Miracle-dz/UniFAR-FTUs huggingface dataset:
Idea generation and planning time in second language academic writing : an empirical investigation at Howard College campus, University of KwaZulu-Natal, Durban, South Africa.

ix LIST OF FIGURES AND TABLES x CHAPTER 1: INTRODUCTION AND BACKGROUND TO THE STUDY 1.1. The background of the study 1 1.2. The rationale for the study 4 1.3. Research objectives 5 1.4. Key research questions 5 1.5. Research methodology/approach to study 5 1.6. Location of the study 6 1.7. Definition of terminologies 7 1.7. 1. Idea generation 7 1.7. 2. Task environment 7 1.7. 3. English as a second language (ESL) 8 1.8. Chapters outline 8
Tweetstorming in the Language Classroom : Impact on EFL Tertiary Students' Ideational Fluency and Syntactic Complexity

The last few years have witnessed a paradigm shift in educational settings where language educators and practitioners have turned their focus from traditional face-to-face classroom practices to more hybrid and virtual language teaching/learning methodologies. This paradigm shift gained momentum with the introduction of Web 2.00 tools and social media applications and the increased tendency in education and workplace towards more technology-driven practices and solutions. The current study reports on an experimental treatment to employ Tweetstorming in writing classes of tertiary students and studying the impact on their ideational fluency and syntactic complexity. Participants were EFL tertiary students enrolled in Writing I course of the English Study program of Abu Dhabi University. Results of the study indicate that using Tweetstorming in the writing classes of tertiary EFL students brought about significant gains in their ideational fluency and syntactic complexity. Details of the instructional treatment along with the tools used in measuring students’ ideational fluency and syntactic complexity will be discussed. Recommendations for language education as well as suggestions for further research will be presented.
Beyond the Traditional Volcano Concept: Overpotential-Dependent Volcano Plots Exemplified by the Chlorine Evolution Reaction over Transition-Metal Oxides

The chlorine evolution reaction (CER) over a single-crystalline RuO2(110) model electrode is one of the best understood model systems in the field of electrocatalysis, which is taken here as a benchmark system to advance the concept of activity-based Volcano plots. Volcano curves can be derived from linear scaling relationships, in which thermodynamic considerations based on Sabatier’s principle and the Bronsted–Evans–Polanyi relation at zero overpotential are assumed to describe activity trends of electrocatalysts within a homologous series of materials. However, the underlying approach does not capture the influence of the applied overpotential on the activity, which is given by the Tafel slope. This may explain, why in certain cases the traditional Volcano analysis at zero overpotential does not reproduce activity trends of highly active catalytic materials with an overpotential-dependent Tafel slope correctly. Herein, a novel approach of overpotential-dependent Volcano plots is presented, which connec.
Theory Of Edge Diffraction In Electromagnetics

Thank you for downloading theory of edge diffraction in electromagnetics. As you may know, people have search numerous times for their favorite readings like this theory of edge diffraction in electromagnetics, but end up in malicious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some infectious bugs inside their laptop. theory of edge diffraction in electromagnetics is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the theory of edge diffraction in electromagnetics is universally compatible with any devices to read.
A preliminary categorization of what mathematics undergraduate students include on exam 'crib sheets'

Many undergraduate examinations permit students to use a limited quantity of previously prepared notes: so-called “crib sheets”, or “cheat-sheets”. The majority of evidence from the literature suggests that students sitting such exams feel less anxious, and that they perform to a higher standard, although such results are idiosyncratic to discipline and course, and few are set in the context of undergraduate mathematics. Less is known about what content students choose to include on such a sheet, and how they interact with this material. This preliminary research report presents the first results from a three-year project investigating students’ use of crib sheets in undergraduate mathematics exams. It explores the content and layout of crib sheets used by students for an end-of-semester calculus exam.
Combinatorial Optimization Networks And Matroids

combinatorial optimization networks and matroids is available in our digital library an online access to it is set as public so you can download it instantly. Our book servers hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the combinatorial optimization networks and matroids is universally compatible with any devices to read.
Ehrlich's reaction of furanoeremophilanes

6-Ethoxyfuranoeremophilane and its synthetic analogue, 4-ethoxy-4,5,6,7-tetrahydrobenzofuran, reacted with p-dimethylaminobenzaldehyde (Ehrlich's reagent) under acidic conditions in a 2:1 ratio to afford condensation products in 34% and 78% yields, respectively, 6-Hydroxyfuranoeremophilane and 4-hydroxy-4,5,6,7-tetrahydrobenzofuran afforded the same products as in the case of ethoxy derivatives, respectively. The intermediate 1:1 adduct was not obtained from 6-ethoxyfuranoeremophilane, but obtained from the synthetic analogue in low yield. It was deduced that the colored compound in Ehrlich's test is the cationic species of the intermediate 1:1 adduct, and that the concentration of the colored intermediate in the reaction mixture is very low.
Dimensionality reduction for k-means clustering

