Miracle-dz/UniFAR-FTUs huggingface dataset:
An innovative teaching method to increase engagement in the classroom: A case study in science and engineering

This Evidence-based practice paper describes a learning process developed and used in several STEM courses. Learning is a process unique to each individual and can be accomplished by watching, reading, doing, experiencing, repetition and even teaching. Learning according to [1] is a two-step process where the first step is to receive information and the second is to process. This study is an active learning method that combines these two steps in a repetitive process that encourages engagement and collaboration in the classroom. Memory related research has identified and confirmed the power of repetition on the recall ability. Repetition has a profound impact on the event related brain potential eliciting a longer recall period and has been reported to speed up the learning process [2,3,4]. In addition, paired learning combined with paired testing has been proven to enhance learning more than it would in paired learning combined with individual testing [5]. This paper discusses a pedagogy that combines the above mentioned theories. This pedagogy aims to maximize learning in within a short time frame. Students learn while creating questions and answers to these questions, preparing for the quiz, taking the quiz and finally grading the quiz. The students work with a partner or in a group throughout the process of the quiz. The paper also discusses the modifications done to accommodate for the change in a classroom size. It also gives the feedback gathered from the students while implementing this pedagogy in a small and a large classroom setting and what improvements have been done in addressing students’ concerns. 93% of the students found the process of creating the questions for the quiz to be helpful in reviewing the material learned in the class and the process of taking the quiz to be helpful in learning the material; 93% of the students indicated that they learned from their classmates; and 13% of the students preferred lecture only class. The project is a result of an international collaboration with professors from two different universities across three different disciplines in STEM including Civil Engineering, Computer Science and, Mechanical and Industrial Engineering, Slight variations of the method was employed based on the specific nature of the class. The project consists of the students working in groups to create questions and answers related to a topic briefly described in class. The questions and answers are presented in an online forum monitored by the class instructor for all the students to see. The students are then quizzed on questions from the pool asked by them online. The project was implemented in a range of class sizes from 20 students to 120 students.
Novel strategies for assessing and managing the risks posed by invasive alien species to global crop production and biodiversity

Summary International actions to combat the threat posed by invasive alien species (IAS) to crops and biodiversity have intensifi ed in recent years. The formulation of 15 guiding principles on IAS by the Convention on Biological Diversity (CBD) stimulated the International Plant Protection Convention (IPPC) to review its role in protecting biodiversity. IPPC standards now demonstrate clearly that the risks posed by any organism that is directly or indirectly injurious to cultivated or uncultivated plants can be assessed and managed under the IPPC. Since the IPPC, unlike the CBD, constitutes an international legal instrument recognised by the World Trade Organization, greater protection from the introduction of IAS is now available. However, phytosanitary measures can only be enacted if they can be justifi ed by risk analysis and we outline some novel strategies to improve the assessment and management of the risks posed by IAS, highlighting some of the key challenges which remain.
With a Little Help from my Spouse : The Role of Trust in Family Busimess

Empirically, self-employed persons often operate with the support of their partners or other household members as co-workers in the firm. The formal or informal employment of the marriage partner in particular seems to have a lot of advantages, such as fiscal benefits and low wage costs. But from a theoretical point of view, these advantages are jeopardized by serious cooperation problems: If the spouse of the self-employed decides to work in the jointly-run business, former qualifications are lost. Hence a one-sided dependence on the part of the co-working spouse arises, which increases with the amount of relation-specific investments. Hence the actors are confronted with a cooperation problem, because the decision for co-working requires trust concerning the future action of the self-employed. Within this framework, this paper is focused on the determinants which influence the decisions concerning collaborating in the business of a self employed life’s companion as well as the extent of labor provided in the case of co-working. It can be assumed that the three types of determinants investigated in this paper affect the assignment of a spouse or cohabitation partner in the business of a self-employment person: (1) economic determinants which are individual productivity and human capital of the life’s companion, (2) the situation of the family and the household, and (3) characteristics of the entrepreneur and the firm. Based on a sample of self-employed empirical analysis indicates that trust and specialization play a crucial role in explaining co-working of the life’s companions of self-employed persons. However, economic determinants like productivity and human capital do not show the expected effects on collaboration. With a Little Help from my Spouse Martin Abraham 3
Title : AFREET : Human-Inspired Spatio-Spectral Feature Construction for Image Classification with Support Vector Machines

