Miracle-dz/UniFAR-FTUs huggingface dataset:
LiDAR mapping of canopy gaps in continuous cover forests: A comparison of canopy height model and point cloud based techniques

In continuous cover forest systems, canopy gaps are created by management activities with an aim of encouraging natural regeneration and of increasing structural heterogeneity. Light Detection and Ranging (LiDAR) may provide a more accurate means to assess gap distribution than ground survey, allowing more effective monitoring. This paper presents a new approach to gap delineation, based on identifying gaps directly from the point cloud and avoiding the need for interpolation of returns to a canopy height model (CHM). Areas of canopy are identified through local maxima identification, filtering and clustering of the point cloud, with gaps subsequently delineated in a GIS environment. When compared to field surveyed gap outlines, the algorithm has an overall accuracy of 88% for data with a high LiDAR point density (11.4 returns per m2) and accuracy of up to 77% for lower density data (1.2 returns per m2). The method provides an increase in overall and Producer's accuracy of 4 and 8% respectively, over a method based on the use of a CHM. The estimation of total gap area is improved by, on average, 16% over the CHM based approach. Results indicate that LiDAR data can be used accurately to delineate gaps in managed forests, potentially allowing more accurate and spatially explicit modelling of understorey light conditions.
Essays on Unobserved Heterogeneity and Endogeneity in Health Econometrics

Microdata in Health Economics During the past two decades, applied econometric analysis has been widely adopted among health economists. Its adoption is accelerating, producing ever-richer research as electronic recording and collection make available more data about individual patients. In addition, computational power for analyzing large, complex datasets is increasing, facilitating econometric analysis involving latent variables, unobserved heterogeneity, and nonlinear models in the field now established as “health econometrics. ” Extensive individual-, household-, and establishment-level microdata are available from cross-sectional and longitudinal sample surveys and the census. Health economics primarily employs cross-sectional data. That is, observations are independent of each other, and pure time series applications are excluded. Microdata used in health econometrics have two notable features. First, they are often measured on a non-continuous scale: data are not only continuous and discrete variables but also on a non-continuous scale, such as quantitative and qualitative (or categorical) variables. This leads inconsistency of linear regression models. For example, analyzing expenditure data is complicated when samples feature a preponderance of observations with zero expenditures. The consistency of standard approaches to the problem relies on the validity of distributional assumptions. To analyze these data, health econometrics requires disparate nonlinear models, including binary responses, multinomial responses, limited dependent variables, integer counts, and measures of duration. Moreover, variables denoting health or quality of life are often unobservable and perhaps measurable only with error (through subjective reports, for example). This situation induces latent variables and selection problems. Second, health data are observational, i.e., they are neither experimental nor collected from surveys and administrative records through randomized experiment. Although availability of
Forgive, or Award, Your Debtor? - A Barrier Option Approach

Dealing with default risk on sovereign debt became an urgent matter with the Financial Crisis of 2008. Depending on both exogenous and endogenous factors, the risk of eventual default may become significant over the lifetime of a loan. Creditors are then faced with a dilemma, since they cannot force the borrowing country into bankruptcy court. Should they ease repayment by reducing the principal of the debt (“forgiveness”), or should they offer an extra award if the borrower repays in full? The latter award could come in the form of lower borrowing costs in the future. In this article, Sun and Chen propose to model the possibility of loan modification for risky sovereign debt as a kind of down-and-in put option: There is a boundary value (the “default threshold”) for the country’s perceived ability to pay, measured as the ratio of debt principal to GDP, at which the terms of the debt are revised, either to reduce the principal or to offer a repayment award. Both approaches have been tried in practice. What the authors show is that forgiveness is better for the creditors than a repayment award. The sensitivity of bond value to default boundary, forgiveness amount, and repayment award within the model are explored through simulation. The authors then conduct an empirical analysis on the bonds of 17 of the G20 countries to estimate the values of these three model parameters, and show that their model performs well in practice.
What is the Motivation of Travellers? An investigation of quotes on TripAdvisor’s facebook page

The online review websites such as TripAdvisor provide the advantages to access this information. If these websites post the reviews on their social media addresses, it is possible to spread the information more. Travellers have also the opportunity to disseminate their motivations and experiences about their travel on social media and online review websites. Content analysis method was applied to understand TripAdvisor travellers’ motivation. The findings of this study suggest that novelty/adventure and knowledge/intellectual are two major travel motivations among TripAdvisor travellers. The findings of the current study are important for both tourism managers and academicians to better understand travel motivation of people on social media. It is expected that the results of this study help tourism businesses and destinations to understand travellers’ opinions and enhances their awareness of customer needs. Thus, they can easily adapt their marketing strategies. No research has been done on examining the motivation of TripAdvisor travellers regarding their quotes on Facebook. Thus, the current study contributes to an understanding of the factors that influence peoples’ travel motivation.
Robotic and machine learning: How to help support to process customer tickets more effectively

