Stem-root flow effect on soil–atmosphere interactions and uncertainty assessments

Abstract. Soil water can rapidly enter deeper layers via vertical redistribution of soil water through the stem–root flow mechanism. This study develops the stem–root flow parameterization scheme and coupled this scheme with the Simplified Simple Biosphere model (SSiB) to analyze its effects on land–atmospheric interactions. The SSiB model was tested in a single column mode using the Lien Hua Chih (LHC) measurements conducted in Taiwan and HAPEX-Mobilhy (HAPEX) measurements in France. The results show that stem–root flow generally caused a decrease in the moisture content at the top soil layer and moistened the deeper soil layers. Such soil moisture redistribution results in significant changes in heat flux exchange between land and atmosphere. In the humid environment at LHC, the stem–root flow effect on transpiration was minimal, and the main influence on energy flux was through reduced soil evaporation that led to higher soil temperature and greater sensible heat flux. In the Mediterranean environment of HAPEX, the stem–root flow significantly affected plant transpiration and soil evaporation, as well as associated changes in canopy and soil temperatures. However, the effect on transpiration could either be positive or negative depending on the relative changes in the moisture content of the top soil vs. deeper soil layers due to stem–root flow and soil moisture diffusion processes

Estimating the impact of whaling on global whale watching

After the commercial whaling moratorium was enacted in 1986, whale watching became one of the fastest growing tourism industries worldwide. As whaling was regarded as an activity incompatible with whale watching, the possible resumption of commercial whaling caused an urgent need to investigate the potential negative effects of whaling on the whale-watching industry. We examine the potential impacts of whaling on the global whale-watching tourism industry using unbalanced panel data model. The empirical results indicate that the resumption of commercial whaling has the potential for a negative effect on the global whale-watching industry, especially for nations that are engaged in whaling.delay-difference equation model;global whale watching;whaling

Thermoelectric and thermal rectification properties of quantum dot junctions

The electrical conductance, thermal conductance, thermal power and figure of merit (ZT) of semiconductor quantum dots (QDs) embedded into an insulator matrix connected with metallic electrodes are theoretically investigated in the Coulomb blockade regime. The multilevel Anderson model is used to simulate the multiple QDs junction system. The charge and heat currents in the sequential tunneling process are calculated by the Keldysh Green function technique. In the linear response regime the ZT values are still very impressive in the small tunneling rates case, although the effect of electron Coulomb interaction on ZT is significant. In the nonlinear response regime, we have demonstrated that the thermal rectification behavior can be observed for the coupled QDs system, where the very strong asymmetrical coupling between the dots and electrodes, large energy level separation between dots and strong interdot Coulomb interactions are required.Comment: 8 page and 14 figure

Application of a failure driven test profile in random testing

Random testing techniques have been extensively used in reliability assessment, as well as in debug testing. When used to assess software reliability, random testing selects test cases based on an operational profile; while in the context of debug testing, random testing often uses a uniform distribution. However, generally neither an operational profile nor a uniform distribution is chosen from the perspective of maximizing the effectiveness of failure detection. Adaptive random testing has been proposed to enhance the failure detection capability of random testing by evenly spreading test cases over the whole input domain. In this paper, we propose a new test profile, which is different from both the uniform distribution, and operational profiles. The aim of the new test profile is to maximize the effectiveness of failure detection. We integrate this new test profile with some existing adaptive random testing algorithms, and develop a family of new random testing algorithms. These new algorithms not only distribute test cases more evenly, but also have better failure detection capabilities than the corresponding original adaptive random testing algorithms. As a consequence, they perform better than the pure random testing

Dynamic test profiles in adaptive random testing: A case study

Random testing (RT) is a basic software testing method. When used to detect software failures, RT usually generates random test cases according to a uniform distribution. Adaptive random testing (ART) is an innovative approach to enhancing the failure-detection capability of RT. Most ART algorithms are composed of two independent processes, namely the candidate generation process and the test case identification process. In these ART algorithms, some program inputs are first randomly generated as the test case candidates; then test cases are identified from these candidates in order to ensure an even spread of test cases across the input domain. Most previous studies on ART focused on the enhancement of the test case identification process, while using the uniform distribution in the candidate generation process. A recent study has shown that using a dynamic test profile in the candidate generation process can also improve the failure-detection capability of ART. In this paper, we develop various test profiles and integrate them with the test case identification process of a particular ART algorithm, namely fixed-size-candidate-set ART. It is observed that all these test profiles can significantly improve the failure-detection capability of ART

Adaptive random testing based on distribution metrics

Random testing (RT) is a fundamental software testing technique. Adaptive random testing (ART), an enhancement of RT, generally uses fewer test cases than RT to detect the first failure. ART generates test cases in a random manner, together with additional test case selection criteria to enforce that the executed test cases are evenly spread over the input domain. Some studies have been conducted to measure how evenly an ART algorithm can spread its test cases with respect to some distribution metrics. These studies observed that there exists a correlation between the failure detection capability and the evenness of test case distribution. Inspired by this observation, we aim to study whether failure detection capability of ART can be enhanced by using distribution metrics as criteria for the test case selection process. Our simulations and empirical results show that the newly proposed algorithms not only improve the evenness of test case distribution, but also enhance the failure detection capability of ART

Distribution metric driven adaptive random testing

Adaptive Random Testing (ART) was developed to enhance the failure detection capability of Random Testing. The basic principle of ART is to enforce random test cases evenly spread inside the input domain. Various distribution metrics have been used to measure different aspects of the evenness of test case distribution. As expected, it has been observed that the failure detection capability of an ART algorithm is related to how evenly test cases are distributed. Motivated by such an observation, we propose a new family of ART algorithms, namely distribution metric driven ART, in which, distribution metrics are key drivers for evenly spreading test cases inside ART. Out study uncovers several interesting results and shows that the new algorithms can spread test cases more evenly, and also have better failure detection capabilities