Miracle-dz/UniFAR-FTUs huggingface dataset:
Farmer to Farmer Extension and Adoption of Improved Farming Practices: Evidence from Manicaland and Masvingo Provinces of Zimbabwe

The farmer to farmer extension support is increasingly being supported in Zimbabwe as a strategy to complementing the public extension service in delivering extension support to smallholder farmers. Despite this increasing support, development practitioners and traditional conservative extensionists still doubt the effectiveness of the model in promoting technology adoption, agricultural productivity and development. This study sought to assess the impact of the farmer to farmer extension approach on improved farming practices adoption amongst 479 smallholder farmers selected randomly from 6 districts in Manicaland and Masvingo provinces of Zimbabwe. The study found that 80.6% of the sample farmers indicated adopting improved farming practices and the most widely adopted technologies were conservation farming related technologies, crop management practices related technologies, soil fertility management related technologies and farming as a business. Results of the binary logistic regression show that the farmer to farmer extension approach positively and significantly influences the adoption of improved farming practices. Other extension related variables that positively and significantly affect adoption of improved farming practices are the number of years of receiving extension support by the household and agricultural extension training. However, private input suppliers and contractors extension has a negative and significant effect on adoption of improved farming practices. The study recommends that the government actively pursue increasing coverage of the farmer to farmer extension support and should also put in place measures to continually upgrade the skills of extension farmers.
Real-Time Twitter Corpus Labelling Using Automatic Clustering Approach

In this paper, we present a novel automatic labelling approach for the classification of large amount of unlabelled real-time twitter datasets for textual-based Twitter Sentiment Analysis. The tweets are labelled or classified as Positive, Negative or Neutral using the novel automatic approach. The proposed approach applies an unsupervised clustering technique that would generate clusters based on the underlying patterns (finding similarities between tweets) in the collected twitter corpus. Twitter search API is used to collect real-time English tweets on several topics such as “#Demonetization”, “#lockdown”, and “#9pm9minutes” by the use of search operator. To analyse the sentiment from real-time tweets, labelling of the corpus is required. Manual annotation of large twitter corpus is time and labor-intensive. Moreover, domain experts are needed for labelling of tweets belonging to a particular domain. Thus, in this work, we propose the use of the improved K-mean clustering approach, which is an unsupervised way of labelling corpus, which could then be used for learning supervised models such as SVM for sentiment analysis. To make the corpus ready for clustering and to get quality clusters, we have applied some basic to advanced cleaning operations known as tweet normalization. Furthermore, extensive feature engineering is conducted to generate different types of features including POS-based (Part-of-Speech), ngrams, Twitter-specific, negation, and lexicon-based features from our collected unlabelled twitter corpus. Those features act as input to the K-mean clustering algorithm and help it in identifying patterns from the data for cluster generation. Moreover, we handle an important linguistic phenomenon namely negation before the cluster generation. Our main focus is on handling those negation tweets in which negation presence has literally no sense of negation (negation exception cases). At the end, cluster analysis is done manually to find out the sentiments expressing by tweets in a particular cluster. Accordingly, cluster classification is done and each cluster is assigned one class that is Positive, Negative, or Neutral. The main contribution of this work is the idea of amalgamation of extensive feature engineering and negation modelling with the unsupervised K-mean clustering approach for classification of large unlabelled twitter corpus. A comparative analysis of our proposed approach is done with or without negation exception cases and with random K-mean using only conventional TF-IDF as features. The proposed automatic labelling approach produces substantial results in terms of cluster quality assessed through two evaluation metrics known as inertia and silhouette score.
University of Birmingham Theory versus practice in planning education

This paper re ﬂ ects on the ongoing debate between theory and practice in planning, using the example of South Africa. Based on survey responses, it discusses how planning education in South Africa is perceived to prepare students for practice. While we acknowledge that the majority of respondents view their planning education positively, the results reveal challenges regarding the practical application of theory, especially in the case of land-use management. We then respond to calls for contextualised practices of knowing, emphasising the ‘ local ’ in planning education so that theory and practice can be combined across contexts.
DOES A RELATIONSHIP EXIST BETWEEN PALAEOPATHOLOGIES AND INSULARITY ? A CASE STUDY OF SOME BONES OF PROLAGUS SARDUS (WAGNER, 1829) FROM SARDINIA (ITALY)