In this thesis we study dimensionality reduction techniques for approximate k-means clustering. Given a large dataset, we consider how to quickly compress to a smaller dataset (a sketch), such that solving the k-means clustering problem on the sketch will give an approximately optimal solution on the original dataset. First, we provide an exposition of technical results of [CEM15], which show that provably accurate dimensionality reduction is possible using common techniques such as principal component analysis, random projection, and random sampling. We next present empirical evaluations of dimensionality reduction techniques to supplement our theoretical results. We show that our dimensionality reduction algorithms, along with heuristics based on these algorithms, indeed perform well in practice. Finally, we discuss possible extensions of our work to neurally plausible algorithms for clustering and dimensionality reduction. This thesis is based on joint work with Michael Cohen, Samuel Elder, Nancy Lynch, Christopher Musco, and Madalina Persu. Thesis Supervisor: Nancy A. Lynch Title: Professor of Electrical Engineering and Computer Science
The Strategies for Quorum Satisfaction in Host-to-Host Meeting Scheduling Negotiation

This paper proposes two strategies for handling conflict schedule of two meetings which invite the same member of personnel at the same time through host-to-host negotiation scheme. The strategy is to let the member attend the other meeting under the condition that the group decision regarding the schedule is not changed and meeting quorum is fulfilled, namely release strategy. Other strategy is to substitute the absent personnel in order to keep the number of attendees above the quorum, namely substitute strategy. This paper adapts a mechanism design approach, namely Clarke Tax Mechanism, to satisfy incentive compatibility and individual rationality principal in meeting scheduling. By using a release strategy and substitute strategy, colliding meetings can still be held according to the schedule without the need for rescheduling. This paper shows the simulation result of using the strategies within some scenarios. It demonstrates that the number of meeting failures can be reduced with negotiation.
Fast online gaming over wireless networks

Interactive, massive online games are becoming popular and begin to receive the attention of researchers. Wireless, mobile gaming represents a particularly interesting case because of the proliferation of smart personal devices, the increasing availability of high speed wireless access points, and the emergence of vehicular networks and applications. With this vision, we propose a holistic solution that enables a top quality online gaming experience regardless whether the player is wired, wireless, even riding a vehicle. Main contributions of this work: (i) synchronization mechanism based on an Internet server overlay that enables scalability, fairness, and interactivity; (ii) elastic (TCP) and real-time (games) coexistence in a wireless access point; (iii) a fast multi-hop broadcast scheme for efficient game event delivery to players in vehicles.
Study and analysis of innovative network protocols and architectures

In the last years, some new paradigms are emerging in the networking area as inspiring models for the definition of future communications networks. A key example is certainly the Content Centric Networking (CCN) protocol suite, namely a novel network architecture that aims to supersede the current TCP/IP stack in favor of a name based routing algorithm, also introducing in-network caching capabilities. On the other hand, much interest has been placed on Software Defined Networking (SDN), namely the set of protocols and architectures designed to make network devices more dynamic and programmable. Given this complex arena, the thesis focuses on the analysis of these innovative network protocols, with the aim of exploring possible design flaws and hence guaranteeing their proper operation when actually deployed in the network. Particular emphasis is given to the security of these protocols, for its essential role in every wide scale application. Some work has been done in this direction, but all these solutions are far to be considered fully investigated. In the CCN case, a closer investigation on problems related to possible DDoS attacks due to the stateful nature of the protocol, is presented along with a full-fledged proposal to support scalable PUSH application on top of CCN. Concerning SDN, instead, we present a tool for the verification of network policies in complex graphs containing dynamic network functions. In order to obtain significant results, we leverage different tools and methodologies: on the one hand, we assess simulation software as very useful tools for representing the most common use cases for the various technologies. On the other hand, we exploit more sophisticated formal methods to ensure a higher level of confidence for the obtained results
Denoising and Demosaicking of Color Images

