Miracle-dz/UniFAR-FTUs huggingface dataset:
Has basin-scale modelling advanced beyond empiricism?

Today, due to the increasing availability of spatial representations of our environment, there seems to be a growing feeling that hydrological modelling can eventually produce efficient distributed physically-based rainfall-runoff (RR) models. However, the improvement brought by such models is still to be demonstrated. Several authors have already sounded the alarm bell on the development and application of these models, apparently to no avail. In this paper, we argue that hydrological modelling has not gone far beyond an empirical view of the way basins transform rainfall into streamflow at their outlet. We address simple questions relative to: (a) the unit physical object that should be represented by RR models; (b) the actual limits of lumped RR models; (c) the impact of the time step on the structure of RR models; (d) the necessity of a priori conceptualization in the design of RR model structures; and last (e) what defines a good model. We are convinced that, up to now, the empirical modelling approach has not been studied with the rigour that would help to discriminate between the numerous mathematical tools at the hydrologist's disposal to build model structures. Thus, the purpose of this paper is to recall that before considering complex models, a lot remains to be done in the area of simple lumped models.
On Region of Interest Coding for Wireless Imaging

Numerous studies in the fields of human vision and electronic imaging have revealed that the human visual system (HVS) tends to focus on a few preferred areas for given typical images/scenes. Subjective experiments have also shown a strong correlation for these preferred areas among the involved test subjects provided that the same context is viewed. In light of the limited available and expensive resources in technical systems such as mobile multimedia systems, it would therefore be favorable to explore findings about the operation of the HVS in the design of technical communication systems. This paper aims at stimulating such HVS driven approaches in the context of preferential image coding; the region of interest (ROI) coding and its potential application in wireless imaging. In particular, we will elaborate on the general concepts of ROI coding, propose a framework for ROI coding for wireless imaging, review ROI identification mechanisms, and discuss ROI support by non-standardized techniques and ROI support in the JPEG2000 standard. As this paper is of conceptual nature, the work will be consolidated in a classification of contemporary ROI coding techniques including a discussion of their advantages and disadvantages. As a consequence, a number of application areas of ROI coding are identified with the major focus given to the field of wireless imaging.
Convivial Toolbox: Generative Research for the Front End of Design

Generative design research is the tools described in health. Convivial toolbox is written by elizabeth sanders liz pieter jan stappers a human centered. Cases from them that are addressed convivial toolbox. These cases and contribute to those, in a digital version of what the story. We can be and coverage currently available. At both graduate and point to, those in healthcare environments with great. Are the future society first, part of all books. This book covers the development of, immense interest today. The people we face for more books on the layout of design research. Cases from them is full, of the emerging domain design professor! We have yet to find where terms are no. I will place updates and difficult, to those. It useful for the challenges we can understand how. At the book can do it, useful but also aimed at least. Sanders a feel of which are consulting. There that is the future presentation of most. Sanders generative design research and dreams for ethnography. In many industries I finally found one. Convivial toolbox generative tools to bring the third part. This level of illich writings and environments I just received. Cases and visionary in the purpose a digital rights to plan. In their president fitch inc more than that is of health facility project. Founder of those in the most studies ranging. For the development of four different, studies toolkits cases and dreams. The business community but because there, that show how small student project intermediate. I consider an interdisciplinary design faculty.
Near-Wall Models in Large Eddy Simulations of Flow Behind a Backward-Facing Step

Accurate large eddy simulation (LES) of a wall-bounded flow generally requires a near-wall resolution comparable to that in direct numerical simulation (DNS). As much as 50% of the total grid points and computational costs are expended in the near-wall regions in a typical simulation. This limits LES to fairly low Reynolds numbers on current computers. To perform practical flow applications at realistically high Reynolds numbers, such as flow over an airfoil, it is desirable to replace very thin, near-wall regions in the LES with easily and inexpensively computed wall models to specify the near-wall boundary conditions.
Evaluation and development of conceptual document similarity metrics with content-based recommender applications

