Until now, the potential of untethered magnetic robots (UMRs), propelled by external time-periodic magnetic fields, has been hindered by the limitations of wireless manipulation systems or noninvasive imaging techniques combined. The need for simultaneous actuation and noninvasive localization imposes a strict constraint on both functionalities. This study addresses this challenge by substantiating the feasibility through experimental validation, showcasing the direct teleoperation of UMRs within a fluid-filled lumen. This teleoperation capability is facilitated by a scalable X-ray-guided robotic platform, extendable to match the dimensions required for in vivo applications, marking a noteworthy advancement. Our methodology is demonstrated by teleoperating a 12-mm-long screw-shaped UMR (5 mm in diameter) within a bifurcated lumen, filled with blood. This navigation is achieved using controlled rotating magnetic fields, guided by real-time X-ray Fluoroscopy images. Incorporating a two-degree-of-freedom control system, we demonstrate the operator’s capability to use X-ray Fluoroscopy images to keep the UMR coupled with the external field during wireless teleoperations, resulting in a success rate of 76.6% when moving along the intended pathways, with a mean absolute position error of 1.6 ± 2.1 mm.

Quantitative business process analysis is a powerful approach for analyzing timing properties of a business process, such as the expected waiting time of customers or the utilization rate of resources. Multiple techniques are available for quantitative business process analysis, which all have their own advantages and disadvantages. This paper presents a novel technique, based on queueing models, that combines the advantages of existing techniques, in that it leads to accurate analyses, is computationally inexpensive, and feature complete with respect to its support for basic process modeling constructs. An extensive quantitative evaluation has been performed that compares the presented queueing model to existing queueing models from literature. This evaluation shows that the presented model outperforms existing models with one order of magnitude on accuracy. The resulting queueing model can be used for fast and accurate timing predictions of business process models. These properties are useful in optimization scenarios.

Traditionally, the optimal resource allocation in a business process is determined by manually exploring a number of options, but whether this leads to the optimal solution is questionable; it is possible that an unexplored option produces even better results. Therefore, an automated approach is needed that explores all possible options in a structured manner to find the best one. To address this need this paper proposes three iterations of a search strategy for resource optimization that searches through the space of possible resource allocations to find the best one. The performance of the search strategy is evaluated theoretically with guided experiments. The experiments show that the proposed search strategies scale very well and obtain the optimal value in only a fraction of the number of steps needed by an exhaustive exploration of the solution space. In this manner, this paper also forms the basis for future research into resource optimization with extensions into multi-process optimization, advanced, and operational resource optimization.

A growing number of business process management systems is under development both in academia and in practice. These systems typically are based on modern system engineering principles, such as service-oriented architecture. At the same time, the advent of big data analytics has changed the scope of these systems, including functionality such as data mining. However, existing reference architectures for business process management systems date back 20 years and, consequently, are not up-to-date with these modern developments. To fill the gap, this article proposes an up-to-date reference architecture, called BPMS-RA, for modern business process management systems. BPMS-RA is based on analysis of recent literature and of existing commercial implementations. This reference architecture aims to provide a guideline template for the development of modern-day business process management systems by specifying functions and interfaces that need to be provided by these systems as well as a set of quality criteria that they need to meet.

Simulation is often used to provide quantitative predictions on the performance of business processes. However, the current available business process simulation engines only support basic resource constructs and even these are sometimes lacking. This leads to simulated performance metrics that can deviate significantly from the real process performance. This paper provides an overview of the quantitative effects of incorporating these advanced resource constructs in business process simulation. Experiments are conducted to assess whether there are significant differences between the effects of using basic resource constructs and using their advanced counterparts. The main conclusions are: that the allocation strategy deployed by the resources is of significant influence on the performance of the process; that the ability to assign resources to multiple roles has a significant influence; and that simulation replications are absolutely necessary to produce reliable simulation results. These insights can be used by researchers and practitioners to understand the validity of the results of their simulation studies and as a direction for future research.

Quantitative analysis of business processes facilitates the computation of quantitative properties of a business process, such as the average sojourn time and cost of cases of the process. Moreover, it allows for what-if analysis, by enabling the computation of such properties for alternative business process designs, without requiring those designs to be implemented in practice. This paper presents a novel technique for quantitative analysis that is both fast and generally applicable. The technique is developed as an extension of the state-of-the-art in queueing network analysis, by lifting some of the restrictions that are imposed by existing techniques. In particular, it enables precise quantitative analysis of business process models that contain many-to-many relations between tasks and resources (via roles). As a consequence, the technique presented in this paper allows for quantitative analysis that is more generally applicable than existing queueing network analysis techniques and only takes a fraction of the execution time required by existing simulation techniques. In doing so, it paves the way to advanced analysis techniques, such as prescriptive analytics.

BACKGROUND: Identification of biological specimens is a requirement for a range of applications. Reference-free methods analyse unprocessed sequencing data without relying on prior knowledge, but generally do not scale to arbitrarily large genomes and arbitrarily large phylogenetic distances. RESULTS: We present Cnidaria, a practical tool for clustering genomic and transcriptomic data with no limitation on genome size or phylogenetic distances. We successfully simultaneously clustered 169 genomic and transcriptomic datasets from 4 kingdoms, achieving 100% identification accuracy at supra-species level and 78% accuracy at the species level. CONCLUSION: CNIDARIA allows for fast, resource-efficient comparison and identification of both raw and assembled genome and transcriptome data. This can help answer both fundamental (e.g. in phylogeny, ecological diversity analysis) and practical questions (e.g. sequencing quality control, primer design).