Miracle-dz/UniFAR-FTUs huggingface dataset:
Precision fertigation for sustainable agriculture in Saudi Arabia

Agriculture in Saudi Arabia is dependent on finite water resources. Efficient use of depleting water resources is a dire necessity to attain sustainability. Precision fertigation technology helps in optimizing input use in agricultural activities. Investigations were carried out to study the response of wheat and alfalfa to precision fertigation. Field experiments were conducted in two center pivot fields each measuring 50 ha. Split plot design was adopted for the two experiments each with three replications. For both wheat and alfalfa, four irrigation treatments allocated to main plots included I1: Irrigation at 100% ETc, I2: Irrigation at 90% ETc, I3: Irrigation at 80% ETc and I4: Irrigation at 70% ETc. Three fertilizer levels formed sub-plot treatments in both the experiments. In wheat experiment, three levels of fertilizer nitrogen, phosphorus (P2O5) and potassium (K2O) formed the sub plot treatments. The three fertilizer levels were defined as F1 (Low): 300:200:200 kg ha -1 ; F2 (Medium): 400:250:250 kg ha -1 and F3 (High): 500:300:300 kg ha -1 . In alfalfa experiment, three fertilizer levels included were Low (126:92:300), Medium (234:138:400) and High (342:184:500) (kg N:P2O5:K2O/ha/year). Results of the studies indicated that precision fertigation could help to save 20 and 30 per cent of water used in the production of alfalfa and wheat without sacrificing the yield.
A Comparative Study of Student Question Generation in Web-based and Paper-based Learning Environment

Over the last few decades, researchers have been investigating the uses of student-generated questions (SGQs) in multifarious areas. In understanding the potential benefits of SGQ, this action research is designed to examine a group of students’ perceptions after experiencing interventions of web-based student generated questions (e-SGQs) and paper-based student-generated questions (PB-SGQs) in the classroom. For this research, ten students were recruited to construct test items for their own mid-term preparation. The student generated items were split fairly evenly with about half of the items being e-SGQs and the other half being PB-SGQs. The two modes were compared through the analyses of a survey, a group interview, and individual in-depth interviews. The data analyses reveal that the participants perceived their experiences of generating questions to be very helpful for understanding their class content. Although many of them clearly recognized the advantages of e-SGQs as an educational tool, most of the participants chose PB-SGQ over e-SGQ. This result can be explained by the survey and interview data which illustrate that the respondents preferred the test taking strategies that they were used to such as marking, underlining, and skimming through the test items on a paper.
CALCULATING VOXEL-POLYHEDRON INTERSECTIONS FOR MESHING IMAGES

Finite element meshes constructed from 3D images are useful in materials science and medical applications when it is necessary to model the actual geometry of a sample, rather than an idealized approximation of it. Constructing the mesh involves computing the intersection of the mesh elements with the voxels (3D pixels) of the image. If done naively, this process is unstable, and small errors in the computed position of an intersection point can lead to large errors in the computed volume. We demonstrate the source of the instability and present a robust and eﬃcient method of doing the computation, based on the r3d algorithm of Powell and Abel. The method was developed in the context of the OOF3D program at NIST, but the freely available code is completely independent of OOF3D
A Model of Social Networks and Migration Decisions∗

This paper is motivated by rich empirical evidence regarding the importance of social contacts abroad for the decision making of potential migrants. It aims at providing a microfoundation for the well-established hypothesis in the chain migration literature that attributes much of the impact of social contacts to their role as information providers about jobs in the foreign labor market. The proposed theoretical model has two countries, a home country and a destination country, and agents in both countries share information about job opportunities through an explicitly modelled network of social relations. The information about jobs affects employment prospects and hence, expected income of the agents in each country. This in turn determines the decisions of the home country residents on whether to migrate or to stay, which in the model is represented by an outcome of the migration decision game. I study the effects of the social network on equilibrium migration decisions and in particular, the effects of a small change in the number of social contacts in the destination country on the size of the migration flow. Using the results of analytical work and numerical simulations, I find that even a marginal increase in the number of links between countries may lead to a substantial increase in migration. JEL Classification: A14, F22, J61, J64
Chemical profile and allelopathic potential of Anacardium humile St. Hill. (cajuzinho-do-cerrado) leaf aqueous extract in the seed germination and seedling growth of lettuce, tomato and sicklepod

