Miracle-dz/UniFAR-FTUs huggingface dataset:
PARTICIPATION IN COMMUNITY BASED NATURAL RESOURCE MANAGEMENT PROGRAMME AND EFFECTS ON WELFARE OF RURAL FAMILIES IN IKWERRE, RIVERS STATE, NIGERIA

The study was on participation in Community Based Natural Resource Management Programme (CBNRMP) and its socio-economic effect on rural families in Ikwerre Area, Rivers State Nigeria. A structured questionnaire was administered to 60 beneficiaries of the programme. Data collected were subjected to descriptive and inferential statistics. Findings of their income before programme intervention indicated that they earned N15,100N22,600 while a tremendous increase in income was observed after the programme’s intervention where most (70%) of them earned N 102,000.00 and above. The multiple regression coefficient (R 2 ) was 0.9215 which indicated that direct relationship exists between participation and socio-economic characteristics of the respondents. Participation in the Community Based Natural Resource Management Programme had empowered the beneficiaries in problem identification, ways of seeking for solution, project planning, implementation, monitoring and evaluation of projects and making of decisions in issues that pertain to their welfare and development. CBNRMP programme had impacted greatly on the welfare of participants in areas such as household items, saving/ investments and increase in livestock herds. However, beneficiaries’ were faced with some constraints which include: insufficient capacity building, insufficient storage facilities and inaccessibility to micro credit. The study therefore recommended that more emphasis should be on capacity building of beneficiaries of the programme, adequate provision of production inputs such as access to credit, farm inputs such as seeds and seedlings, fertilizers and reduction in cost of inputs should be taken into cognizance and proper mainstreaming of target beneficiaries of the programme intervention.
Perceptions of Professional Chemical Engineers Toward Immersive Virtual Reality in Health and Safety Training

Following the rapid advancement and growing market of immersive virtual reality (IVR), it is important to understand the impacts caused by these technological innovations. Research on feasibility, reliability, and ease of use of IVR has received considerable attention, but little is known about the specific factors weighing in the decision to adopt IVR in the health and safety (H&S) training setting. Since the success of implementing IVR in H&S training depends on the individuals willing to use it, this paper investigates the interrelationship between influential factors and behavioural intention to adopt IVR among different professional groups. To understand this, a conceptual framework was developed by adaptation of the Unified Theory of Acceptance and Use of Technology (UTAUT2). Data were collected using an online survey from professional chemical engineers. Partial least squares structural equation modelling (PLS-SEM) based on SmartPLS 3 was used to analyse the intention of the population sampled to adopt IVR, followed by multi-group analysis (MGA) to explore the differences between groups of professionals. The findings from this study contribute to the literature of the UTAUT2 model on IVR adoption and inform stakeholders in formulating appropriate strategies to improve the adoption of IVR in different group settings.
An IoT platform for occupancy prediction using support vector machine

ii DEDICATION . iii ACKNOWLEDGEMENTS . iv TABLE OF CONTENTS . v LIST OF TABLES . vii LIST OF FIGURES . viii CHAPTER 1: INTRODUCTION . 1 1.2. Scientific contributions . 4 1.3 Research Questions and Objectives.5 1.4. Organization of this Thesis . 6 CHAPTER 2: BACKGROUND . 7 2.1 Sensors . 8 2.2 NodeMCU . 14 2.3 Raspberry Pi 3 B+ Gateway . 15 2.4 Wireless Networks . 16 2.5 MQTT Protocol . 18 2.6 Computing Environments . 19 CHAPTER 3: LITERATURE. 24 3.1 IoT Platforms . 25 3.2 Occupancy Detection Methods . 29 3.3 Non-intrusive General-Purpose Sensing and SVM . 34 3.4 Summary . 36 CHAPTER 4: METHODOLOGY . 38 4.1 Overview of the proposed IoT Platform . 39 4.2 Fundamentals of SVM . 43 vi 4.3 The analytical pipeline for the SVM predictive model . 45 CHAPTER 5: EXPERIMENTS AND DISCUSSION OF RESULTS . 51 5.1 Description of the experiments . 51 5.2 Data pre-processing . 58 5.3 SVM implementation . 61 5.4 Discussion of Results . 63 CHAPTER 6: CONCLUSIONS AND FUTURE RESEARCH WORK . 70 6.1. Summary . 70 6.2 Research questions . 71 6.3 Future Research Work . 75 REFERENCES . 77
New Root-based Stemmer for Arabic Language

