Integration of Abductive and Deductive Inference Diagnosis Model and Its Application in Intelligent Tutoring System

This dissertation presents a diagnosis model, Integration of Abductive and Deductive Inference diagnosis model (IADI), in the light of the cognitive processes of human diagnosticians. In contrast with other diagnosis models, that are based on enumerating, tracking and classifying approaches, the IADI diagnosis model relies on different inferences to solve the diagnosis problems. Studies on a human diagnosticians\u27 process show that a diagnosis process actually is a hypothesizing process followed by a verification process. The IADI diagnosis model integrates abduction and deduction to simulate these processes. The abductive inference captures the plausible features of this hypothesizing process while the deductive inference presents the nature of the verification process. The IADI diagnosis model combines the two inference mechanisms with a structure analysis to form the three steps of diagnosis, mistake detection by structure analysis, misconception hypothesizing by abductive inference, and misconception verification by deductive inference. An intelligent tutoring system, Recursive Programming Tutor (RPT), has been designed and developed to teach students the basic concepts of recursive programming. The RPT prototype illustrates the basic features of the IADI diagnosis approach, and also shows a hypertext-based tutoring environment and the tutoring strategies, such as concentrating diagnosis on the key steps of problem solving, organizing explanations by design plans and incorporating the process of tutoring into diagnosis

Budget-Constrained Regression Model Selection Using Mixed Integer Nonlinear Programming

Regression analysis fits predictive models to data on a response variable and corresponding values for a set of explanatory variables. Often data on the explanatory variables come at a cost from commercial databases, so the available budget may limit which ones are used in the final model. In this dissertation, two budget-constrained regression models are proposed for continuous and categorical variables respectively using Mixed Integer Nonlinear Programming (MINLP) to choose the explanatory variables to be included in solutions. First, we propose a budget-constrained linear regression model for continuous response variables. Properties such as solvability and global optimality of the proposed MINLP are established, and a data transformation is shown to signicantly reduce needed big-Ms. Illustrative computational results on realistic retail store data sets indicate that the proposed MINLP outperforms the statistical software outputs in optimizing the objective function under a limit on the number of explanatory variables selected. Also our proposed MINLP is shown to be capable of selecting the optimal combination of explanatory variables under a budget limit covering cost of acquiring data sets. A budget-constrained and or count-constrained logistic regression MINLP model is also proposed for categorical response variables limited to two possible discrete values. Alternative transformations to reduce needed big-Ms are included to speed up the solving process. Computational results on realistic data sets indicate that the proposed optimization model is able to select the best choice for an exact number of explanatory variables in a modest amount of time, and these results frequently outperform standard heuristic methods in terms of minimizing the negative log-likelihood function. Results also show that the method can compute the best choice of explanatory variables affordable within a given budget. Further study adjusting the objective function to minimize the Bayesian Information Criterion BIC value instead of negative log-likelihood function proves that the new optimization model can also reduce the risk of overfitting by introducing a penalty term to the objective function which grows with the number of parameters. Finally we present two refinements in our proposed MINLP models with emphasis on multiple linear regression to speed branch and bound (B&B) convergence and extend the size range of instances that can be solved exactly. One adds cutting planes to the formulation, and the second develops warm start methods for computing a good starting solution. Extensive computational results indicate that our two proposed refinements significantly reduce the time for solving the budget constrained multiple linear regression model using a B&B algorithm, especially for larger data sets. The dissertation concludes with a summary of main contributions and suggestions for extensions of all elements of the work in future research

Theory and Application of No-Till Reseeding Technology in Degraded Grasslands in China

Grasslands occupy nearly 400 million hectares in China, accounting for about 40.7% of the total land area, provide multiple ecological and economic benefits. However, due to over-grazing and over-cultivation, more than 90% grasslands in China are threatened by degradation that has caused significant negative impact on biodiversity and ecosystem functioning, such as biodiversity losses, decreased productivity, increased soil erosion etc. Thus, restoration of degraded grassland is urgent for sustainable grassland management in China. No-till reseeding has been found to be an effective way for grassland vegetation regeneration with improved productivity and increased plant diversity via reseeding suitable species with minimum disturbance for the soil. Here, we present a conceptual framework integrating plant-soil feedback theory and subclimax management model. We show that field experiments with reseeding legumes into the degraded grasslands can restore forage production and plant diversity in degraded grassland. We also applied the no-till reseeding technology in degraded grasslands in China, such as Shanxi, Gansu, Qinghai and found that reseeding leguminous and gramineous forages are effective in improving productivity and nutritional quality of degraded grassland in China. Overall, no-till reseeding is an effective way in restoring degraded grassland and could play an important role for sustainable grassland management in China

