Dreissenid veliger dynamics along a nearshore to offshore transect in Lake Michigan

Dreissenid mussel veligers compose a substantial component of pelagic biomass in the Great Lakes, yet their dynamics are poorly understood. To evaluate seasonal, spatial, and inter-annual variation in veliger density, we used a 64-μm mesh plankton net (2008, 2013–2016) and a 153-μm mesh plankton net (2007–2016) to collect dreissenid veligers at nearshore (15–25?m depth), transitional (45?m) and offshore (93–110?m) sites in southeast Lake Michigan during March–December. We also evaluated trends in density of recently settled mussels relative to veliger abundance and the density of the standing stock of adult mussels. Veliger density peaked during both summer and fall at all sites, but peak densities in summer were generally higher nearshore, whereas peak densities in the fall were generally higher offshore. The density of veligers in the 153-μm net was overall 28% of that in the 64-μm net, but there was high variability in this comparison among months. Smaller veligers were much more abundant in the 64-μm net, but there was little difference in the size distribution and abundance between nets for the 210–300?μm size classes. Thus, the 153-μm net could still be a useful tool for assessing density trends of larger veligers just prior to their settlement. Newly settled mussels (≤2?mm) were most abundant in summer or fall at the nearshore and offshore sites but were nearly absent at the transitional site despite the high density of veligers there. Factors other than veliger density must play an important role in mussel recruitment.     

Mimicking the Brain Extracellular Matrix in Vitro: A Review of Current Methodologies and Challenges

The brain's extracellular matrix (ECM), a 3-dimensional macromolecular network that supports cell growth and viability, has crucial roles in homeostasis and disease. Accurate recapitulation of the chemical, structural, and mechanical properties of the ECM in brain cell cultures is essential for the development of translatable in vitro models; however, thus far, this task has proven highly challenging. This review provides an in-depth discussion of this challenge, including an overview of the properties of the ECM that in vitro models should endeavour to capture, a survey of ECM analogues that are currently used for this purpose, and a discussion of the main hindrances to developing more effective ECM-like coating materials.     

Discovery of path-attribute dependency in manufacturing environments: A process mining approach

The more knowledge industrial practitioners detain of their production processes, the more they are capable of performing process improvements. Nonetheless, there may exist process characteristics and dependencies that are not easily extractable from business models, such as routing dependent attributes. This paper introduces an algorithm-driven framework to establish whether process path decisions influence the attributes in non-direct sequences, e.g., deploying machine A instead of machine B affects the % of rejected parts on the process, 4 stages down the line. This problem is shown to bears similarities with sequential pattern mining problems. The basis of the solution framework relies on process mining and data mining techniques. The approach proposed is applied on a real industrial log, unveiling deficiencies in the system and providing further improvement recommendations.     

A Reduced Reachability Tree for a Class of Unbounded Petri Nets

As a powerful analysis tool of Petri nets, reachability trees are fundamental for systematically investigating many characteristics such as boundedness, liveness and reversibility. This work proposes a method to generate a reachability tree, called ωRT for short, for a class of unbounded generalized nets called ω-independent nets based on new modified reachability trees (NMRTs). ωRT can effectively decrease the number of nodes by removing duplicate and ω-duplicate nodes in the tree, and verify properties such as reachability, liveness and deadlocks. Two examples are provided to show its superiority over NMRTs in terms of tree size.      

Lean production system design for fishing net manufacturing using lean principles and simulation optimization

Value stream mapping (VSM) is a useful tool for describing the manufacturing state, especially for distinguishing between those activities that add value and those that do not. It can help in eliminating non-value activities and reducing the work in process (WIP) and thereby increase the service level. This research follows the guidelines for designing future state VSM. These guidelines consist of five factors which can be changed simply, without any investment. These five factors are (1) production unit; (2) pacemaker process; (3) number of batches; (4) production sequence; and (5) supermarket size. The five factors are applied to a fishing net manufacturing system. Using experimental design and a simulation optimizing tool, the five factors are optimized. The results show that the future state maps can increase service level and reduce WIP by at least 29.41% and 33.92% respectively. For the present study, the lean principles are innovatively adopted in solving a fishing net manufacturing system which is not a well-addressed problem in literature. In light of the promising empirical results, the proposed methodologies are also readily applicable to similar industries.     

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.     

Semantics for consistent activation in context-oriented systems

ContextContext-oriented programming languages provide dedicated programming abstractions to define behavioral adaptations and means to combine those adaptations dynamically according to sensed context changes. Some of these languages feature programming abstractions to explicitly define interaction dependencies among contexts. However, the semantics of context activation and the meaning of dependency relations have been described only informally, which in some cases has led to incorrect specifications, faulty implementations and inconsistent system behavior.ObjectiveWith the aim of avoiding faulty implementations and inconsistencies during system execution, this paper proposes both a formal and run-time model of contexts, context activation and context interaction.MethodAs a formal and computational basis, we introduce context Petri nets, a model based on Petri nets, which we found to match closely the structure of contexts in context-oriented systems. The operational semantics of Petri nets permits the modeling of run-time context activations. Existing Petri net analyses allow us to reason about system properties. As validation, we carried out small and medium-sized case studies.ResultsIn the cases explored, context Petri nets served effectively as underlying run-time model to ensure that declared context interaction constraints remain consistent during context manipulation. Moreover, context Petri nets enabled us to analyze certain properties regarding the activation state of particular contexts.ConclusionContext Petri nets thus proved to be appropriate to encode and manage the semantics of context activation, both formally and computationally, so as to preserve the consistency of context-oriented systems.     

基于LCC管理的变电站主接线的风险评估

全寿命周期成本(life-cycle Cost,LCC)管理是资产全寿命周期管理(life-cycle management,LCM)的基础和技术手段。Petri网具有图形化表达方式,可以对故障树分析、马尔可夫方程等进行改进并补充更多特征和信息,提高了整个风险评估系统的速度和准确性。采用基于LCC管理的风险评估的方法,在变电站主接线运行分析过程中建立四状态模型。讨论了元件发生计划检修事件和断路器拒动现象,提出利用Petri网的基本方程确定系统状态并计算相应概率,定量分析变电站主接线运行的断供成本,寻求最佳效益下的运行方式。在算例中讨论计算了不同主接线方案的风险指标断供成本,计算结果验证了所提算法的可行性。