This research describes pathological bones of Prolagus sardus (Lagomorpha, Ochotonidae) from four Middle-Late Pleistocene to Early Holocene sites of Sardinia (Italy), and presents a short inventory of the main Sardinian localities recording the species, with the intent to infer the viability of the Sardinian ochotonid populations during the Middle-Late Pleistocene and Early Holocene. The macroscopical analysis evidences that the pathologies were mostly of a traumatic origin, rather than due to other pathological conditions, which have been only observed in a few individuals inhabiting the Tavolara islet. The significance of the occurrence of palaeopathological signatures in bones of insular, mammals and their usefulness in detecting the life-style, behaviour and niche occupancy of endemic mammalian species, and reconstructing some aspects of population dynamics in the peculiar island environments, are briefly discussed.
In vitro assessment of natural extract as irrigant on root dentin microhardness

Sneha Mali1, Abhijeet Phase2, Nootan Mali*3 1Department of Conservative Dentistry & Endodontics, School of Dental Sciences,Kristna Institute of Medical Sciences Deemed To Be University Karad Dist-Satara, Maharashtra, India 2Department of Orthodontics & Dentofacial orthopedic,School of Dental Sciences,Kristna Institute of Medical Sciences Deemed To Be University Karad Dist-Satara, Maharashtra, India 3Department of Medical Surgical Nursing Institute of Nursing Education & Paramedical Sciences, Dombivli (E), Maharashtra, India
Three-Dimensional, Vision-Based Proportional Navigation for UAV Collision Avoidance

As the number of potential applications for Unmanned Aerial Vehicles (UAVs) keeps rising steadily, the chances that these devices will operate in close proximity to static or dynamic obstacles also increases. Therefore, collision avoidance is an important challenge to overcome for Unmanned Aerial Vehicle operations. Electro-optical devices have several advantages such as light weight, low cost, low algorithm requirements with respect to computational power and possibly night vision capabilities. Therefore, visionbased Unmanned Aerial Vehicle collision avoidance has received considerable attention. Although much progress has been made in collision avoidance systems (CAS), most approaches are focused on two-dimensional environments. In order to operate in complex three-dimensional urban environments, three-dimensional collision avoidance systems are required. This thesis develops a three-dimensional vision-based collision avoidance system to providesense and avoid capabilities for unmanned aerial vehicles (UAVs) operating in complex urban environments with multiple static and dynamic collision threats. This collision avoidance system is based on the principle of proportional navigation (Pro-Nav), which states that a collision will occur when the line-of-sight (LOS) angles to another object remain constant. According to this guidance law, monocular electro-optical devices can be implemented on Unmanned Aerial Vehicles, which can provide measurements of the line-of-sight angles, indicating potential collision threats. In this thesis, the guidance laws were applied to a nonlinear, six degree-offreedom Unmanned Aerial Vehicles model in different two-dimensional or threedimensional simulation environments with a varying number of static and dynamic obstacles.
Multilocus Sequence Typing of Pasteurella multocida
 Isolates from Acute Fowl Cholera Outbreak in Layer

Fowl cholera is an infectious disease caused by Pasteurella multocida that contributes to high economic loss in the commercial chicken industry. Three Pasteurella multocida strains were isolated from outbreaks of acute fowl cholera in the Korean layer farms from 2018 to 2019. One strain was identified and serotyped using capsular PCR typing. This strain was also genotyped by lipopolysaccharide (LPS) PCR typing as A: L3, whereas other strains were non-typable. The multilocus sequence typing (MLST) result showed that the A: L3 strain is sequence type (ST) 134; the non-typable strains were recorded as the following new STs: ST 366 and ST 374. Using phylogenetic tree analysis based on MLST sequences, we determined that ST 366 and ST 374 are closely related to the reference strains that were previously isolated from duck and chicken in Korea, and they were highly prevalent within the Korean cluster. In conclusion, Pasteurella multocida strains were identified and isolated in this study. Furthermore, this is the first report of using MLST to determine the prevalence of fowl cholera in Korea. (
Obtaining new Ruscus products using Gamma radiation

Ruscus is an evergreen shrub that offers dark-green glossy foliage used as green additions to bouquets and flower arrangements. One of the most significant ways to obtain new varieties of crop and ornamental plants is to induce mutations by radiation. Gamma radiation is most commonly used to obtain mutants in commercial food as well as feed crops and ornamental plants. In this study, we developed tissue culture methods for Ruscus proliferation from rhizomes to obtain rhizomes clusters. These clusters were subsequently irradiated with Gamma rays to obtain unique phenotypes, such as: elongated narrow phylloclades modified stem symmetry and dwarfed growth habit. Such Ruscus types can contribute to the expansion of the floral industry.
Grit and Flow as Prescriptions for Self-Actualization

