A Buffer Stocks Model for Stabilizing Price of Commodity under Limited Time of Supply and Continuous Consumption

Staple foods, in developing countries especially in Indonesia, have extremely volatile among harvest and planting season caused by inelastic of supply-demand and price disparity. When a staple food is shortage in market, it will trigger crisis of economics, political and social because it concerns with food security. This paper develops a buffer stock model for stabilizing price of commodity under limited time of supply and continuous consumption. The performance criterion of model will consider financial loss of producer, consumer and government side when market is interfered by price-stabilization program and price-support program simultaneously. The price fluctuation will be stabilized by market operation where buffer stocks are bought from domestic and import supply point. This paper provides a price band policy that attempts to bound domestic price variation between a set of upper and lower bounds on the level of domestic prices. We consider three sets of problems reflecting different three prices elasticity from 4 period of supply and demand. Numerical examples are found to be consistent with empirical estimates regarding the relationship price elasticity with price band and with government budget for the agenda of assisting household to assure availability a staple food with enough amounts at rational prices. Keywords: buffer stocks, price band, stabilization, limited time of supply, staple foods

More Bang for Your Buck: Improved use of GPU Nodes for GROMACS 2018

We identify hardware that is optimal to produce molecular dynamics trajectories on Linux compute clusters with the GROMACS 2018 simulation package. Therefore, we benchmark the GROMACS performance on a diverse set of compute nodes and relate it to the costs of the nodes, which may include their lifetime costs for energy and cooling. In agreement with our earlier investigation using GROMACS 4.6 on hardware of 2014, the performance to price ratio of consumer GPU nodes is considerably higher than that of CPU nodes. However, with GROMACS 2018, the optimal CPU to GPU processing power balance has shifted even more towards the GPU. Hence, nodes optimized for GROMACS 2018 and later versions enable a significantly higher performance to price ratio than nodes optimized for older GROMACS versions. Moreover, the shift towards GPU processing allows to cheaply upgrade old nodes with recent GPUs, yielding essentially the same performance as comparable brand-new hardware.Comment: 41 pages, 13 figures, 4 tables. This updated version includes the following improvements: - most notably, added benchmarks for two coarse grain MARTINI systems VES and BIG, resulting in a new Figure 13 - fixed typos - made text clearer in some places - added two more benchmarks for MEM and RIB systems (E3-1240v6 + RTX 2080 / 2080Ti

Agent-based Sensor-Mission Assignment for Tasks Sharing Assets

(c) IFAAMASPeer reviewedPostprin

Best bang for your buck: GPU nodes for GROMACS biomolecular simulations

The molecular dynamics simulation package GROMACS runs efficiently on a wide variety of hardware from commodity workstations to high performance computing clusters. Hardware features are well exploited with a combination of SIMD, multi-threading, and MPI-based SPMD/MPMD parallelism, while GPUs can be used as accelerators to compute interactions offloaded from the CPU. Here we evaluate which hardware produces trajectories with GROMACS 4.6 or 5.0 in the most economical way. We have assembled and benchmarked compute nodes with various CPU/GPU combinations to identify optimal compositions in terms of raw trajectory production rate, performance-to-price ratio, energy efficiency, and several other criteria. Though hardware prices are naturally subject to trends and fluctuations, general tendencies are clearly visible. Adding any type of GPU significantly boosts a node's simulation performance. For inexpensive consumer-class GPUs this improvement equally reflects in the performance-to-price ratio. Although memory issues in consumer-class GPUs could pass unnoticed since these cards do not support ECC memory, unreliable GPUs can be sorted out with memory checking tools. Apart from the obvious determinants for cost-efficiency like hardware expenses and raw performance, the energy consumption of a node is a major cost factor. Over the typical hardware lifetime until replacement of a few years, the costs for electrical power and cooling can become larger than the costs of the hardware itself. Taking that into account, nodes with a well-balanced ratio of CPU and consumer-class GPU resources produce the maximum amount of GROMACS trajectory over their lifetime

Phase change material in automated window shades

The purpose of this report is to detail the development process for a phase change material window shading system, which stores solar thermal energy and later releases it indoors to provide nighttime space heating. To do this, wax-filled louvers with thermally absorptive front faces were developed and outfitted with a control system, which utilized historical weather data to orient the louvers to specific solar azimuthal angles, thus maximizing the thermal absorption. The system was tested against other common window treatments in a pair of thermally comparable testing structures, and was found to provide energy savings as high as 50%

Unscented Bayesian Optimization for Safe Robot Grasping

We address the robot grasp optimization problem of unknown objects considering uncertainty in the input space. Grasping unknown objects can be achieved by using a trial and error exploration strategy. Bayesian optimization is a sample efficient optimization algorithm that is especially suitable for this setups as it actively reduces the number of trials for learning about the function to optimize. In fact, this active object exploration is the same strategy that infants do to learn optimal grasps. One problem that arises while learning grasping policies is that some configurations of grasp parameters may be very sensitive to error in the relative pose between the object and robot end-effector. We call these configurations unsafe because small errors during grasp execution may turn good grasps into bad grasps. Therefore, to reduce the risk of grasp failure, grasps should be planned in safe areas. We propose a new algorithm, Unscented Bayesian optimization that is able to perform sample efficient optimization while taking into consideration input noise to find safe optima. The contribution of Unscented Bayesian optimization is twofold as if provides a new decision process that drives exploration to safe regions and a new selection procedure that chooses the optimal in terms of its safety without extra analysis or computational cost. Both contributions are rooted on the strong theory behind the unscented transformation, a popular nonlinear approximation method. We show its advantages with respect to the classical Bayesian optimization both in synthetic problems and in realistic robot grasp simulations. The results highlights that our method achieves optimal and robust grasping policies after few trials while the selected grasps remain in safe regions.Comment: conference pape

Systems design analysis applied to launch vehicle configuration

As emphasis shifts from optimum-performance aerospace systems to least lift-cycle costs, systems designs must seek, adapt, and innovate cost improvement techniques in design through operations. The systems design process of concept, definition, and design was assessed for the types and flow of total quality management techniques that may be applicable in a launch vehicle systems design analysis. Techniques discussed are task ordering, quality leverage, concurrent engineering, Pareto's principle, robustness, quality function deployment, criteria, and others. These cost oriented techniques are as applicable to aerospace systems design analysis as to any large commercial system