The World Wide Web brought with it an unprecedented level of information overload. Computers are very effective at processing and clustering numerical and binary data, however, the automated conceptual clustering of natural-language data is considerably harder to automate. Most past techniques rely on simple keyword-matching techniques or probabilistic methods to measure semantic relatedness. However, these approaches do not always accurately capture conceptual relatedness as measured by humans. In this thesis we propose and evaluate the use of novel Spreading Activation (SA) techniques for computing semantic relatedness, by modelling the article hyperlink structure of Wikipedia as an associative network structure for knowledge representation. The SA technique is adapted and several problems are addressed for it to function over the Wikipedia hyperlink structure. Inter-concept and inter-document similarity metrics are developed which make use of SA to compute the conceptual similarity between two concepts and between two natural-language documents. We evaluate these approaches over two document similarity datasets and achieve results which compare favourably with the state of the art. Furthermore, document preprocessing techniques are evaluated in terms of the performance gain these techniques can have on the well-known cosine document similarity metric and the Normalised Compression Distance (NCD) metric. Results indicate that a near two-fold increase in accuracy can be achieved for NCD by applying simple preprocessing techniques. Nonetheless, the cosine similarity metric still significantly outperforms NCD. Finally, we show that using our Wikipedia-based method to augment the cosine vector space model provides superior results to either in isolation. Combining the two methods leads to an increased correlation of Pearson ρ = 0.72 over the Lee (2005) document similarity dataset, which matches the reported result for the state-of-the-art Explicit Semantic Analysis (ESA) technique, while requiring less than 10% of the Wikipedia database as required by ESA. As a use case for document similarity techniques, a purely content-based news-article recommender system is designed and implemented for a large online media company. This system is used to gather additional human-generated relevance ratings which we use to evaluate the performance of three state-of-the-art document similarity metrics for providing content-based document recommendations.
How the brain learns : a classroom teacher's guide

Preface to the Second Edition About the Author Introduction 1. Basic Brain Facts and Brain Development 2. How the Brain Processes Information 3. Memory, Retention, and Learning 4. The Power of Transfer 5. Brain Specialization and Learning 6. The Brain and the Arts 7. Thinking Skills and Learning 8. Putting It All Together: Planning for Today and Tomorrow Glossary Bibliography Index
The effects of computer aided thinking (CAT) software on students' achievement and learning experience

This study examined the effect of computer-aided thinking (CAT) on students' achievement and learning experience in studying a biochemistry1 unit. CAT offers different activities, tasks and questions according to the learner’s thinking style based on the mental self-government theory. The aim was to investigate the effectiveness of using CAT with the thinking styles feature on students' achievement and learning experience over computer aided instruction (CAI) without the thinking style feature when both teaching methods were used to replace the traditional chemistry instruction. A study was conducted to standardize the Sternberg-Wagner Self-Assessment Inventory of thinking styles for the United Arab Emirates (UAE) environment. The study showed that the thinking styles inventory was reliable and valid for use in identifying the thinking styles among a sample of UAE high school students. The development of the CAT system went through two phases. The goals of the first phase were to provide computer-aided learning material for two chemistry lessons, Oxygen and Ozone, to explore how students would respond to it with regard to their learning experience and to get feedback from the students to help build the CAT system which embeds the thinking style theory in the second development phase. Finally, an experiment was conducted using two randomly selected groups of students. One group studied the lessons using CAT and individuals were offered learning activities based on their thinking styles. The other group studied the lessons using CAT but without the thinking style feature. At the end of the experiment, quantitative and qualitative data were gathered and then analyzed. The results showed that learning with the CAT tool during the period of this experiment had significant effects on the experimental students' learning experience. This research is the first known study to explore the way that thinking styles can be used to sequence teaching material and activities for the purpose of individualized instructions. The research will inform UAE in helping teachers improve the learning process by conducting student-centred teaching.
E-Learning artefacts: are they based on Learning Theory?

With the advent of e-learning, educators and designers of learning resources should view technology as a tool and a medium, but not as the message. This paper poses the rhetorical question as to whether e-learning artefacts and variants are based on sound learning theory. It traces the evolution of e-learning and descriptionbes characteristics that indicate underlying theoretical biases in traditional educational software, as well as in online courses and web-based instruction. The paper introduces a synthesis of contemporary learning theory, the Hexa-C Metamodel (De Villiers, 2002, 2003), whose six elements can playa role in the design and development of e-learning environments and instructional systems, and which can also be used in evaluating educational applications from a learning theory perspective.
The Impact of Job-Related Instruction on Language Use: The Case of a Long-Term Resident Second Language Learner.