Psychology as a discipline has historically focused on psychopathology and associated evidence-based modalities to alleviate symptoms of mental illness and distress. In recent decades, however, there has been a shift towards studying the positive aspects of human experience that make life worth living. This Positive Psychological perspective was utilized by Humanistic Psychologist Abraham Maslow when he created his theory of Self-Actualization, in which he postulated that the highest aim a person can strive towards is to become the absolute best version of themselves, and to maximize their abilities and potential to the highest possible degree. Aside from describing a hierarchy of needs required before Self-Actualization could be realized, Maslow left little recommendation for how the phenomenon could be enacted. This article recommends the combined utilization of the non-cognitive trait of Grit along with the implementation of activities that promote the full-immersion state of Flow as prescriptions for Self-Actualization. Additional emphasis is placed on the importance of healthcare professionals utilizing the concepts Grit and Flow in their clinical practice with patients.
A step-by-step guide to design, implement, and analyze a discrete choice experiment

Discrete Choice Experiments (DCE) have been widely used in health economics, environmental valuation, and other disciplines. However, there is a lack of resources disclosing the whole procedure of carrying out a DCE. This document aims to assist anyone wishing to use the power of DCEs to understand people's behavior by providing a comprehensive guide to the procedure. This guide contains all the code needed to design, implement, and analyze a DCE using only free software.
Keeping up Appearances: Computational Modeling of Face Acts in Persuasion Oriented Discussions

The notion of face refers to the public self-image of an individual that emerges both from the individual's own actions as well as from the interaction with others. Modeling face and understanding its state changes throughout a conversation is critical to the study of maintenance of basic human needs in and through interaction. Grounded in the politeness theory of Brown and Levinson (1978), we propose a generalized framework for modeling face acts in persuasion conversations, resulting in a reliable coding manual, an annotated corpus, and computational models. The framework reveals insights about differences in face act utilization between asymmetric roles in persuasion conversations. Using computational models, we are able to successfully identify face acts as well as predict a key conversational outcome (e.g. donation success). Finally, we model a latent representation of the conversational state to analyze the impact of predicted face acts on the probability of a positive conversational outcome and observe several correlations that corroborate previous findings.
Nonlinear stability of chemotactic clustering with discontinuous advection

We perform the nonlinear stability analysis of a chemotaxis model of bacterial self-organization, assuming that bacteria respond sharply to chemical signals. The resulting discontinuous advection speed represents the key challenge for the stability analysis. We follow a perturbative approach, where the shape of the cellular profile is clearly separated from its global motion, allowing us to circumvent the discontinuity issue. Further, the homogeneity of the problem leads to two conservation laws, which express themselves in differently weighted functional spaces. This discrepancy between the weights represents another key methodological challenge. We derive an improved Poincare inequality that allows to transfer the information encoded in the conservation laws to the appropriately weighted spaces. As a result, we obtain exponential relaxation to equilibrium with an explicit rate. A numerical investigation illustrates our results.
Understanding the origin of white dwarf atmospheric pollution by dynamical simulations based on detected three-planet systems

Between 25-50 % of white dwarfs (WD) present atmospheric pollution by metals, mainly by rocky material, which has been detected as gas/dust discs, or in the form of photometric transits in some WDs. Planets might be responsible for scattering minor bodies that can reach stargazing orbits, where the tidal forces of the WD can disrupt them and enhance the chances of debris to fall onto the WD surface. The planet-planet scattering process can be triggered by the stellar mass-loss during the post main-sequence evolution of planetary systems. In this work, we continue the exploration of the dynamical instabilities that can lead to WD pollution. In a previous work we explored two-planet systems found around main-sequence (MS) stars and here we extend the study to three-planet system architectures. We evolved 135 detected three-planet systems orbiting MS stars to the WD phase by scaling their orbital architectures in a way that their dynamical properties are preserved by using the $N$-body integrator package Mercury. We find that 100 simulations (8.6 %) are dynamically active (having planet losses, orbit crossing and scattering) on the WD phase, where low mass planets (1-100 $\mathrm{M}_\oplus$) tend to have instabilities in Gyr timescales while high mass planets ($>100~\mathrm{M}_\oplus$) decrease the dynamical events more rapidly as the WD ages. Besides, 19 simulations (1.6 %) were found to have planets crossing the Roche radius of the WD, where 9 of them had planet-star collisions. Our three-planet simulations have an slight increase percentage of simulations that may contribute to the WD pollution than the previous study involving two-planet systems and have shown that planet-planet scattering is responsible of sending planets close to the WD, where they may collide directly to the WD, become tidally disrupted or circularize their orbits, hence producing pollution on the WD atmosphere.
Principled Uncertainty Estimation for High Dimensional Data

The ability to quantify the uncertainty in the prediction of a Bayesian deep learning model has signiﬁcant practical implications—from more robust machine-learning based systems to more effective expert-in-the loop processes. While several general measures of model uncertainty exist, they are often intractable in practice when dealing with high dimensional data such as long sequences. Instead, researchers often resort to ad hoc approaches or to introducing independence assumptions to make computation tractable. We introduce a principled approach to estimate uncertainty in high dimensions that circumvents these challenges, and demonstrate its beneﬁts in de novo molecular design.
A REVIEW PAPER ON EMOTION RECOGNITION

