The purpose of this work was to investigate the use of nanoscale Fe3O4 as a catalytst for destruction of trichloroethylene (TCE) and 1,2-dichloroethane (1,2-DCA) by persulfate in spiked water and soil. First, nanoscale Fe3O4 was prepared by chemical coprecipitation. X-ray powder diffraction (XRD) was used to confirm the crystal structure; And size identification was performed using the scanning electron microscopy (SEM). The effectiveness of using 3 wt% soluble starch (SS) to stabilize nanoscale Fe3O4 was also studied. It was found that SS could effectively disperse the nanoparticles for more than one month. Therefore, SS was chosen to prepare the nanoscale Fe3O4 slurry. The efficiency of nanoscale Fe3O4 as an activator for persulfate remediation of TCE and 1,2-DCA in aqueous solutions (DI water, simulated groundwater, and actual groundwater) was then investigated. The results showed that all test removal efficiency of TCE and 1,2-DCA was more than 95%. Use of the persulfate for destruction of TCE and 1,2-DCA produced some by-products. The primary reaction products were cis-1,2-Dichloroethylene (cis-1,2-DCE) and trans-1,2-Dichloroethylene (trans-1,2-DCE)¡F The secondary daughter prodnct was vinyl chloride (VC). The VC produced is gradually degraded to safer substances (ethene, ethane, and methane). The nanoscale Fe3O4 slurry and the persulfate injection coupled with the electrokinetic (EK) process was tested for remediation of TCE and 1,2-DCA in saturated soil. The results showed that injection of persulfate into the EK reservoir could decrease the electrode polarization, and increase the electroosmotic flow and current density. When persulfate was injected into the cathode reservoir, the derived sulfate radicals would transfer into the soil compartment by ion migration. The injection of persulfate into the cathode reservoir was more efficient than injection of persulfate into the anode reservoir. The removal efficiency for TCE and 1,2-DCA was more than 96% in all tests. The remediation system was assessed for potential application in-situ. Soil was spiked with high TCE and 1,2-DCA and aged for a week. The injection of persulfate and nanoscale Fe3O4 slurry coupled with the EK process was tested for remediation of the aged contaminated soil. The results showed that the target contaminants (TCE and 1,2-DCA) met the Taiwan¡¦s EPA¡¦s control standard. After 30 d of remediation, the by-products (cis-1,2-DCE, trans-1,2-DCE, and VC) had also been removed to below the action limit. A cost analysis was performed in order to demonstrate the economic feasibility of the remediation method in this study. Operating costs (chemicals + electricity bill) of all tests were assessed. The results showed that the costs were 8000-17000 NT$/m3, which is economically reasonable.

Pa-Chang River is in Southwestern Taiwan. It originates from Fenchihu, Alishan and flows between Chiayi County and Tainan County. Its total length is 81 kilometer and total basin area is 475 square kilometer. The natural forest communities are mostly in the upper section and fragmentarily spread in the small areas that locate between farms or in complicated topography, e.g. steep slope, area difficult to reach. It is necessary to make inventory and research about these natural forest communities now to gather sufficient vegetation data which will be served as basic reference in the future local environmental management. This study is to investigate and analyze the species composition of broad-leaved forest vegetation in the upper section of Pa-Chang River. The analysis results of the vegetation data by detrended correspondence analysis¡]DCA¡^ and two-way indicator species analysis¡]TWINSPAN¡^classified the sampling plots into four forest types: I. Litsea hypophaea forest type The range of elevation is 743~1003 m. The topographic position is mainly in ridge and upper slope. II. Ficus irisana -Dendrocnide meyeniana forest type The range of elevation is 381~825 m¡CThe topographic position is mainly in lower slope and valley. III. Machilus japonica var. kusanoi -Ficus fistulosa forest type The range of elevation is 764~1229 m¡C IV. Prunus phaeosticta forest type The range of elevation is 1559~1804 m. The topographic position is mainly in upper slope. The study of correlations among environmental gradients and ordination axes indicated that the elevation is the most important environmental factor affecting the vegetation composition and distribution. Topographic position also has obvious influence on vegetation. In addition, the study of population structure shows that regeneration is continuous in present broad-leaved forest, but the forest is also continuously disturbed by artificial activities. The area of natural forest in this area might continuously be decreased, so it is important to gather sufficient vegetation data in time.

