Miracle-dz/UniFAR-FTUs huggingface dataset:
Advancing log analysis of student interactions with cognitive tools

We have developed a software application (gStudy) that supports learning with multimedia documents. Students use gStudy to create and link notes, highlight and label text and images, construct glossaries and concept maps, exchange information objects through a chat interface, and perform other operations on multimedia content. The detailed log files recorded by gStudy constitute a wealth of data about how students process information as they learn. In this paper we describe log parsing and data mining methods we have borrowed from computer science and apply to researching self-regulated learning. The analysis software we developed identifies coherent learner actions from the complex series of low-level events recorded by gStudy and detects sequential patterns of these actions that may be interspersed with unrelated actions. It applies a data mining algorithm to discover action patterns which are the longest subsequences common to a group of participants. The use of these methods is illustrated through an analysis of gStudy log files generated by 103 university students. Log Analysis of Student Interactions with Cognitive Tools 3 gStudy is a software application for researching self-regulated learning (Winne et al., 2006). Learners use gStudy to operate on multimedia documents packaged in learning kits. Because gStudy presents documents through a web browser, learning kits may contain media commonly found on the web such as text, video, diagrams, and animations. As learners work in a learning kit, they use gStudy tools to create information objects and hyperlinks connecting them. Learners can highlight and categorize selections of content. They can create notes, glossary entries, concept maps, HTML documents and other objects. Learners can collaborate with others through a chat tool, sharing the information objects they create. To make a note linked to content in a learning kit a learner chooses a note template. Note templates are schemas that instructional designers, teachers, researchers or learners can design to structure the note content. Note templates are often designed to scaffold cognitive or metacognitive processing. Creating a note as guided by a template in gStudy is an instance of using a tool for learning. As a learner uses this tool, gStudy records in detail the events involved in creating a note: which content was selected, when the selection was made, which note template the learner chose, which fields of the note template the learner filled in, what information was entered in those fields, and when the learner closed the note window. All these …
Parental Investment in Offspring in Variable Environments: Theoretical and Empirical Considerations

It has been argued that environmental variability favors variation in offspring size. We have examined theoretically the effects of three types of environmental heterogeneity on parental investment: spatial variation, spatial variation with density-dependent offspring fitness, and temporal variability. We concluded that environmental heterogeneity rarely favors the production of variable offspring. With spatial heterogeneity and no density dependence in fitness, a single offspring size is always optimal. Variable offspring may be favored if density-dependent fitness loss to offspring is large and if parents can control the dispersal of offspring to the appropriate habitats. If environments vary temporally, the predicted optimal parental investment strategy depends on the measure of fitness used. If the arithmetic mean of fitness is used, a single offspring size is always favored. Variable offspring sizes are sometimes selected if fitness is measured by the arithmetic mean discounted for the variance or by the geometric mean. Our results differ from those of R. Kaplan and Cooper (1984) because of differences in our assumptions about the relationship among offspring fitness, offspring size, and environmental quality. Although a single offspring size is often the optimal parental investment strategy according to our models, considerable variation in the size of eggs/seeds/young has been found in animals and plants. We suggest that an organism's ability to control investment in offspring precisely and to reduce the variation in offspring size may be constrained by such factors as pleiotropy and positional, temporal, and environmental effects during offspring development. We suggest that animals are more likely to exhibit adaptive offspring size variation than plants because of their greater ability to assess environmental conditions and to disperse offspring to appropriate habitats.
Human capital and productivity growth: A comparative analysis of Turkey

This paper analyzes the role of human capital in pr oductivity growth for a panel of around 50 countries from different l evels of development, including Turkey. We particularly focus on the rele vance of different quantitative measures of human capital in explainin g productivity growth and a detailed examination of the case of the Turki sh economy. The analysis covers the period 1981-2002 and the averag e years of education of the labor force and schooling rates for differen t ducation levels are proxy variables used for human capital. The initial year productivity level, physical investment, foreign direct investment, exp ort intensity and the share of agricultural employment in total employmen t are other * The views expressed in this study are the sole re sponsibility of the authors and do not necessarily reflect those of the Banking Regulation and Supervision Agency, the State Planning Organization and the Turkish Industrialist s’ and Businessmen’s Association. We would like to thank anonymous referees for their valuable suggestions. We also thank Yusuf I şık, Zafer Mustafao ğlu, and Mehmet N. Eri ş for their constructive comments. An version of this study was presented at the International Conference on Sustainable Growth Strategies for Turkey on June 17 , 2005 in Đstanbul, organized by TÜSĐAD-Koç University Economic Research Forum. The autho rs are responsible for any errors. Şeref SAYGILI – Cengiz CĐHAN – Zafer Ali YAVAN 490 productivity growth explanatory variables considere d in the study. Confirming the majority of other relevant studies, t his study finds a positive contribution from human capital to product ivity growth for the whole sample of countries considered. However, we f ind that this relation is not valid for the Turkish economy. Beside the qua lity dimension of human capital, the low level of human capital accum ulation and lack of adequate environment conducive for both ordinary pr oduction and technological activities are regarded as possible e xplanations for this finding.
DEVELOPMENT OF AN INSPIRAL SEARCH PIPELINE USING THE NESTED SAMPLING TECHNIQUE

