Simulation study of China's net primary production

Spatial and temporal distribution of vegetation net primary production (NPP) in China was studied using three light-use efficiency models (CASA, GLOPEM and GEOLUE) and two mechanistic ecological process models (CEVSA, GEOPRO). Based on spatial and temporal analysis (e.g. monthly, seasonally and annually) of simulated results from ecological process mechanism models of CASA, GLOPEM and CEVSA, the following conclusions could be made: (1) during the last 20 years, NPP change in China followed closely the seasonal change of climate affected by monsoon with an overall trend of increasing; (2) simulated average seasonal NPP was: 0.571 +/- 0.2 GtC in spring, 1.573 +/- 0.4 GtC in summer, 0.6 +/- 0.2 GtC in autumn, and 0.12 +/- 0.1 GtC in winter. Average annual NPP in China was 2.864 +/- 1 GtC. All the five models were able to simulate seasonal and spatial features of biomass for different ecological types in China. This paper provides a baseline for China's total biomass production. It also offers a means of estimating the NPP change due to afforestation, reforestation, conservation and other human activities and could aid people in using for-mentioned carbon sinks to fulfill China's commitment of reducing greenhouse gases.

A Review of Active Appearance Models

Active appearance model (AAM) is a powerful generative method for modeling deformable objects. The model decouples the shape and the texture variations of objects, which is followed by an efficient gradient-based model fitting method. Due to the flexible and simple framework, AAM has been widely applied in the fields of computer vision. However, difficulties are met when it is applied to various practical issues, which lead to a lot of prominent improvements to the model. Nevertheless, these difficulties and improvements have not been studied systematically. This motivates us to review the recent advances of AAM. This paper focuses on the improvements in the literature in turns of the problems suffered by AAM in practical applications. Therefore, these algorithms are summarized from three aspects, i.e., efficiency, discrimination, and robustness. Additionally, some applications and implementations of AAM are also enumerated. The main purpose of this paper is to serve as a guide for further research.

A Scheduling Algorithm for Flexible Manufacturing Cell

In this paper, a new scheduling algorithm for the flexible manufacturing cell is presented, which is a discrete time control method with fixed length control period combining with event interruption. At the flow control level we determine simultaneously the production mix and the proportion of parts to be processed through each route. The simulation results for a hypothetical manufacturing cell are presented.

装配车间的多品种变批量的生产调度优化模型

针对装配车间调度问题,提出了综合考虑生产中物料配送能力,车间存储物料能力以及装配工艺序列问题的多品种变批次的优化调度方法。主要是分两步解决:首先考虑不同类型产品的装配加工的顺序优化问题;然后,根据生产约束建立各类型产品的分批问题。最后,实现计划调度衔接,并应用于生产。该方法对于实际装配生产具有很强的可操作性。