Secondary bile acids have long been postulated to be tumor promoters in the colon but their mechanism of action are yet to be delineated. Though most bile acids are chemically similar, they have been found to exert contrasting signaling effects in the colonic epithelium. Particularly, hydrophobic bile acids such as deoxycholic acid (DCA) are found to be tumor promoters while their hydrophilic counterparts such as ursodeoxycholic acid (UDCA) are chemopreventive. Given the fact that colon cells do not possess bile acid transporters, the question that arises is how do bile acids activate intracellular signaling? In our studies, we examined the actions of bile acids at the cell membrane and found that hydrophobic bile acids can perturb membrane structure. This membrane perturbation was found to be characterized by a change in membrane fluidity and by cholesterol aggregation. Additionally, several membrane associated proteins were found to be deregulated in response to DCA further supporting the above conclusion regarding membrane perturbation. Moreover, caveolin, a negative regulator of membrane microdomains was seen to be dephosphorylated and disassociated from the membrane microdomains, implicating membrane microdomains as a possible target of the effects of DCA on the membrane. Consistent with this, we found that DCA was able to cause rapid and sustained activation of the receptor tyrosine kinase, EGFR and that this activation was ligand-independent. Using fluorescent-tagged bile acids we showed increased aggregation and clustering in the membranes treated with FITC-DCA in a manner that was reminiscent of receptor activation in immune cells. Collectively, these data suggest that bile-acid induced signaling is likely to be initiated through alterations of the plasma membrane structure in colon cancer cells.

This paper examines the use of infinitival and gerundial constructions by intermediate Spanish learners. The use of those two patterns creates problems for second language learners at intermediate and advanced levels. However, there are only few studies on their second language acquisition, and fewer focus on Spanish learners. This study tries to resolve this and to this end, I retrieved all hits of the two constructions from the Spanish component of the International Learner Corpus of English (SP-ICLE). I run a distinctive collexeme analysis (DCA) to identify the verbs that are associated with either pattern. The results are discussed at three different levels: (i) the identification of verbs that Spanish learners associate with each construction; (ii) a systematic comparison with previous studies on native speakers to show possible similarities/discrepancies; and (iii) a comparison of the results with findings on German learners to discuss possible effects of language similarity and transfer.

The Florida District Courts of Appeal have undergone many changes over the last three years, including the adoption of video conferencing due to the Covid-19 pandemic, and the creation of a brand-new district for the first time since 1979. Included in this series of changes was a new rule that moves most of the circuit court’s appellate jurisdiction into the jurisdiction the District Courts of Appeals (DCAs). This change has added to the systemic pressures of the Florida DCAs. While the creation of a new district is a step in the right direction to protect the effectiveness and perception of the state’s intermediate appellate courts, more needs to be done. Unfortunately getting data from the courts is difficult; thus, there is little way for the public to sense their effectiveness. While the integration of technology has been positive, the current resources available to the courts to dispose of its cases are in need of expansion. Finally, there is a general need for more support for judges and their staff. Overall, the way that Appellate Courts operate has significantly changed, and the stress they are under has in turn increased because of these reasons; the creation of a new district—while expensive— was an important step to preserving the integrity of the courts.