We introduce a new technique for gravitational wave signal detection and parameter estimation which we hope will provide a more sensitive algorithm to search for wave signals originating from compact binary coalesences (CBCs) associated with gamma-ray burst events (GRBs). In order to provide a better search algorithm, we focus on applying Bayesian inference to the search for signal around GRB events, using a nested sampling technique to estimate the evidence integral, a notoriously difficult to calculate quantity which often limits the applicability of Bayes Theorem. We design a pipeline tailored to the search for GRB-associated inspirals, and test its performance on both software injection recovery and off-source data. We also explore the usefulness of a Bayesian coherency test as a means of glitch rejection. Date: August 9, 2010. 1
Molecular detection and identification of Tomato leaf curl New Delhi virus isolate causing yellow mosaic disease in Bitter gourd (Momordica charantia), a medicinally important plant in India

Bitter gourd (Momordica charantia) is extensively cultivated in India, China and South East Asia for vegetable and traditional medicines. A severe yellow mosaic disease on bitter gourd (M. charantia) with a significant disease incidence was observed during the survey of different locations of Eastern U.P., India in the year 2007. The disease consisted of yellow mosaic accompanied with slight curling on leaves. The whitefly (Bemisia tabaci) population was also observed in the vicinity. The characteristic disease symptoms and whitefly population indicated the possibility of begomovirus infection. PCR was carried out using the total DNA isolated from infected leaf samples and a pair of begomovirus specific primers which resulted in the expected size (∼800 bp) amplicon in 3/6 samples indicating presence of begomovirus. For further identification of the begomovirus, the PCR amplicons were cloned and sequenced (GenBank accession no. EU439260). The sequence data analysis revealed highest 98–93% similarities with several isolates of Tomato leaf curl New Delhi virus (ToLCNDV) at both nucleotide and amino acid levels. The phylogenetic analysis also showed closest relationships of the isolate (EU439260) with various variant of ToLCNDV. Based on highest sequence similarities and closest relationships with ToLCNDV the virus isolated from bitter gourd was considered as an isolate of Tomato leaf curl New Delhi virus.
Visual Analysis of Thematic, Social and Geospatial Patterns of Microblogging Content Using D3

User generated content from microblogging platforms often have a direct or indirect thematic, social, geospatial and temporal reference. Visual analysis tools can give an insight into the structure of the data provided by such platforms. This paper describes a simple way to analyze and visualize the thematic, social and geospatial patterns by using microblogging content from the platform Twitter. For this study we used tweets collected during a soccer match. We mapped the network of the thematic relation by examining the co-occurence of hashtags in a single tweet and the network of the social relation by examining the relationships between the users. The Louvain unsupervised community detection algorithm was applied on the networks to discover different groups of topics and users. To map the geospatial distribution of the users, we extracted the location information from the user profile and from the geolocated tweets. The results of this work were presented by using the JavaScript library D3 in order to produce a dynamic and interactive data visualization.
Role of salicylic acid and ascorbic acid in alleviating the harmful effects of water stress in Maize (Zea mays L.)

Water scarcity is threatening productivity of crops throughout the globe. Different osmoprotectants such as salicylic acid and ascorbic acid have potential to mitigate the harmful effects of water deficit. Current study was conducted to examine the possible role of salicylic acid and ascorbic acid individually and in combination in improving the yield and yield components of maize (Zea mays L.) exposed to drought at different growth stages. Drought was imposed by skipping irrigation at two water sensitive crop growth stages i.e. 6 fully expanded leaves (V6 as per Feekes scale) and initiation of silking (R1 as per Feekes scale). Water deficit significantly decreased cob diameter (19.2%), cob weight (13.8%), grain rows per cob (14.4%), cob length (12.8%), 1000grain weight (6.2%), stover yield (17.7%), grain yield (10.6%), biological yield (8.2) as compared to normal irrigation. Drought imposed at R1 (as per Feekes scale) was more lethal in terms of reduction in different yield and yield components including 1000-grain weight, grain yield and biological yield by 0.2, 6.9 and 0.9 %, respectively. Exogenous application of combination of salicylic acid + ascorbic acid @ 0.5mM each + tween 20 (0.1%) applied at V6 stage (as per Feekes scale) was more effective in mitigating the harmful effects of water deficit by improving cob diameter (6%) biological yield (1%) and grain yield of maize (6.9%) as compared to drought imposed at R1(as per Feekes scale).
Seed Germination of a Halophytic Grass Aeluropus lagopoides

