Dynamics of nitrogen and carbon cycling associated with greenhouse gas emissions in the salt-affected soils.

Salinity is one of the most severe environmental factors limiting the productivity of aquaculture and agriculture. The worldwide area of salt-affected soils is predicted to become even more widespread in the future due to climate change and sea-level rise. However, the soil nitrogen and carbon dynamics associated with soil-induced gas emissions under salinity are not well understood. The main objective of this study was to investigate changes of soil carbon and nitrogen cycling associated with greenhouse gas emissions, plant growth and fertilizer recovery under effects of different salinity levels. This study addressed research issues with the following main objectives. The main aim of the study reported in Chapter 2 was to analyse greenhouse gas production from different soils with different times of lid closure and to assess the effects of different activation time on gas emissions from soils. The results showed that the 20-min sampling interval at the closure time of maximum 80 minutes had good results with less variance either for soil types or monitored gases. Lengthening activation times for the incubation study may affect emission rates due to differences in soil properties. The study in Chapter 3 examined the effects of salinity and additional sources of nitrogen and carbon on soil nitrogen and carbon cycling in an acid sulphate soil (ASS) and an alluvial soil. The findings of this study demonstrated that salinity significantly decreased N2O emissions from the acid sulphate soil but did not affect emissions from the alluvial soil. The addition of glucose and nitrate enhanced N2O production in both salt-affected soils. This investigation indicated that salinity altered the carbon and nitrogen cycles in the acid sulphate soil; it recommends that future fertiliser and crop management will need to account for the changed nutrient cycling caused by saline water intrusion and climate change. The objective of the study reported in Chapter 4 was to identify a relationship between induced-soil gas emissions and the abundance of denitrification genes in a salt-affected soil. Increased salinity caused a decrease in both flux and cumulation of the N2O-N production and soil respiration from the incubated soil. The study result also showed that elevated salinity increased the denitrifying genes in the incubated acid sulphate soil. Abundance of the nir genes was usually high between the first and second week of incubation, while number of copies of the nosZ gene were significantly low at those times. Another study presented in Chapter 5 investigated changes in soil properties, the dynamics of N and its effects on rice growth and yield under different salinity levels by using a 15N label fertilizer technique. Flooding soils for two weeks by saline water greatly decreased rice yield and yield components in the acid sulphate soil. High salinity significantly lowered the recovery of fertilizer N by rice plants, especially in the acid sulphate soil where the crop did not produce any grain. The loss of fertilizer nitrogen was highly controlled by the interaction effect of soil types and salinity. Findings from the thesis substantially and originally contribute to the literature on salt-affected soils and will assist in developing new managemental interventions and strategies for soils where increased salinity is a real possibility in the future

Fourier Neural Network Approximation of Transition Densities in Finance

This paper introduces FourNet, a novel single-layer feed-forward neural network (FFNN) method designed to approximate transition densities for which closed-form expressions of their Fourier transforms, i.e. characteristic functions, are available. A unique feature of FourNet lies in its use of a Gaussian activation function, enabling exact Fourier and inverse Fourier transformations and drawing analogies with the Gaussian mixture model. We mathematically establish FourNet's capacity to approximate transition densities in the $L_2$-sense arbitrarily well with finite number of neurons. The parameters of FourNet are learned by minimizing a loss function derived from the known characteristic function and the Fourier transform of the FFNN, complemented by a strategic sampling approach to enhance training. Through a rigorous and comprehensive error analysis, we derive informative bounds for the $L_2$ estimation error and the potential (pointwise) loss of nonnegativity in the estimated densities. FourNet's accuracy and versatility are demonstrated through a wide range of dynamics common in quantitative finance, including L\'{e}vy processes and the Heston stochastic volatility models-including those augmented with the self-exciting Queue-Hawkes jump process.Comment: 27 pages, 5 figure

