A Risk-Based Interval Two-Stage Programming Model for Agricultural System Management under Uncertainty

Nonpoint source (NPS) pollution caused by agricultural activities is main reason that water quality in watershed becomes worse, even leading to deterioration. Moreover, pollution control is accompanied with revenue's fall for agricultural system. How to design and generate a cost-effective and environmentally friendly agricultural production pattern is a critical issue for local managers. In this study, a risk-based interval two-stage programming model (RBITSP) was developed. Compared to general ITSP model, significant contribution made by RBITSP model was that it emphasized importance of financial risk under various probabilistic levels, rather than only being concentrated on expected economic benefit, where risk is expressed as the probability of not meeting target profit under each individual scenario realization. This way effectively avoided solutions' inaccuracy caused by traditional expected objective function and generated a variety of solutions through adjusting weight coefficients, which reflected trade-off between system economy and reliability. A case study of agricultural production management with the Tai Lake watershed was used to demonstrate superiority of proposed model. Obtained results could be a base for designing land-structure adjustment patterns and farmland retirement schemes and realizing balance of system benefit, system-failure risk, and water-body protection

Finite element analysis using fe-based neural networks

The thesis is based on the research work that was carried out to investigate Finite Element Analysis (FEA) using Artificial Neural Networks (ANN). A novel ANN model, Finite Element-based Neural Networks (FE-based NN) was proposed and applied to dynamic problems in mechanics. Firstly the variational approach to a functional in solid mechanics and the structural analogies between FEM and ANN were introduced. The computation energy functional of the FE-based NN was defined. Furthermore the architecture of FE-based NN was constructed and its algorithm was derived with the variational approach to the computational energy functional. The convergence of the FE-based NN was proved and the range of the main parameters was determined. The dynamic analysis of a beam element structure was considered as an application evaluator. The index of speedup was investigated for the measure of the computational efficiency of the FE-based NN. The simulation results were promising, which were verified by the experiment results and the computations with the commercial software package, ANSYS for the finite element analysis. Finally, the conclusion and the recommendations for further work of the investigation were discussed

A Risk-Based Interval Two-Stage Programming Model for Agricultural System Management under Uncertainty

Nonpoint source (NPS) pollution caused by agricultural activities is main reason that water quality in watershed becomes worse, even leading to deterioration. Moreover, pollution control is accompanied with revenue’s fall for agricultural system. How to design and generate a cost-effective and environmentally friendly agricultural production pattern is a critical issue for local managers. In this study, a risk-based interval two-stage programming model (RBITSP) was developed. Compared to general ITSP model, significant contribution made by RBITSP model was that it emphasized importance of financial risk under various probabilistic levels, rather than only being concentrated on expected economic benefit, where risk is expressed as the probability of not meeting target profit under each individual scenario realization. This way effectively avoided solutions’ inaccuracy caused by traditional expected objective function and generated a variety of solutions through adjusting weight coefficients, which reflected trade-off between system economy and reliability. A case study of agricultural production management with the Tai Lake watershed was used to demonstrate superiority of proposed model. Obtained results could be a base for designing land-structure adjustment patterns and farmland retirement schemes and realizing balance of system benefit, system-failure risk, and water-body protection

Stimuli-Responsive Macromolecular Self-Assembly

Macromolecular self-assembly has great potential for application in the field of the design of molecular machines, in molecular regulation, for biological tissue, and in biomedicine for the optical, electrical, and biological characteristics that the assembly unit does not possess. In this paper, the progress in macromolecular self-assembly is systematically reviewed, including its conception, processes and mechanisms, with a focus on macromolecular self-assembly by stimuli. According to the difference in stimuli, macromolecular self-assembly can be classified into temperature-responsive self-assembly, light-responsive self-assembly, pH-responsive self-assembly, redox-responsive self-assembly, and multi-responsive self-assembly. A preliminary study on constructing dynamic macromolecular self-assembly based on a chemical self-oscillating reaction is described. Furthermore, the problems of macromolecular self-assembly research, such as the extremely simple structure of artificial self-assembly and the low degree of overlap between macromolecular self-assembly and life sciences, are analyzed. The future development of stimuli-responsive macromolecular self-assembly should imitate the complex structures, processes and functions in nature and incorporate the chemical-oscillation reaction to realize dynamic self-assembly