— In the past decade a lot of research has gone into Speech Emotion Recognition (SER). Recognition of emotion is always a difficult problem, particularly if the recognition of emotion is done by using speech signal. In human machine interface application, emotion recognition from the speech signal has been research topic since many years. This paper reviews similar works done in the domain of Speech Emotion Recognition (SER) system. It highlights various feature extraction methods, prosodic and spectral features, and various models, Gaussian Mixture Model (GMM), k-Nearest Neighbours (k-NN), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) etc, used resulting in varying levels of accuracy. After analysing various similar papers, this paper attempts to provide a comparative study on effect of models and feature vectors on SER system accuracy.
The Quotient in Preorder Theories

Seeking the largest solution to an expression of the form A x <= B is a common task in several domains of engineering and computer science. This largest solution is commonly called quotient. Across domains, the meanings of the binary operation and the preorder are quite different, yet the syntax for computing the largest solution is remarkably similar. This paper is about finding a common framework to reason about quotients. We only assume we operate on a preorder endowed with an abstract monotonic multiplication and an involution. We provide a condition, called admissibility, which guarantees the existence of the quotient, and which yields its closed form. We call preordered heaps those structures satisfying the admissibility condition. We show that many existing theories in computer science are preordered heaps, and we are thus able to derive a quotient for them, subsuming existing solutions when available in the literature. We introduce the concept of sieved heaps to deal with structures which are given over multiple domains of definition. We show that sieved heaps also have well-defined quotients.
Effects of Non-locality in Gravity and Quantum Theory

Spacetime—the union of space and time—is both the actor and the stage during physical processes in our fascinating Universe. In Lorentz invariant local theories, the existence of a maximum signalling speed (the “speed of light”) determines a notion of causality in spacetime, distinguishing the past from the future, and the cause from the effect.This thesis is dedicated to the study of deviations from locality. Focussing on a particular class of non-local theories that is both Lorentz invariant and free of ghosts, we aim to understand the effects of such non-local physics in both gravity and quantum theory. Non-local ghost-free theories are accompanied by a parameter l of dimension length that parametrizes the scale of non-locality, and for that reason we strive to express all effects of non-locality in terms of this symbol. In the limiting case of l = 0 one recovers the local theory, and the effects of non-locality vanish.In order to address these questions we develop the notion of non-local Green functions, study their causal properties, and demonstrate that non-locality leads to a violation of causality on small scales but may be recovered at macroscopic distances much larger than the scale of non-locality. In particular, we utilize non-local Green functions to construct the stationary gravitational field of point particles and extended bodies in the weak-field limit of non-local gravity and demonstrate explicitly that non-locality regularizes gravitational singularities at the linear level. Boosting these solutions to the speed of light in a suitable limit, we obtain a class of geometries corresponding to non-local regular ultrarelativistic objects.In the context of quantum mechanics we demonstrate that non-locality affects the scattering coefficients of a scalar field in the presence of a δ-shaped potential: for a critical frequency, the potential becomes completely opaque and reflects 100% of the incoming wave of that frequency. In the realm of non-local quantum field theory we first illustrate that non-locality smoothes the vacuum polarization and thermal fluctuations in the vicinity of a δ-shaped potential and then prove the fluctuation-dissipation theorem in this particular case. Turning towards quantum field theory in curved spacetime, we construct a non-local ghost-free generalization of the Polyakov effective action and evaluate the resulting quantum average of the energy-momentum tensor in the background of a two-dimensional black hole. While non-locality does not affect the asymptotic flux of Hawking radiation in this model, the entropy of the black hole is sensitive to the presence of non-locality. The results presented in this thesis establish several effects of a Lorentz invariant, ghost-free non-locality in the areas of both gravitational and quantum physics.
Measuring students' interest in physics

Verena Spatz, Jan-Philipp Burde, Thomas Wilhelm, Lana Ivanjek, Martin Hopf Thomas Schubatzky, and Claudia Haagen-Schützenhöfer TU Darmstadt, Department of Physics, Physics Education, Hochschulstraße 12, Darmstadt, Germany 2 University of Tübingen, Physics Education Research Group, Auf der Morgenstelle 14, Tübingen, Germany Goethe University, Department of Physics Education Research, Max-von-Laue-Str. 1, Frankfurt a. M. , Germany University of Vienna, Austrian Educational Competence Centre Physics, Porzellangasse 4, Vienna, Austria University of Graz, Regional Centre for Didactics of Physics, Universitätsplatz 5, Graz, Austria