Lot Splitting Under Learning Effects with Minimal Revenue Requirements and Multiple Lot Types

The lot splitting problem in the presence of learning is addressed. This work is an extension of an approach proposed for splitting in the case of a single item. We address the issue of a minimal revenue requirement from partial deliveries until a predetermined time. This is achieved by imposing a constraint on what is originally an unconstrained optimization problem. When sublots of different items are involved, the optimal splitting decisions have to be combined with the sequencing of the deliveries. Numerical examples are presented to demonstrate the proposed approach.     

Image statistics and the perception of apparent motion.

The short- and long-range apparent motion processes are discussed in terms of the statistical properties of images. It is argued that the short-range process, exemplified by the random-dot kinematogram, is primarily sensitive to the dipole statistics, whereas the long-range process, exemplified by illusory occlusion, is treated by the visual system primarily in terms of the tripole and higher statistical correlation functions. The studies incorporate the balanced dot, which is a unique stimulus element that permits high pass filtering while preserving detailed positional information. Low spatial frequencies are shown to be critical for texture segregation in random-dot kinematograms, independent of the grain size or number density of texture elements. Illusory path perception in the long-range process is shown not to require low spatial frequencies, but is sensitive rather to global temporal phase coherency. These results are interpreted in terms of the respective roles of the power and phase spectra in perceptual organization. The construction of balanced dots is discussed in detail. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Disparity disambiguation by fusion of signal- and symbolic-level information

We describe a method for resolving ambiguities in low-level disparity calculations in a stereo-vision scheme by using a recurrent mechanism that we call signal-symbol loop. Due to the local nature of low-level processing it is not always possible to estimate the correct disparity values produced at this level. Symbolic abstraction of the signal produces robust, high confidence, multimodal image features which can be used to interpret the scene more accurately and therefore disambiguate low-level interpretations by biasing the correct disparity. The fusion process is capable of producing more accurate dense disparity maps than the low- and symbolic-level algorithms can produce independently. Therefore we describe an efficient fusion scheme that allows symbolic- and low-level cues to complement each other, resulting in a more accurate and dense disparity representation of the scene.     

Global sensitivity analysis based on Gini’s mean difference

Global sensitivity analysis has been widely used to detect the relative contributions of input variables to the uncertainty of model output, and then more resources can be assigned to the important input variables to reduce the uncertainty of model output more efficiently. In this paper, a new kind of global sensitivity index based on Gini’s mean difference is proposed. The proposed sensitivity index is more robust than the variance-based first order sensitivity index for the cases with non-normal distributions. Through the decomposition of Gini’s mean difference, it shows that the proposed sensitivity index can be represented by the energy distance, which measures the difference between probability distributions. Therefore, the proposed sensitivity index also takes the probability distribution of model output into consideration. In order to estimate the proposed sensitivity index efficiently, an efficient Monte Carlo simulation method is also proposed, which avoids the nested sampling procedure. The test examples show that the proposed sensitivity index is more robust than the variance-based first order sensitivity index for the cases with non-normal distributions.     

Photosensitization of PVC dehydrochlorination by hydroquinone for improved optical and electrical properties

Hydroquinone (HQ) is incorporated into the PVC films containing methyl violet or polyaniline (emeraldine base) for sensitizing the UV induced optical or electrical changes, respectively. It is observed that introduction of a small amount (less than 10% by weight) of HQ not only brings the dehydrochlorination onset down to 310 nm but also sensitizes the process by more than one order of magnitude as well as leading to strong polyene formation. UV-Vis-NIR spectroscopy is used to characterize the changes and investigate the mechanism. Accordingly, it is postulated that this HQ assisted photo-dehydrochlorination involves predominantly the formation of an excited triplet via an efficient intersystem crossing in HQ followed by abstraction of hydrogen from the poly(vinyl chloride) to initiate a zipping reaction in the PVC matrix.     

Nonlinear negative transmittance at a CW 980‐nm laser diodes pumping in Yb3+:CaF2 nanocrystals‐embedded glass ceramics

Cooperative upconversion luminescence (CUCL) occurs in spectral regions in which single ions do not have energy levels. However, all results reported so far are concentrated on luminescence properties from Yb³⁺ ions‐doped various hosts. Here, we report the observation of nonlinear negative transmittance (NNT) at continuous‐wavelength (CW) 980‐nm laser diodes (LDs) pumping in silicate oxyfluoride glass ceramics (GCs)‐containing CaF₂:Yb³⁺ nanocrystals. The unique optical nonlinearity is analyzed based on energy‐level transitions, dynamic evolution, rate equation, and power transmission equation, which can be explained as the cooperative optical absorption for the intense CUCL of Yb³⁺ ions. The NNT in the CaF₂:Yb³⁺ nanocrystals‐embedded GCs can be tailored with the power of a CW 980‐nm LDs, which possesses potential for the development of future optical limiters and switches.     