A factorial environment-oriented input-output model for diagnosing urban air pollution

The excessive emissions of various air pollutants seriously hinder the sustainability of cities. It is imperative to conduct a thorough diagnose of urban emission systems. The objective of this study is to develop a factorial environment-oriented input-output (FEIO) model to reveal the urban emission system's structural characteristics and possible internal interactions. Through constructing urban emissions networks regarding multiple air pollutants, the crucial transfer sectors and their relevant transactions are identified. Based on the results of identification, factorial analysis (FA) is further introduced to explore the effects of designed factors and their combinations. The results of the case study for Guangdong Province, China demonstrate that the urban emission system differs from the natural ecosystem because of its complex structure. The impact of various air pollutants on the urban ecosystem is different from what they are expected, both the emitting sectors and their energy consumption structures are decisive. In Guangdong province, Manufacture of Metal and Non-metallic Mineral Products (MMN), Electric Power (EP), Electronic and Telecommunications Equipment (ETE) and Chemical Products (CP) are identified as transfer centers in the emission network. The contribution of air pollution derived from coal consumption is more than 48.30%. Both in 2012 and 2015, the contribution of interactions exceeded 17.46%. The VOCs emissions emitted by MMN and SO2 emissions emitted by EP have the greatest impact on the regional ecosystem. These findings can provide reliable information for ensuring regional air environmental protection targets

Transfer of virtual water embodied in food: A new perspective

Food and water are inextricably linked. With the increase of water consumption in irrigation and food growth, water shortage has become an urgent issue. Irrational cross-regional transfer of water embodied in food exacerbates water scarcity and restrict China's sustainable development. Given that, a Virtual Water-Food Nexus Model is developed to quantify the inter-provincial transfer of water embodied in food and to identify the complicated interactions between different provinces. In detail, Environmental Input-Output Analysis is applied to quantitatively estimate the inter-provincial water transfer embodied in food trades. Based on the network constructed by interrelated nature of nexus, the mutual interactions, control situation, and the dominant and weak pathways are examined through the combination of Ecological Network Analysis and Principal Component Analysis. Two new indictors water consumption intensity and water supply capacity are first performed to measure the role of each province from the supply and consume side respectively. It is revealed that interregional food transactions failed to realize water resources dispatching management. Many water-deficient regions suffered from massive virtual water losses through food exports, but water-rich areas still import large quantities of food containing virtual water. Results show that exploitation and competition dominate the ecological relationships between provinces. Agricultural GDP ratio is the indicator which most affect water consumption intensity and water supply capacity. Network-based research contributes more insights into the recognition of water management responsibilities across provinces and municipalities. These findings will provide a scientific support to adjust unreasonable allocation of water resources in China in an attempt to addressing the contradiction between food demand and water shortages

A Multi-Objective Optimization Model for a Non-Traditional Energy System in Beijing under Climate Change Conditions

In recent years, with the increase of annual average temperature and the decrease of annual precipitation in Beijing, the fragility of Beijing’s energy system has become more and more prominent, especially the balance of electricity supply and demand in extreme weather. In the context of unstable supply of new and renewable energies, it is imperative to strengthen the ability of the energy system to adapt to climate change. This study first simulated climate change in Beijing based on regional climate data. At the same time, the Statistical Program for Social Sciences was used to perform multiple linear regression analysis on Beijing’s future power demand and to analyze the impact of climate change on electricity supply in both the RCP4.5 and RCP8.5 (representative concentration pathway 4.5 and 8.5) scenarios. Based on the analysis of the impact of climate change on energy supply, a multi-objective optimization model for new and renewable energy structure adjustment combined with climate change was proposed. The model was then used to predict the optimal power generation of the five energy types under different conditions in 2020. Through comparison of the results, it was found that the development amount and development ratio of various energy forms underwent certain changes. In the case of climate change, the priority development order of new and renewable energies in Beijing was: external electricity > other renewable energy > solar energy > wind energy > biomass energy. The energy structure adjustment program in the context of climate change will contribute to accelerating the development and utilization of new and renewable energies, alleviating the imbalance between power supply and demand and improving energy security