Importance of Arabic language stemming algorithm is not less than that of other languages stemming in Information Retrieval (IR) field. Lots of algorithms for finding the Arabic root are available and they are mainly categorized under two approaches which are light (stem)-based approach and root-based approach. The latter approach is somehow better than the first approach. A new root-based stemmer is proposed and its performance is compared with Khoja stemmer which is the most efficient root-based stemmers. The accuracy ratio of the proposed stemmer is (99.7) with a difference (1.9) with Khoja stemmer.
Attentive Normalization for Conditional Image Generation Supplementary Material

Attentive normalization consists of the semantic layout learning module and subsequent regional normalization. The semantic layout learning module includes selfsampling regularization, similar to that of self-attention [5] except that we need an additional convolutional layer (with n filters) to simulate the semantic entities and avoid computing pair-wise relationship between every two feature points from the input. The implementation of regional normalization is given in Algorithm. 1.
Intrusion Detection System for SDN based IoT Devices using Deep Neural Network

— One of the emerging technologies in the field of networking is the Software Defined Networking (SDN). Since it is a centrally controlled networks, it provides us with a better control to improve the security within our network against the potential threats. In this work we are using Deep Neural Network (DNN) model to detect the flow-based anomaly within the network. The model was trained on NSL-KDD dataset and out of forty-one features only six of the most relevant features of NSL-KDD were used. The results show that Deep Learning approach shows some promising results in detecting the anomaly in the SDN environment.
Effects of post-harvest storage conditions on the levels of glucosinolates in broccoli sprouts (Brassica oleracea var. italica) grown under different temperature regimes

Summary Broccoli sprouts have been recognised as a rich source of glucosinolates, particularly 4-methylsulphinylbutyl glucosinolate, the precursor of the potent anti-cancer compound, sulphoraphane. Previous results have shown that temperature can significantly affect the levels of glucosinolates. In this study, we showed how sprout age and storage temperature affected glucosinolate levels in broccoli sprouts grown under different temperature regimes. Experiments were conducted in growth cabinets with day/night temperature regimes of 30°/15°C, 22°/15°C and 18°/12°C. At 9, 10 and 11 d after sowing in the first temperature regime, 10, 11 and 12 d in the second, and 12, 13 and 14 in the third, sprouts were submitted to 4°C or 20°C to simulate refrigerated and room temperature storage. Sampling was done after 1 d or 2 d of exposure to these conditions. The results showed that total glucosinolate levels and the potential health effects of broccoli sprouts depended on the growth temperature regime (P < 0.05), the age of the sprouts (P < 0.001), and the storage conditions (P < 0.01). The highest total glucosinolate levels (65.7 µmoles g–1 dry weight) were obtained under the 30º/15°C temperature regime for the youngest sprouts (harvested 9 d after emergence), after being submitted to a storage temperature of 4°C for 24 h. However, these levels were much lower than in 3-d-old sprouts. Consuming old sprouts provides less health-protective effects due to reduced levels of glucosinolates.
Functional cortical networks of early preterm infants and their relationship to neurological performance

of the master’s thesis Author Pauliina Yrjölä Title Functional cortical networks of early preterm infants and their relationship to neurological performance Degree programme Life Science Technologies Major Biomedical Engineering Code of major SCI3059 Supervisor Prof. Matias Palva Advisors Dr. Anton Tokariev, Prof. Sampsa Vanhatalo Date 31.1. 2020 Number of pages 47 Language English
Correlators on non-supersymmetric Wilson line in N = 4 SYM and AdS 2 / CFT 1