胜利勘探数据中心及勘探辅助决策系统研究

In the paper through extensive study and design, the technical plan for establishing the exploration database center is made to combine imported and self developed techniques. By research and repeated experiment a modern database center has been set up with its hardware and network having advanced performance, its system well configured, its data store and management complete, and its data support being fast and direct. Through study on the theory, method and model of decision an exploration decision assistant schema is designed with one decision plan of well location decision support system being evaluated and put into action. 1. Study on the establishment of Shengli exploration database center Research is made on the hardware configuration of the database center including its workstations and all connected hardware and system. The hardware of the database center is formed by connecting workstations, microcomputer workstations, disk arrays, and those equipments used for seismic processing and interpretation. Research on the data store and management includes the analysis of the contents to be managed, data flow, data standard, data QC, data backup and restore policy, optimization of database system. A reasonable data management regulation and workflow is made and the scientific exploration data management system is created. Data load is done by working out a schedule firstly and at last 200 more projects of seismic surveys has been loaded amount to 25TB. 2. Exploration work support system and its application Seismic data processing system support has the following features, automatic extraction of seismic attributes, GIS navigation, data order, extraction of any sized data cube, pseudo huge capacity disk array, standard output exchange format etc. The prestack data can be accessed by the processing system or data can be transferred to other processing system through standard exchange format. For supporting seismic interpretation system the following features exist such as auto scan and store of interpretation result, internal data quality control etc. the interpretation system is connected directly with database center to get real time support of seismic data, formation data and well data. Comprehensive geological study support is done through intranet with the ability to query or display data graphically on the navigation system under some geological constraints. Production management support system is mainly used to collect, analyze and display production data with its core technology on the controlled data collection and creation of multiple standard forms. 3. exploration decision support system design By classification of workflow and data flow of all the exploration stages and study on decision theory and method, target of each decision step, decision model and requirement, three concept models has been formed for the Shengli exploration decision support system including the exploration distribution support system, the well location support system and production management support system. the well location decision support system has passed evaluation and been put into action. 4. Technical advance Hardware and software match with high performance for the database center. By combining parallel computer system, database server, huge capacity ATL, disk array, network and firewall together to create the first exploration database center in China with reasonable configuration, high performance and able to manage the whole data sets of exploration. Huge exploration data management technology is formed where exploration data standards and management regulations are made to guarantee data quality, safety and security. Multifunction query and support system for comprehensive exploration information support. It includes support system for geological study, seismic processing and interpretation and production management. In the system a lot of new database and computer technology have been used to provide real time information support for exploration work. Finally is the design of Shengli exploration decision support system. 5. Application and benefit Data storage has reached the amount of 25TB with thousand of users in Shengli oil field to access data to improve work efficiency multiple times. The technology has also been applied by many other units of SINOPEC. Its application of providing data to a project named Exploration achievements and Evaluation of Favorable Targets in Hekou Area shortened the data preparation period from 30 days to 2 days, enriching data abundance 15 percent and getting information support from the database center perfectly. Its application to provide former processed result for a project named Pre-stack depth migration in Guxi fracture zone reduced the amount of repeated process and shortened work period of one month and improved processing precision and quality, saving capital investment of data processing of 30 million yuan. It application by providing project database automatically in project named Geological and seismic study of southern slope zone of Dongying Sag shortened data preparation time so that researchers have more time to do research, thus to improve interpretation precision and quality.

东营凹陷中央隆起带次生油藏成藏机制和预测

It is the key project of SINOPEC at ninth five years period with a lot of work and very difficult, which the main object are the study of pool-forming mechanism, distribution rule and pool-forming model of complex secondary pool at Dongying formation in high mature exploration area, and building theories and methods of research, description and prediction of secondary fault block pool. This paper apply comprehensively with various theories, method and techniques of geology, seismic, well log, reservoir engineering, meanwhile apply with computer means, then adopt combination of quality and quantitative to develop studies of pool-forming mechanism, model and pool prediction of fault block pool. On the based of stretch, strike-slip, reversal structure theories, integrated the geometry, kinematics, and dynamics of structure, it is show that the structure framework, the structure evolve, formation mechanism of central uplift belt of Dongying depression and control to formation and distribute of secondary complex fault block pool. The opening and sealing properties, sealing mechanism and sealing models of pool-controlling fault are shown by using quality, direction of normal stress, relations between interface and rock of two sides of fault and shale smear factor (SSF), as well as the juxtaposition of fault motion stage and hydrocarbon migration, etc. The sealing history of controlling fault, formation mechanism and distribute the regulation are established by combining together with bury history, structure evolve history, fault growth history stress field evolve history, which can be guide exploration and production oil field. It were bring up for the first time the dynamics mechanism of Dongying central uplift which were the result of compound tress field of stretch, strike-slip and reversal, companion with reversal drag structure, arcogenesis of paste and salt beds. The dual function of migration and sealing of fault were demonstrated in the research area. The ability of migration and sealing oil of pool-controlling fault is controlled by those factors of style of fault combination, activity regulation and intensity of fault at the period of oil migration. The four kinds of sealing model of pool-controlling fault were established in the research area, which the sealing mechanism of fault and distribution regulation of oil in time and space. The sealing ability of fault were controlled by quality, direction of normal stress, relations between interface and rock of two sides of fault and shale smear factor (SSF), as well as the juxtaposition of fault motion stage and hydrocarbon migration, etc. The fuzzy judge of fault sealing is the base of prediction of secondary pool. The pool-forming model of secondary was established in the research area, which the main factors are ability migration and sealing. The transform zone of fault, inner of arc fault and the compound area of multi fault are enrichment region of secondary pool of Dongying formation, which are confirm by exploration with economic performance and social performance.