Aeluropus lagopoides (Linn.) Trin. Ex Thw. (Poaceae) is a perennial grass distributed from coastal Sindh and Balochistan to saline flats of Punjab, Pakistan. Seeds collected from an inland population of A. lagopoides located on the University of Karachi campus were germinated under various levels of salinity (0, 100, 200, 300, 400 and 500 mM NaCl) and temperature regimes (10/20, 15/25, 20/30 and 25/35 8C) in a 12 h dark/12 h light photoperiod. Highest germination was obtained under non-saline conditions, and an increase in NaCl concentration progressively inhibited germination. Inhibition of germination was greater at cooler temperatures (10/20 8C) when no seed germinated above a concentration of 300 mM NaCl. The germination response at moderate temperatures (20/30 8C) was optimal, with 30% of seeds germinating in 500 mM NaCl. The rate of germination decreased as salinity increased. Germination rate was highest at 20/30 8C and lowest at 10/20 8C. Seeds were transferred from salt solutions to distilled water after 20 d and those from high salinities recovered quickly at warmer temperatures with an optimal response at 20/30 8C. # 2001 Annals of Botany Company
Gravitational-Wave Memory Waveforms: A Generalized Approach

Binary black hole coalescences can produce a nonlinear memory effect besides emitting oscillatory gravitational waves. The purpose of the paper is to present a simple and general approach for calculating the Christodoulou gravitational-wave memory waveform, along with some preliminary results. We compute memories based on the oscillatory waveforms, which contain information of the energy flux of the wave. We compare this approach with the minimal-waveform model.
The Effect of Health Status on Economic Growth

This paper will explore the effect of health status on economic growth. Over the past half century, economists and statisticians alike have estimated the effect of health status on economic growth through the use of a proxies, such as health indicators. A health indicator with which many are familiar, life expectancy, is a statistical measure of average lifespan based on birth year, sex, and other demographics. Economists like Bloom (2004) have reported the “positive, sizable, and statistically significant effect” of life expectancy on economic growth. Despite such findings, other economists, such as Pascale (2005), argue that life expectancy is an imperfect measurement of global health status. Such discrepancies between economists on the significance and magnitude of health indicators effect on economic growth are not unheard of. The debate over the efficacy of health indicators constitutes reason to investigate the validity of any measure of health status in its effects on economic growth. Life expectancy is one measure of health of health status; however, there is a refined metric developed in the 1990s by the World Health Organization, disability adjusted life years (DALY). DALY is a measurement of health loss which effectively sums the years of life lost as a result of premature death and the years of life lived with a disability. The initial motivation behind DALY was to develop a health metric capable of providing “consistent estimates of the incidence, prevalence, duration, and case-fatality” for disabling diseases around the world (Murray, 1996). In
Physical Activity and Quality of Life after Renal Transplantation

Background: The survival rate after renal transplantation (RT) has improved across the world. Physical activity (PA) remains a way to sustain health benefits after successful RT. But, the PA and quality of life (QOL) after renal transplantation remains underexplored. Methods: This cross sectional study was done on 63 eligible, consented RT recipients who underwent transplant between 2010 and 2014. The Blood urea, serum creatinine, Physical Activity level (IPAQ) and QOL (RAND 36) were assessed. Results: The PA was found to be low, moderate and high respectively among 28, 24 and 11 subjects. The mean score of PA, physical health (QOL) was 932 ±720, 73.68±10.85 respectively. Physical health QOL positively correlated with PA, negatively with Post RT duration (p<0.05). Conclusions: The PA and QOL remained low with time than the recommendation. The significant correlation between the PA, duration of transplantation and QOL warrants tailored interventions in post renal transplantation patients.
Impedance measurement and computation for the steam- generator tube integrity using the ECNDT technique

The purpose of this research paper is to characterize and extract crack defects from steam-generator (SG) tubes, operating in nuclear-power plants by using an eddy-current nondestructive testing (ECNDT) technique. Modeling and simulation using this technique make it possible to characterize the eddy-current sensors and to improve their performances while limiting the number of experimental prototypes and thus the development costs. The paper describes an effective ECNDT technique enabling detection of a small crack defect or discontinuity in the form of a narrow crack in cylindrical structures by using a particular sensor configuration based on the numerical technique. The results prove the efficiency of a 3D FEM model implemented based on the Comsol multiphysics software, which can understand , quantifies and accurately predicts the eddy-current signals determining the interaction between defected SG tube wall, and the eddy-current probe response. By using the multifrequency technique the entire crack depth of a conductive-test specimen is scanned. Our calculation results are verified by comparing them with the experimental ones and a good agreement between them is observed.
Evaluation of a ramjet model in a supersonic high-enthalpy tunnel

