On the Transformation Mechanism for Formulating a Multiproduct Two-Layer Supply Chain Network Design Problem as a Network Flow Model

The multiproduct two-layer supply chain is very common in various industries. In this paper, we introduce a possible modeling and algorithms to solve a multiproduct two-layer supply chain network design problem. The decisions involved are the DCs location and capacity design decision and the initial distribution planning decision. First we describe the problem and give a mixed integer programming (MIP) model; such problem is NP-hard and it is not easy to reduce the complexity. Inspired by it, we develop a transformation mechanism of relaxing the fixed cost and adding some virtual nodes and arcs to the original network. Thus, a network flow problem (NFP) corresponding to the original problem has been formulated. Given that we could solve the NFP as a minimal cost flow problem. The solution procedures and network simplex algorithm (INS) are discussed. To verify the effectiveness and efficiency of the model and algorithms, the performance measure experimental has been conducted. The experiments and result showed that comparing with MIP model solved by genetic algorithm (GA) and Benders, decomposition algorithm (BD) the NFP model and INS are also effective and even more efficient for both small-scale and large-scale problems

Sedimentation of the Lower Cretaceous Xiagou formation and Its Response to Regional Tectonics in the Qingxi Sag, Jiuquan Basin, NW China

Under the constraint of an isochronous sequence stratigraphic framework, sediment infill of the Xiagou Formation reflects the overall control of dynamic tectonic movements and episodic sedimentations in the Qingxi Sag. Structure reactivity during post-depositional processes could cause stratigraphic variations in longitudinal time and lateral space. This study documents sediment infill features and their response to the tectonic evolutions of the Qingxi Sag. The data sets include comparison of cores, well drilling, 3D seismic, inter-well correlation, wave impedance inversion profiles, original strata recovery data, sedimentary fades spatial evolution and their superimposition with paleogeomorphology. The Jiuquan Basin is a Mesozoic-Cenozoic superposition basin comprising an early rifting graben phase and a later compression phase. Since the Early Cretaceous, the basin has undergone four major tectonic episodes: 1) extension during the Early Cretaceous, 2) tectonic inversion caused by northwest-southeast contraction from the Late Cretaceous to the Paleocene, 3) weak extension from the Eocene to the Miocene and 4) contraction from the Miocene to the present. Therefore, the Jiuquan Basin is the product of taphrogenic, collisional and shearing movements. Seismic interpretations of sequence and maximum flooding surface divide the Xiagou Formation into three third order sequences: SQK1g(0), SQK1g(1) and SQK1g(2+3). Five sedimentary facies associations are identified: the shoreland plain, fan delta dominated sedimentary systems, turbidite deposits, shallow lakes and half-deep lake systems. From K1g(0) to K1g(2+3), decreased sandstone percentages in three fan delta areas indicate a continuously transgressive process, which shows the transition from proximal to distal sites in most statistic wells and an obvious decrease of fan delta scales. The northeast-southwest faults control the lakeward distributions of delta fronts and turbidite fans. The correspondence of sedimentary infill and its response to tectonic movements have been demonstrated in the Qingxi Sag. The more active eastern part of the northeastern boundary fault has an important influence on the northeastward migration of depocenters in the Xiagou Formation. The topography developed continuously from K1g(0) to K1g(2+3), but the diminished subsidence indicates the dominant geological process varying from intense fault rifting in an early period to relatively gentle and overall subsidence in a later period during the Early Cretaceous. (C) 2013 Elsevier Ltd. All rights reserved.Key Laboratory of Tectonics and Petroleum Resources, Ministry of Education TPR-2011-09Project of "Double strong effect, driving mechanism and hydrocarbon significance of tectonic activity during depositional period of Dongying Formation in Qikou and Nanpu Sag, Eastern China" 41272122Geological Science