Author Title Number of Pages Date Dmitry Khramov Robotic and machine learning: How to help support to process customer tickets more effectively 41 pages 12 April 2018 Degree Bachelor of Engineering Degree Programme Information technology Professional Major Software engineering Instructors Marko Lähde, Senior Solution Manager, Basware Oy Peter Hjort, Senior Lecturer The objective of the thesis work was to explore opportunities provided by emerging digital technologies, such as robotic process automation and machine learning and apply these technologies for enhancing the efficiency of company’s business process. Developed automation was embedded into existing process of handling incoming customer issue tickets. It allowed a significant increase in the tickets’ processing speed and will relieve human workforce from repetitive, low value-added tasks. Process automation was combined with machine learning in order to introduce decision making capabilities for the robotics workflow. Historical data from issue tickets was used to train supervised machine learning algorithm for prediction, based on textual content. This method indicated whether a ticket belongs to the particular group. Several classification algorithms were evaluated by accuracy performance with different representations of textual data. Superior performance results were achieved by using linear support vector machines and logistic regression which were trained on the data. Finally, the results were transformed into binary vector representation with n-gram range from 1 to 3. Implemented automation identifies correct support group with 75% accuracy and 76% precision, which allowed taking under RPA coverage 16% of English subset of processed tickets. At the time of writing this study, the automation was evaluated in test environment and considered for further extensions and accuracy improvements. RPA powered by machine learning enables to overcome the limitations of plain rule-based robotics automation. Also, it provides potential for developing sophisticated, intelligent automations, capable of handling a higher range of business processes.
Worthy Transfers ? A Dynamic Analysis of Turkey’s Accession to the European Union

In this paper, we build a two-country dynamic general equilibrium model to study whether European citizens would benefit from the eventual accession of Turkey to the European Union. The results of the simulations show that Turkey's accession to the European Union is welfare enhancing for Europeans, provided that Turkish total factor productivity (TFP) increases sufficiently after enlargement. In the model with no capital mobility, the Europeans are better off it the Turkish TFP increase bridges more than 31% of the initial TFP gap between Turkey and the European Union. That figure becomes 45% when capital mobility is introduced.
Perceived eWOM and Students’ University Enrolment Intentions: The Corporate Image as a Mediator

The advancement of internet technologies and the emergence of social media platforms have made the influence of electronic word of mouth (eWOM) more pronounced than ever before on consumers’ purchase decisions, especially young consumers who are more engaged with new technologies and social networks. Through social media platforms, young consumers nowadays are always connected to friends, families and organizations on smartphones, tablets or computers and actively look for eWOM before making purchase decisions. This study is aimed at examining the effect of perceived eWOM on the university enrolment intentions by the Saudi high school students mediated by the corporate image of the university. The sample comprised of 133 high school students studying in Saudi high secondary schools. The findings revealed that electronic word of mouth (eWOM) had a significant and positive association with the students’ enrolment intentions, and the corporate image mediates this relationship. This research’s findings and recommendations can largely help who are in charge of the private and public universities in Saudi Arabia to understand how eWOM shapes the university choices of students.
Genetic evidence for the origin of the agrimi goat ( Capra aegagrus cretica )

The agrimi goat Capra aegagrus cretica is unique to Crete and its offshore islands. It has been identified as a sub-species of the wild bezoar goat Capra aegagrus aegagrus Erxleben, 1777, which it closely resembles in horn shape, body form and coloration. This classification has been disputed by some researchers who claim that the agrimi are feral goats, derived from early domestic stock brought to the island by the first Neolithic settlers. In order to clarify this issue, DNA analyses (cytochrome b and D loop sequences) were carried out on tissue of live and skeletonized agrimi and compared to sequences of wild and domestic caprines. Results conclusively show the agrimi to be a feral animal, that clades with domestic goats ( Capra hircus ) rather than with wild Asiatic bezoar. This study demonstrates that morphometric criteria do not necessarily reflect genetic affinities, and that the taxonomic classification of agrimi should be revised.
Tilburg University Correcting ' Wrong-Column ' Errors in Text Databases

We present a novel data-driven approach for detecting and correcting errors in text databases. We focus on information that was accidentally entered in an incorrect column. Unlike machine-learning approaches to data cleaning that assume the database cells to contain atomic or numeric content, our method takes into account substrings of textual cells, and treats error detection and correction as a text categorisation task. Errors are detected at points where the classifier disagrees with the data; corrections are the suggestions put forward by the classifier. We demonstrate that the method is suited for high-recall detection of errors in freetext columns of a zoological database, with a high correction accuracy as well.
Development of a novel and versatile affinity tagging system using an anti-podoplanin antibody NZ-1