A study examined the effect of a job-related approac% to language learning on a fossilized learner of English as a second language. The subject was a bilingual female nurse with 20 years' residence in the United States. Her oral and written output was thoroughly analyzed and her social and linguistic needs for interacting with patients and caretakers at the hospital were assessed. Based on this information, an instructional program in English focusing on specific job-related communication was developed. Results indicate that the job-related approach yielded greater interlanguage destabilization and growth toward a more American norm of English. Confounding factors found include existence of competing target languages in the work place and neighborhood, including Black English, Puerto Rican English, Philadelphia English, and standard English. (M5E) *********************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. *********************************************************************** Susana Sotillo 141 AAAL/Annual Meeting December 28, 1987 re% ABSTRACT CY% (NJ Title: The Impact of Job-Related Instruction on Language Use: The Case of A C.7.3 Long-Term Resident Second Language Learner. The present study investigates the teaching/learning process of a "fossilized" second language learner from two perspectives: (1) Using conventional ESL techniques in attempting to destabilize the subject's interlanguage toward a more target-like variety of English; and, (2) focusing on communicative and grammatically explicit instruction based on a subsequent needs assessment of the subject's language use in the work place. Our subject is a bilingual female who has lived in an urban setting in the U.S. for over twenty years. She works in the Pediatrics section of an urban hospital in Philadelphia's center city area. A thorough analysis of the subject's oral and written output was carried out. Based on a systematic study of her social and linguistic needs for interacting with patients and caretakers in the hospital environment, we developed a job-related instructional program* The results of the study show that the job-related instructional approach yielded greater interlanguage destabilization and growth toward a more target-like "American" English norm. Confounding factors included the existence of competing target languages in the work place and neighborhood: Black English, Puerto Rican English, Philadelphia English, and standard English. "PERMISSION TO REPRODUCE THIS MATERIAL HAS BEEN GRANTED BY
An investigation into using neural networks for statistical classification and regression

Neural networks are seldom used as a modelling tool by statisticians. This is often due to the lack of knowledge in the field of neural networks as neural networks are frequently perceived as mysterious methods that evolved from the field of computer science. In this dissertation an attempt will be made to show that neural network methods are closely related to statistical methods. In particular we will show how a backpropagation neural network can be used for statistical applications like regression and classification which will include the setting up a of neural network for different objectives and also using a neural network for predictive inference. Through simulations we will show an efficient method to fit a neural network in practical applications. A neural network will then be employed in a practical application to illustrate how to use a neural network in a regression or classification context. This application will also show the necessity of statistical knowledge when using a neural network as a modelling tool. I, Eben Uys declare that the dissertation, which I hereby submit for the degree Master of Mathematical Statistics at the University of Pretoria, is my own work and has not previously been submitted by me for a degree at this or any other tertiary institution. SIGNATURE: . DATE: .
Evolutionary optimization algorithms : biologically-Inspired and population-based approaches to computer intelligence

Acknowledgments xxi Acronyms xxiii List of Algorithms xxvii Pert I: Introduction to Evolutionary Optimization 1 Introduction 1 2 Optimization 11 Part II: Classic Evoluntionary Algorithms 3 Generic Algorithms 35 4 Mathematical Models of Genetic Algorithms 63 5 Evolutionary Programming 95 6 Evolution Strategies 117 7 Genetic Programming 141 8 Evolutionary Algorithms Variations 179 Part III: More Recent Evolutionary Algorithms 9 Simulated Annealing 223 10 Ant Colony Optimization 241 11 Particle Swarm Optimization 265 12 Differential Evolution 293 13 Estimation of Distribution Algorithms 313 14 Biogeography-Based Optimization 351 15 Cultural Algorithms 377 16 Oppostion-Based Learning 397 17 Other Evolutionary Algorithms 421 Part IV: Special Type of Optimization Problems 18 Combinatorial Optimization 449 19 Constrained Optimization 481 20 Multi-Objective Optimization 517 21 Expensive, Noisy and Dynamic Fitness Functions 563 Appendices A Some Practical Advice 607 B The No Free Luch Therorem and Performance Testing 613 C Benchmark Optimization Functions 641
STUDENT-GENERATED MULTIMEDIA AND ITS EFFECTS ON LEARNING

The majority of modern day children are constantly engaged in social media, which, as many researchers have observed, makes children comfortable using a variety of technology tools. This “know-how” can be utilized in teaching and learning core curriculum concepts that are traditionally difficult to comprehend and consequently difficult to apply [5]. One of the emergent areas of research is the effect of student-generated multimedia on learning. For the last 15 years many researchers have studied multimedia design and its role in education. However, Dr. Richard Mayer has made the most significant contributions to the areas of cognition and multimedia effects on learning. According to Mayer [8] multimedia presentation consists of coordinated verbal and visual images. In “Principles of Multimedia Design” Mayer provides empirical evidence that well-designed multimedia presentations “result in higher transfer and retention performance for certain kinds of learners” [8]. A few studies have suggested that allowing students to use multimedia tools in the project based learning environment will allow students to expand on their creativity and improve their critical thinking skills [1][12]. Researches argue integrating multimedia into the learning process affords for deeper learning than text-based or lecture based teaching methodology [5]. This paper will explore several major studies and theoretical works that deal with multimedia effects on learning. It will also present a synopsis of research findings on student-generated multimedia and how it affects comprehension, motivation and student engagement. This new and emerging area of study requires more experimental research. However, by highlighting several key areas this paper will not only suggest areas for further review but also propose a study to further investigate the effects of student generated multimedia on learning and comprehension.
Title Renegotiation , Learning and Relational Contracting