A multi-level dimension reduction Monte-Carlo method for jump-diffusion models

This paper develops and analyses convergence properties of a novel multi-level Monte-Carlo (mlMC) method for computing prices and hedging parameters of plain-vanilla European options under a very general b-dimensional jump–diffusion model, where b is arbitrary. The model includes stochastic variance and multi-factor Gaussian interest short rate(s). The proposed mlMC method is built upon (i)\ua0the powerful dimension and variance reduction approach developed in Dang et\ua0al. (2017) for jump–diffusion models, which, for certain jump distributions, reduces the dimensions of the problem from b to 1, namely the variance factor, and (ii)\ua0the highly effective multi-level MC approach of Giles (2008) applied to that factor. Using the first-order strong convergence Lamperti–Backward-Euler scheme, we develop a multi-level estimator with variance convergence rate O(h), resulting in an overall complexity O(ϵ) to achieve a root-mean-square error\ua0of\ua0 ϵ. The proposed mlMC can also avoid potential difficulties associated with the standard multi-level approach in effectively handling simultaneously both multi-dimensionality and jumps, especially in computing hedging parameters. Furthermore, it is considerably more effective than existing mlMC methods, thanks to a significant variance reduction associated with the dimension reduction. Numerical results illustrating the convergence properties and efficiency of the method with jump sizes following normal and double-exponential distributions are presented

A Shannon wavelet method for pricing foreign exchange options under the Heston multi-factor CIR model

We present a robust and highly efficient Shannon wavelet pricing method for plain-vanilla foreign exchange European options under the jump-extended Heston model with multi-factor CIR interest rate dynamics. Under a Monte Carlo and partial differential equation hybrid computational framework, the option price can be expressed as an expectation, conditional on the variance factor, of a convolution product that involves the densities of the time-integrated domestic and foreign multi-factor CIR interest rate processes. We propose an efficient treatment to this convolution product that effectively results in a significant dimension reduction, from two multi-factor interest rate processes to only a single-factor process. By means of a state-of-the-art Shannon wavelet inverse Fourier technique, the resulting convolution product is approximated analytically and the conditional expectation can be computed very efficiently. We develop sharp approximation error bounds for the option price and hedging parameters. Numerical experiments confirm the robustness and efficiency of the method

A dimension reduction Shannon-wavelet based method for option pricing

We present a robust and highly efficient dimension reduction Shannon-wavelet method for computing European option prices and hedging parameters under a general jump-diffusion model with square-root stochastic variance and multi-factor Gaussian interest rates. Within a dimension reduction framework, the option price can be expressed as a two-dimensional integral that involves only (i) the value of the variance at the terminal time, and (ii) the time-integrated variance process conditional on this value. A Shannon wavelet inverse Fourier technique is developed to approximate the conditional density of the time-integrated variance process. Furthermore, thanks to the excellent approximation properties of Shannon wavelets, the overall pricing procedure is reduced to the evaluation of just a single integral that involves only the density of the terminal variance value. This single integral can be accurately evaluated, since the density of the variance at the terminal time is known in closed-form. We develop sharp approximation error bounds for the option price and hedging parameters. Numerical experiments confirm the robustness and impressive efficiency of the method

A Machine Learning-based Approach to Vietnamese Handwritten Medical Record Recognition

Handwritten text recognition has been an active research topic within computer vision division. Existing deep-learning solutions are practical; however, recognizing Vietnamese handwriting has shown to be a challenge with the presence of extra six distinctive tonal symbols and extra vowels. Vietnam is a developing country with a population of approximately 100 million, but has only focused on digitalization transforms in recent years, and so Vietnam has a significant number of physical documents, that need to be digitized. This digitalization transform is urgent when considering the public health sector, in which medical records are mostly still in hand-written form and still are growing rapidly in number. Digitization would not only help current public health management but also allow preparation and management in future public health emergencies. Enabling the digitalization of old physical records will allow efficient and precise care, especially in emergency units. We proposed a solution to Vietnamese text recognition that is combined into an end-to-end document-digitalization system. We do so by performing segmentation to word-level and then leveraging an artificial neural network consisting of both convolutional neural network (CNN) and a long short-term memory recurrent neural network (LSTM) to propagate the sequence information. From the experiment with the records written by 12 doctors, we have obtained encouraging results of 6.47% and 19.14% of CER and WER respectively

Mean-Quadratic Variation portfolio optimization: a desirable alternative to time-consistent mean-variance optimization?

