REDUCED GRAPHENE OXIDE HYDROGELS, DEPOSITED IN NICKEL FOAM BY ELECTROPHORETIC DEPOSITION, FOR SUPERCAPACITOR

Supercapacitors, a class of electrochemical energy storage devices with superior power densities and long cycling lifetimes, have attracted great attention for the last decade due to their widespread application in backup power supply systems, portable devices, power tools, and hybrid electric vehicles. Graphene is considered as an ideal supercapacitor electrode material due to its large surface area, superior electrical conductivity, good chemical stability, and high mechanical strength. The theoretical specific capacitance of graphene is as high as ~ 550 F/g. The assembly of graphene sheets into three-dimensional interconnected porous microstructures, namely graphene hydrogels, has been considered the most effective approach to utilize these materials in supercapacitors that can achieve high specific capacitances. However, graphene hydrogels typically consist of large amount of water, up to 99 wt. %, resulting in very low graphene packing density. Therefore, the usual volumetric capacitance of graphene hydrogels is very poor, limiting their practical application. Please click Additional Files below to see the full abstract

ARSENIC DISTRIBUTION IN NATURE AND THE ENVIRONMENTAL POLLUTION BY ARSENIC IN VIETNAM

Joint Research on Environmental Science and Technology for the Eart

Support Vector Machine for Regression of Ultimate Strength of Trusses: A Comparative Study

Thanks to the rapid development of computer science, direct analyses have been increasingly used in the design of structures in lieu of member-based design methods using the effective length factor. In a direct analysis, the ultimate strength of a whole structure can be sufficiently estimated, so that the need for member capacity checks is eliminated. However, in complicated structural design problems where many structural analyses are required, the use of direct analyses requires an excessive computation cost. In such cases, Machine Learning (ML) algorithms are used to build metamodels that can predict the structural responses without performing costly structural analysis. In this paper, the support vector machine (SVM) algorithm is employed for the first time to develop a metamodel for predicting the ultimate strength of trusses using direct analysis. Several kernel functions for the SVM model, including linear, sigmoid, polynomial, radial basis function (RBF), are considered. A planar 39-bar nonlinear inelastic steel truss is taken to study the performance of the kernel functions. The results confirm the applicability of the SVM-based metamodel for predicting the ultimate strength of trusses. In particular, the RBF appears to be the best kernel among others. This investigation also provides a deeper understanding of the effect of the parameters on the efficiency of the kernel functions

On the distribution of the maximum and the sojourn time of stationary centered Gaussian fields

Dans cette thèse, nous étudions les propriétés de la surface d'un champ aléatoire. Plus précisément, nous nous intéressons à la loi du maximum d'un champ gaussien centré stationnaire et au volume de l'ensemble d'excursion (le temps de séjour). Nous améliorons la "méthode des records" en dimension 2 et la prolongeons à dimension 3 pour donner des bornes supérieures pour la queue de la distribution du maximum. Nous donnons aussi la formule asymptotique de cette queue en dimension 2. Il y a une correspondance entre la formule asymptotique et les coefficients de la formule de Steiner du domaine considéré. Il s'agit d'une prolongation du résultat de Adler. Nous étudions la vitesse de convergence dans le théorèmes de la limite centrale pour le temps de séjour dans deux cas: à niveau fixe et à niveau variable.In this thesis, we study the properties of the paths of random fields. More precisely, we are interested in the distribution of the maximum of stationary centered Gaussian field and the volume of the excursion set (sojourn time). We extend slightly the "record method" in dimension 2 and develop it in dimension 3 to give an upper bound for the tail of the distribution of the maximum. We also give an asymptotic formula for this tail in dimension 2. There is a correspondence between the asymptotic formula and the coefficients of the Steiner formula of the domain considered. This can be viewed as an extension of some results of Adler. We study the rate of convergence of the central limit theorems of the sojourn time in both cases: fixed and moving level

