Stochastic Optimal Control with Neural Networks and Application to a Retailer Inventory Problem

Overwhelming computational requirements of classical dynamic programming algorithms render them inapplicable to most practical stochastic problems. To overcome this problem a neural network based Dynamic Programming (DP) approach is described in this study. The cost function which is critical in a dynamic programming formulation is approximated by a neural network according to some designed weight-update rule based on Temporal Difference(TD)learning. A Lyapunov based theory is developed to guarantee an upper error bound between the output of the cost neural network and the true cost. We illustrate this approach through a retailer inventory problem

Effects of Stocking Rate on the Variability of Ecosystem Productivity in Desert Steppe

Management practices can increase biodiversity and generate corresponding compensatory effects on biomass production, which may reduce inter-annual variability of productivity in some grassland ecosystems. However, it remains unclear how stocking rate influences variability of ecosystem productivity. Four stocking rates were compared in a completely randomized block experiment in the desert steppe of Inner Mongolia, China: non-grazed control (0 sheep/ha/mo), lightly grazed (LG, 0.15 sheep/ha/mo), moderately grazed (MG, 0.30 sheep/ha/mo) and heavily grazed (HG, 0.45 sheep/ha/mo). Aboveground net primary productivity (ANPP) was measured every August for eight consecutive years (2004-2011). ANPP decreased significantly (P \u3c 0.05) with increasing stocking rate. Coefficients of variation for community (CVcomm) in LG and MG were lower than in the control and HG treatments. Coefficients of variation for both species (CVsp) and functional groups (CVPFG) showed logarithmic relationships with relative density (P \u3c 0.05). Thus, both stocking rate and annual precipitation significantly affected the biodiversity and stability of desert steppe in terms of interannual variability of ANPP. As in other grazed systems, our results indicate that grazing management can alter dominant species and functional group components within the grassland community

High-Performance Nanofluidic Osmotic Power Generation Enabled by Exterior Surface Charges under the Natural Salt Gradient

High-performance osmotic energy conversion (OEC) requires both high ionic selectivity and permeability in nanopores. Here, through systematical explorations of influences from individual charged nanopore surfaces on the performance of OEC, we find that the charged exterior surface on the low-concentration side (surfaceL) is essential to achieve high-performance osmotic power generation, which can significantly improve the ionic selectivity and permeability simultaneously. Detailed investigation of ionic transport indicates that electric double layers near charged surfaces provide high-speed passages for counterions. The charged surfaceL enhances cation diffusion through enlarging the effective diffusive area, and inhibits anion transport by electrostatic repulsion. Different areas of charged exterior surfaces have been considered to mimic membranes with different porosities in practical applications. Through adjusting the width of the charged ring region on the surfaceL, electric power in single nanopores increases from 0.3 to 3.4 pW with a plateau at the width of ~200 nm. The power density increases from 4200 to 4900 W/m2 and then decreases monotonously that reaches the commercial benchmark at the charged width of ~480 nm. While, energy conversion efficiency can be promoted from 4% to 26%. Our results provide useful guide in the design of nanoporous membranes for high-performance osmotic energy harvesting.Comment: 30 pages and 7 figure

Quantifying the Economic Value of Evidence-Based Animal Selection on the inner Mongolian Desert Steppe

Inner Mongolian desert steppe in northwestern China suffers from significant grassland degradation, causing a decrease in producers\u27 income as well as negative off-site impacts (Kemp et al., 2013). Recent studies attribute this problem to a sudden increase in the stocking rate over the last half century, and thus development of an alternative farming system to reduce the animal number is urgently needed (Wang et al., 2011). Scientific experiments and modelling analyses have shown the potential of innovative systems that could deliver a win-win solution to local producers and environment (Li et al., 2015). However, the uptake of the proposed new technologies is generally slow because of the scepticism amongst producers, which is often augmented by the traditional herding culture whereby a large flock of animals is a symbol of social success (Kemp and Michalk, 2007). The objective of the present paper is to quantify the economic value of evidenced-based ewe selection, vis-à-vis random selection, the former of which could reduce the negative economic impact to producers due to the reduced stocking rate or, in some cases, even improve their long-term income (Kemp et al., 2011). A particular attention is paid to the carryover effect of an ewe\u27s body condition at an early stage of pregnancy on her lamb\u27s bodyweight at the annual sales time, a relationship relatively understudied in the preceding literature. Because lambs\u27 bodyweight is the most closely linked to economic benefits enjoyed by local producers specializing in meat production, a positive result from this study would be valuable information to convince them to adopt an alternative farming strategy

On the Evolution of Knowledge Graphs: A Survey and Perspective

Knowledge graphs (KGs) are structured representations of diversified knowledge. They are widely used in various intelligent applications. In this article, we provide a comprehensive survey on the evolution of various types of knowledge graphs (i.e., static KGs, dynamic KGs, temporal KGs, and event KGs) and techniques for knowledge extraction and reasoning. Furthermore, we introduce the practical applications of different types of KGs, including a case study in financial analysis. Finally, we propose our perspective on the future directions of knowledge engineering, including the potential of combining the power of knowledge graphs and large language models (LLMs), and the evolution of knowledge extraction, reasoning, and representation

Grassland Rehabilitation through Re-Designing Livestock Management Systems

Grasslands are one of the most important land types supplying critical ecosystem services including feed for livestock grazing. They occupy ~54% of the world’s ice-free land surface. China contains the third largest area of grassland in the world, ~400 M ha, ~40% of China’s land surface. Chinese grasslands are severely degraded primarily due to overgrazing, which contributes to local poverty because of poor livestock production. To both recover the degraded grassland and to enhance the local herders’ income, a large farm-scale experiment was conducted in a desert steppe of Inner Mongolia, China from 2007 to 2012. We used a baseline survey, production models, and extension with government and private companies to test a redesigned grassland livestock management system. The new system employed summer grazing, winter greenhouse shed feeding, a reduction of overall stocking rate, lambing in summer (July), livestock infrastructure structure improvements, use of animal nutrient supplements, and incorporating crossbred Dorper and Mongolian sheep. This system showed positive advantages on animal production and household net income and transformed livestock production from a survival to a production enterprise. Of critical additional importance was that grassland rehabilitation occurred with the new management system, albeit slower than the more immediate positive changes to animal performance and herder net incomes. The integration of science, government and industry were key for this successful large-scale farm experiment