Neuroprotective and immunomodulatory properties of the new PPARγ agonist MDG548: an in vitro and in vivo study in PD models

Neuroinflammation is a key component of Parkinson’s disease (PD) neuropathology. Skewed microglia activation with pro-inflammatory prevailing over anti-inflammatory phenotypes may contribute to neurotoxicity via the production of cytokines and neurotoxic species. Therefore, microglia polarization has been proposed as a target for neuroprotection. The peroxisome proliferator-activated receptor gamma (PPARγ) is expressed in microglia and peripheral immune cells, where it is involved in macrophages polarization and in the control of inflammatory responses, by modulating gene transcription. Several studies have shown that PPARγ agonists are neuroprotective in experimental PD models in rodents and primates. however safety concerns have been raised about PPARγ agonists thiazolidinediones (TZD) currently available, prompting for the development of non-TZD compounds. Aim of this study was to characterize a novel PPARγ agonist non TZD, MDG548, for its potential neuroprotective effect in PD models and its immunomodulatory activity as the underlying mechanism of neuroprotection. The neuroprotective activity of MDG548 was assessed in vivo in the subacute MPTP model and in the chronic MPTP/probenecid (MPTPp) model of PD. MDG548 activity on microglia activation and phenotype was investigated in the substantia nigra pars compacta (SNc) via the evaluation of pro- (TNF-α and iNOS) and anti-inflammatory (CD206) molecules, with fluorescent immunohistochemistry. Moreover, cultured murine microglia MMGT12 were treated with MDG548 in association with the inflammagen LPS, pro- and anti-inflammatory molecules were measured in the medium by ELISA assay and phagocytosis was evaluated by fluorescent immunohistochemistry for CD68. MDG548 arrested dopaminergic cells degeneration in the SNc in both the subacute MPTP and the chronic MPTPp models of PD, and reverted MPTPp-induced motor impairment. Moreover, MDG548 reduced microglia activation, iNOS and TNF-α production, while induced CD206 in microglia. In cultured unstimulated microglia, LPS increased TNF-α production and CD68 expression, while decreased CD206 expression. MDG548 reverted LPS effect on TNF-α and CD206 restoring physiological levels, while strongly increased CD68 expression. Results suggest that the PPARγ agonist MDG548 is neuroprotective in experimental models of PD. MDG548 targets microglia polarization by correcting the imbalance between pro- over antiinflammatory molecules, offering a novel immunomodulatory approach to neuroprotection.

Automating Mechanical FMEA Using Functional Models

Hughes, N., Chou E., Price, C. J. Lee M. H.(1999). Automating Mechanical FMEA Using Functional Models, Proceedings 12th Int. Florida AI Research Soc. Conf. (FLAIRS-99), AAAI Press, May 1999, pp. 394-398.

Layered Graphical Models for Tracking Partially-Occluded Objects

Partial occlusions are commonplace in a variety of real world computer vision applications: surveillance, intelligent environments, assistive robotics, autonomous navigation, etc. While occlusion handling methods have been proposed, most methods tend to break down when confronted with numerous occluders in a scene. In this paper, a layered image-plane representation for tracking people through substantial occlusions is proposed. An image-plane representation of motion around an object is associated with a pre-computed graphical model, which can be instantiated efficiently during online tracking. A global state and observation space is obtained by linking transitions between layers. A Reversible Jump Markov Chain Monte Carlo approach is used to infer the number of people and track them online. The method outperforms two state-of-the-art methods for tracking over extended occlusions, given videos of a parking lot with numerous vehicles and a laboratory with many desks and workstations.

Learning and Production of Movement Sequences: Behavioral, Neurophysiological, and Modeling Perspectives

A growing wave of behavioral studies, using a wide variety of paradigms that were introduced or greatly refined in recent years, has generated a new wealth of parametric observations about serial order behavior. What was a mere trickle of neurophysiological studies has grown to a more steady stream of probes of neural sites and mechanisms underlying sequential behavior. Moreover, simulation models of serial behavior generation have begun to open a channel to link cellular dynamics with cognitive and behavioral dynamics. Here we summarize the major results from prominent sequence learning and performance tasks, namely immediate serial recall, typing, 2XN, discrete sequence production, and serial reaction time. These populate a continuum from higher to lower degrees of internal control of sequential organization. The main movement classes covered are speech and keypressing, both involving small amplitude movements that are very amenable to parametric study. A brief synopsis of classes of serial order models, vis-à-vis the detailing of major effects found in the behavioral data, leads to a focus on competitive queuing (CQ) models. Recently, the many behavioral predictive successes of CQ models have been joined by successful prediction of distinctively patterend electrophysiological recordings in prefrontal cortex, wherein parallel activation dynamics of multiple neural ensembles strikingly matches the parallel dynamics predicted by CQ theory. An extended CQ simulation model-the N-STREAMS neural network model-is then examined to highlight issues in ongoing attemptes to accomodate a broader range of behavioral and neurophysiological data within a CQ-consistent theory. Important contemporary issues such as the nature of working memory representations for sequential behavior, and the development and role of chunks in hierarchial control are prominent throughout.

