山东栖霞—大柳行地区金矿找矿靶区地质评价及优选

栖霞—大柳行地区位于胶东中部栖霞—蓬莱金矿成矿带内，是胶东重要的金成矿区，成矿条件优越。为了对圈定靶区进行优选分类，通过对该区地质背景、找矿靶区评价、找矿靶区优选方面展开综合研究，并结合勘查工作程度和矿业权设置情况，划分出3类预测区，选定了5个靶区为今后优先部署勘查工作的方向。     

Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes

A Takagi-Sugeno adaptive neuro-fuzzy inference system (TSFIS) model is developed and applied to a dataset of wellhead flow-test data for the Resalat oil field located offshore southern Iran, the objective is to assist in the prediction and control of multi-phase flow rates of oil and gas through the wellhead chokes. For this purpose, 182 test data points (Appendix 1) related to the Resalat field are evaluated. In order to predict production flow rate (Q_L) expressed as stock-tank barrels per day (STB/D), this dataset includes four selected input variables: upstream pressure (Pwh); wellhead choke sizes (D64); gas to liquid ratio (GLR); and, base solids and water including some water-soluble oil emulsion (BS&W). The test data points evaluated include a wide range of oil flow rate conditions and values for the four input variables recorded. The TSFIS algorithm applied involves five data processing steps: a) pre-processing, b) fuzzification, c) rules base and adaptive neuro-fuzzy inference engine, d) defuzzification, and e) post-processing of the fuzzy model. The developed TSFIS model for the Resalat oil field database predicted oil flow rate to a high degree of accuracy (root mean square error = 247 STB/D, correlation coefficient = 0.9987), which improves substantially on the commonly used empirical algorithms used for such predictions. TSFIS can potentially be applied in wellhead choke fuzzy controllers to stabilize flow in specific wells based on real-time input data records.     

石油套管质量评价方法研究

石油套管是油气田开采中重要的物资装备,其质量直接影响油气田的生产安全与开采效率。对石油套管质量评价方法进行研究,优选反映石油套管质量的性能参数,通过归一化处理,结合专家知识,建立评价模型,进而得到石油套管质量的评价结果。将所研究的评价方法应用于P110石油套管质量评价中,评价结果准确、直观,方法有效实用。     

Multiple Empirical Kernel Learning with Majority Projection for imbalanced problems

Traditional Multiple Empirical Kernel Learning (MEKL) expands the expressions of the sample and brings better classification ability by using different empirical kernels to map the original data space into multiple kernel spaces. To make MEKL suit for the imbalanced problems, this paper introduces a weight matrix and a regularization term into MEKL. The weight matrix assigns high misclassification cost to the minority samples to balanced misclassification cost between minority and majority class. The regularization term named Majority Projection (MP) is used to make the classification hyperplane fit the distribution shape of majority samples and enlarge the between-class distance of minority and majority class. The contributions of this work are: (i) assigning high cost to minority samples to deal with imbalanced problems, (ii) introducing a new regularization term to concern the property of data distribution, (iii) and modifying the original PAC-Bayes bound to test the error upper bound of MEKL-MP. Through analyzing the experimental results, the proposed MEKL-MP is well suited to the imbalanced problems and has lower generalization risk in accordance with the value of PAC-Bayes bound.     

Predicting hydrogen adsorption and desorption rates in cylindrical metal hydride beds: Empirical correlations and machine learning

A major limitation in chemisorptive hydrogen storage in metal hydrides is the long time required for the adsorption reaction during charging. This study investigates how the shape and material of the reaction chamber influences the adsorption and desorption rates. Numerical simulations of hydrogen storage in a cylindrical reaction chamber filled with LaNi₅ hydride are conducted for a range of chamber thermal conductivities and aspect ratios. The results show that adsorption and desorption processes are limited by thermal diffusion in the hydride bed and storage chamber. A storage efficiency is proposed based on an ideal isothermal process and used to evaluate the impact of chamber thermal conductivity and aspect ratio on the adsorption and desorption rates. Empirical correlations are proposed for predicting the adsorption and desorption efficiency of cylindrical LaNi₅ hydride beds. Finally, a machine-learning based data model for predicting storage efficiency in metal hydride chambers is presented. Comparison against the empirical correlations highlights that the machine learning-based data model can predict the storage efficiency more accurately.     