We investigate the mean-quadratic variation (MQV) portfolio optimization problem and its relationship to the time-consistent mean-variance (TCMV) portfolio optimization problem. In the case of jumps in the risky asset process and no investment constraints, we derive analytical solutions for the TCMV and MQV problems. We study conditions under which the two problems are (i) identical with respect to MV trade-offs, and (ii) equivalent, i.e., have the same value function and optimal control. We provide a rigorous and intuitive explanation of the abstract equivalence result between the TCMV and MQV problems developed in [T. Bjork and A. Murgoci, A General Theory of Markovian Time Inconsistent Stochastic Control Problems, working paper, 2010] for continuous rebalancing and no-jumps in risky asset processes. We extend this equivalence result to jump-diffusion processes (both discrete and continuous rebalancings). In order to compare the MQV and TCMV problems in a more realistic setting which involves investment constraints and modeling assumptions for which analytical solutions are not known to exist, using an impulse control approach we develop an efficient partial integro-differential equation (PIDE) method for determining the optimal control for the MQV problem. We also prove convergence of the proposed numerical method to the viscosity solution of the corresponding PIDE. We find that the MQV investor achieves essentially the same results concerning terminal wealth as the TCMV investor, but the MQV-optimal investment process has more desirable risk characteristics from the perspective of long-term investors with fixed investment time horizons. As a result, we conclude that MQV portfolio optimization is a potentially desirable alternative to TCMV

Time-consistent mean-variance portfolio allocation: a numerical impulse control approach

We investigate the time-consistent mean–variance (MV) portfolio optimization problem, popular in investment–reinsurance and investment-only applications, under a realistic context that involves the simultaneous application of different types of investment constraints and modelling assumptions, for which a closed-form solution is not known to exist. We develop an efficient numerical partial differential equation method for determining the optimal control for this problem. Central to our method is a combination of (i) an impulse control formulation of the MV investment problem, and (ii) a discretized version of the dynamic programming principle enforcing a time-consistency constraint. We impose realistic investment constraints, such as no trading if insolvent, leverage restrictions and different interest rates for borrowing/lending. Our method requires solution of linear partial integro-differential equations between intervention times, which is numerically simple and computationally effective. The proposed method can handle both continuous and discrete rebalancings. We study the substantial effect and economic implications of realistic investment constraints and modelling assumptions on the MV efficient frontier and the resulting investment strategies. This includes (i) a comprehensive comparison study of the pre-commitment and time-consistent optimal strategies, and (ii) an investigation on the significant impact of a wealth-dependent risk aversion parameter on the optimal controls

Comparison of sensory characteristics of green tea in Thai Nguyen and Phu Tho, Vietnam

Green tea is a popular consumption product in Vietnam. Especially, tea which origins from Tan Cuong, Thai Nguyen has been known for long by its better quality than those coming from other regions on the country. The study aims at comparing and finding out if the difference between tea in Thai Nguyen and Phu Tho can be figured by sensory tasting. Two products picked from Tan Cuong, Thai Nguyen province and two others from Phu Ho district, Phu Tho are were evaluated by a panel of twelve judges (eleven women and one man) who was set from a group of thirty eight peoples, had completed a general training and sensory tasting on tea. The experiment on dry tea (eleven descriptors) was carried out separately of the experiment on brewed tea (twenty-one descriptors) and brewed leaf (five descriptors). All attributes are made notes on the sensory unstructured intensity scale. Statistic analyses have shown typical differences by region among all of trees groups of attributes: dry leaf (10/11 attributes), liquor (6/21 attributes) and brewed leaf (5/5 attributes)