Correlators of local operators inserted on a straight or circular Wilson loop in a conformal gauge theory have the structure of a one-dimensional “defect” CFT. As was shown in arXiv:1706.00756, in the case of supersymmetric Wilson-Maldacena loop in N = 4 SYM one can compute the leading strong-coupling contributions to 4-point correlators of the simplest “protected” operators by starting with the AdS5 × S5 string action expanded near the AdS2 minimal surface and evaluating the corresponding treelevel AdS2 Witten diagrams. Here we perform the analogous computations in the nonsupersymmetric case of the standard Wilson loop with no coupling to the scalars. The corresponding non-supersymmetric “defect” CFT1 should have an unbroken SO(6) global symmetry. The elementary bosonic operators (6 SYM scalars and 3 components of the SYM field strength) are dual respectively to the S5 embedding coordinates and AdS5 coordinates transverse to the minimal surface ending on the line at the boundary. The SO(6) symmetry is preserved on the string side provided the 5-sphere coordinates satisfy Neumann boundary conditions (as opposed to Dirichlet in the supersymmetric case); this implies that one should integrate over the S5 expansion point. The massless S5 fluctuations then have logarithmic propagator, corresponding to the boundary scalar operator having dimension ∆ = 5 √ λ + . at strong coupling. The resulting functions of 1d cross-ratio appearing in the 4-point functions turn out to have a more complicated structure than in the supersymmetric Also at Lebedev Institute and ITMP, Moscow State University. Open Access, c © The Authors. Article funded by SCOAP. https://doi.org/10.1007/JHEP05(2019)122 J H E P 0 5 ( 2 0 1 9 ) 1 2 2 case, involving polylog (Li3 and Li2) functions. We also discuss consistency with the operator product expansion which allows extracting the leading strong coupling corrections to the anomalous dimensions of the operators appearing in the intermediate channels.
A theoretical exercise of Marine Spatial Planning in the Flemish Cap and Flemish Pass (NAFO Divs. 3LM): implications for fisheries management in the high seas

A theoretical exercise of Maritime Spatial Planning (MSP) is currently being conducted within the Regulatory Area of the Northwest Atlantic Fisheries Organization (NAFO), using the MESMA framework to assess whether the existing science base is sufficient to support a potential spatially managed area. The case study is located in the high seas within the Flemish Cap – Flemish Pass area. It includes cold-water coral and deep-sea sponge vulnerable marine ecosystems (VMEs), bottom fishing closed areas, and different blue economy activities, such as high seas fisheries and offshore oil and gas. The paper summarizes the context setting for MSP, in a theoretical scenario to accommodate an emergent offshore hydrocarbon exploration and exploitation, minimizing impacts on VMEs and existing high seas fisheries. Biophysical and socio-economic components of the ecosystem are mapped, including the spatial overlapping between new and traditional uses of the marine space, focused on potential conflicts user-user (e.g. hydrocarbon industry and deep-sea fisheries) or userenvironment (e.g. hydrocarbon industry and VMEs) and considering transboundary conflicts (e.g. recent oil spills). Current and potential management measures are described. This is followed by a discussion on the role and challenges of MSP in Areas Beyond National Jurisdiction, as lessons learned from the present exercise. Finally, the future work is briefly outlined.
Improving exploration in reinforcement learning with temporally correlated stochasticity

— Reinforcement learning is a useful approach to solve machine learning problems by self-exploration when training samples are not provided. However, researchers usually ignore the importance of the choice of exploration noise. In this paper, I show that temporally self-correlated exploration stochasticity, generated by Ornstein-Uhlenbeck process, can sig-niﬁcantly enhance the performance of reinforcement learning tasks by improving exploration.
Evaluation of antimicrobial and antioxidant activity of areal part methanolic extract of Paeonia mascula (L.) Mill