Dans cette thèse, nous étudions des approches basées sur la programmation DC (Difference of Convex functions) et DCA (DC Algorithm) pour la programmation mathématique avec des contraintes d'équilibre, notée MPEC (Mathematical Programming with Equilibrum Constraints en anglais). Etant un sujet classique et difficile de la programmation mathématique et de la recherche opérationnelle, et de par ses diverses applications importantes, MPEC a attiré l'attention de nombreux chercheurs depuis plusieurs années. La thèse se compose de quatre chapitres principaux. Le chapitre 2 étudie une classe de programmes mathématiques avec des contraintes de complémentarité linéaire. En utilisant quatre fonctions de pénalité, nous reformulons le problème considéré comme des problèmes DC standard, i.e minimisation d'une fonction DC sous les contraintes convexes. Nous développons ensuite des algorithmes appropriés basés sur DCA pour résoudre les problèmes DC résultants. Deux d'entre eux sont reformulés encore sous la forme des problèmes DC généraux (i.e. minimisation d'une fonction DC sous des contraintes DC) pour que les sous-problèmes convexes dans DCA soient plus faciles à résoudre. Après la conception de DCA pour le problème considéré, nous développons ces schémas DCA pour deux cas particuliers: la programmation quadratique avec des contraintes de complémentarité linéaire, et le problème de complémentarité aux valeurs propres. Le chapitre 3 aborde une classe de programmes mathématiques avec des contraintes d'inégalité variationnelle. Nous utilisons une technique de pénalisation pour reformuler le problème considéré comme un programme DC. Une variante de DCA et sa version accélérée sont proposées pour résoudre ce programme DC. Comme application, nous résolvons le problème de détermination du prix de péages dans un réseau de transport avec des demandes fixes (" the second-best toll pricing problem with fixed demands" en anglais). Le chapitre 4 se concentre sur une classe de problèmes d'optimisation à deux niveaux avec des variables binaires dans le niveau supérieur. En utilisant une fonction de pénalité exacte, nous reformulons le problème considéré comme un programme DC standard pour lequel nous développons un algorithme efficace basé sur DCA. Nous appliquons l'algorithme proposé pour résoudre le problème d'interdiction de flot maximum dans un réseau ("maximum flow network interdiction problem" en anglais). Dans le chapitre 5, nous nous intéressons au problème de conception de réseau d'équilibre continu ("continuous equilibrium network design problem" en anglais). Il est modélisé sous forme d'un programme mathématique avec des contraintes de complémentarité, brièvement nommé MPCC (Mathematical Program with Complementarity Constraints en anglais). Nous reformulons ce problème MPCC comme un programme DC général et proposons un schéma DCA approprié pour le problème résultant / In this dissertation, we investigate approaches based on DC (Difference of Convex functions) programming and DCA (DC Algorithm) for mathematical programs with equilibrium constraints. Being a classical and challenging topic of nonconvex optimization, and because of its many important applications, mathematical programming with equilibrium constraints has attracted the attention of many researchers since many years. The dissertation consists of four main chapters. Chapter 2 studies a class of mathematical programs with linear complementarity constraints. By using four penalty functions, we reformulate the considered problem as standard DC programs, i.e. minimizing a DC function on a convex set. The appropriate DCA schemes are developed to solve these four DC programs. Two among them are reformulated again as general DC programs (i.e. minimizing a DC function under DC constraints) in order that the convex subproblems in DCA are easier to solve. After designing DCA for the considered problem, we show how to develop these DCA schemes for solving the quadratic problem with linear complementarity constraints and the asymmetric eigenvalue complementarity problem. Chapter 3 addresses a class of mathematical programs with variational inequality constraints. We use a penalty technique to recast the considered problem as a DC program. A variant of DCA and its accelerated version are proposed to solve this DC program. As an application, we tackle the second-best toll pricing problem with fixed demands. Chapter 4 focuses on a class of bilevel optimization problems with binary upper level variables. By using an exact penalty function, we express the bilevel problem as a standard DC program for which an efficient DCA scheme is developed. We apply the proposed algorithm to solve a maximum flow network interdiction problem. In chapter 5, we are interested in the continuous equilibrium network design problem. It was formulated as a Mathematical Program with Complementarity Constraints (MPCC). We reformulate this MPCC problem as a general DC program and then propose a suitable DCA scheme for the resulting problem

Cette thèse s'articule autour des fonctions booléennes liées à la cryptographie et la cryptanalyse de certains schémas d'identification. Les fonctions booléennes possèdent des propriétés algébriques fréquemment utilisées en cryptographie pour constituer des S-Boxes (tables de substitution).Nous nous intéressons, en particulier, à la construction de deux types de fonctions : les fonctions courbes et les fonctions équilibrées de haut degré de non-linéarité.Concernant la cryptanalyse, nous nous focalisons sur les techniques d'identification basées sur les problèmes de perceptron et de perceptron permuté. Nous réalisons une nouvelle attaque sur le schéma afin de décider de sa faisabilité.Nous développons ici des nouvelles méthodes combinant l'approche déterministe DCA (Difference of Convex functions Algorithm) et heuristique (recuit simulé, entropie croisée, algorithmes génétiques...). Cette approche hybride, utilisée dans toute cette thèse, est motivée par les résultats intéressants de la programmation DC. / In cryptography especially in block cipher design, boolean functions are the basic elements.A cryptographic function should have high non-linearity as it can be attacked by linear method. There are three goals for the research presented in this thesis :_ Finding a new construction algorithm for the highest possible nonlinear boolean functions in the even dimension, that is bent functions, based on a detreministic model._ Finding highly non linear boolean functions._ Cryptanalysing an identification scheme based on the perceptron problem.Optimisation heuristic algorithms (Genetic algorithm and simulated annealing) and a deterministicone based on DC programming (DCA) were used together.

