80 research outputs found
An approximate dynamic programming approach to food security of communities following hazards
Food security can be threatened by extreme natural hazard events for
households of all social classes within a community. To address food security
issues following a natural disaster, the recovery of several elements of the
built environment within a community, including its building portfolio, must be
considered. Building portfolio restoration is one of the most challenging
elements of recovery owing to the complexity and dimensionality of the problem.
This study introduces a stochastic scheduling algorithm for the identification
of optimal building portfolio recovery strategies. The proposed approach
provides a computationally tractable formulation to manage multi-state,
large-scale infrastructure systems. A testbed community modeled after Gilroy,
California, is used to illustrate how the proposed approach can be implemented
efficiently and accurately to find the near-optimal decisions related to
building recovery following a severe earthquake.Comment: As opposed to the preemptive scheduling problem, which was addressed
in multiple works by us, we deal with a non-preemptive stochastic scheduling
problem in this work. Submitted to 13th International Conference on
Applications of Statistics and Probability in Civil Engineering, ICASP13
Seoul, South Korea, May 26-30, 201
Solving Markov decision processes for network-level post-hazard recovery via simulation optimization and rollout
Computation of optimal recovery decisions for community resilience assurance
post-hazard is a combinatorial decision-making problem under uncertainty. It
involves solving a large-scale optimization problem, which is significantly
aggravated by the introduction of uncertainty. In this paper, we draw upon
established tools from multiple research communities to provide an effective
solution to this challenging problem. We provide a stochastic model of damage
to the water network (WN) within a testbed community following a severe
earthquake and compute near-optimal recovery actions for restoration of the
water network. We formulate this stochastic decision-making problem as a Markov
Decision Process (MDP), and solve it using a popular class of heuristic
algorithms known as rollout. A simulation-based representation of MDPs is
utilized in conjunction with rollout and the Optimal Computing Budget
Allocation (OCBA) algorithm to address the resulting stochastic simulation
optimization problem. Our method employs non-myopic planning with efficient use
of simulation budget. We show, through simulation results, that rollout fused
with OCBA performs competitively with respect to rollout with total equal
allocation (TEA) at a meagre simulation budget of 5-10% of rollout with TEA,
which is a crucial step towards addressing large-scale community recovery
problems following natural disasters.Comment: Submitted to Simulation Optimization for Cyber Physical Energy
Systems (Special Session) in 14th IEEE International Conference on Automation
Science and Engineerin
Design and analysis of truck body for increasing the payload capacity
Truck industry is a major source of transportation in India. With an average truck travelling about 300 kilometers per day [1], every kilogram of truck weight is of concern to the industry in order to get the best out of the truck. The main objective of this project is to increase the payload capacity of automotive truck body. Every kilogram of increased vehicle weight will decrease the vehicle payload capacity in turn increasing the manufacturing cost and reducing the fuel economy by increase the fuel consumption. With the intension of weight reduction, standard truck body has been designed and analyzed in ANSYS software. C-cross section beams were used instead of conventional rectangular box sections to reduce the weight of the body. Light-weight Aluminum alloy Al 6061 T6 is used to increase the payload capacity. The strength of the Truck platform is monitored in terms of deformation and stress concentration
Gaspé Flint corn as a seed-to-seed model to study the effect of phosphorus on maize growth and development.
Phosphorus (P) is a vital macronutrient for plant growth and development. Thus, P deficiency represents a bottleneck in the production of maize (Zea mays, L.). Therefore, there is a need for a prompt identification of new P-use efficient lineages and hybrids. The Canadian landrace Gaspé Flint (GF) race of maize was used to identify molecular and morphological traits due to its short life cycle and ease of growing in a hydroponic system under controlled conditions. First, GF was grown in a hydroponic system containing different Pi regimes for 15 d, and harvested tissues were assayed for various morphophysiological and molecular traits. Second, GF was grown in hydroponics under P+ (250 µM) and P-(10 µM) conditions until seed maturity. Pi deficiency led to a lack of synchrony between male and female reproductive organs, reducing fertilization, cob development, and productivity. Although typical Pi deficiency-mediated morphophysiological responses, such as increased root biomass relative to the shoot, accumulation of anthocyanins in the roots and leaves, and elevated acid phosphatase activity in the shoot could be observed in any maize variety, the use of GF abbreviated the analysis of these traits from 120 days in commercial varieties to 40 days. Furthermore, Pi transporters ZmPT5 and ZmPT6 were induced in Pi-deprived roots and leaves and suppressed upon Pi replenishment, suggesting a transcriptional regulation. The study validated the efficacy of GF for accelerating studies on agronomic traits and plant response to stress, from seeds to seeds, in the grass family. The Gaspé Flint corn was confirmed as a plant model to study the effect of phosphorus on the growth and development of maize in a hydroponic system
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