Anacardium genus, Anacardiaceae, stands out for the presence of phenolic compounds. One of its species, investigated for its different potential uses, is Anacardium humile; however, little is known about its allelopathic effects. Therefore, the present study aimed to determine the chemical profile and evaluate the herbicide potential of your leaves in the germination of seeds and growth of seedlings of Lactuca sativa (lettuce), Lycopersicon esculentum (tomato) and Senna obtusifolia (sicklepod), both in vitro and in greenhouse. Leaves of A. humile were obtained from 20 matrices of Cerrado fragments in the municipality of Campo Grande, Mato Grosso do Sul state, Brazil. A voucher specimen was deposited at the herbarium (no. 8448). The aqueous extract was obtained from dried and crushed leaves using the extraction method of ultrasonic bath (30 min) with subsequent static maceration. After solvent evaporation, 12.78 g of extract were obtained. The chemical profile of the aqueous extract included determination of total phenolic and flavonoid contents, pH, electrical conductivity, and soluble solids concentration. For the in vitro bioassays, the extract was used at different concentrations, namely, 25, 50, 100, 150, and 200 mg mL-1. In greenhouse, the substrates were prepared using 950 g of vermiculite and 50 g of A. humile leaf powder at the following ratios: (950/50) (5%), 900/100 (10%), 800/200 (20%), and a control (100% vermiculite), under a completely randomized experimental design. Phenolic compounds and anthraquinones were predominant in the chemical profile of the extract, which presented different levels of allelopathic effect on seed germination and seedling growth of plants both in germination chamber and in greenhouse, indicating strong allelopathic activity associated with the different compounds found in the leaves. Therefore, the results showed germination and growth inhibition at different levels, indicating that the substances contained in the leaves of Anacardium humile may be a promising alternative for the control of invasive species.
EFFECTIVENESS OF NORDIC WALKING AND PHYSICAL TRAINING IN IMPROVING BALANCE AND BODY COMPOSITION OF PERSONS WITH DOWN SYNDROME

Aim The aim of this study was to evaluate the effects of Nordic Walking training and physical training on the balance and body composition in adult people with Down syndrome. Basic procedures We enrolled 32 subjects with Down syndrome, aged 25-40 years with moderate intellectual disability. They were randomly divided into three groups: Nordic Walking training group, physical training group and control group with no intervention. Training sessions were held for 10 weeks at a frequency of 3 times a week. Subjects were examined twice: 1 week before training and a week immediately after intervention. To evaluate balance we applied modified Clinical Test for Sensory Integration and Balance (mCTSIB) on BioSway platform. Evaluation of body composition was assessed with Tanita Body Composition Analyzer TBF – 300. Results After training, in the mCTSIB statistically significant changes were observed only in the Nordic Walking group. In the control group in the mCTSIB the changes were not reported. There were observed improvements in the body composition after Nordic Walking training and in the control group there were deteriorated. Conclusions The research shows that regular physical activity such as Nordic Walking training has positive influence on the balance in people with Down syndrome. The changes were greater in people participated in Nordic Walking training rather than physical training. Both of the study groups presented improvement compared to controls. Key words: Down syndrome, Nordic Walking, disability, balance, stability, BMI, mCTSIB, body composition, rehabilitation
Abstract and Compare: A Framework for Defining Precision Measures for Automated Process Discovery

Automated process discovery techniques allow us to extract business process models from event logs. The quality of process models discovered by these techniques can be assessed with respect to various quality criteria related to simplicity and accuracy. One of these criteria, namely precision, captures the extent to which the behavior allowed by a discovered process model is observed in the log. While numerous measures of precision have been proposed in the literature, a recent study has shown that none of them fulfils a set of five axioms that capture intuitive properties behind the concept of precision. In addition, several existing precision measures suffer from scalability issues when applied to models discovered from real-life event logs. This paper presents a versatile framework for defining precision measures based on behavior abstractions. The key idea is that a precision measure can be defined by three ingredients: a function that abstracts a process model (e.g. as a transition system), a function that does the same for an event log, and a function that compares the behavior abstraction of the model with that of the log. We show empirically that different instances of this framework allow us to strike different tradeoffs between scalability and sensitivity. We also show that two instances of the framework based on lossless abstraction functions yield a precision measure that fulfils all the above-mentioned axioms.
Reducing uncertainties in satellite estimates of aerosol-cloud
interactions over the subtropical ocean by integrating vertically
resolved aerosol observations