Peptide-based epitope tagging technology is universally used in nearly all kind of research projects that involve biochemical characterization of a target protein, but not many systems are fully compatible with purification purpose. By utilizing an anti-human podoplanin antibody NZ-1, I constructed a novel epitope tagging system. NZ-1 possesses exceptionally high affinity toward a dodecapeptide (GVAMPGAEDDVV) dubbed “PA tag”, with a characteristic slow dissociation kinetics. Because of its high affinity, PA-tagged proteins in a dilute sample can be captured by immobilized NZ-1 resin in a near complete fashion and eluted by a solution of free PA peptide. This enabled efficient one-step purification of various proteins including soluble (e.g., an ectodomain fragment of neuropilin-1 and a human serum albumin) and membrane proteins expressed in mammalian cells. Mild regeneration condition of the peptide-bound antibody ensures repeated use of the antibody resin, indicating a cost-efficient nature of the system. PA tag/NZ-1 system also exhibits an outstanding performance in the immunodetection experiments (i.e., Western blotting and flow cytometry). An alanine scanning mutagenesis experiment revealed that many amino acid residues within the PA tag sequence contribute to the binding by NZ-1. To understand the molecular details of the PA tag-NZ-1 interaction at atomic resolution, I determined the X-ray crystal structure of the NZ-1 Fab fragment in complex with the PA tag peptide. In the structure, the PA peptide is docked in the antigen-binding cleft of the NZ-1, and there are numerous contacts involving the peptide region encompassing Met4-Asp10. In the NZ-1-bound conformation, the PA peptide assumed a tight 2-residue type II β-turn conformation at the Pro5-Gly6 sequence. This binding mode suggested an interesting possibility that PA tag may be inserted into a loop region of proteins, without losing its high affinity toward NZ-1. Using a cell adhesion receptor integrin αIIbβ3 as a model protein, I successfully inserted the PA tag into multiple loop regions in the ectodomain of αIIbβ3 and confirmed that all proteins were reactive with NZ-1. Moreover, by choosing the site of PA tag insertion, it was possible to modulate the function of resultant integrin by inducing conformational change upon the addition of NZ-1. As it is generally difficult to graft linear epitope tag in a structured protein domain, the PA tag system provide unique opportunity to attach purification/labeling handle to a target protein.
Rethinking the problem of faculty resistance to engaging with students as partners in learning and teaching in higher education

Engaging students as partners in the scholarship of learning and teaching (SoTL) is a principle guiding good practice. Enthusiasm for student-faculty partnerships in learning and teaching continues to grow. In this essay, I want to invite readers to reflect with me about concerns of resistance to partnership practices. I interweave stories from my experiences with selected literature that is shifting the conversation about the ‘challenge of resistance’ in partnership work in learning and teaching. Positioning students as partners work as a values-based practice and in the context of ‘scaling-up’ partnership programs, I argue that our pre-occupation with resistance is problematic. Instead, we should accept resistance as part of a natural sense-making process that allows us to think together about the complexity of genuine pedagogical partnership.
To Speak Out Matters: Implication of Rural Road Network on Healthcare Accessibility among Residents in the Nzema East Municipality, Ghana

Road transport plays a significant role in rural healthcare accessibility and utilization. This study assessed the implication of poor rural road transport network on healthcare accessibility and utilization in Akango, Gbwira Eshiem and Ampanshie in the Nzema East Municipality of Ghana. The study employed mixed method approach using questionnaires and interviews to investigate the subject matter. A total of 253 respondents were randomly sampled for the survey and 13 were purposively selected for the interviews. Descriptive and regression analysis was used to analyze the questionnaire while thematic analysis was used to analyze the interviews. On the nature of road network linking the communities to healthcare facilities, the study found that, residents road network were muddy and slippery, had a lot of potholes, dusty during the dry season, eroded along hills, bridges randomly collapsed, and marshy areas easily got flooded. Importantly, the regression results revealed that complication of diseases, self-medication, difficulty in accessing referral services, challenge of transporting medical supplies to health facilities, difficulty of accessing maternity and childcare and the use of life-risking routes to access healthcare have a significant relationship with poor road network in the area. The study recommends the need for comprehensive and pragmatic measures to improve rural road transport network for improved healthcare.
LEARNING, COMMITMENT, AND MONETARY POLICY