Large lakes in climate models: A Great Lakes case study on the usability of CMIP5

Large lakes have an impact on regional weather. In addition, they can be both sensitive to and influence regional climate changes. In the climate models that are used to investigate future climate changes, lakes are greatly simplified and sometimes absent. At the regional scale, this can have strong implications for the quality of the model information about the future. Through our work with climate information users in the Laurentian Great Lakes region, we have found that basic credibility of the information requires the underlying climate models simulate lake-atmosphere-land interactions. We are not aware of efforts within the scientific community to make known how individual large lakes are represented in models and how those representations translate to the quality of the data for particular regions. We share our framework for identifying how the Laurentian Great Lakes are represented in the Coupled Model Intercomparison Project (CMIP) version 5 climate models. We found that most CMIP5 models do not simulate the Great Lakes in a way that captures their impact on the regional climate, which is a credibility issue for their projections. We provide a perspective on the usability of CMIP5 for practitioners in the Great Lakes region and offer recommendations for alternative options.     

An integrated DEMATEL-MMDE-ISM based approach for analysing the barriers of IoT implementation in the manufacturing industry

Incorporation of smart devices within the older framework has brought along significant challenges. This paper presents a detailed analysis of the barriers faced during the implementation of Internet of Things (IoT) within the manufacturing sector. In addition, the authors aim to obtain a hierarchical structure, which will help the policymakers to identify the most crucial barriers enabling them to make an informed decision. With the help of databases like Scopus, Web of Science, etc. a comprehensive list of 22 barriers was initially obtained. This list was further narrowed down to 10 critical barriers. The first step of the analysis involved the application of Decision Making Trial and Evaluation Laboratory (DEMATEL) technique, which quantifies the influence of the barriers amongst one another. Maximum Mean De-Entropy (MMDE) technique is then used to obtain a scientific threshold value, which is later used in the Interpretive Structural Modelling (ISM) technique from which a hierarchical structure of the barriers is obtained. The results of this study are expected to highlight the most crucial barriers wherein the researchers and practitioners can focus their strategic efforts. This will facilitate the addressal of implicit issues while implementing IoT Techniques in the manufacturing industry.     

基于儿童使用主体的建成环境评价尺度研究

评价尺度是建筑使用后评价的基 本问题之一，它是使用者评判建成环境好 坏、优劣的重要测度标准。为弥补目前环境 评价尺度研究方面的缺失，建立与儿童使用 主体相一致的建成环境评价尺度，对儿童常 用语义量词进行汇总和筛选，采用五级线 分刻度方法进行问卷调查研究。基于儿童的 理解，通过问卷试验分析，结果表明：双侧 5级区间评价尺度为：很（负面）；有点（负 面）；中等；有点（正面）；很（正面）；单侧 5级区间评价尺度为：不；好像有点；比较； 很；特别。     

Multi-stage power source and grid coordination planning method considering grid uniformity

Given the increasing uncertainties in power supply and load, this paper proposes the concept of power source and grid coordination uniformity planning. In this approach, the standard deviation of the transmission line load rate is considered as the uniformity evaluation index for power source and grid planning. A multi-stage and multi-objective optimization model of the power source and grid expansion planning is established to minimize the comprehensive cost of the entire planning cycle. In this study, the improved particle swarm optimization algorithm and genetic algorithm are combined to solve the model, thus improving the efficiency and accuracy of the solution. The analysis of a simple IEEE Garver’s 6-node system shows that the model and solution method are effective and feasible. Moreover, they are suitable for the coordinated planning of the power source and grid under a diversified nature of power supply and load.     

The effects of augmented virtual science laboratories on middle school students' understanding of gas properties

The Next Generation Science Standards (NGSS) emphasize authentic scientific practices such as developing models and constructing explanations of phenomena. However, research documents how students struggle to explain observable phenomena with molecular-level behaviors with current classroom experiences. For example, physical laboratory experiences in science enable students to interact with observable scientific phenomena, but students often fail to make connections to underlying molecular-level behaviors. Virtual laboratory experiences and computer-based visualizations enable students to interact with unobservable scientific concepts, but students can have difficulties connecting to actual instantiations of the observed phenomenon. This paper investigates how combining physical and virtual experiences into augmented virtual science laboratories can help students build upon intuitive ideas and develop molecular-level explanations of macroscopic phenomena. Specifically, this study uses the Frame, a sensor-augmented virtual lab that uses sensors as physical inputs to control scientific simulations. Eighth-grade students (N = 45) engaged in a Frame lab focused on the properties of gas. Results demonstrate that students using the Frame lab made progress developing molecular-level explanations of gas behavior and refining alternative and partial ideas into normative ideas about gases. This study offers insights for how augmented virtual labs can be designed to enhance science learning and encourage scientific practices as called for in the NGSS.