Nous abordons dans cette thèse les approches de la Programmation DC et DCAen Data Mining (fouille de données). Plus particulièrement, nous nous intéressons aux problèmes de parcimonie en modélisation parcimonieuse de données. Le travail porte sur des recherches théoriques et algorithmiques et la principale approche utilisée est la programmation DC et DCA.Nous avons établi des propriétés intéressantes, des reformulations DC, voire quadratiques,équivalentes pour ces problèmes grâce à de nouvelles techniques de pénalité exacte développées durant cette thèse. Ces résultats donnent une nouvelle facette et une nouvelle manière de voir ces problèmes de parcimonie afin de permettre une meilleure compréhension et prise en main de ces problèmes. Ces nouvelles techniques ont été appliquées dans le cadre de la modélisation parcimonieuse pour le problème de la valeur propre maximale et dans le cadre de la modélisation parcimonieuse dans les modèles de régression linéaire.La structure simple des reformulations obtenues se prête bien à la programmation DC et DCA pour la résolution. Les simulations numériques, obtenues avec DCA et un algorithme combiné DCA et la procédure Séparation et Evaluation pour l’optimisation globale, sont très intéressantes et très prometteuses et illustrent bien le potentiel de cette nouvelle approche. / In this thesis, we investigate the DC Programming and DCA approaches in DataMining. More precisely, we are interested in the sparse approximation problems in sparse modelling. The work focuses on theoretical and algorithmic studies, mainly based on DC Programming and DCA. We established interesting properties concerning DC and quadratic reformulations for these problems with the help of new exact penalty techniques in DC programming. These results give new insights on these sparse approximation problems and so allow a better understanding and a better handling of these problems. These novel techniques were applied in both contexts of sparse eigenvalue problem and sparse approximation in linear models.The simple and nice structure of the obtained reformulations are suitably adapted to DC programming and DCA. Computational experiments are very interesting and promising, illustrating the potential of the novel approach.

Dans cette thèse, nous nous concentrons sur le développement des méthodes d'optimisation pour résoudre certaines classes de problèmes d'apprentissage avec la parcimonie et/ou avec l'incertitude des données. Nos méthodes sont basées sur la programmation DC (Difference of Convex functions) et DCA (DC Algorithms) étant reconnues comme des outils puissants d'optimisation. La thèse se compose de deux parties : La première partie concerne la parcimonie tandis que la deuxième partie traite l'incertitude des données. Dans la première partie, une étude approfondie pour la minimisation de la norme zéro a été réalisée tant sur le plan théorique qu'algorithmique. Nous considérons une approximation DC commune de la norme zéro et développons quatre algorithmes basées sur la programmation DC et DCA pour résoudre le problème approché. Nous prouvons que nos algorithmes couvrent tous les algorithmes standards existants dans le domaine. Ensuite, nous étudions le problème de la factorisation en matrices non-négatives (NMF) et fournissons des algorithmes appropriés basés sur la programmation DC et DCA. Nous étudions également le problème de NMF parcimonieuse. Poursuivant cette étude, nous étudions le problème d'apprentissage de dictionnaire où la représentation parcimonieuse joue un rôle crucial. Dans la deuxième partie, nous exploitons la technique d'optimisation robuste pour traiter l'incertitude des données pour les deux problèmes importants dans l'apprentissage : la sélection de variables dans SVM (Support Vector Machines) et le clustering. Différents modèles d'incertitude sont étudiés. Les algorithmes basés sur DCA sont développés pour résoudre ces problèmes. / In this thesis, we focus on developing optimization approaches for solving some classes of optimization problems in sparsity and robust optimization for data uncertainty. Our methods are based on DC (Difference of Convex functions) programming and DCA (DC Algorithms) which are well-known as powerful tools in optimization. This thesis is composed of two parts: the first part concerns with sparsity while the second part deals with uncertainty. In the first part, a unified DC approximation approach to optimization problem involving the zero-norm in objective is thoroughly studied on both theoretical and computational aspects. We consider a common DC approximation of zero-norm that includes all standard sparse inducing penalty functions, and develop general DCA schemes that cover all standard algorithms in the field. Next, the thesis turns to the nonnegative matrix factorization (NMF) problem. We investigate the structure of the considered problem and provide appropriate DCA based algorithms. To enhance the performance of NMF, the sparse NMF formulations are proposed. Continuing this topic, we study the dictionary learning problem where sparse representation plays a crucial role. In the second part, we exploit robust optimization technique to deal with data uncertainty for two important problems in machine learning: feature selection in linear Support Vector Machines and clustering. In this context, individual data point is uncertain but varies in a bounded uncertainty set. Different models (box/spherical/ellipsoidal) related to uncertain data are studied. DCA based algorithms are developed to solve the robust problems