Most digital cameras capture images through Color Filter Arrays (CFA), and reconstruct the full color image from the CFA image. Each CFA pixel only captures one primary color component at each pixel location; the other primary components will be estimated using information from neighboring pixels. During the demosaicking algorithm, the unknown color components will be estimated at each pixel location. Most of the demosaicking algorithms use the RGB Bayer CFA pattern with Red, Green and Blue filters. Some other CFAs contain four color filters. The additional filter is a panchromatic/white filter, and it usually receives the full light spectrum. In this research, we studied and compared different four channel CFAs with panchromatic/white filter, and compared them with three channel CFAs. An appropriate demosaicking algorithm has been developed for each CFA. The most well-known three-channel CFA is Bayer. The Fujifilm X-Trans pattern has been studied in this work as another three-channel CFA with a different structure. Three different four-channel CFAs have been discussed in this research: RGBW-Kodak, RGBW-Bayer and RGBW5×5. The structure and the number of filters for each color are different for these CFAs. Since the Least-Square Luma-Chroma Demultiplexing method is a state of the art demosaicking method for the Bayer CFA, we designed the Least-Square method for RGBW CFAs. The effect of noise on different CFA patterns will be discussed for four channel CFAs. The Kodak database has been used to evaluate our non-adaptive and adaptive demosaicking methods as well as the optimized algorithms with the least square method. The captured values of white (panchromatic/clear) filters in RGBW CFAs have been estimated using red, green and blue filter values. Sets of optimized coefficients have been proposed to estimate the white filter values accurately. The results have been validated using the actual white values of a hyperspectral image dataset. A new denoising-demosaicking method for RGBW-Bayer CFA has been presented in this research. The algorithm has been tested on the Kodak dataset using the estimated value of white filters and a hyperspectral image dataset using the actual value of white
Large-Scale Image Segmentation with Convolutional Networks

Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge variations in pose, appearance, illumination and occlusion, and a visual system need to be robust to all these changes. In the present thesis, we are interested in pixel-level recognition problems, ie, problems in which the objective is to partition a given image into multiple regions (overlapping or not) that are considered meaningful according to some criterion. Our interests are in algorithms that require the least amount of feature engineering and are easy to scale. Deep learning methods fit very well with this objective: these models alleviate the need of engineered features by discriminatively training a system from raw data (pixels). More precisely, we propose different convolutional neural network (CNN) based algorithms to deal with three important segmentation problems: semantic segmentation, object proposal generation and object detection with segments. The objective of semantic segmentation is to generate a categorical label to each pixel present in a scene. We first study the problem of fully supervised semantic segmentation. We propose a recurrent CNN that is able to consider a large input context (while limiting its capacity), which is essential to model long range pixel label dependencies. This approach achieves state-of-the-art performance without relying on any post-processing smoothing step. However, having densely labeled images to train a model can be expensive and require a lot of human labor. We also propose a CNN-based model that is able to infer object semantic segmentation by leveraging only the object category information from images. This is achieved by casting the problem into a multiple instance learning framework. This approach beats previous state of the art in weakly supervised semantic segmentation by a large margin. Object proposal algorithms generate a set of regions (segments) that are likely to contain objects, independent of their semantic category. Contrary to most approaches (which rely on low-level vision cues), we propose a CNN-based discriminative approach that is able to learn segmentation proposals from raw pixels. This approach is proven to be quite effective in this setting, achieving substantially higher recall using fewer proposals than other methods. The state of the art is pushed further with the introduction of a new top-down network augmentation. The resulting bottom-up/top-down network combines low-level rich spatial information with high-level object semantic information to improve segmentation, while remaining fast at test time. Finally, we show that the proposals generated by our approach, when coupled with a standard state-of-the-art object detection pipeline, achieve considerably better performance than previous proposals methods.
The Effect of the Immediate Feedback by the Collaborative Education Tool ViLLE on Learning for Business Mathematics in Higher Education

This article outlines the effect of the collaborative educational tool ViLLE when learning business mathematics in higher education. ViLLE validates students’ answers during the assessment process and provides immediate feedback, enabling students to receive feedback and guidance about the correctness of their answers. The learning results in the business mathematics course in Turku University of Applied Sciences are used for the study. The effect of the ViLLE tool is researched by quasi-experimental study using the 2013 course as the control group and 2014 course as the treatment group. This research confirmed that the usage of the ViLLE tool improved the students’ learning of business mathematics.
It’s not a math lesson - we’re learning to draw! Teachers’ use of visual representations in instructing word problem solving in sixth grade of elementary school.

