Robust optimisation of sustainable food grain transportation with uncertain supply and intentional disruptions

The proliferating need for sustainability intervention in food grain transportation planning is anchoring the attention of researchers in the interests of stakeholders and environment at large. Uncertainty associated with food grain supply further intensifies the problem steering the need for designing robust, cost-efficient and sustainable models. In line with this, this paper aims to develop a robust and sustainable intermodal transportation model to facilitate single type of food grain commodity shipments while considering procurement uncertainty, greenhouse gas emissions, and intentional hub disruption. The problem is designed as a mixed integer non-linear robust optimisation model on a hub and spoke network for evaluating near optimal shipment quantity, route selection and hub location decisions. The robust optimisation approach considers minimisation of total relative regret associated with total cost subject to several real-time constraints. A version of Particle Swarm Optimisation with Differential Evolution is proposed to tackle the resulting NP-hard problem. The model is tested with two other state-of the art meta-heuristics for small, medium, and large datasets subject to different procurement scenarios inspired from real time food grain operations in Indian context. Finally, the solution is evaluated with respect to total cost, model and solution robustness for all instances.     

Fast handoff with reducing waste rate approach for wireless resources management in UMTS mobile networks

3G Wideband CDMA systems adopt the Orthogonal Variable Spreading Factor code tree as the channelization codes management for achieving high data rate transmission in personal multimedia communications. It assigns a single channelization code for each accepted connection. Nevertheless, it wastes the system capacity when the required rate is not powers of two of the basic rate. One good solution is to assign multiple codes for each accepted connection but it causes two inevitable drawbacks: long handoff delay and new call setup delay due to high complexity of processing with multiple channelization codes, and high cost of using more number of rake combiners. Especially, long handoff delay may result in more call dropping probability and higher Grade of Service, which will degrade significantly the utilization and revenue of the 3G cellular systems. Therefore, we propose herein an adaptive efficient codes determination algorithm based on the Markov Decision Process analysis approach to reduce the waste rate and reassignments significantly while providing fast handoff. Numerical results demonstrate that the proposed approach yields several advantages, including the lowest GOS, the least waste rate, and the least number of reassignments. Meanwhile, the optimal number of rake combiners is also analyzed in this paper. This research was supported in part by the National Science Council of Taiwan, ROC, under contract NSC-93-2213-E-324-018.     

多带抗噪声语音识别算法研究

根据Flether等人的研究,基于感知独立性假设的子带识别方法被用于抗噪声鲁棒语音识别。本文拓展子带方法,采用基于噪声污染假定的多带框架来减少噪声影响。论文不仅从理论上分析了噪声污染假定多带框架在识别性能上的潜在优势,而且提出了多带环境下的鲁棒语音识别算法。研究表明：多带框架不仅回避了独立感知假设要求,而且与子带方法相比,多带方法能更好的减少噪声影响,提高系统识别性能。     

Monte Carlo simulation of gas phase polymerization of 1,3‐butadiene. Part II: Kinetics optimization and confirmation

Studies on the deactivations and initiations of gas phase polymerizations of 1,3‐butadiene have been achieved by Monte Carlo simulation. Initiation and deactivation control the reaction before and after the peak of the polymerization rate, respectively. The influence of polymerization temperature has been studied. Monte Carlo modeling of polymerization kinetics and mechanism was confirmed by the agreement of experimental data and simulation results of polymerizations run with a temporary evacuation of monomer. The balance of catalysts and active chains is established by both initiation and chain transfer reactions with cocatalyst, which causes a ‘pseudo‐stability’ stage. © 2003 Society of Chemical Industry     

Structural changes in isothermal crystallization process of polyoxymethylene investigated by time-resolved FTIR, SAXS and WAXS measurements

Structural evolution in the isothermal crystallization process of polyoxymethylene from the molten state has been investigated by carrying out the time-resolved measurements of infrared spectra and synchrotron small angle X-ray scattering (SAXS) and wide angle X-ray scattering. In case of isothermal crystallization at 130 °C, for example, the infrared bands intrinsic of folded chain crystal (FCC) morphology appeared at first, and then the bands of extended chain crystal (ECC) morphology were detected with time delay of ca. 150 s. In the SAXS experiment at 130 °C, the lamellar stacking structure of the long period of ca. 15 nm was observed at first, which changed rapidly to ca. 12 nm in a short time. The SAXS peak with the long period of ca. 6 nm started to appear with a time delay of ca. 150 s after the initial lamellae appeared and coexisted with the initially-observed 12 nm peak. Judging from the timing to detect these characteristic infrared and SAXS signals, a good correspondence was found to exist between the stacked lamellar structure of 12 nm long period and FCC morphology and between the structure of 6 nm long period and ECC morphology. The quantitative analysis was made for the SAXS data on the basis of the lamellar insertion model combined with the paracrystalline theory of the second-kind of disorder. The following structural evolution was deduced from all these results. Immediately after the temperature jump from the melt to 130 °C, the stacked lamellar structure of FCC morphology was generated at first. New lamellae were formed from the amorphous region in between the originally-existing lamellae about 150 s later, where the random chain segments bridging the adjacent lamellae were extended to form the taut tie chains, giving infrared bands of ECC morphology. This inserted lamellar structure of 6 nm long period coexisted at a population of ca. 6% with the initially-formed lamellar stacking structure of 12 nm long period. When the experiment was made at 150 °C, only the formation of stacked lamellar structure of FCC morphology was observed and the insertion of new lamella did not occur.     