Abstract. Satellite quantification of aerosol effects on clouds relies on aerosol optical depth (AOD) as a proxy for aerosol concentration or cloud condensation nuclei (CCN). However, the lack of error characterization of satellite-based results hampers their use for the evaluation and improvement of global climate models. We show that the use of AOD for assessing aerosol-cloud interactions (ACI) is inadequate over vast oceanic areas in the subtropics. Instead, we postulate that a more physical approach that consists of matching vertically resolved aerosol data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite at the cloud-layer height with Aqua Moderate-resolution Imaging Spectroradiometer (MODIS) cloud retrievals reduces uncertainties in satellite-based ACI estimates. Combined aerosol extinction coefficients (σ) below cloud-top (σBC) from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and cloud droplet number concentrations (Nd) from Aqua-MODIS yield high correlations across a broad range of σBC values, with σBC quartile correlations > 0.78. In contrast, CALIOP-based AOD yields correlations with MODIS Nd of less than 0.62 for the two lower AOD quartiles. Moreover, σBC explains 41 % of the spatial variance in MODIS Nd, whereas AOD only explains 17 %, primarily caused by the lack of spatial covariability in the eastern Pacific. Compared with σBC, near-surface σ weakly correlates in space with MODIS Nd, accounting for a 16 % variance. It is concluded that the linear regression calculated from ln(Nd)−ln(σBC) (the standard method for quantifying ACI) is more physically meaningful than that derived from the Nd−AOD pair.
Labor market sorting and health insurance system design

This paper develops and estimates a life-cycle equilibrium labor search model in which heterogeneous firms determine health insurance provisions and heterogeneous workers sort themselves into jobs with different compensation packages over the life cycle. I study the optimal joint design of major policies in the Affordable Care Act (ACA) and the implications of targeting these policies to certain individuals. Compared with the health insurance system under the ACA, the optimal structure lowers the tax benefit of employer-sponsored health insurance and makes individual insurance more attractive to younger workers. Through changes in firms’ insurance provisions, a greater number of younger workers sort into individual markets, which contributes to improving the risk pool in individual insurance and lowering the uninsured risk.
Termite: Emulation Test-Bed for Encounter Networks

The mobile device presence in everyday life is growing faster and faster. Devices like smartphones and tablets keep growing in performance, battery life and communication. The communication improvement allows mobile applications to take advantage of encounters between co-located users. Nevertheless, the capability of testing and debugging tools available is not enough to deal with the demand of encounter network applications. So we present Termite. Termite is an emulation test-bed for encounter network applications. Based on the necessity to support application testing and debugging in encounter networks emulations, Termite allow developers to model their own encounter network. By using a unique Petri Net variant it is possible to model topology in a dynamic way and translate user interactions with virtual devices. Through its breakpoints and step-by-step execution, Termite helps in the developers job. Termite’s architecture was designed to be efficient in managing multiple hosting infrastructures to help developers. Termite design also allows to support heterogeneous mobile platforms. The developed prototype implementation is done in Android, in virtual devices through Wi-Fi Direct networks. These networks can run on top of local or cloud infrastructure. The evaluation made can not only prove that Termite is expressive and performs well, but also that the system have good usability and corresponds to the expectations under real-case scenarios.
Learning optimal nonlinearities for iterative thresholding algorithms /Author=Kamilov, Ulugbek; Mansour, Hassan /CreationDate=May 26, 2016 /Subject=Computational Sensing

Iterative shrinkage/thresholding algorithm (ISTA) is a well-studied method for finding sparse solutions to illposed inverse problems. In this letter, we present a data-driven scheme for learning optimal thresholding functions for ISTA. The proposed scheme is obtained by relating iterations of ISTA to layers of a simple feedforward neural network and developing a corresponding error backpropagation algorithm for fine-tuning the thresholding functions. Simulations on sparse statistical signals illustrate potential gains in estimation quality due to the proposed data adaptive ISTA. 2016 IEEE Signal Processing Letters This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c © Mitsubishi Electric Research Laboratories, Inc., 2016 201 Broadway, Cambridge, Massachusetts 02139 LEARNING OPTIMAL NONLINEARITIES FOR ISTA 1 Learning optimal nonlinearities for iterative thresholding algorithms Ulugbek S. Kamilov, Member, IEEE and Hassan Mansour, Member, IEEE Abstract—Iterative shrinkage/thresholding algorithm (ISTA) is a well-studied method for finding sparse solutions to illposed inverse problems. In this letter, we present a data-driven scheme for learning optimal thresholding functions for ISTA. The proposed scheme is obtained by relating iterations of ISTA to layers of a simple feedforward neural network and developing a corresponding error backpropagation algorithm for fine-tuning the thresholding functions. Simulations on sparse statistical signals illustrate potential gains in estimation quality due to the proposed data adaptive ISTA.
His strength is my strategies: Experience of an English teacher in Indonesia teaching English for hyperactive students in inclusive class

