Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the nutritional chemistry of lupin (Lupinus spp.) seed to improve livestock production efficiency

In their raw, unprocessed form, lupins have many desirable characteristics for feeding both ruminants and single-stomached animals. An emphasis on these desirable characteristics when formulating diets, combined with an advanced knowledge of how components of lupins can influence nutritional value, will ensure they make a cost-effective contribution to livestock diets. The main lupin species used in livestock diets include Lupinus albus , L. angustifolius and L. luteus. Supplementation of ruminant diets with lupins has been shown to have many positive effects in terms of growth and reproductive efficiency, comparable with supplements of cereal grain. The true value of lupins in ruminants, however, can only be determined following a better definition of animal requirements and a closer match of ration specifications. Pigs can effectively utilize L. angustifolius and L. luteus , but detailed research has yet to reveal the reason for poor utilization of diets containing L. albus. Poultry can tolerate high levels of lupins in their diets but levels are often restricted to avoid problems associated with excess moisture in the excreta. Variable responses to enzymes have been observed when attempting to rectify this problem. Lupins have unique carbohydrate properties characterized by negligible levels of starch, high levels of soluble and insoluble NSP, and high levels of raffinose oligosaccharides, all of which can affect the utilization of energy and the digestion of other nutrients in the diet. In addition to carbohydrates, an understanding of lupin protein, lipid and mineral composition together with a knowledge of potential anti-nutritional compounds is required if the use of this legume is to be optimized.
QUALITY RESEARCH OF THE KRUMOVGRAD 90 ORIENTAL TOBACCO CULTIVAR, GROWN IN THE MESTA TOBACCO REGION UNDER VARIED FERTILIZING CONDITIONS NurettinTahsin,

The quest for quality-specific raw tobacco aimed at obtaining homogenous lots preferred on the international market has led to the replacement of the typical Nevrokop tobacco cultivars by the Krumovgrad tobacco ecotypes and their growing in untypical regions such as the Mesta tobacco region. The mass entering of theKrumovgrad cultivars into almost all tobacco growing regions in Bulgaria has to a certain extent changed also the quality characteristics of the raw material. That fact posed the need of changing the traditional agro techniques applied for those types of tobacco in line with the peculiarities of the region and with a view to obtaining high quality raw tobacco. The traditional agro techniques for the Nevrokop tobaccos, used for the growing of the Krumovgrad 90 in the Mesta tobacco region is most likely one of the reasons for obtaining raw tobacco inferior in its consumer and technological characteristics to that produced in its typical regions. The present research proves the assertion that the use of the combined soil fertilizer – Hydro 4-2121+ammonium nitrate and the combined soil fertilizer – Cropcare 12-22-8 + foliar spray – Ferticare 6-14-31 influence positively the quality of the Krumovgrad 90 cultivar.
Large-Scale Linear Algebra and Applications

Many applications of science and engineering reduce to solving large linear systems of equations. Accordingly, the solution to the equation Ax = b, where A ∈ Cn×m and b ∈ C, is important to our understanding of physical systems. One of the simplest algorithms for solving these problems is Gaussian elimination, which is usually taught in an introductory linear algebra course. By the 20th century however, very large systems motivated iterative methods, which could potentially compute solutions more efficiently than could traditional direct methods [7]. Furthermore, advancements in computing revealed the effectiveness of these relatively new algorithms. Iterative methods work by computing increasingly accurate approximations for the solution at each step of the algorithm. The earliest iterative solvers included Gauss-Seidel (1823), Jacobi (1846) [2], and the Successive Over-relaxation, or SOR, (1950) methods. These solvers are called relaxation methods and, in general, are computed by splitting the matrix into its lower triangular, diagonal, and upper triangular components and modifying and replacing one entry at a time. These methods were the state of the art until the 1980s, at which point, iterative methods using Krylov subspaces began to dominate [5]. Theoretically, these methods approximate the solution by iteratively building the Krylov subspace, Km = span{b, Ab, Ab, . , Am−1b}, in m iterations and extracting an approximate solution from Km at each step. Examples of Krylov subspace methods include the conjugate gradient (CG) method and generalized minimum residual (GMRES) method. The CG method was introduced in 1952 as a direct solver [3]. Today, it is used as an iterative solver for symmetric positive-definite systems of linear equations. GMRES was introduced in 1986 for solving asymmetric systems [8]. Combined with preconditioning, a technique which helps the approximations converge, GMRES is a powerful algorithm with some elegant mathematical properties [6]. This semester, we will implement some of these Krylov subspace methods, including the Arnoldi Iteration, the Full Orthogonalization method (FOM), CG, and GMRES as well as investigate their theoretical properties. We will also study preconditioning techniques and some applications to physical systems.
Treating the Tiers