New mixed adaptive detection algorithm for moving target with big data

Aiming at the troubles (such as complex background, illumination changes, shadows and others on traditional methods) for detecting of a walking person, we put forward a new adaptive detection algorithm through mixing Gaussian Mixture Model (GMM), edge detection algorithm and continuous frame difference algorithm in this paper. In time domain, the new algorithm uses GMM to model and updates the background. In spatial domain, it uses the hybrid detection algorithm which mixes the edge detection algorithm, continuous frame difference algorithm and GMM to get the initial contour of moving target with big data, and gets the ultimate moving target with big data. This algorithm not only can adapt to the illumination gradients and background disturbance occurred on scene, but also can solve some problems such as inaccurate target detection, incomplete edge detection, cavitation and ghost which usually appears in traditional algorithm. As experimental result showing, this algorithm holds better real-time and robustness. It is not only easily implemented, but also can accurately detect the moving target with big data

Cyclic thermo-mechanical performance of granular beds: Effect of elastoplasticity

Understanding the coupled thermo-mechanical behaviour of compacted granular beds can benefit various industrial applications, such as pebble bed design in fusion reactors. In this study, a thermo-mechanical discrete element method based on our previous work is improved and adapted to investigate the cyclic thermo-mechanical performance of gas-filled granular materials composed of elastoplastic grains. An interparticle contact model is developed considering the plastic deformation of grains. Through the simulation on a representative volume element of beryllium pebble beds, we provide grain-scale insight into the evolution of thermal conductivity and stress. The simulation results suggest that the network of thermal contacts is impeded by plastic deformation leading to a significant drop of thermal conductivity during cooling. This effect can be suppressed by increasing the initial packing factor. Not limited to pebble bed design, the conclusion of this work can also pave the way for optimizing powder-based manufacturing and energy storage, where combined thermo-mechanical loading conditions and elastoplastic deformation of individual particles are involved

Novel fusion computing method for bio-medical image of WSN based on spherical coordinate

In bio-medical field, embedded numerous sensing nodes can be used to monitor and interact with physical world based on signal analysis and processing. Data from many different sources can be collected into massive data sets via localized sensor networks. Understanding the environment requires collecting and analyzing data from thousands of sensors monitoring, this is big data environment. The application of bio-medical image fusion for big-data computing has strong development momentum, big-data bio-medical image fusion is one of key problems, so the fusion method study is a hot topic in the field of signal analysis and processing. The existing methods have many limitations, such as large delay, data redundancy, more energy cost, low quality, so novel fusion computing method based on spherical coordinate for big-data bio-medical image of WSN is proposed in this paper. In this method, the three high-frequency coefficients in wavelet domain of bio-medical image are pre-processed. This pre-processing strategy can reduce the redundant ratio of big-data bio-medical image. Firstly, the high-frequency coefficients are transformed to the spherical coordinate domain to reduce the correlation in the same scale. Then, a multi-scale model product (MSMP) is used to control the shrinkage function so as to make the small wavelet coefficients and some noise removed. The high-frequency parts in spherical coordinate domain are coded by improved SPIHT algorithm. Finally, based on multi-scale edge of bio-medical image, it can be fused and reconstructed. Experimental results indicate the novel method is effective and very useful for transmission of big-data bio-medical image, which can solve the problem of data redundancy, more energy cost and low quality

Prompting GPT-3 To Be Reliable

Large language models (LLMs) show impressive abilities via few-shot prompting. Commercialized APIs such as OpenAI GPT-3 further increase their use in real-world language applications. However, the crucial problem of how to improve the reliability of GPT-3 is still under-explored. While reliability is a broad and vaguely defined term, we decompose reliability into four main facets that correspond to the existing framework of ML safety and are well-recognized to be important: generalizability, social biases, calibration, and factuality. Our core contribution is to establish simple and effective prompts that improve GPT-3's reliability as it: 1) generalizes out-of-distribution, 2) balances demographic distribution and uses natural language instructions to reduce social biases, 3) calibrates output probabilities, and 4) updates the LLM's factual knowledge and reasoning chains. With appropriate prompts, GPT-3 is more reliable than smaller-scale supervised models on all these facets. We release all processed datasets, evaluation scripts, and model predictions. Our systematic empirical study not only sheds new insights on the reliability of prompting LLMs, but more importantly, our prompting strategies can help practitioners more reliably use LLMs like GPT-3.Comment: ICLR 202