Understanding the formation of interdisciplinary research from the perspective of keyword evolution: a case study on joint attention

We measure the knowledge flows between countries by analysing publication and citation data, arguing that not all citations are equally important. Therefore, in contrast to existing techniques that utilize absolute citation counts to quantify knowledge flows between different entities, our model employs a citation context analysis technique, using a machine-learning approach to distinguish between important and non-important citations. We use 14 novel features (including context-based, cue words-based and text-based) to train a Support Vector Machine (SVM) and Random Forest classifier on an annotated dataset of 20,527 publications downloaded from the Association for Computational Linguistics anthology (http://allenai.org/data.html). Our machine-learning models outperform existing state-of-the-art citation context approaches, with the SVM model reaching up to 61% and the Random Forest model up to a very encouraging 90% Precision–Recall Area Under the Curve, with 10-fold cross-validation. Finally, we present a case study to explain our deployed method for datasets of PLoS ONE full-text publications in the field of Computer and Information Sciences. Our results show that a significant volume of knowledge flows from the United States, based on important citations, are consumed by the international scientific community. Of the total knowledge flow from China, we find a relatively smaller proportion (only 4.11%) falling into the category of knowledge flow based on important citations, while The Netherlands and Germany show the highest proportions of knowledge flows based on important citations, at 9.06 and 7.35% respectively. Among the institutions, interestingly, the findings show that at the University of Malaya more than 10% of the knowledge produced falls into the category of important. We believe that such analyses are helpful to understand the dynamics of the relevant knowledge flows across nations and institutions.     

Multi-objective feasibility enhanced particle swarm optimization

This article introduces a new method entitled multi-objective feasibility enhanced partical swarm optimization (MOFEPSO), to handle highly-constrained multi-objective optimization problems. MOFEPSO, which is based on the particle swarm optimization technique, employs repositories of non-dominated and feasible positions (or solutions) to guide feasible particle flight. Unlike its counterparts, MOFEPSO does not require any feasible solutions in the initialized swarm. Additionally, objective functions are not assessed for infeasible particles. Such particles can only fly along sensitive directions, and particles are not allowed to move to a position where any previously satisfied constraints become violated. These unique features help MOFEPSO gradually increase the overall feasibility of the swarm and to finally attain the optimal solution. In this study, multi-objective versions of a classical gear-train optimization problem are also described. For the given problems, the article comparatively evaluates the performance of MOFEPSO against several popular optimization algorithms found in the literature.     

钠离子电池TiO_2/还原氧化石墨烯负极材料的制备及储钠性能

由于钠离子半径比锂离子半径大70%,使得钠离子在石墨电极材料中脱嵌较困难,需要对石墨负极材料进行改性。以天然石墨为原料,采用Hummers法制备氧化石墨烯;在此基础上以钛酸丁酯为原料,采用溶胶-凝胶法制备了TiO_2前驱体/氧化石墨烯(TiO_2/GO)复合材料,通过热处理获得锐钛矿型TiO_2/还原氧化石墨烯(TiO_2/RGO)复合材料。电化学测试结果表明:TiO_2含量为15wt%的TiO_2/RGO复合材料在电流密度为20mA·g~(-1)下的首次放电比容量为74.08mAh·g~(-1),随着循环次数的增加,放电比容量逐渐增大,循环50次后达109.10mAh·g~(-1);充放电效率也呈现出逐渐增大的趋势,循环50次后达65.59%。而纯还原氧化石墨烯首次放电比容量为41.43mAh·g~(-1),循环50次后仅为20.47mAh·g~(-1)。     

Forecasting Daily Streamflow for Basins with Different Physical Characteristics through Data-Driven Methods

Modelling streamflow is essential for activities, such as flood control, drought mitigation, and water resources utilization and management. Artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machines (SVM) are techniques that are frequently used in hydrology to specifically model streamflow. This study compares the accuracy of ANN, ANFIS, and SVM in forecasting the daily streamflow with the traditional approach known as autoregressive (AR) model for basins with different physical characteristics. The accuracies of the models are compared for three basins, that is, 1801, 1805, and 1822, at the Seyhan River Basin in Turkey. The comparison was performed by using coefficient of efficiency, index of agreement, and root-mean-square error. Results indicate that ANN and ANFIS are more accurate than AR and SVM for all the basins. ANN and ANFIS perform similarly, while ANN outperformed ANFIS. Among the models used, the ANN demonstrates the highest performance in forecasting the peak flood values. This study also finds that physical characteristics, such as small area, high slope, and high elevation variation, and streamflow variance deteriorate the accuracy of the methods.