Original Research Article Paeonia mascula is a plant used in folk medicine for treatment of antispasmodic, antitussive, tonic, inflammation, disturbances, the healing of wounds and gynecology. In this study, total phenolic, flavonoid and tannins contents, antioxidant and antimicrobial activities were evaluated. Antioxidant activity of areal part methanol extract was carried out by four different methods (DPPH, β-carotene, reduction power, and Iron chelation). However, the antimicrobial activity was realized by disc diffusion methods in using: Escherichia coli ATCC 25922 Salmonella typhimurium ATCC 13311, Staphylococcus aureus ATCC25923, klebsela pneumoniae ATCC70060, Shigella flexheri NCCB1406, Proteus mirabilis ATCC35659, Aspergillus niger 2CA936, Aspergillus flavius NRRL3357 and Candida albicans ATCC1024. Our results exhibit high antioxidant and antimicrobial activities.
Maize Kernel Abortion Recognition and Classification Using Binary Classification Machine Learning Algorithms and Deep Convolutional Neural Networks

Maize kernel traits such as kernel length, kernel width, and kernel number determine the total kernel weight and, consequently, maize yield. Therefore, the measurement of kernel traits is important for maize breeding and the evaluation of maize yield. There are a few methods that allow the extraction of ear and kernel features through image processing. We evaluated the potential of deep convolutional neural networks and binary machine learning (ML) algorithms (logistic regression (LR), support vector machine (SVM), AdaBoost (ADB), Classification tree (CART), and the K-Neighbor (kNN)) for accurate maize kernel abortion detection and classification. The algorithms were trained using 75% of 66 total images, and the remaining 25% was used for testing their performance. Confusion matrix, classification accuracy, and precision were the major metrics in evaluating the performance of the algorithms. The SVM and LR algorithms were highly accurate and precise (100%) under all the abortion statuses, while the remaining algorithms had a performance greater than 95%. Deep convolutional neural networks were further evaluated using different activation and optimization techniques. The best performance (100% accuracy) was reached using the rectifier linear unit (ReLu) activation procedure and the Adam optimization technique. Maize ear with abortion were accurately detected by all tested algorithms with minimum training and testing time compared to ear without abortion. The findings suggest that deep convolutional neural networks can be used to detect the maize ear abortion status supplemented with the binary machine learning algorithms in maize breading programs. By using a convolution neural network (CNN) method, more data (big data) can be collected and processed for hundreds of maize ears, accelerating the phenotyping process.
Backtesting Macroprudential Stress Tests

In this paper, we consider models of price-mediated contagion in a banking networkof common asset holdings. For these models, the literature proposed two alternativeclasses of liquidation dynamics:threshold dynamics(banks liquidate their invest-ment portfolios only after they have defaulted), andleverage targeting dynamics(banks constantly rebalance their portfolios to maintain a target leverage ratio). We introduce a one-parameter family of non-linear liquidation functions that inter-polates between these two extremes. We then test the capability of these modelsto predict actual bank defaults (and survivals) in the United States for the years2008-10. We show that the model performance depends on the type of shock be-ing imposed (idiosyncratic versus systematic). We identify the two most relevantasset classes, for which the model has predictive power when these asset classes areexposed to an initial shock. In these cases, the model performs better than alter-native benchmarks that do not account for the network of common asset holdings,irrespective of the assumed liquidation dynamics. We also show how the best per-forming liquidation dynamics depend on the combination of the initial shock leveland the market impact parameter, on the cross-sectional variation in the marketimpact parameter, and on the number of asset liquidation rounds.
An Improvement On The Naïve Bayes Classifier

Naïve Bayes classifier is a simple and efficient classification method. Classification is a process which classifies data into one of several predefined categorical classes. In Naïve Bayes classifier, all the features are assumed as independent of each other within each class. Structural learning among the features thus helps in the classification problem. Structural learning using a hill-climbing algorithm is used in this study to learn the causal relationship and dependencies among the attributes. The accuracy of prediction by structural learning of the features using hill climbing is better than Naïve Bayes classifier using a tenfold cross validation.
COMPUTATIONAL FLUID DYNAMICS FOR MODELING AND SIMULATION OF INTRAOCULAR DRUG DELIVERY AND WALL SHEAR STRESS IN PULSATILE FLOW