We examine the task of pixel-by-pixel classification of the multispectral and grayscale images typically found in remote-sensing and medical applications. Simple machine learning techniques have long been applied to remote-sensed image classification, but almost always using purely spectral information about each pixel. Humans can often outperform these systems, and make extensive use of spatial context to make classification decisions. We present Afreet: an SVM-based learning system which attempts to automatically construct and refine spatio-spectral features in a somewhat human-inspired fashion. Comparisons with traditionally used machine learning techniques show that Afreet achieves significantly higher performance. The use of spatial context is particularly useful for medical imagery, where multispectral images are still rare.
Chemical Screening of Urochloa humidicola : Methods for Characterizing Secondary Metabolites and Allelopathic Activity on Forage Legumes

The aim of this study was to identify the main classes of secondary metabolites present in the root and shoot crude extracts and fractions from the forage grass Urochloa humidicola (Rendle) Morrone & Zuloaga and to evaluate the allelopathic effect of these metabolites on forage legumes for intercropping. Phytochemical prospecting analyses, 1H NMR and capillary electrophoresis were performed on extracts of U. humidicola. Allelopathic activity was evaluated in germination of Stylosanthes, Macrotyloma axillare and Lactuca sativa L. (standard) in the presence of crude extracts, isolated saponins, flavonoids and trans-cinnamic acid. The metabolite classes present in the extracts could be determined by the combined use of the tested analytical techniques, but their use alone was usually not sufficient to chemically characterize the species. Capillary electrophoresis was effective in detecting phenolic compounds. Macrotyloma axillare was tolerant to crude extracts of U. humidicola. Saponins and trans-cinnamic acid, but not the flavonoids, reduced germination of the target plants.
Investigating the Role of Denial of Cognition, Capacity for Morality and Perception of Child-likeness in Infrahumanization

Abstract Secondary emotions (for example, guilt, shame, and tenderness) are uniquely human, indicate higher cognition, moral capacity, and develop with age, compared with primary emotions (for example, pain, fear, happiness), which humans also share with animals. According to Leyers and colleagues people designate outgroups as less human, that is, infrahumanize them through ascribing them less uniquely human emotions. This paper reports two studies that examined the role of denial of cognition, capacity for morality and perception of child-likeness in denial of secondary emotions to outgroups. In Study 1, it was hypothesized that a sample of psychology students would deny secondary emotions to people with a learning disability, Down syndrome, as a function of denying them cognitive capacities, compared with people with physical disabilities and the ingroup. Study 2 tested denial of secondary emotions to the three target groups as a function of not only denial of cognition, but also of moral capacities, and a tendency to liken the mental capacities of adults to children’s. Both studies showed that only people with Down syndrome are denied secondary emotions. In Study 1, denial of secondary emotions to people with Down syndrome was mediated by denial of cognitive capacities. In Study 2, denial of secondary emotions was mediated by an average index of denial of cognitive and moral capacities, and likening mental capacities of the target groups to children’s. Both studies also showed greater denial of positive than negative secondary emotions to people with Down syndrome. Results are discussed in terms of infrahumanization theory and romanticization of learning disabilities. Implications of results to people with Down syndrome are also discussed.
Days Suitable for Fieldwork in the US Corn Belt:Climate, Soils and Spatial Heterogeneity

Days suitable for field work (DSFW) is an important piece of data for production agriculture and agricultural extension focused on practical decision making about investment in farm machinery and cropping systems management. It is, however, noteworthy that there has been limited attention paid to DSFW. To fill this gap, this study tries to answer two research questions: (1) what is the trend in DSFW during the planting and harvest period from 1980-2010? (2) what is the accuracy of a predictive econometric model of DSFW based on agro-environmental data? To tackle the economic dimensions of DSFW, we model DSFW consistent with two major approaches in climate change impacts on agriculture: the Ricardian approach and the panel estimation approach. We first specify the regression model of DSFW in panel model from two conceptual approaches: the response function and the factor demand function of cost minimization. Both approaches provide consistent regression specification of fixed and random effects models. We construct an unbalanced panel of weekly DSFW observations, historic weather data, and soil data in five Corn Belt States for 1980-2010 at the Crop Reporting District (CRD) level to implement out-of-sample and in-sample prediction analysis. The results show that the random effects model is the most suitable model to perform climate change response analysis for our data. This paper contributes to the literature in three ways. First, the analytical derivation of two econometric interpretations of DSFW and link them to econometric model specification strategies are easily extended to other agro-environmental analysis. Second, the estimation results for panel models empirically demonstrate that random effects model could be proper model specification taking into account soil effects. Lastly, we discuss that DSFW could be an important constraints for policy corresponding to climate change and its adaptation.
Experimental Comparisons of Multi-class Classifiers