Non-routine word problem solving is an essential feature of the mathematical development of elementary school students worldwide. Many students experience difficulties in solving these problems due to erroneous problem comprehension. These difficulties could be alleviated by instructing students how to use visual representations that clarify the problem structure and the relations between solution-relevant elements (so-called visual-schematic representations). Research shows that instructional effectiveness depends largely on teachers’ mathematical knowledge for teaching. Teachers’ knowledge of visual representations is therefore essential to instructing word problem comprehension in this way. As there is little to no literature investigating teachers’ practices in this area, the goal of the present study is to examine teachers’ use of visual representations to support non-routine word problem solving. Eight mainstream elementary school teachers implemented an innovative approach focused on the use of visual-schematic representations. After a short training, teachers were able to produce these representations during instruction. However, some teachers seemed unclear about what these representations comprise and what function they serve within the word problem solving context. Teachers seemed to base their use of representations on personal preferences rather than on an optimal fit with the word problem characteristics. These aspects need to be addressed in teacher training and professional development programs. This study makes an unique contribution to research in the important and problematic area of word problem solving in regular classrooms. The results of this study are relevant for educational researcher, teachers, and teacher educators who deal with difficulties in instructing mathematical word problems.
Villiramulins A and B: new phenol derivatives from Piper villiramulum

Two new phenol derivatives, villiramulin A (1) and B (2), have been isolated from the Panamanian plant Piper oilliramulum and assigned structures on the basis of a variety of spectroscopic data. After a model study wherein the (+)- and (-)-O-methylmandelate esters of 2-heptanol were shown to present significantly different 1 H NMR spectra, villiramulin A was esterified with both of these acids. On the basis of the 1 H NMR data of the resulting esters, the S stereochemistry was assigned to the natural product. Finally, villiramulin B was shown to be significantly active in a bioassay that measures repellency to a captive colony of leafcutter ants
Adsorption Capacity and Isotherm of Methylene Blue Removal in Aqueous Solution onto Regenerated Activated Carbon

Adsorption is one of the potential ways to removal of methylene blue in aqueous solution. The adsorbent used in the adsorption process is the activated carbon of betel nuts that produced from regeneration. The purpose of this study was to determine the effect of regeneration method to capacity and isotherm adsorption. The research occurs in a batch with three various regeneration conditions that is chemical, physical, and chemical-physical. In chemical, activated carbon is regenerated with HCl with various concentrations of 5 N, 6 N, and 7N. In physical, activated carbon is regenerated by heating it at temperatures of 160°C, 175°C, and 190°C. In chemical-physical regeneration, the condition is combination of the two. The concentration of methylene blue was analyzed using ultraviolet-visible spectrophotometer (UV-visible). The result showed the best regeneration conditions occur in chemical-physical regeneration with HCl concentration of 7 N, temperature of 190°C, and contact time of 25 minutes with an adsorption capacity on the surface is 3.57 mg/g and efficiency of 98.98%. Adsorption isotherm of removal methylene blue onto regenerated of betel nuts activated carbon follows the Langmuir model with regression value 1 (R2 = 1).
Identification and quantification of some phytohormones in seaweeds using UPLC-MS/MS

Abstract The aim of this work was to identify main plant growth regulators (PGRs), such as, indole-3-acetic acid (IAA), abscisic acid (ABA), gibberellic acid (GA), zeatin, kinetin and 6-benzyl amino purine (BAP) and to determine corresponding quantity in nine seaweed varieties. The samples were extracted by methanol-water (75%) solvent from lyophilized seaweeds for 16 h at −20 °C. A rapid and sensitive UPLC-MS/MS method was developed and validated. Both individual and mixed standards were used to study the peak separation and identification of hormones. The precision of the method was tested with good RSD values of 0.67–2.01 for intra-day and 1.21–3.50 for inter-day in six and four replicates, respectively. Accuracy was in the range of 82.75–98.94% by spiking phytohormone standards to eliminate plant matrix. Excellent linearities were found within the ranges of 0.5–50 ng mL−1 for zeatin and 0.5–10 ng mL−1 for BAP, 5–500 ng mL−1 for GA and kinetin, and 2.5–500 ng mL−1 for IAA and ABA. The results showed that the applied SPE-UPLC-MS/MS method was very sensitive and can be used extensively for the routine analysis of trace amounts of multi-class phytohormones in seaweeds. The method developed greatly reduced both sample preparation and total analysis time. Graphical Abstract
Mobile Learning Application Interfaces: First Steps to a Cognitive Load Aware System.

Mobile learning is a cognitively demanding application and more frequently the ubiquitous nature of mobile computing means that mobile devices are used in cognitively demanding environments. This paper examines the nature of this use of mobile devices from a Learning, Usability and Cognitive Load Theory perspective. It suggests scenarios where these fields interact and presents an experiment which determined that several sources of cognitive load can be measured simultaneously by the learner. The experiment also looked at the interaction between these cognitive load types and found that a distraction did not affect the performance or cognitive load associated with a learning task but it did affect the perception of the cognitive load associated with using the application interface. This paper concludes by suggesting ways in which mobile learning (and mobile computing) can benefit by developing cognitive load aware systems that could detect and change the difficulty of the learning task based on the cognitive state of the learner.