Model repair — aligning process models to reality

Process mining techniques relate observed behavior (i.e., event logs) to modeled behavior (e.g., a BPMN model or a Petri net). Process models can be discovered from event logs and conformance checking techniques can be used to detect and diagnose differences between observed and modeled behavior. Existing process mining techniques can only uncover these differences, but the actual repair of the model is left to the user and is not supported. In this paper we investigate the problem of repairing a process model w.r.t. a log such that the resulting model can replay the log (i.e., conforms to it) and is as similar as possible to the original model. To solve the problem, we use an existing conformance checker that aligns the runs of the given process model to the traces in the log. Based on this information, we decompose the log into several sublogs of non-fitting subtraces. For each sublog, either a loop is discovered that can replay the sublog or a subprocess is derived that is then added to the original model at the appropriate location. The approach is implemented in the process mining toolkit ProM and has been validated on logs and models from several Dutch municipalities.     

An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

Fault detection, isolation and optimal control have long been applied to industry. These techniques have proven various successful theoretical results and industrial applications. Fault diagnosis is considered as the merge of fault detection (that indicates if there is a fault) and fault isolation (that determines where the fault is), and it has important effects on the operation of complex dynamical systems specific to modern industry applications such as industrial electronics, business management systems, energy, and public sectors. Since the resources are always limited in real-world industrial applications, the solutions to optimally use them under various constraints are of high actuality. In this context, the optimal tuning of linear and nonlinear controllers is a systematic way to meet the performance specifications expressed as optimization problems that target the minimization of integral- or sum-type objective functions, where the tuning parameters of the controllers are the vector variables of the objective functions. The nature-inspired optimization algorithms give efficient solutions to such optimization problems. This paper presents an overview on recent developments in machine learning, data mining and evolving soft computing techniques for fault diagnosis and on nature-inspired optimal control. The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system. New research challenges with strong industrial impact are highlighted.     

A novel approach to adaptive relational association rule mining

The paper focuses on the adaptive relational association rule mining problem. Relational association rules represent a particular type of association rules which describe frequent relations that occur between the features characterizing the instances within a data set. We aim at re-mining an object set, previously mined, when the feature set characterizing the objects increases. An adaptive relational association rule method, based on the discovery of interesting relational association rules, is proposed. This method, called ARARM (Adaptive Relational Association Rule Mining) adapts the set of rules that was established by mining the data before the feature set changed, preserving the completeness. We aim to reach the result more efficiently than running the mining algorithm again from scratch on the feature-extended object set. Experiments testing the method's performance on several case studies are also reported. The obtained results highlight the efficiency of the ARARM method and confirm the potential of our proposal.     

Understanding value creation in closed loop supply chains – Past findings and future directions

The topic of value creation through the recovery of returned products in closed loop supply chains is scattered across various bodies of literature. We undertake a systematic literature review of 144 articles in relevant green, reverse and closed loop supply chain literature to synchronize existing knowledge on value creation. Value manifestations of four types of value, namely economic, environmental, information and customer value, are identified. Value adding concepts from the forward- and reverse supply chain may leverage the process of value creation. They are classified into six subclasses, namely partnerships and collaboration, product design characteristics, service concepts, IT solutions, supply chain processes and organizational characteristics. We present a conceptual framework on a strategic level. In this way companies can create competitive advantages by closing the loop. The results of the literature analysis suggest avenues for future research on the operational and strategic level.     

Geothermal energy utilization trends from a technological paradigm perspective

The use of geothermal energy and its associated technologies has been increasing worldwide. However, there has been little paradigmatic research conducted in this area. This paper proposes a systematic methodology to research the development trends for the sustainable development of geothermal energy. A novel data analysis system was created to research the geothermal energy utilization trends, and a technological paradigm theory was adopted to explain the technological changes. A diffusion velocity model was used to simulate and forecast the geothermal power generation development in the diffusion phase. Simulation results showed that the development of installed capacity for geothermal generation had a strong inertia force along with the S-curve. Power generation from geothermal power sources reached a peak in 2008 and is estimated to be saturated by 2030. Geothermal energy technologies in hybrid power systems based on other renewable energy sources look to be more promising in the future.     

一种基于Spark的不确定数据集频繁模式挖掘算法

如何在海量不确定数据集中提高频繁模式挖掘性能是目前研究的热点.传统算法大多是以期望、概率或者权重等单一指标为数据项集支持度，在大数据背景下，同时考虑概率和权重支持度的算法难以兼顾其执行效率.为此，本文提出一种基于Spark的不确定数据集频繁模式挖掘算法（UWEFP），首先，为了同时兼顾数据项的概率和权重，计算一项集的最大概率权重值并进行剪枝；然后，为了减少对数据集的多次扫描，结合Spark框架的优点，设计了一种具有FP-tree特征的新颖的UWEFP-tree结构进行模式树的构建及挖掘；最后在Spark环境下，以UCI数据集进行实验验证.实验结果表明本文的方法在保证挖掘结果的同时，提高了效率.     

Prominent consequences of role stress: A meta-analytic review.

Role stress has received a lot of research attention in psychological, sociological, and organizational studies over the last several decades. Based on a literature review of about 300 journal articles, this article examines prominent consequences of role stress. Specific focus is on researching differences in relationships between facets of role stress (i.e., role ambiguity, role conflict, and role overload) and frequently cited consequences using techniques of meta-analysis. Findings indicate that each role stress facet has a different relationship with the eight consequences studied. Role stress research can benefit from looking at each facet individually in addition to role stress generally. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

稠油微生物开采技术现状及进展

综述了用微生物方法开采稠油的技术现状与进展，论题如下。①概述。②基本方法：异源微生物采油，包括微生物吞吐和微生物驱；本源微生物采油压大港孔店油田的实例。③主要机理，包括产表面活性剂，降解稠油中重质组分及其他。④技术研究，包括机理性、可行性及经济效益研究，列举了国内外6个实例。⑤现场应用，包括国外1个、国内6个实例。⑥该技术的优势及问题。参22。     

OTC药品的包装设计

结合儿童OTC(非处方药)药品的包装设计，分析了我国OTC药品包装设计的现状，讨论了OTC药品包装的设计思想及设计步骤。文章指出，在OTC药品包装设计时需要着重研究其设计理念、消费群体及消费定位问题。