Abootorabi, Seyedalireza, M.S.M.E, Purdue University, August 2020. Computational Fluid Dynamics for Modeling and Simulation of Intraocular Drug Delivery and Wall Shear Stress in Pulsatile Flow. Major Professor: Dr. Huidan Yu . The thesis includes two application studies of computational fluid dynamics. The first is new and efficient drug delivery to the posterior part of the eye, a growing health necessity worldwide. Current treatment of eye diseases, such as age-related macular degeneration (AMD), relies on repeated intravitreal injections of drug-containing solutions. Such a drug delivery has significant drawbacks, including short drug life, vital medical service, and high medical costs. In this study, we explore a new approach of controlled drug delivery by introducing unique porous implants. Computational modeling contains physiological and anatomical traits. We simulate the IgG1 Fab drug delivery to the posterior eye to evaluate the effectiveness of the porous implants to control the drug delivery. The computational model was validated by established computation results from independent studies and experimental data. Overall, the results indicate that therapeutic drug levels in the posterior eye are sustained for eight weeks, similar to those performed with intravitreal injection of the same drug. We evaluate the effects of the porous implant on the time evaluation of the drug concentrations in the sclera, choroid, and retina layers of the eye. Subsequent simulations were carried out with varying porosity values of a porous episcleral implant. Our computational results reveal that the time evolution of drug concentration is distinctively correlated to drug source location and pore size. The response of this porous implant for controlled drug delivery applications was examined. A correlation between porosity and fluid properties for the porous implants was revealed in this study. The second application lays in the computational modeling of the oscillating
Improving Participation of Non-Traditional Extension Audiences: The Empower Ocala Garden Project

Marion County Extension created the Empower Ocala Garden project to increase participation among low-income minority populations and address "food desert" conditions around its office. The project built trusting relationships, created a community garden for 12 households, and provided bi-weekly garden skills trainings. Participation, attitudinal changes, and knowledge gains were evaluated using pre- and post-project questionnaires. On average participants attended 53.4% of sessions. Attitudes improved by 9.82% across four gardening-related indicators, while knowledge increased by 19.57% across eight indicators. Overall, the project successfully engaged new clients, positively changed attitudes and knowledge, and may benefit other Extension professionals serving these audiences.
Comparative performance evaluation of self-adaptive differential evolution with GA, SCE and DE algorithms for the automatic calibration of a computationally intensive distributed hydrological model

Recently, physically-based hydrological models have been gaining much popularity in various activities of water resources planning and management, such as assessment of basin water availability, floods, droughts, and reservoir operation. Every hydrological model contains some parameters that must be tuned to the catchment being studied to obtain reliable estimates from the model. This study evaluated the performance of different evolutionary algorithms, namely genetic algorithm (GA), shuffled complex evolution (SCE), differential evolution (DE), and self-adaptive differential evolution (SaDE) algorithm for the parameter calibration of a computationally intensive distributed hydrological model, variable infiltration capacity (VIC) model. The methodology applied and tested for a case study of the upper Tungabhadra River basin in India and the performance of the algorithms is evaluated in terms of reliability, variability, efficacy measures in a limited number of function evaluations, their ability for achieving global convergence, and also by their capability to produce a skilful simulation of streamflows. The results of the study indicated that SaDE facilitates an effective calibration of the VIC model with higher reliability and faster convergence to optimal solutions as compared to the other methods. Moreover, due to the simplicity of the SaDE, it provides easy implementation and flexibility for the automatic calibration of complex hydrological models.
Sparse bayesian polynomial chaos approximations of elasto-plastic material models

In this paper we studied the uncertainty quantification in a functional approximation form of elastoplastic models parameterised by material uncertainties. The problem of estimating the polynomial chaos coefficients is recast in a linear regression form by taking into consideration the possible sparsity of the solution. Departing from the classical optimisation point of view, we take a slightly different path by solving the problem in a Bayesian manner with the help of new spectral based sparse Kalman filter algorithms.