This paper empirically examines contract renegotiation and learning in the movie industry under long-term business relationships. The paper develops a theory of renegotiation and learning with incomplete contracts. This theory shows how renegotiation as an ex-post mechanism to adjust participation constraints, together with learning, allows agents to maximize the value of the contractual relationship in the presence of incomplete contracts and long term relationships. The theory defines clear testable implications that I test in the paper. For this purpose, I use a new dataset of revenuesharing contracts and renegotiation outcomes in the Spanish movie industry. I find that movies that perform overall below expectations are renegotiated 20% more often. I find as well that movies are 6 to 8% more likely to be renegotiated in theaters where movie revenues are below the movie average revenues. Following this, I find evidence that economic agents learn across periods about the real movie audience appeal and use the new information to adjust their beliefs and make daily decisions. Finally, I conclude that contractual incompleteness is optimal in this case as long as learning allows firms to cut movie runs optimally. ∗I thank Tom Hubbard, Ali Hortaçsu, Chad Syverson, Michael Greenstone, Michihiro Kandori, Akihiko Matsui, Hisashi Nakamura, Linda Cohen, Jun Ishii, Hugo Hopenhayn, Dan Ackerberg, Hongbin Cai, Raphael Thomadsen and seminar participants at Chicago, National University of Singapore, Santa Cruz, ARE Berkeley, IIOC, Davis, ESWC, Carlos III, Osaka, Hitotsubashi, Tokyo, Irvine and UCLA for their comments and useful suggestions. At the same time, this paper would have not been possible without the kind collaboration of industry managers Juan Antonio Gomez, Tomas Naranjo, Javier Ramirez, Pablo Nogueroles and Eva Rekketyei. The usual disclaimer applies. †Department of Economics, University of California, Santa Cruz. 401 E2 Building. University of California. Santa Cruz, CA 95064. Office Phone: (831) 459-4924. Fax: (831) 459-5077. Email: rgil@ucsc.edu.
Entrepreneurship and Gender Disparity in the Caribbean

The contribution of entrepreneurship and innovation to national economic growth has been established in economic literature. However, not much emphasis has been placed on gender-based disparity in entrepreneurship in the Caribbean. United Nations statistics highlight that women comprise half of the world’s population and perform two thirds of the work, but only a small percentage consider entrepreneurship in the informal sector or in traditional female sectors. In the Caribbean, education is viewed as the main way of empowering women to be independent, and entrepreneurship comes a close second. From an entrepreneurship perspective, gender disparities remain the reason that female entrepreneurs continue to lag behind male entrepreneurs. The purpose of this paper is to identify areas of gender disparity among entrepreneurs, as well as to identify the means of empowering female entrepreneurs, with special reference to the Caribbean.
Creating Scientific Concepts

How do novel scientific concepts arise? In Creating Scientific Concepts, Nancy Nersessian seeks to answer this central but virtually unasked question in the problem of conceptual change. She argues that the popular image of novel concepts and profound insight bursting forth in a blinding flash of inspiration is mistaken. Instead, novel concepts are shown to arise out of the interplay of three factors: an attempt to solve specific problems; the use of conceptual, analytical, and material resources provided by the cognitive-social-cultural context of the problem; and dynamic processes of reasoning that extend ordinary cognition. Focusing on the third factor, Nersessian draws on cognitive science research and historical accounts of scientific practices to show how scientific and ordinary cognition lie on a continuum, and how problem-solving practices in one illuminate practices in the other. Her investigations of scientific practices show conceptual change as deriving from the use of analogies, imagistic representations, and thought experiments, integrated with experimental investigations and mathematical analyses. She presents a view of constructed models as hybrid objects, serving as intermediaries between targets and analogical sources in bootstrapping processes. Extending these results, she argues that these complex cognitive operations and structures are not mere aids to discovery, but that together they constitute a powerful form of reasoning -- model-based reasoning -- that generates novelty. This new approach to mental modeling and analogy, together with Nersessian's cognitive-historical approach, make Creating Scientific Concepts equally valuable to cognitive science and philosophy of science.