In the framework of a cooperation agreement between SPLab of Politecnico di Milano and ICMATECNR (National Research Council), two different configurations of a rectangular geometry small scale model engine fuelled with gaseous hydrogen were tested at Mach 5.4 in a high enthalpy pulse facility. A dual-mode scramjet setup was first studied and the main propulsion parameters were obtained for a wide ER range. A second geometry, corresponding to a ramjet configuration, was then evaluated under the same nominal freestream flow and fuelling conditions. Under the tested flight conditions, the ramjet setup featured higher specific impulses.
Decentralized teaching and learning in cooperative multiagent systems

Cooperative multiagent decision making is a ubiquitous problem with many real-world applications, including organization of driverless car fleets [1, 2], target surveillance [3], and warehouse automation [4–6]. The unifying challenge in these real-world settings is the presence of domain stochasticity (due to noisy sensors and actuators) and partial observability (due to local perspectives of agents), which can obfuscate the underlying state. In many practical applications, it is desirable for teams of agents to be capable of executing well-coordinated policies despite these uncertainty challenges. The core assumption of standard multiagent planning approaches is knowledge of an accurate, high-fidelity environment model. In practice, models may be unavailable or inaccurate. In the former case, models necessary for planning-based approaches must be generated or learned (which may be difficult and/or expensive). In the latter, execution of policies optimized for incorrect models may have dire economic and/or social consequences for systems deployed in the real world. While many works have introduced learning (rather than planning) approaches for multiagent systems, few address the partially observable setting, and even fewer do so in a scalable manner deployable to real-world settings, such as multi-robot systems that face collections of tasks [7]. The primary objective of this thesis is to develop technologies for scalable learningbased coordination in multiagent settings. Specifically, this thesis introduces methods for hierarchical learning of models and policies that enable multiagent coordination with more realistic sensors, execution in settings where underlying environment contexts may be non-unique or non-stationary, and acceleration of cooperative learning using inter-agent advice exchange. The algorithms developed are demonstrated in a variety of hardware and simulation settings, including those with complex sensory inputs and realistic dynamics and/or learning objectives, extending beyond the usual task-specific performance objectives to meta-learning (learning to learn) and multitask learning objectives. Thesis Supervisor: Jonathan P. How Title: R. C. Maclaurin Professor of Aeronautics and Astronautics, MIT
Cost-effectiveness analysis of prognostic-based depression monitoring

Abstract Chronic depression monitoring often relies on one-size-fits-all routine monitoring guidelines. Without considering heterogeneity in patients’ disease progression, routine monitoring guidelines may lead to inadequate monitoring of sick individuals and unnecessary monitoring of healthy individuals. Prognostic-based monitoring that stratifies the individual’s disease progression risk into different levels and adaptively allocates monitoring resources to high-risk individuals has the potential to improve patient health outcomes and cost-effectiveness of the monitoring service. However, challenges include how to best apply prognostic models to inform the design of monitoring strategies and identify the cost-effective strategies. To address these challenges, we develop a decision support framework that integrates individual prognostics, monitoring strategy design and cost-effectiveness analysis. We apply the proposed framework to simulate the adaptive monitoring of a depression treatment population from electronic health record data. Several prediction algorithms with increasing complexity, including natural history matching, logistic regression, rule-based method and Markov-based collaborative model, are simulated to monitor high-risk individuals for severe depression over time. We find six cost-effective monitoring strategies and demonstrate that two routine monitoring strategies are dominated by the prognostic-based monitoring strategies. Methods from this research show promise for implementing prognostic-based monitoring of chronic conditions in clinical practice.
A Monad for Probabilistic Point Processes

A point process on a space is a random bag of elements of that space. In this paper we explore programming with point processes in a monadic style. To this end we identify point processes on a space X with probability measures of bags of elements in X. We describe this view of point processes using the composition of the Giry and bag monads on the category of measurable spaces and functions and prove that this composition also forms a monad using a distributive law for monads. Finally, we define a morphism from a point process to its intensity measure, and show that this is a monad morphism. A special case of this monad morphism gives us Wald's Lemma, an identity used to calculate the expected value of the sum of a random number of random variables. Using our monad we define a range of point processes and point process operations and compositionally compute their corresponding intensity measures using the monad morphism.