This article explores the potential of play therapy as a school-based intervention for children who are experiencing behavioral difficulties within the K-12 school setting. A model is presented describing the use of play therapy within the Response to Intervention (RtI) model at the Tier 2 and Tier 3 level. This article explores the RtI model and how play therapy can be incorporated by school counselors or school-based play therapists using individual and small group sessions.
Skeleton-Aware 3D Human Shape Reconstruction From Point Clouds

This work addresses the problem of 3D human shape reconstruction from point clouds. Considering that human shapes are of high dimensions and with large articulations, we adopt the state-of-the-art parametric human body model, SMPL, to reduce the dimension of learning space and generate smooth and valid reconstruction. However, SMPL parameters, especially pose parameters, are not easy to learn because of ambiguity and locality of the pose representation. Thus, we propose to incorporate skeleton awareness into the deep learning based regression of SMPL parameters for 3D human shape reconstruction. Our basic idea is to use the state-of-the-art technique PointNet++ to extract point features, and then map point features to skeleton joint features and finally to SMPL parameters for the reconstruction from point clouds. Particularly, we develop an end-to-end framework, where we propose a graph aggregation module to augment PointNet++ by extracting better point features, an attention module to better map unordered point features into ordered skeleton joint features, and a skeleton graph module to extract better joint features for SMPL parameter regression. The entire framework network is first trained in an end-to-end manner on synthesized dataset, and then online fine-tuned on unseen dataset with unsupervised loss to bridges gaps between training and testing. The experiments on multiple datasets show that our method is on par with the state-of-the-art solution.
#28 - Testing the Effectiveness of Mnemonic Strategies in Learning and Retaining New Information

Learning new information can sometimes be a very demanding process, especially in very intense courses such as hard sciences, which can require a lot of reading, studying, and memorization. Simply reading the large walls of text within textbooks can be overwhelming to some students. Thus, applying mnemonic strategies may help in the simplification of otherwise overwhelming information and improving knowledge retention. This study was designed to test the effectiveness of such mnemonics in the memorization and retention of information both long-term and shortterm. Twenty-five students separated two different lab sections of a Human Anatomy and Physiology II course were given a mnemonic phrase each week to help them retain information. After six weeks, they were tested on the information, and given a survey. Results were compared both within each group, and between the two groups. While one group of students showed better fourto five-week-long knowledge retention after applying two mnemonic phrases in their study, they did not show any significantly better retention after one week. Comparatively, the second group showed better knowledge retention overall, regardless of whether or not they had the mnemonic devices to assist them on a certain question, and even showed generally better retention of information for which they were not given mnemonic phrases after three weeks. Despite student responses claiming the effectiveness of mnemonic phrases, the data suggests other factors, aside from simply having mnemonic phrases to assist learning, are important to knowledge retention. Even still, mnemonic triggers are designed to assist learning difficult topics. Understanding their benefits, as well as where they fall short, can assist teachers in presenting students with knowledge in a manner that may optimize learning and retaining information.
PHYTOCHEMICAL, ANTIBACTERIAL AND ANTI-OXIDANT ACTIVITY OF PIPER LONGUM LEAVES

Piper longum L. (Piperaceae) commonly known as “long pepper” is a well-known medicinal plant in ayurveda. Different parts of this plant, such as root, seed, fruit, whole plant etc. are traditionally used in various ailments. The methanol extract of leaves of Piper longum was subjected to evaluate phytochemical, antibacterial and antioxidant activity. In order to investigate the antioxidant activity, different complementary test systems, namely, 2,2-diphenyl-1-picryl-hydrazil (DPPH) free radical scavenging, total phenolic, total tannin and flavonoid contents were used. Total flavonoids content was spectrophotometrically determined using aluminum chloride colorimetric assay while total phenolic and tannin content by Folin Chiocalteu’s reagent. Antibacterial activity was evaluated by disc diffusion assay. Phytochemical analysis of the extract indicated the presence of saponins, tannins, glycosides, alkaloids, and flavonoids. Extract contains greater amount of total phenolic (93.94 mg GAE/g dry extract), flavonoids (44.15 mg QE/g dry extract) and tannin content (58.06 mg QE/g dry extract). Extract showed DPPH scavenging activity (149.92 μg/mL) that was comparable to standard ascorbic acid (25.42 μg/mL). In disc diffusion assay experimental extract showed potent antibacterial activity.
Bio-optical properties and radiative energy budgets in fed and unfed scleractinian corals (Pocillopora sp.) during thermal bleaching

