Hydrogen sorption mechanisms in lithium amide and metal hydride reactive systems.

PhDConsiderable effort has been devoted to the M-N-H system for solid-state hydrogen storage. However, the desorption mechanism is still unclear and the desorption temperature is too high for practical considerations. Here, the desorption characteristics of LiNH2 and a mixture of (LiNH2+LiH) were firstly comparatively studied by simultaneous then-nogravimetry, differential scanning calorimetry and mass spectrometry for further understanding of H2 desorption in the (LiNH2+LiH) system. Mass spectrometry and thermal analysis of (LiNH2+LiH) mixtures indicate that approximately 5 mass % of H2 is released at 180*C after four hours of milling without any apparent release of NH3, whereas insufficient mixing of the two compounds cannot stop the escaping of NH3 from the mixture. Non-unifon-ri mixing can lead to the escape of NH3 from the mixture. The evidence further supports the notion that NH3 intermediated reaction is a possible reaction path within the thermal desorption of the (LiNH2+ LiH) mixture. BN additive, among selected nitrides shows the best effect on desorption from (LiNH2+ LiH). (LiNH2+MgH2)materials with different molar ratios (4: 3,4: 2 and 4: 1) were also studied for their sorption properties and mechanisms. Results show that more than 6 mass% H2 is desorbed from 1500C for the (4LiNH2 +3MgH2)mixture, with two H2peaks at 200 and 320'C. Meanwhile, there is only -5 mass% for (4LiNH2 +2MgH2) mixture with one H2 peak at 200 T. Reversibility measurements suggest that LiNH2 and MgH2 cannot be recovered after absorption; instead, Li2NH and Mg(NH2)2 (or MgNH) take over to perform the H2 storage functions. The (4LiNH2+3MgH2 ) mixture possess a greater H2 capacity in first desorption, but shows less than 2 mass% reversible capacity in subsequent cycles. However, there is only about I mass% capacity loss during the reversibility measurement for the (4LiNH2 +2MgH2)mixture. Other M-N-H systems, mainly NaH, KH, AlH3 and CaH2, were also investigated, and only CaH2 shows the capability of reacting with LiNH2 to produce H2 among these candidates

The Implications Of Accounting Conservatism For The Relation Between Earnings And Stock Returns

Characterizing accounting conservatism as the accountants’ tendency to require a higher degree of verification for recognizing good news than bad news, Basu (1997) predicts that the slope coefficient and R2 in a regression of earnings on concurrent stock returns will be higher for bad news (negative stock returns) than for good news (positive stock returns). However, standard econometric analysis indicates that the R2 is a function of the sensitivity of earnings to returns and the noise ratio, which is defined as the ratio of the variance of noise in earnings to the variance of noise in returns. I show that the R2 from the regression of earnings on stock returns is not necessarily higher for bad news than for good news. So the test of R2 is not a robust test of accounting conservatism. Consistent with the prediction, I find that the slope coefficient is higher for bad news firms reporting losses than for good news firms reporting profits, but R2 is lower for bad news firms reporting losses than for good news firms reporting profits

How Different Apology Components Drive Trust Repair: The Moderating Effect of Social Value Orientation

Trust is commonly recognized as a significant part in social life. Exploring how to repair violated trust is necessary because trust transgressions occur frequently. Apologizing is one of the methods commonly used to repair trust. Rather than simply regarding it as a dichotomous phenomenon in most extant researches, additional details on the effect mechanisms of apology components for trust repair must be investigated. Thus this study explores how three apology components drive trust repair through forgiveness, considering the moderating effect of social value orientation. Forgiveness mediates the effect of compensation, empathy, and acknowledgment on trust repair, and that prosocials react more positively to the effect of acknowledgment on trust repair in forgiveness than proselfs. This study can contribute to promoting understanding on how apology really works and how to apology in accordance with people’s tendencies. Keywords：Apology components, Forgiveness, Social value orientation, Trust repai

Amortising the Cost of Mutation Based Fault Localisation using Statistical Inference

Mutation analysis can effectively capture the dependency between source code and test results. This has been exploited by Mutation Based Fault Localisation (MBFL) techniques. However, MBFL techniques suffer from the need to expend the high cost of mutation analysis after the observation of failures, which may present a challenge for its practical adoption. We introduce SIMFL (Statistical Inference for Mutation-based Fault Localisation), an MBFL technique that allows users to perform the mutation analysis in advance against an earlier version of the system. SIMFL uses mutants as artificial faults and aims to learn the failure patterns among test cases against different locations of mutations. Once a failure is observed, SIMFL requires either almost no or very small additional cost for analysis, depending on the used inference model. An empirical evaluation of SIMFL using 355 faults in Defects4J shows that SIMFL can successfully localise up to 103 faults at the top, and 152 faults within the top five, on par with state-of-the-art alternatives. The cost of mutation analysis can be further reduced by mutation sampling: SIMFL retains over 80% of its localisation accuracy at the top rank when using only 10% of generated mutants, compared to results obtained without sampling

J-model: an open and social ensemble learning architecture for classification

Ensemble learning is a promising direction of research in machine learning, in which an ensemble classifier gives better predictive and more robust performance for classification problems by combining other learners. Meanwhile agent-based systems provide frameworks to share knowledge from multiple agents in an open context. This thesis combines multi-agent knowledge sharing with ensemble methods to produce a new style of learning system for open environments. We now are surrounded by many smart objects such as wireless sensors, ambient communication devices, mobile medical devices and even information supplied via other humans. When we coordinate smart objects properly, we can produce a form of collective intelligence from their collaboration. Traditional ensemble methods and agent-based systems have complementary advantages and disadvantages in this context. Traditional ensemble methods show better classification performance, while agent-based systems might not guarantee their performance for classification. Traditional ensemble methods work as closed and centralised systems (so they cannot handle classifiers in an open context), while agent-based systems are natural vehicles for classifiers in an open context. We designed an open and social ensemble learning architecture, named J-model, to merge the conflicting benefits of the two research domains. The J-model architecture is based on a service choreography approach for coordinating classifiers. Coordination protocols are defined by interaction models that describe how classifiers will interact with one another in a peer-to-peer manner. The peer ranking algorithm recommends more appropriate classifiers to participate in an interaction model to boost the success rate of results of their interactions. Coordinated participant classifiers who are recommended by the peer ranking algorithm become an ensemble classifier within J-model. We evaluated J-model’s classification performance with 13 UCI machine learning benchmark data sets and a virtual screening problem as a realistic classification problem. J-model showed better performance of accuracy, for 9 benchmark sets out of 13 data sets, than 8 other representative traditional ensemble methods. J-model gave better results of specificity for 7 benchmark sets. In the virtual screening problem, J-model gave better results for 12 out of 16 bioassays than already published results. We defined different interaction models for each specific classification task and the peer ranking algorithm was used across all the interaction models. Our research contributions to knowledge are as follows. First, we showed that service choreography can be an effective ensemble coordination method for classifiers in an open context. Second, we used interaction models that implement task specific coordinations of classifiers to solve a variety of representative classification problems. Third, we designed the peer ranking algorithm which is generally and independently applicable to the task of recommending appropriate member classifiers from a classifier pool based on an open pool of interaction models and classifiers