The multi-class classification algorithms are widely used by many areas such as machine learning and computer vision domains. Nowadays, many literatures described multi-class algorithms, however there are few literature that introduced them with thorough theoretical analysis and experimental comparisons. This paper discusses the principles, important parameters, application domain, runtime performance, accuracy, and etc. of twelve multi-class algorithms: decision tree, random forests, extremely randomized trees, multi-class adaboost classifier, stochastic gradient boosting, linear and nonlinear support vector machines, K nearest neighbors, multi-class logistic classifier, multi-layer perceptron, naive Bayesian classifier and conditional random fields classifier. The experiment tested on five data sets: SPECTF heart data set, Ionosphere radar data set, spam junk mail filter data set, optdigits handwriting data set and scene 15 image classification data set. Our major contribution is that we study the relationships between each classifier and impact of each parameters to classification results. The experiment shows that gradient boosted trees, nonlinear support vector machine, K nearest neighbor reach high performance under the circumstance of binary classification and minor data capacity; Under the condition of high dimension, multi-class and big data, however, gradient boosted trees, linear support vector machine, multi-class logistic classifier get good results. At last, the paper addresses the development and future of multi-class classifier algorithms. Povzetek: V prispevku je podan pregled klasifikatorjev z več razredi.
A Novel Surface Completion Method

Surface completion is a classical and significant research topic in the area of digital geometry processing. Due to the expensive time consumption and the unreliability of the existing popular algorithm, a surface completion method is proposed in this paper that can recover the significant geometric information of the underlying model. Firstly, the hole is filled by a smooth patch by modified advancing front mesh technique. Secondly, the fine-scaled details are added by fitting quadric surface from the boundary of the hole to its center. The experimental results demonstrate that our surface completion scheme can effectively fill the holes and recover the fine-scale details of the hole region.
KRAMERS-KRONIG COMPUTATIONS IN LOGARITHMIC FREQUENCY DOMAIN Lăcrimioara GRAMA

The Kramers-Kronig transformation has been extensively applied in optical spectroscopy to calculate the real component of an optical quantity from the imaginary component, such as the determination of the dispersive mode from a measurement of the absorptive mode and vice versa. In this paper some methods for Kramers-Kronig transform computation are presented, without employing numerical integration and extrapolation or assumptions about the behavior of the tested signals outside the measured range, and the obtained results are compared.
DIURNAL DYNAMIC OF NITRITE AND AMMONIA GAS IN A NILE TILAPIA FISH POND IN SUBTROPICAL REGION

The purpose of this paper is to describe the diurnal oscillations found in the concentrations of nitrite and gas ammonia in a fish pond of Nile Tilapia; identify the exact moment these substances reach inappropriate levels for cultivation, and show the best handling practices. The sampling was performed in 2006 from December 16 th until December 20 th , from 6 am till 8 pm every two hours. It was observed that the concentrations of nitrate and ammonia are affected by diurnal fluctuations, and that the higher levels of gas ammonia and nitrate can be found at 4 pm: 12.4 µg/L and 12.6 mg/L, respectively. The concentration of nitrite reached levels considered inadequate for cultivation. It was also observed that the parameters nitrite and ammonia are strongly influenced by subaqueous radiation and that the afternoon is the best period for obtaining the sampling for parameters. Results like that could be related to feeding management and mainly to the phytoplankton metabolism. The nonutilization of part of the ration spread contributed to the increment of forms of nitrogen into the system. It is suggested to adequate quantity and to improve quality of the food administered.
Embedded Markov chain approximations in Skorokhod topologies

We prove a J1-tightness condition for embedded Markov chains and discuss four Skorokhod topologies in a unified manner. To approximate a continuous time stochastic process by discrete time Markov chains, one has several options to embed the Markov chains into continuous time processes. On the one hand, there is a Markov embedding which uses exponential waiting times. On the other hand, each Skorokhod topology naturally suggests a certain embedding. These are the step function embedding for J1, the linear interpolation embedding forM1, the multistep embedding for J2 and a more general embedding for M2. We show that the convergence of the step function embedding in J1 implies the convergence of the other embeddings in the corresponding topologies. For the converse statement, a J1-tightness condition for embedded time-homogeneous Markov chains is given.Additionally, it is shown that J1 convergence is equivalent to the joint convergence in M1 and J2.
Increasing the efficiency of GEOS-Chem Adjoint model runs using a Python ensemble manager

