Draft bills and research reports on: reducing judicial corruption and child labor in Nepal

These two draft bills and accompanying research report comprise the work of two teams of Nepali officials from Nepal's Ministry of Law and Justice who prepared them in the context of the Boston University School of Law Program on Legislative Drafting for Democratic Social Change. They attended that Program as part of a larger Ministry of Law and Justice Program, funded by the United Nations Development Program (UNDP), to strengthen Nepal's legal framework and the Rule of Law. Using the bills and reports as case studies, the four officials aimed to learn legislative theory, methodology and techniques. The Ministry had assigned them, on their return to Nepal, to play a significant role in institutionalizing an on-going learning process to strengthen Nepali drafters' capacity to prepare the effectively implementable legislation necessary to ensure good governance and development

Effects of salmon lice treatment on the barrier properties of salmon skin mucus

Salmon skin mucus forms a thin physical barrier between the external environment and internal milieu acting as a first line of defence against infection through skin epidermis. In addition, mucus has role in defence mechanism of fish acting as a biological barrier. Reduced function of this barrier can cause threat to the health of fish. The current problem in the salmon farming industry is due to ectoparasitism with sea lice feeding off the flesh and skin of salmon fish. For the efficient control of sea lice, diverse treatment has been tried over the years. Despite the importance of the skin mucosa in the first line defence against environmental pathogens, the effect on barrier properties of fish skin mucus due to lice treatment have yet not been well characterized. The aim of this project was to investigate the effects of treatments applied against salmon lice on the mucus barrier properties. In this master s thesis, variance in immobilization of 200nm diameter Carboxylate-modified microsphere in two different groups of samples i.e., in untreated samples and freshwater treated samples for sea lice were investigated and compared regarding the mucus thickness. The nanoparticles were placed on the top of salmon skin and the mucus ability to immobilize the given nanoparticles was characterized by using confocal laser scanning microscopy. We found that there was considerable variability in the mucus and scales structure between two different groups of samples. The variability in mucus and scales was also observed in same fish skin samples and within the same treatment group samples as well. It can be concluded from this study that there was an effect of treatment on salmon skin mucus indicating that freshwater treatment can cause substantial increase in mucus thickness

DeepEval: An Integrated Framework for the Evaluation of Student Responses in Dialogue Based Intelligent Tutoring Systems

The automatic assessment of student answers is one of the critical components of an Intelligent Tutoring System (ITS) because accurate assessment of student input is needed in order to provide effective feedback that leads to learning. But this is a very challenging task because it requires natural language understanding capabilities. The process requires various components, concepts identification, co-reference resolution, ellipsis handling etc. As part of this thesis, we thoroughly analyzed a set of student responses obtained from an experiment with the intelligent tutoring system DeepTutor in which college students interacted with the tutor to solve conceptual physics problems, designed an automatic answer assessment framework (DeepEval), and evaluated the framework after implementing several important components. To evaluate our system, we annotated 618 responses from 41 students for correctness. Our system performs better as compared to the typical similarity calculation method. We also discuss various issues in automatic answer evaluation

Measuring Semantic Textual Similarity and Automatic Answer Assessment in Dialogue Based Tutoring Systems

This dissertation presents methods and resources proposed to improve onmeasuring semantic textual similarity and their applications in student responseunderstanding in dialogue based Intelligent Tutoring Systems. In order to predict the extent of similarity between given pair of sentences,we have proposed machine learning models using dozens of features, such as thescores calculated using optimal multi-level alignment, vector based compositionalsemantics, and machine translation evaluation methods. Furthermore, we haveproposed models towards adding an interpretation layer on top of similaritymeasurement systems. Our models on predicting and interpreting the semanticsimilarity have been the top performing systems in SemEval (a premier venue for thesemantic evaluation) for the last three years. The correlations between our models\u27predictions and the human judgments were above 0.80 for several datasets while ourmodels being very robust than many other top performing systems. Moreover, wehave proposed Bayesian. We have also proposed a novel Neural Network based word representationmapping approach which allows us to map the vector based representation of a wordfound in one model to the another model where the word representation is missing,effectively pooling together the vocabularies and corresponding representationsacross models. Our experiments show that the model coverage increased by few toseveral times depending on which model\u27s vocabulary is taken as a reference. Also,the transformed representations were well correlated to the native target modelvectors showing that the mapped representations can be used with condence tosubstitute the missing word representations in the target model. models to adapt similarity models across domains. Furthermore, we have proposed methods to improve open-ended answersassessment in dialogue based tutoring systems which is very challenging because ofthe variations in student answers which often are not self contained and need thecontextual information (e.g., dialogue history) in order to better assess theircorrectness. In that, we have proposed Probabilistic Soft Logic (PSL) modelsaugmenting semantic similarity information with other knowledge. To detect intra- and inter-sentential negation scope and focus in tutorialdialogs, we have developed Conditional Random Fields (CRF) models. The resultsindicate that our approach is very effective in detecting negation scope and focus intutorial dialogue context and can be further developed to augment the naturallanguage understanding systems. Additionally, we created resources (datasets, models, and tools) for fosteringresearch in semantic similarity and student response understanding inconversational tutoring systems

Bio-electrochemical production of hydrogen by using electroactive materials

Due to the thermodynamic barrier, volatile fatty acids formed during dark fermentation cannot be oxidized further to produce hydrogen. In this work, we have designed an H-type bio-electrochemical cell to oxidize the acetate, a common intermediate product of dark fermentation reaction, to produce energy in the form of electricity and hydrogen gas. The hydrogen gas can be used as a fuel source and as an energy vector to supply the energy during the drought season. The inoculation was done on carbon felt and carbon cloth electrodes by daily replacing the medium for up to 7 weeks. Acetate was used as a model. The generation of voltage in the cells suggested successful degradation of the organic matter by microorganisms. The overpotential existing in the cells was determined by modelling the polarization behaviour, and the internal resistance of the cells was found in the range 405 Ω-715 Ω. The system was efficient in reducing the COD level by 80% in a batch operation. The electrochemical characterization of the cathode materials was done in a half-cell setup, and Nickel foam catalyzed with platinum (0.5 mg/cm2) exhibited better performance in the reduction zone. The coulombic efficiencies of the cells ranged from 2.28% to 15.80%. The coulombic efficiency was determined as a function of charge transferred in the circuit due to the oxidation of substrate at the anode. The influence of competing microorganisms at the anode and the internal resistance on the efficiency of the cells were also studied. The hydrogen production rate was determined by translating the charge available in the circuit using Faraday’s law. Voltage and Concentration were linearly increased to see their effect on the hydrogen production. At lower voltage (0.5 V, 0.8 V) and lower concentrations (5 mM, 10 mM), the production of hydrogen was almost insignificant; while at higher concentrations (20 mM) and voltage (1.0 V), hydrogen gas was produced at a rate of 0.005-0.006 m3/m3/day