Dans cette thèse, nous développons certaines techniques avancées d'apprentissage automatique dans le cadre de l'apprentissage en ligne et de l'apprentissage par renforcement (« reinforcement learning » en anglais -- RL). L'épine dorsale de nos approches est la programmation DC (Difference of Convex functions) et DCA (DC Algorithm), et leur version en ligne, qui sont reconnues comme de outils puissants d'optimisation non convexe, non différentiable. Cette thèse se compose de deux parties : la première partie étudie certaines techniques d'apprentissage automatique en mode en ligne et la deuxième partie concerne le RL en mode batch et mode en ligne. La première partie comprend deux chapitres correspondant à la classification en ligne (chapitre 2) et la prédiction avec des conseils d'experts (chapitre 3). Ces deux chapitres mentionnent une approche unifiée d'approximation DC pour différents problèmes d'optimisation en ligne dont les fonctions objectives sont des fonctions de perte 0-1. Nous étudions comment développer des algorithmes DCA en ligne efficaces en termes d'aspects théoriques et computationnels. La deuxième partie se compose de quatre chapitres (chapitres 4, 5, 6, 7). Après une brève introduction du RL et ses travaux connexes au chapitre 4, le chapitre 5 vise à fournir des techniques efficaces du RL en mode batch basées sur la programmation DC et DCA. Nous considérons quatre différentes formulations d'optimisation DC en RL pour lesquelles des algorithmes correspondants basés sur DCA sont développés. Nous traitons les problèmes clés de DCA et montrons l'efficacité de ces algorithmes au moyen de diverses expériences. En poursuivant cette étude, au chapitre 6, nous développons les techniques du RL basées sur DCA en mode en ligne et proposons leurs versions alternatives. Comme application, nous abordons le problème du plus court chemin stochastique (« stochastic shortest path » en anglais -- SSP) au chapitre 7. Nous étudions une classe particulière de problèmes de SSP qui peut être reformulée comme une formulation de minimisation de cardinalité et une formulation du RL. La première formulation implique la norme zéro et les variables binaires. Nous proposons un algorithme basé sur DCA en exploitant une approche d'approximation DC de la norme zéro et une technique de pénalité exacte pour les variables binaires. Pour la deuxième formulation, nous utilisons un algorithme batch RL basé sur DCA. Tous les algorithmes proposés sont testés sur des réseaux routiers artificiels / In this dissertation, we develop some advanced machine learning techniques in the framework of online learning and reinforcement learning (RL). The backbones of our approaches are DC (Difference of Convex functions) programming and DCA (DC Algorithm), and their online version that are best known as powerful nonsmooth, nonconvex optimization tools. This dissertation is composed of two parts: the first part studies some online machine learning techniques and the second part concerns RL in both batch and online modes. The first part includes two chapters corresponding to online classification (Chapter 2) and prediction with expert advice (Chapter 3). These two chapters mention a unified DC approximation approach to different online learning algorithms where the observed objective functions are 0-1 loss functions. We thoroughly study how to develop efficient online DCA algorithms in terms of theoretical and computational aspects. The second part consists of four chapters (Chapters 4, 5, 6, 7). After a brief introduction of RL and its related works in Chapter 4, Chapter 5 aims to provide effective RL techniques in batch mode based on DC programming and DCA. In particular, we first consider four different DC optimization formulations for which corresponding attractive DCA-based algorithms are developed, then carefully address the key issues of DCA, and finally, show the computational efficiency of these algorithms through various experiments. Continuing this study, in Chapter 6 we develop DCA-based RL techniques in online mode and propose their alternating versions. As an application, we tackle the stochastic shortest path (SSP) problem in Chapter 7. Especially, a particular class of SSP problems can be reformulated in two directions as a cardinality minimization formulation and an RL formulation. Firstly, the cardinality formulation involves the zero-norm in objective and the binary variables. We propose a DCA-based algorithm by exploiting a DC approximation approach for the zero-norm and an exact penalty technique for the binary variables. Secondly, we make use of the aforementioned DCA-based batch RL algorithm. All proposed algorithms are tested on some artificial road networks