A Neural Network Model for Cursive Script Production

This article describes a neural network model, called the VITEWRITE model, for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a. hand with redundant degrees of freedom. The neural trajectory generator is the Vector Integration to Endpoint (VITE) model for synchronous variable-speed control of multijoint movements. VITE properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. The proposed controller launches transient directional commands to independent hand synergies at times when the hand begins to move, or when a velocity peak in a given synergy is achieved. The VITE model translates these temporally disjoint synergy commands into smooth curvilinear trajectories among temporally overlapping synergetic movements. The separate "score" of onset times used in most prior models is hereby replaced by a self-scaling activity-released "motor program" that uses few memory resources, enables each synergy to exhibit a unimodal velocity profile during any stroke, generates letters that are invariant under speed and size rescaling, and enables effortless. connection of letter shapes into words. Speed and size rescaling are achieved by scalar GO and GRO signals that express computationally simple volitional commands. Psychophysical data concerning band movements, such as the isochrony principle, asymmetric velocity profiles, and the two-thirds power law relating movement curvature and velocity arise as emergent properties of model interactions.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Shop scheduling problems under precedence constraints

The paper considers a scheduling model that generalizes the well-known open shop, flow shop, and job shop models. For that model, called the super shop, we study the complexity of finding a time-optimal schedule in both preemptive and non-preemptive cases assuming that precedence constraints are imposed over the set of jobs. Two types of precedence rela-tions are considered. Most of the arising problems are proved to be NP-hard, while for some of them polynomial-time algorithms are presented.

Optimizing the operation of straight-grate iron-ore pellet induration systems using process models

Mathematical models of straight-grate pellet induration processes have been developed and carefully validated by a number of workers over the past two decades. However, the subsequent exploitation of these models in process optimization is less clear, but obviously requires a sound understanding of how the key factors control the operation. In this article, we show how a thermokinetic model of pellet induration, validated against operating data from one of the Iron Ore Company of Canada (IOCC) lines in Canada, can be exploited in process optimization from the perspective of fuel efficiency, production rate, and product quality. Most existing processes are restricted in the options available for process optimization. Here, we review the role of each of the drying (D), preheating (PH), firing (F), after-firing (AF), and cooling (C) phases of the induration process. We then use the induration process model to evaluate whether the first drying zone is best to use on the up- or down-draft gas-flow stream, and we optimize the on-gas temperature profile in the hood of the PH, F, and AF zones, to reduce the burner fuel by at least 10 pct over the long term. Finally, we consider how efficient and flexible the process could be if some of the structural constraints were removed (i.e., addressed at the design stage). The analysis suggests it should be possible to reduce the burner fuel lead by 35 pct, easily increase production by 5+ pct, and improve pellet quality.

The development and validation of computational MHD techniques for the modelling of metal production processes

Magnetic fields are used in a number of processes related to the extraction of metals, production of alloys and the shaping of metal components. Computational techniques have an increasingly important role to play in the simulation of such processes, since it is often difficult or very costly to conduct experiments in the high temperature conditions encountered and the complex interaction of fluid flow, heat transfer and magnetic fields means simple analytic models are often far removed from reality. In this paper an overview of the computational activity at the University of Greenwich is given in this area, covering the past ten years. The overview is given from the point of view of the modeller and within the space limitations imposed by the format it covers the numerical methods used, attempts at validation against experiments or analytic procedures; it highlights successes, but also some failures. A broad range of models is covered in the review (and accompanying lecture), used to simulate (a) A-C field applications: induction melting, magnetic confinement and levitation, casting and (b) D-C field applications such as: arc welding and aluminium electroloysis. Most of these processes involve phase change of the metal (melting or solidification), the presence of a dynamic free surface and turbulent flow. These issues affect accuracy and need to be address by the modeller.