Switching Diffusions: Applications To Ecological Models, And Numerical Methods For Games In Insurance

Recently, a class of dynamic systems called ``hybrid systems containing both continuous dynamics and discrete events has been adapted to treat a wide variety of situations arising in many real-world situations. Motivated by such development, this dissertation is devoted to the study of dynamical systems involving a Markov chain as the randomly switching process. The systems studied include hybrid competitive Lotka-Volterra ecosystems and non-zero-sum stochastic differential games between two insurance companies with regime-switching. The first part is concerned with competitive Lotka-Volterra model with Markov switching. A novelty of the contribution is that the Markov chain has a countable state space. Our main objective is to reduce the computational complexity by using the two-time-scale formulation. Because the existence and uniqueness as well as continuity of solutions for Lotka-Volterra ecosystems with Markovian switching in which the switching takes place in a countable set are not available, such properties are studied first. The two-time scale feature is highlighted by introducing a small parameter into the generator of the Markov chain. When the small parameter goes to 0, there is a limit system or reduced system. It is established in this work that if the reduced system possesses certain properties such as permanence and extinction, etc., then the complex original system also has the same properties when the parameter is sufficiently small. These results are obtained by using the perturbed Lyapunov function methods. The second part develops an approximation procedure for a class of non-zero-sum stochastic differential games for investment and reinsurance between two insurance companies. Both proportional reinsurance and excess-of-loss reinsurance policies are considered. We develop numerical algorithms to obtain the approximation to the Nash equilibrium by adopting the Markov chain approximation methodology. We establish the convergence of the approximation sequences and the approximation to the value functions. Numerical examples are presented to illustrate the applicability of the algorithms

INVESTIGATING DIFFICULTIES OF ENGLISH-MAJORED STUDENTS IN WRITING ACADEMIC ESSAYS

The purpose of this study was to investigate the difficulties faced by English-majored students in writing academic essays. To collect the data for the investigation, the sample involved was twenty-seven English-majored students who have been studying English as their specialization at a public university in Vietnam. The instruments of this study conducted a mix-method as a qualitative and quantitative approach. To achieve the purpose of this study, the questionnaire was administered as the quantitative data collection and the semi-structured interview as the qualitative data collection. Then, five of the total participants were randomly interviewed to gain insight ideas of the difficulties that they face when writing academic essays in their writing course. The results of this study demonstrated that the majority of students had obstacles related to insufficient linguistic knowledge namely, lexicon-grammar, vocabulary, and the structure of sentences. The findings from the interviews also indicated that the participants had encountered linguistic knowledge difficulties. Based on the findings, both some recommendations and implications are presented.  Article visualizations

Seed priming with sodium nitroprusside enhances the growth of peanuts (Arachis hypogaea L.) under drought stress

Peanuts are a nutrient-dense legume with high lipid, protein, vitamin and mineral content. Peanut development is harmed by drought stress, particularly during the germination and seedling stages. Finding ways to mitigate the impacts of drought stress will have positive effects on peanut production. Seed priming, a short-gun strategy for modulating the impact of abiotic stressors on agricultural plants, has lately piqued the attention of researchers to instill drought tolerance in important crops. In this study, peanut seeds (VD01-2 cultivar) were used as material to investigate the role of priming with sodium nitroprusside at different concentrations (10, 15, 20 and 25 mg L-1) in preventing the damage of peanuts triggered by drought stress. Morphological, physiological and biochemical changes during the development of peanuts in the drought stress condition were analyzed. The results show that moderate drought stress (60% of field capacity) reduced germination and seedling growth. Drought stress reduced relative water content, photosynthesis, and the content of chlorophyll and starch significantly over the control. Seed priming with 20 mg L-1 sodium nitroprusside was effective in increasing these above mentioned growth parameters. Further, the priming of 20 mg L-1 sodium nitroprusside enhanced respiration rate and carotenoid, soluble sugar and proline content compared to the control

A robust algorithm for detection and classification of traffic signs in video data

—The accurate identification and recognition of the traffic signs is a challenging problem as the developed systems have to address a large number of imaging problems such as motion artifacts, various weather conditions, shadows and partial occlusion, issues that are often encountered in video traffic sequences that are captured from a moving vehicle. These factors substantially degrade the performance of the existing traffic sign recognition (TSR) systems and in this paper we detail the implementation of a new strategy that entails three distinct computational stages. The first component addresses the robust identification of the candidate traffic signs in each frame of the video sequence. The second component discards the traffic sign candidates that do not comply with stringent shape constraints, and the last component implements the classification of the traffic signs using Support Vector Machines (SVMs). The main novel elements of our TSR algorithm are given by the approach that has been developed for traffic sign classification and by the experimental evaluation that was employed to identify the optimal image attributes that are able to maximize the traffic sign classification performance. The TSR algorithm has been validated using video sequences that include the most important categories of signs that are used to regulate the traffic on the Irish and UK roads, and it achieved 87.6% sign detection, 99.2% traffic sign classification accuracy and 86.7% overall traffic sign recognition