Discovering social networks from event logs

Process mining techniques allow for the discovery of knowledge based on so-called “event logs”, i.e., a log recording the execution of activities in some business process. Many information systems provide such logs, e.g., most WFM, ERP, CRM, SCM, and B2B systems record transactions in a systematic way. Process mining techniques typically focus on performance and control-flow issues. However, event logs typically also log the performer, e.g., the person initiating or completing some activity. This paper focuses on mining social networks using this information. For example, it is possible to build a social network based on the hand-over of work from one performer to the next. By combining concepts from workflow management and social network analysis, it is possible to discover and analyze social networks. This paper defines metrics, presents a tool, and applies these to a real event log from a Dutch organization

Discovering simulation models

Process mining is a tool to extract non-trivial and useful information from process execution logs. These so-called event logs (also called audit trails, or transaction logs) are the starting point for various discovery and analysis techniques that help to gain insight into certain characteristics of the process. In this paper we use a combination of process mining techniques to discover multiple perspectives (namely, the control-flow, data, performance, and resource perspective) of the process from historic data, and we integrate them into a comprehensive simulation model. This simulation model is represented as a Coloured Petri net (CPN) and can be used to analyze the process, e.g., evaluate the performance of different alternative designs. The discovery of simulation models is explained using a running example. Moreover, the approach has been applied in two case studies; the workflows in two different municipalities in the Netherlands have been analyzed using a combination of process mining and simulation. Furthermore, the quality of the CPN models generated for the running example and the two case studies has been evaluated by comparing the original logs with the logs of the generated models

Discovering simulation models

Process mining is a tool to extract non-trivial and useful information from process execution logs. These so-called event logs (also called audit trails, or transaction logs) are the starting point for various discovery and analysis techniques that help to gain insight into certain characteristics of the process. In this paper we use a combination of process mining techniques to discover multiple perspectives (namely, the control-flow, data, performance, and resource perspective) of the process from historic data, and we integrate them into a comprehensive simulation model. This simulation model is represented as a Coloured Petri net (CPN) and can be used to analyze the process, e.g., evaluate the performance of different alternative designs. The discovery of simulation models is explained using a running example. Moreover, the approach has been applied in two case studies; the workflows in two different municipalities in the Netherlands have been analyzed using a combination of process mining and simulation. Furthermore, the quality of the CPN models generated for the running example and the two case studies has been evaluated by comparing the original logs with the logs of the generated models

Deriving social relations among organizational units from process models

For companies to sustain competitive advantages, it is required to redesign and improve business processes continuously by monitoring and analyzing process enactment results. Furthermore, organizational structures must be redesigned according to the changes in business processes. However, there are few scientific approaches to redesigning organizational structures. This paper presents a method for deriving and analyzing organizational relations from process models using social network analysis. Process models contain information on who performs which processes or activities, along with the assignment of organizational units such as departments and roles to related activities. To derive social relations among organizational units from process models, three types of metrics are formally defined: transfer of work metrics, subcontracting metrics, and cooperation metrics. By applying these metrics, various relations among organizational units can be derived and analyzed, which can suggest how organizational structure must be redesigned. To verify the method, the proposed metrics are applied to standard process models of the semiconductor and electronic industry in Korea

EP-1518: Evaluation of dynamic delivery quality assurance process for internal target based RapidArc

n/

Comparisons of physique, body composition, and somatotype by weight division between male and female collegiate taekwondo athletes

The aim of the study was to compare the physique, body composition and somatotype between male and female collegiate taekwondo athletes and specially focus on differences by weight division. 60 collegiate taekwondo athletes (male: 29, female: 31) voluntarily participated in the study. They were divided into four Olympic weight divisions (male for -58 kg, -68 kg, -80 kg, +80 kg, female for -49 kg, -57 kg, -67 kg, +67 kg). Anthropometric measurements included body weight, height, sitting height, body circumferences (relaxed arm, flexed arm, chest, waist, hip, thigh, and calf), bone widths (humerus and femur), and skinfold thicknesses (triceps, subscapular, suprailiac, thigh, and calf) were measured. The three somatotype components were assessed by Heath-Carter anthropometric method (Carter & Heath, 1990). Independent t-test and one-way ANOVA were applied to analyze difference of dependent variables. Significant level was set at .05. Male athletes were taller and heavier than female athletes. However, sum of skinfold thickness was significantly higher in female athletes than male athletes. The three somatotype components for male athletes were 3.4-3.5-3.1 and characterized with balanced mesomorphy. On the other hand, the somatotype of female athletes were 6.1-3.4-2.6 and characterized with mesomorphic endomorph. In male athletes -80 kg and +80 kg weight divisions were higher mesomorphy, but lower ectomorphy than -58 kg and -68 kg weight divisions. In female, -57 kg, -67 kg and +67 kg weight divisions were higher endomorphy and mesomorphy, but lower ectomorphy than -49 kg weight divisions. In conclusion, male athletes had higher anthropometric characteristics than female athletes except for the skinfold thickness. Female athletes had higher endomorphy, whereas male athletes had higher ectomorphy. Physique and somatotype were different between weight divisions both male and female athletes. This study provides a reference data of morphological characteristics of collegiate elite taekwondo athletes