A stochastic model for localized disturbances and its applications

A stochastic model for local disturbances, particularly for a temporal harmonic with random modulations in amplitude and/or phase, is proposed in this paper. Results for the second moment responses of a linear single-degree-of-freedom system to this type of stochastic loading demonstrate a significant change in response characteristics due to a small uncertainty. A local phenomenon may last much longer and resonance may be smeared to a broad range. Integrated with wavelet transform, the proposed approach may be used to model a random process with non-stationary frequency content. Especially, it can be effectively used for Monte Carlo simulation to generate large size of samples that have similar characteristics in time and frequency domains as a pre-selected mother sample has. The technique has a great potential for the case where uncertainty study is warranted but the available samples are limited.     

创建互联网业务价值链

为促进互联网的良性发展,要使设备制造商、电信运营商、虚拟运营商/集团用户、内容提供商和最终用户构成一条互联网业务价值链,价值链的创建对互联网接入设备提出了更高的要求,华为公司的Quid-wayTMA8010Expert多业务综合接入平台可构建宽带城域网分组语音解决方案、可平滑演进为下一代网络中的媒体网关、提供窄带价值链、提供唯一号码业务以及实现多业务综合接入。     

Simulation of Strength Distribution in Ground Ceramics by Incorporating Residual Stress Effect

Strength of ground ceramics may be affected by residual stress as well as surface flaws induced by grinding. Strength prediction for ground ceramics is convenient for mechanical design of ceramic components. In this article, a numerical procedure based on fracture mechanics was proposed to estimate strength distribution of ground ceramics by considering grinding-induced residual stress. Bending strength and residual stress of ground ceramics were measured for three grinding-conditions. By comparison of simulated results with experimental ones, it was revealed that strength characteristics in experiments were well simulated by using the proposed procedure.     

Dynamic crack propagation with cohesive elements: a methodology to address mesh dependency

In this paper, two brittle fracture problems are numerically simulated: the failure of a ceramic ring under centrifugal loading and crack branching in a PMMA strip. A three‐dimensional finite element package in which cohesive elements are dynamically inserted has been developed. The cohesive elements' strength is chosen to follow a modified weakest link Weibull distribution. The probability of introducing a weak cohesive element is set to increase with the cohesive element size. This reflects the physically based effect according to which larger elements are more likely to contain defects. The calculations illustrate how the area dependence of the Weibull model can be used to effectively address mesh dependency. On the other hand, regular Weibull distributions have failed to reduce mesh dependency for the examples shown in this paper. The ceramic ring calculations revealed that two distinct phenomena appear depending on the magnitude of the Weibull modulus. For low Weibull modulus, the fragmentation of the ring is dominated by heterogeneities. Whereas many cracks were generated, few of them could propagate to the outer surface. Monte Carlo simulations revealed that for highly heterogeneous rings, the number of small fragments was large and that few large fragments were generated. For high Weibull modulus, signifying that the ring is close to being homogeneous, the fragmentation process was very different. Monte Carlo simulations highlighted that a larger number of large fragments are generated due to crack branching. Copyright © 2003 John Wiley & Sons, Ltd.     

A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm

The estimation of the differences among groups in observational studies is frequently inaccurate owing to a bias caused by differences in the distributions of covariates. In order to estimate the average treatment effects when the treatment variable is binary, Rosenbaum and Rubin [1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55] proposed an adjustment method for pre-treatment variables using propensity scores. Imbens [2000. The role of the propensity score in estimating dose-response functions. Biometrika 87, 706-710] extended the propensity score methodology for estimation of average treatment effects with multivalued treatments.However, these studies focused only on estimating the marginal mean structure. In many substantive sciences such as the biological and social sciences, a general estimation method is required to deal with more complex analyses other than regression, such as testing group differences on latent variables. For latent variable models, the EM algorithm or the traditional Monte Carlo methods are necessary. However, in propensity score adjustment, these methods cannot be used because the full distribution is not specified.In this paper, we propose a quasi-Bayesian estimation method for general parametric models that integrate out the distributions of covariates using propensity scores. Although the proposed Bayes estimates are shown to be consistent, they can be calculated by existing Markov chain Monte Carlo methods such as Gibbs sampler. The proposed method is useful to estimate parameters in latent variable models, while the previous methods were unable to provide valid estimates for complex models such as latent variable models.We also illustrated the procedure using the data obtained from the US National Longitudinal Survey of Children and Youth (NLSY1979-2002) for estimating the effect of maternal smoking during pregnancy on the development of the child's cognitive functioning.