Need analysis had been conducted as studies to get the strategies of teaching English for hyperactive students in the inclusive class. Some of the studies wanted to see the problem of hyperactive students while others saw the barrier of hyperactive students in learning English. However, there were only a few studies giving attention to analyze the strength of hyperactive students in learning English. This study aimed to describe how an English teacher did the analysis of the strength of hyperactive students then used it to find a strategy (Role models) in teaching English for hyperactive students in the inclusive class. The data of this study were collected by semi-structured interview and observation. Descriptive qualitative approach as the research design of the research was used to analyze the data. The analysis of results showed that an English teacher not only did the analysis to the students needs such as problem and barrier but also in which more focused on the strength of hyperactive students. This study also provided the implication of role models strategy to teach English especially speaking skill for hyperactive students in inclusive class as the result of the analysis of the strength. The positive result from need analysis on the strength of Attention Deficit Hyperactive Disorder (ADHD) student in finding an appropriate strategy to teach English might also bring positive impact for the students with other types of special needs in inclusive class.
Design of Bio-Medical Embedded System for Tremor Analysis

Tremor is an involuntary action caused due to contraction of muscles. Doctors diagnose the illness of a patient based on tremor, as it is considered to be an important clinical symptom. Normally in medical diagnosis the method used presently to identify the tremor is Electromyography recorders (EMG), these readings depend on the emotions of the patient. So readings should be taken when the patient is in calm and stable state. The instrument is not easily portable because of its large size. These conditions definitely impose certain limitations on the utility of EMG in the diagnosis of tremors in patients. To overcome such limitations, the authors tried to propose and design a novel embedded system in which the average intensity over the measurement interval is recorded. The device is also highly portable for long-term measurement and recording of tremor movements. The measured tremor signals from MEMS sensor (accelerometer) through a Microcontroller are displayed on a 2×16 LCD (liquid crystal display), at the same time the tremor signals are transmitted to remote places, using MAX 232 IC and RS 232. A 1014 Bhamidi Rama and I.V. Subba Reddy serial communication is achieved between Personal Computer and the microcontroller. This microcontroller based portable embedded system for tremor analysis is a very innovative development in bio-medical field which can be used in the hospitals for diagnosis applications. The developed system consists of a MEMS sensor (accelerometer), RF (radio frequency) module, 2×16 LCD display, and microcontroller with PC (personal computer) interface. The MEMs sensor readings along X-axis and Y-axis for different age groups are taken and a Statistical Software tool IBM SPSS 20.0 VERSION is used to analyze the readings. The overall power consumption of the prototype board is also calculated and found to be 0.218 watthour.
SOUND EVENT DETECTION IN REAL LIFE AUDIO USING MULTI-MODEL SYSTEM

In this paper, we present a polyphonic sound event detection (SED) system based on a multi-model system. In the proposed multi-model system, we use one model based on Deep Neural Networks (DNN) to detect sound events of car, and five models based on Bi-directional Gated Recurrent Units Recurrent Neural Networks (BGRU-RNN) to detect other sound events including: brakes squeaking, children, large vehicle, people speaking and people walking. Since different classes sound events have different audio characteristics, we use different models to detect each class. The proposed multi-model system is trained and tested based on IEEE DCASE2017 Challenge: Sound Event Detection in Real Life Audio (Task 3) Development Dataset, the result yields up to 58.92% and 0.60 in terms of F-Score and error rate on segment-based metric respectively.
Improving Grasping Performance by Segmentation of Large Planar Surfaces

Grasping objects is a task that humans do without major concerns. This results from learning and observing other skilled humans doing such task and with previous information, unconsciously, we know how to pick up different types of objects. However, grasping novel objects in unknown positions for a robot is a complex task which encounters many problems, such as the performance rates that are not perfect and the time consumption. In this paper we present a method that complements the state-ofthe-art grasping by removing the largest planar surface of the image of the world before the grasp detector receives them. The proposed method improves the performance rate and is also capable of reducing the time consumption.
Hierarchical Energy Management System for Microgrid Operation Based on Robust Model Predictive Control

This paper presents a two-level hierarchical energy management system (EMS) for microgrid operation that is based on a robust model predictive control (MPC) strategy. This EMS focuses on minimizing the cost of the energy drawn from the main grid and increasing self-consumption of local renewable energy resources, and brings benefits to the users of the microgrid as well as the distribution network operator (DNO). The higher level of the EMS comprises a robust MPC controller which optimizes energy usage and defines a power reference that is tracked by the lower-level real-time controller. The proposed EMS addresses the uncertainty of the predictions of the generation and end-user consumption profiles with the use of the robust MPC controller, which considers the optimization over a control policy where the uncertainty of the power predictions can be compensated either by the battery or main grid power consumption. Simulation results using data from a real urban community showed that when compared with an equivalent (non-robust) deterministic EMS (i.e., an EMS based on the same MPC formulation, but without the uncertainty handling), the proposed EMS based on robust MPC achieved reduced energy costs and obtained a more uniform grid power consumption, safer battery operation, and reduced peak loads.