Model of a fluid at small and large length scales and the hydrophobic effect

We present a statistical field theory to describe large length scale effects induced by solutes in a cold and otherwise placid liquid. The theory divides space into a cubic grid of cells. The side length of each cell is of the order of the bulk correlation length of the bulk liquid. Large length scale states of the cells are specified with an Ising variable. Finer length scale effects are described with a Gaussian field, with mean and variance affected by both the large length scale field and by the constraints imposed by solutes. In the absence of solutes and corresponding constraints, integration over the Gaussian field yields an effective lattice gas Hamiltonian for the large length scale field. In the presence of solutes, the integration adds additional terms to this Hamiltonian. We identify these terms analytically. They can provoke large length scale effects, such as the formation of interfaces and depletion layers. We apply our theory to compute the reversible work to form a bubble in liquid water, as a function of the bubble radius. Comparison with molecular simulation results for the same function indicates that the theory is reasonably accurate. Importantly, simulating the large length scale field involves binary arithmetic only. It thus provides a computationally convenient scheme to incorporate explicit solvent dynamics and structure in simulation studies of large molecular assemblies

Knowledge graph applications in medical imaging analysis : a scoping review

There is an increasing trend to represent domain knowledge in structured graphs, which provide efficient knowledge representations for many downstream tasks. Knowledge graphs are widely used to model prior knowledge in the form of nodes and edges to represent semantically connected knowledge entities, which several works have adopted into different medical imaging applications. We systematically search over five databases to find relevant articles that apply knowledge graphs to medical imaging analysis. After screening, evaluating, and reviewing the selected articles, we performed a systematic analysis. We look at four applications in medical imaging analysis, including disease classification, disease localization and segmentation, report generation, and image retrieval. We also identify limitations of current work, such as the limited amount of available annotated data and weak generalizability to other tasks. We further identify the potential future directions according to the identified limitations, including employing semi-supervised frameworks to alleviate the need for annotated data and exploring task-agnostic models to provide better generalizability. We hope that our article will provide the readers with aggregated documentation of the state-of-the-art knowledge graph applications for medical imaging.Electrical and Computer Engineerin

Enhanced production of tropane alkaloids in transgenic Scopolia parviflora hairy root cultures over-expressing putrescine N-methyl transferase (PMT) and hyoscyamine-6β-hydroxylase (H6H)

Scopolia parviflora adventitious roots were metabolically engineered by co-expression of the two gene putrescine N-methyl transferase (PMT) and hyoscyamine-6β-hydroxylase (H6H) cDNAs with the aid of Agrobacterium rhizogenes. The transformed roots developed into morphologically distinct S. parviflora PMT1 (SpPMT1), S. parviflora PMT1 (SpPMT2), and S. parviflora H6H (SpH6H) transgenic hairy root lines. Consequent to the introduction of these key enzyme genes, the production of the alkaloids hyoscyamine and scopolamine was enhanced. Among the transgenic hairy root lines, SpPMT2 line possessed the highest growth index. The treatment of transgenic hairy roots with growth regulators further enhanced the production of scopolamine. Thus, the results suggest that PMT1, PMT2, and H6H genes may not only be involved in the metabolic regulation of alkaloid production but also that these genes may play a role in the root development

Z boson pair production at LHC in a stabilized Randall-Sundrum scenario

We study the Z boson pair production at LHC in the Randall-Sundrum scenario with the Goldberger-Wise stabilization mechanism. It is shown that comprehensive account of the Kaluza-Klein graviton and radion effects is crucial to probe the model: The KK graviton effects enhance the cross section of $g g \to Z Z$ on the whole so that the resonance peak of the radion becomes easy to detect, whereas the RS effects on the $q\bar{q} \to Z Z$ process are rather insignificant. The $p_T$ and invariant-mass distributions are presented to study the dependence of the RS model parameters. The production of longitudinally polarized Z bosons, to which the SM contributions are suppressed, is mainly due to KK gravitons and the radion, providing one of the most robust methods to signal the RS effects. The $1 \sigma$ sensitivity bounds on $(\Lambda_\pi, m_\phi)$ with $k/M_{\rm Pl} =0.1$ are also obtained such that the effective weak scale $\Lambda_\pi$ of order 5 TeV can be experimentally probed.Comment: 28 pages, LaTex file, 18 eps figure