When does a perturbation of the equations preserve the normal cone

Let $(R,\mathfrak m)$ be a local ring and $I, J$ two arbitrary ideals of $R$. Let $\operatorname{gr}_J(R/I)$ denote the associated ring of $R/I$ with respect to $J$, which corresponds to the normal cone in geometry. The main result of this paper shows that if $I = (f_1,...,f_r)$, where $f_1,...,f_r$ is a $J$-filter regular sequence, there exists a number $N$ such that if $f_i' \equiv f_i \mod J^N$ and $I' = (f_1',...,f_r')$, then $\operatorname{gr}_J(R/I) \cong \operatorname{gr}_J(R/I')$. If $J$ is an $\mathfrak m$-primary ideal, this result implies a long standing conjecture of Srinivas and Trivedi on the invariance of the Hilbert-Samuel function under small perturbations, which has been solved recently by Ma, Quy and Smirnov. As a byproduct, the Artin-Rees number of $I$ and $I'$ with respect to $J$ are the same. Furthermore, we give explicit upper bounds for the smallest number $N$ with the above property. These results solve two problems raised by Ma, Quy and Smirnov. There are other interesting consequences on the invariance of the Achilles-Manaresi function, the relation type, the Castelnuovo-Mumford regularity, the Cohen-Macaulayness and the Gorensteiness of the Rees algebra of $R/I$ with respect to $J$ under small perturbation of $I$. We also prove a converse of the main result showing that the condition $I$ being generated by a $J$-filter regular sequence is the best possible for its validity. The main result can be also extended to perturbations with respect to filtrations of ideals. As a consequence, if $R$ is a power series ring, $f_1,...,f_r$ is a filter regular sequence, and $f_i'$ is the $n$-jet of $f_i$ for $n \gg 0$, then $I$ and $I'$ have the same initial ideal with respect to any Noetherian monomial order. A special case of this consequence was a conjecture of Adamus and Seyedinejad on approximations of analytic complete intersection singularities.Comment: 22 pages. This replacement contains more applications of the main resul

Improving the Reliability of Deep Learning Software Systems

For the last decade, deep learning (DL) has emerged as a new effective machine learning approach that is capable of solving difficult challenges. Due to their increasing effectiveness, DL approaches have been applied widely in commercial products such as social media platforms and self-driving cars. Such widespread application in critical areas means that mistakes caused by bugs in such DL systems would lead to serious consequences. Our research focuses on improving the reliability of such DL systems. At a high level, the DL systems development process starts with labeled data. This data is then used to train the DL model with some training methods. Once the model is trained, it can be used to create predictions for some unlabeled data in the inference stage. In this thesis, we present testing and analysis techniques that help improve the DL system reliability for all stages. In the first work, CRADLE, we improve the reliability of the DL system inference by applying differential testing to find bugs in DL libraries. One key challenge of testing DL libraries is the difficulty of knowing the expected output of DL libraries given an input instance. We leverage equivalent DL libraries to overcome this challenge. CRADLE focuses on finding and localizing bugs in DL software libraries by performing cross-implementation inconsistency checking to detect bugs, and leveraging anomaly propagation tracking and analysis to localize faulty functions that cause the bugs. CRADLE detects 12 bugs in three libraries (TensorFlow, CNTK, and Theano), and highlights functions relevant to the causes of inconsistencies for all 104 unique inconsistencies. Our second work is the first to study the variance of DL systems training and the awareness of this variance among researchers and practitioners. Our experiments show large overall accuracy differences among identical training runs. Even after excluding weak models, the accuracy difference is 10.8%. In addition, implementation-level factors alone cause the accuracy difference across identical training runs to be up to 2.9%. Our researcher and practitioner survey shows that 83.8% of the 901 participants are unaware of or unsure about any implementation-level variance. This work raises awareness of DL training variance and directs SE researchers to challenging tasks such as creating deterministic DL implementations to facilitate debugging and improving the reproducibility of DL software and results. DL systems perform well on static test sets coming from the same distribution as training sets but may not be robust in real-world deployments because of the fundamental assumption that the training data represents the real world data well. In cases where the training data misses samples from the real-world distribution, it is said to contain blindspots. In practice, it is more likely a training dataset contains weakspots (i.e., a weaker form of blindspots, where the training data contains some samples that represent the real world but it does not contain enough). In the third work, we propose a new procedure to detect weakspots in training data and to improve the DL system with minimum labeling effort. This procedure leverages the variance of the DL training process to detect highly varying data samples that could indicate the weakspots. Metrics that measure such variance can also be used to rank new samples to prioritize the labeling of additional training data that can improve the DL system accuracy when applied to the real world. Our evaluation shows that, in scenarios where the weakspots are severe, our procedure improves the model accuracy on weakspot samples by 25.2% requiring 2% of additional training data. This is an improvement of 4.5 percentage points compared to the traditional single model metric with the same amount of additional training data

ULTRASONICALLY ASSISTED PREPARATION OF BIO-DIESEL FUEL AS CLEAN ENERGY FOR GLOBAL WARMING AND EMMISION OF FINE PARTICULATE MATTER

Joint Research on Environmental Science and Technology for the Eart