This paper examines a class of interest rate rules that respond to public expectations and to lagged variables and considers varying levels of commitment that correspond to varying degrees of response to lagged output. Under commitment the policymaker adjusts the nominal rate with lagged output to impact public expectations. Within this class of rules, I provide a condition for nonexplosive and determinate solutions. Expectational stability obtains for any nonnegative response to lagged output. Simulation results show that modified commitment is best under least-squares learning. However, in the presence of parameter uncertainty and/or measurement error in the policymaker's data on public expectations, the best policy is one of partial commitment, where the response to lagged output is less than under modified commitment. The case for partial commitment is strengthened if the gain parameter in the learning mechanism is high, which can be interpreted as the use of few lags by public agents in the formation of expectations or as an indication of low credibility of the policymaker. The appointment of a conservative central banker ameliorates these concerns about modified commitment.
Optimizing the Multi-Objective Deployment Problem of Mlat System

Multilateration (MLAT) systems are powerful means for air traffic surveillance. These systems aim to extract, and display to air traffic controllers identification of aircrafts or vehicles equipped with a transponder. They provide an accurate and real-time data without human intervention using a number of ground receiving stations, placed in some strategic locations around the coverage area, and they are connected with a Central Processing Subsystem (CPS) to compute the target (i.e., aircraft or vehicle) position. The MLAT performance strongly depends on system layout design which consists on deploying the minimum number of stations, in order to obtain the requested system coverage and performance, meeting all the regulatory standards with a minimum cost. In general, choosing the number of stations and their locations to cope with all the requirements is not an obvious task and the system designer has to make several attempts, by trial and error, before obtaining a satisfactory spatial distribution of the stations. In this work we propose a new approach to solve the deployment of Mlat stations problem by focusing on the number of deployed stations and the coverage as the main objectives to optimize. The Non-dominated Sorting Genetic Algorithm II(NSGA-II) was used in order to minimize the total number of stations required to identify all targets in a given area, with the aim to minimize the deployment cost, accelerating processes, and achieve high availability and reliability. The proposed approach is more efficient and converge rapidly which makes it ideal for our research involving optimal deployment of Mlat station.
Philosophical Challenges of Computing Ethics: UNESCO and Information Ethics

Ethics and information activities are related in a number of ways. This paper discusses the role of UNESCO regarding information ethics. The main commitment of UNESCO is “free flow of information and access to knowledge sources,” and the organization has held various meetings and congresses to this purpose. Then the author’s project on mental mapping is discussed. The main focus of the project is on the question how interpersonal understanding is possible, and how this question is related to the problem of how human beings make decision in moral dilemma situations. The paper ends with a discussion of what is novel about information ethics in the age of computers .
The relationship between the amounts of time spent in one school during early field experiences and student teacher performance

James P. Gregory. THE RELATI ONSHIP BETWEEN THE AMOUNTS OF TIME SPENT IN ONE SCHOOL DURING EARLY FIELD EXPERIENCES AND STUDENT TEACHER PERFORMANCE. (under the direction of Dr. Daniel Baer) School of Education, Liberty University, March 2013. This quantitative non-experimental correlational study was designed to investigate the correlation between the amount of time a teacher candidate spent during their early field experiences at one school location and performance ratings on their student teacher evaluation sheet. The rating scale was based on the Interstate New Teacher Assessment and Support Consortium (INTASC) Standards. The purpose of the study was to determine if there is a difference in the level of teacher candidates’ performance as outlined by the INTASC standards for secondary students in relationship to the number of hours of early field experience spent at one school location. All candidates were scheduled for a minimum of 100 hours in early field experiences, yet the actual amount of time spent in any one school varied widely between candidates. Using a Likert scale, the host teachers in cooperation with the candidates’ college supervisor rated the preservice teacher based on each of the ten INTASC standards. The data for the study was provided by the cooperating teachers hosting the pre-service teachers and housed in the TK-20 database. The Product-Moment Correlation Coefficient (Pearson r) statistic was chosen to provide insight into the magnitude of relationship between variables. Analysis of the data revealed a correlation at a significance level exists for one of the participating groups.
A Correntropy-Based Proportionate Affine Projection Algorithm for Estimating Sparse Channels with Impulsive Noise

A novel robust proportionate affine projection (AP) algorithm is devised for estimating sparse channels, which often occur in network echo and wireless communication channels. The newly proposed algorithm is realized by using the maximum correntropy criterion (MCC) and the data reusing scheme used in AP to overcome the identification performance degradation of the traditional PAP algorithm in impulsive noise environments. The proposed algorithm is referred to as the proportionate affine projection maximum correntropy criterion (PAPMCC) algorithm, which is derived in the context of channel estimation framework. Many simulation results were obtained to verify that the PAPMCC algorithm is superior to early reported AP algorithms with different input signals under impulsive noise environments.