: Corals live in symbiosis with algal dinoflagellates, which can achieve outstanding photo synthetic energy efficiencies in hospite approaching theo retical limits. However, how such photosynthetic efficiency varies with environmental stress remains poorly known. Using fiber-optic and electrochemical microsensors in combination with variable chlorophyll fluorescence imaging, we investigated the combined effects of thermal stress and active feeding on the radiative energy budget and photosynthetic efficiency of the symbiotic coral Pocillopora sp. At ambient temperature (25°C), the percentage of ab sorbed light energy used for photosynthesis under low irradiance was higher for fed (~5−6%) compared to unfed corals (4%). Corals from both feeding treatments responded equally to stress from high light ex posure (2400 µmol photons m −2 s −1 ), exhibiting a de crease in photosynthetic efficiency, down to 0.5− 0.6%. Fed corals showed increased resilience to thermal-induced bleaching (loss of symbionts) compared to unfed corals. In addition, while unfed corals decreased their photosynthetic efficiency almost immediately when exposed to thermal stress, fed corals maintained a constant and high photosynthetic efficiency for 5 more days after onset of thermal stress. We conclude that active feeding is beneficial to corals by prolonging coral health and resilience during thermal stress as a result of an overall healthier symbiont population.
Energy and Net Ecosystem CO2 Exchange between Agro- ecosystems and the Atmosphere over a Complex Terrain in Korea

(1) Department of Micrometeorology, University of Bayreuth, Germany, peng.zhao@unibayreuth.de, johannes.lueers@uni-bayreuth.de, thomas.foken@uni-bayreuth.de (2) Department of Plant Ecology, University of Bayreuth, Germany, bumsuk.seo@unibayreuth.de, steve.lindner@yahoo.de, puplebr@gmail.com, john.tenhunen@uni-bayreuth.de (3) Department of Environmental Science College of Natural Science, Kangwon National University, South Korea, kjc25@kangwon.ac. kr, cbl@kangwon.ac. kr
RACAI’s System at PharmaCoNER 2019

This paper describes the Named Entity Recognition system of the Institute for Artificial Intelligence “Mihai Drăgănescu” of the Romanian Academy (RACAI for short). Our best F1 score of 0.84984 was achieved using an ensemble of two systems: a gazetteer-based baseline and a RNN-based NER system, developed specially for PharmaCoNER 2019. We will describe the individual systems and the ensemble algorithm, compare the final system to the current state of the art, as well as discuss our results with respect to the quality of the training data and its annotation strategy. The resulting NER system is language independent, provided that language-dependent resources and preprocessing tools exist, such as tokenizers and POS taggers.
Applying Variable Coefficient functions to Self-Organizing Feature Maps for Network Intrusion Detection on the 1999 KDD Cup Dataset

Self-Organizing Feature Maps (SOFM’s) can be a valuable element in a network intrusion detection system. When classification is performed on a segment of network traffic, the usual method for class determination is selecting the class which has the smallest measurement of the Euclidean distance from the multi-dimensional network traffic sample to the class’ multi-dimensional prototype. This minimum distance is calculated with equivalent weights for each dimension of data in the network traffic sample. In this paper we explore the possibility of applying different randomly generated weightings to each dimension of data in the network traffic sample to increase positive classifications of the network sample data provided by the 1999 KDD Cup Dataset. We show that there is improvement, and recommend that further studies be done in choosing the right evolutionary functions to help modify the hotspots and achieve better results.
Innovation, the Flying Geese Model, IPR Protection, and Sustainable Economic Development in China

China has achieved an “economic miracle” with 40 years of continual high-speed growth and the simultaneous realization of global innovation prowess. In this study, a large panel dataset from 1985 to 2017 was used in an effort to explore how innovation (at the enterprise level), the flying geese model (at the global and national level), and intellectual property rights (IPR) protection (at the governmental level) have facilitated China’s sustainable economic development (SED). We employed ridge regression to compensate for the obvious multicollinearity among independent variables. For control purposes, we included multiple variables, namely, population, the labor force, the exchange rate, human capital, and research and development (R&D) expenditures. The results show that all three factors have significant explanatory power for China’s SED. First, either Total domestic patent applications or Total domestic patent grants by Chinese enterprises have overtaken those by their foreign counterparts and become a powerful engine for China’s SED. However, we understand that patent applications as a measure of innovation could overestimate China’s innovation capability more than patent grants. Second, the flying geese model can explain not only China’s SED but also China’s innovation diffusion at the global and national levels. From 1985 to 1992, China’s industrialization and innovation were mainly driven endogenously; from 1993 to 2008, they were simultaneously driven exogenously and endogenously. Since 2009, they have gradually become predominantly endogenously driven. Third, China’s IPR protection has grown increasingly tighter at the governmental level, which has further facilitated China’s SED. About 98.5% (annually) of patent infringement disputes through the administrative protection system could be closed promptly between 2014 and 2017. Additionally, the proportion of infringed patent rights holders from foreign countries has been declining in recent years. The results significantly enrich the extant theories, and the analysis also has several key implications for actions that should be taken to maintain China’s sustainable economic growth; specifically, China should (1) keep its opening-up policy so as to continually expand exports and attract foreign direct investment (FDI); (2) encourage innovation activities from domestic enterprises since they have been the stimulus of China’s SED; (3) improve its IPR protection system, particularly its judicial protection system, so as to form a virtuous circle of innovation in China.
Improving Test Data Generation for Java Programs using Search-based Approach and d-criterion