Methane is a powerful greenhouse gas with uncertainty regarding the strengths and trends of various individual sources. These uncertainties make it difficult for researchers to determine the exact reasons behind methane’s variable annual growth rate and the stabilization of the atmospheric concentration over the past three decades. It is possible to estimate individual methane source emission values using satellite measurements and inverse modeling techniques, although data quality limits how well individual sources are resolved. GEOS-Chem Adjoint is the combination of an atmospheric chemical transport model (GEOS-Chem) with an adjoint model, and it can be used to test the emissions source resolving power for actual and theoretical satellite retrievals of methane. In order to test the resolving power, the mathematical calculations require a large number of individual simulations to be run, but currently in the standard version of GEOS-Chem Adjoint each simulation needs to be manually set up and initiated. To overcome the need for manual setup and executions of model runs, a manager script was created using the Python programming language. The ensemble manager script, PyEnsemble, automates the process of creating multiple unique simulations, and can run variable numbers of simulations on many different types of clustered computer systems. PyEnsemble marks the first step in a larger project of testing the current accuracy of methane surface emission estimates, and helping to develop ways to help further constrain them. This work was performed under the auspices of the Significant Opportunities in Atmospheric Research and Science Program. SOARS is managed by the University Corporation for Atmospheric Research and is funded by the National Science Foundation, the National Oceanic and Atmospheric Administration, the Cooperative Institute for Research in Environmental Science, the University of Colorado at Boulder, and by the Center for Multiscale Modeling of Atmospheric Processes.
On the structure of scientific texts

1. In the study of text structure there are basically two approaches which we may adopt. On the one hand, we may seek to understand the mechanisms of textual cohesion, to discover what makes a sequence of sentences into a coherent text. Such a study involves the discussion of anaphora, of reference and substitution, of ellipsis, of the role of conjunctions and ‘sentence adverbs’, of lexical and semantic cohesion between sentences, and so forth. The alternative approach is to take a more global perspective, to seek to understand the overall organisation of texts, to understand how one episode of a narrative develops from another and how paragraphs and chapters are built into cohesive wholes. Following van Dijk (1972) we may call the first approach the study of ‘micro-structure’ and the second the study of ‘macro-structure’. The latter has long been the province of the literary critic, the rhetorician, and the analyst of folk-tales (e.g. Kinneavy 1971, Propp 1968). It is an area which linguists have neglected. Indeed it is only relatively recently that they have turned their attention to text structures at all, and it is perhaps natural that they should have concentrated primarily on ‘micro-structure’ (e.g. Grimes 1975, Halliday and Hasan 1976) since it is here that techniques used in the analysis of sentences can be most easily applied. The two approaches to the study of text have thus proceeded with relatively little interaction. There has been scarcely any attempt to relate the two aspects of text structure within a comprehensive linguistic framework; the first real effort in this direction is represented by the work of Grimes (1975). Furthermore, the study of ‘macro-structure’ has been concentrated almost wholly on narrative texts, and primarily on fictional texts, short stories, novels and folk-tales. Narrative texts of a non-literary nature such as newspaper reports and historical writings have been generally neglected, and non-narrative texts have been almost completely ignored. In this paper it is my intention to examine the structure of an important type of nonnarrative text. It is the expository text as represented by the article of a learned journal which argues for the revision of some accepted opinion in some area of academic study, i. e. the ‘scientific paper’. I shall be concerned mainly with the macro-structure, of such texts, and I shall deal only with those aspects of microstructure which are linked most closely to the global organisation of texts.
An improved ICP algorithm based on Generalized Procrustes Analysis

Point cloud registration often use ICP algorithm to match ,which is the classic algorithm of point cloud registration, due to the presence of some defects in ICP algorithm, scholars later have done a lot of improvements. In this paper, we firstly view the traditional ICP algorithms, then we propose the Procrustes analysis into the ICP algorithm for multi view point cloud registration. Simulation results show that this method can resolve the phenomenon in ICP registration algorithm of bifurcation and bloated.
Scaling laws of coronal loops compared to a 3D MHD model of an Active Region

Context. The structure and heating of coronal loops are investigated since decades. Established scaling laws relate fundamental quantities like the loop apex temperature, pressure, length, and the coronal heating. Aims. We test such scaling laws against a large-scale 3D MHD model of the Solar corona, which became feasible with nowadays high-performance computing. Methods. We drive an active region simulation a with photospheric observations and found strong similarities to the observed coronal loops in X-rays and EUV wavelength. A 3D reconstruction of stereoscopic observations showed that our model loops have a realistic spatial structure. We compare scaling laws to our model data extracted along an ensemble of field lines. Finally, we fit a new scaling law that represents well hot loops and also cooler structures, which was not possible before only based on observations. Results. Our model data gives some support for scaling laws that were established for hot and EUV-emissive coronal loops. For the RTV scaling law we find an offset to our model data, which can be explained by 1D considerations of a static loop with a constant heat input and conduction. With a fit to our model data we set up a new scaling law for the coronal heat input along magnetic field lines. Conclusions. RTV-like scaling laws were fitted to hot loops and therefore do not predict well the coronal heat input for cooler structures that are hardly observable. The basic differences between 1D and self-consistent 3D modeling account for deviations between our and earlier scaling laws. We also conclude that a heating mechanism by MHD-turbulent dissipation within a braided flux tube would heat the corona stronger than consistent with our model corona.