Open structure and gating of the Arabidopsis mechanosensitive ion channel MSL10

Plants are challenged by drastically different osmotic environments during growth and development. Adaptation to these environments often involves mechanosensitive ion channels that can detect and respond to mechanical force. In the model plant Arabidopsis thaliana, the mechanosensitive channel MSL10 plays a crucial role in hypo-osmotic shock adaptation and programmed cell death induction, but the molecular basis of channel function remains poorly understood. Here, we report a structural and electrophysiological analysis of MSL10. The cryo-electron microscopy structures reveal a distinct heptameric channel assembly. Structures of the wild-type channel in detergent and lipid environments, and in the absence of membrane tension, capture an open conformation. Furthermore, structural analysis of a non-conductive mutant channel demonstrates that reorientation of phenylalanine side chains alone, without main chain rearrangements, may generate the hydrophobic gate. Together, these results reveal a distinct gating mechanism and advance our understanding of mechanotransduction

One-step preparation of optically transparent Ni-Fe oxide film electrocatalyst for oxygen evolution reaction

Optically transparent cocatalyst film materials is very desirable for improved photoelectrochemical (PEC) oxygen evolution reaction (OER) over light harvesting photoelectrodes which require the exciting light to irradiate through the cocatalyst side, i.e., front-side illumination. In view of the reaction overpotential at electrode/electrolyte interface, the OER electrocatalysts have been extensively used as cocatalysts for PEC water oxidation on photoanode. In this work, the feasibility of a one-step fabrication of the transparent thin film catalyst for efficient electrochemical OER is investigated. The Ni-Fe bimetal oxide films, 200 nm in thickness, are used for study. Using a reactive magnetron co-sputtering technique, transparent (> 50% in wavelength range 500-2000 nm) Ni-Fe oxide films with high electrocatalytic activities were successfully prepared at room temperature. Upon optimization, the as-prepared bimetal oxide film with atomic ratio of Fe/Ni = 3:7 demonstrates the lowest overpotential for the OER in aqueous KOH solution, as low as 329 mV at current density of 2 mA cm 2, which is 135 and 108 mV lower than that of as-sputtered FeOx and NiOx thin films, respectively. It appears that this fabrication strategy is very promising to deposit optically transparent cocatalyst films on photoabsorbers for efficient PEC water splitting.This work was financially supported by the National Natural Science Foundation of China (No. 21090340), 973 National Basic Research Program of the Ministry of Science and Technology (No. 2014CB239400) and Solar Energy Action Plan of Chinese Academy of Sciences (KGCX2-YW-399+7-3)

Simulating Spatiotemporal Dynamics of Sichuan Grassland Net Primary Productivity Using the CASA Model and In Situ Observations

Net primary productivity (NPP) is an important indicator for grassland resource management and sustainable development. In this paper, the NPP of Sichuan grasslands was estimated by the Carnegie-Ames-Stanford Approach (CASA) model. The results were validated with in situ data. The overall precision reached 70%; alpine meadow had the highest precision at greater than 75%, among the three types of grasslands validated. The spatial and temporal variations of Sichuan grasslands were analyzed. The absorbed photosynthetic active radiation (APAR), light use efficiency (ε), and NPP of Sichuan grasslands peaked in August, which was a vigorous growth period during 2011. High values of APAR existed in the southwest regions in altitudes from 2000 m to 4000 m. Light use efficiency (ε) varied in the different types of grasslands. The Sichuan grassland NPP was mainly distributed in the region of 3000–5000 m altitude. The NPP of alpine meadow accounted for 50% of the total NPP of Sichuan grasslands