Java program testing professionals have required effective test suites in fault detection within limited budget. To generate more effective test suites, they have evaluated test suites by mutation analysis. Therefore, researchers have been suggested more powerful mutation adequacy criterion. Also, they have studied on automatizing the process of test data generation and reducing the test suite size by prioritization to reduce the cost it takes to test programs. In this paper, we suggest an automated test suite generation approach using genetic algorithm and powerful mutation adequacy criterion. The proposed approach is implemented in the previous tool, EvoSuite. The test suite generated by our approach is up to four time more effective than test suites generated by original EvoSuite. Keywords-automated test generation; genetic algorithm; mutation analysis; d-criterion
Predictive Performance of Hargreaves Model in Estimating Solar Radiation in Yola, Adamawa State Nigeria Using Minimum Climatological Data

Mathematical models have been very useful in reducing challenges encountered by researchers due to the inability of having solar radiation data or lack of instrumental sites at every point on the Earth. This work aimed at investigating the prediction performance of Hargreaves-Samani’s model in estimating global solar radiation (GSR) out of the many other empirical models so far formulated for this purpose. This model basically uses maximum and minimum temperature data and basically used in mid-latitudes. The paper attempts to assess the predictive performance of HargreavesSamani’s model in the Savanna region using Yola as a case study. Estimated values of GSR from one month data adopted from the Meteorological station of the Department of Geography, Federal University of Technology, Yola, Nigeria was used for this purpose. Using this model shows a 95% index of agreement (IA) with the observed values; which suggests a good model performance and can also be used in estimating global solar radiation in the Savanna region particularly in areas with little or no such climatic data. Original Research Article Samuel et al.; JERR, 1(2): 1-8, 2018; Article no.JERR. 40655 2
Distributed Reinforcement Learning using Bi-directional Decision Making for Multi- criteria Control of Multi-Stage Flow Systems

Autonomous control systems have been requested recently for large-scale real systems. Distributed reinforcement learning is attracting attention specifically in control of physical flow systems such as lifelines. In this paper, we will introduce a model of Multi-Stage Flow System (MSFS). MSFS is a framework which can describe various physical flow systems. Furthermore, it is effective in handling multi-criteria, multiple constraints under uncertainty and so on that are difficult to solve in conventional methods because of its features. We propose a new bi-directional decision making algorithm based on a least commitment strategy. We apply our method to controlling of real sewerage systems. The simulation results show that only our method satisfies permissible levels and attains the performance within an acceptance level.
REDONDO-CABRERA ET AL.: CONTINUOUS POSE ESTIMATION WITH HOUGH FOREST 1 All together now: Simultaneous Object Detection and Continuous Pose Estimation using a Hough Forest with Probabilistic Locally Enhanced Voting

Simultaneous object detection and pose estimation is a challenging task in computer vision. In this paper, we tackle the problem using Hough Forests. Unlike most methods in the literature, we focus on the problem of continuous pose estimation. Moreover, we aim for a probabilistic output. We first introduce a new pose purity criterion for splitting a node during the forest training. Second, we propose the concept of Probabilistic Locally Enhanced Voting (PLEV), a novel regression strategy which consists in modulating the regression with a kernel density estimation to consolidate the votes in a local region near the maxima detected in the Hough space. And third, we propose a pose-based backprojection strategy to improve the bounding box estimation. With these three additions, we show that our Hough Forest can achieve state-of-the-art results without needing 3D CAD models. We present a quite versatile method, showing results for different categories (cars as well as faces) and for different modalities (RGB as well as depth images).
Applying a resource model of stress to the cancer caregiver experience

Objectives: The aim of this study was to apply a resource model of stress and coping to explaining the relationship between burden of care, perceived stress, optimism, resilience, self-efficacy, perceived support and psychological distress, and to test the role of satisfaction with medical care in the process. Methods: A cross-sectional survey using questionnaire measures was conducted with a group of 269 female cancer caregivers who were caring for a spouse, a parent or a child with cancer. Participants were accessed via an oncology clinic. Results: Analysis supported the resource model showing that psychological resources and social support mediate the impact of burden of care on caregiver mental health. Satisfaction with medical care added to the explanatory power of the model. Conclusions: This study provides support for a resource model of stress and the potential utility of resource building interventions in terms of increased social support and the development of psychological resources such as resilience skills. In addition it points to the importance of positive experience of health care services for caregivers and suggests that this may be a fruitful way to improve their quality of life.