Probability Models of Distributed Proof Generation for zk-SNARK-Based Blockchains

With digital transformation, physician review websites have also revolutionised the healthcare industry, which has significantly impacted patient consultation behaviour by improving the service delivery process. Also, physician review websites offer substantial information on physician profiles, including operational attributes such as pre-process wait time and appointment slot duration, to reduce uncertainty regarding service. However, there is a paucity of research on pre-process wait time and appointment slot duration and its influence on patient preference for a physician in physician review websites. To bridge the gap, this study explores the influence of pre-process wait time and appointment slot duration on patient preference using the real-time physician review website dataset. The findings of this study reveal that pre-process wait time exhibits a U-shaped relationship with patient preference. Appointment slot duration has a positive influence on patient preference. This study highlights the moderation effect of appointment slot duration between pre-process wait time and patient preference. Our study advances online healthcare literature with theoretical and practical implications on wait time and appointment slot duration for healthcare service providers and managers. With digital transformation, physician review websites have also revolutionised the healthcare industry, which has significantly impacted patient consultation behaviour by improving the service delivery process. Also, physician review websites offer substantial information on physician profiles, including operational attributes such as pre-process wait time and appointment slot duration, to reduce uncertainty regarding service. However, there is a paucity of research on pre-process wait time and appointment slot duration and its influence on patient preference for a physician in physician review websites. To bridge the gap, this study explores the influence of pre-process wait time and appointment slot duration on patient preference using the real-time physician review website dataset. The findings of this study reveal that pre-process wait time exhibits a U-shaped relationship with patient preference. Appointment slot duration has a positive influence on patient preference. This study highlights the moderation effect of appointment slot duration between pre-process wait time and patient preference. Our study advances online healthcare literature with theoretical and practical implications on wait time and appointment slot duration for healthcare service providers and managers.

This letter studies the effect of rate adaptation in time slotted Internet of things (IoT) networks. For a given time slot duration and packets size, rate adaptation necessitates packet fragmentation to fit the time slot duration. Accounting for the quality and time resolution of the underlying traffic, this letter characterizes the tradeoff between transmission rate and packet latency in IoT networks. Using tools from stochastic geometry and queueing theory, a novel mathematical framework is developed for static and dynamic rate adaptation schemes. The results show that there is an optimal static rate that minimizes latency, which depends on the network parameters. Furthermore, the dynamic rate is shown to be resilient to different variations in the network parameters without sacrificing packet latency.

Surgical process modelling is an innovative approach that aims to simplify the challenges involved in improving surgeries through quantitative analysis of a well-established model of surgical activities. In this paper, surgical process model strategies are applied for the analysis of different Minimally Invasive Liver Treatments (MILTs), including ablation and surgical resection of the liver lesions. Moreover, a generic surgical process model for these differences in MILTs is introduced. The generic surgical process model was established at three different granularity levels. The generic process model, encompassing thirteen phases, was verified against videos of MILT procedures and interviews with surgeons. The established model covers all the surgical and interventional activities and the connections between them and provides a foundation for extensive quantitative analysis and simulations of MILT procedures for improving computer-assisted surgery systems, surgeon training and evaluation, surgeon guidance and planning systems and evaluation of new technologies.

The determination of ideal process parameters for mechanical joining processes such as self-pierce riveting currently requires a comprehensive understanding of the process, the availability of the materials to be joined and the corresponding system technology. General process models can simplify the use of these joining technologies, accelerate development cycles and thereby reduce the effort for implementation into production. In this paper, the development of general data-based process models for the mechanical joining method self-pierce riveting with semi-tubular rivet is described. Extensive experimental and numerical investigations with more than 2300 joint combinations for steel and aluminum sheets with tensile strengths between 240 - 1020 MPa were generated for the building of the models. Based on these results, different meta-models are fused into general data-based process models for the self-pierce riveting process in order to show the general relationships between material properties, process parameters and joining results. The paper discusses the acquisition of the experimental and numerical data, the statistical methods for evaluation and the application of the data-based process models.

Product-Service Systems (PSS) are seen as key enablers of circularity. However, better sustainability performance of PSS when compared to transactional business models is not given and it must be carefully considered in the design process. The early-stage PSS design is considered crucial because it determines most of the future offerings' impact. Nevertheless, manufacturing companies struggle with PSS design and often rely on ad hoc intuitive approaches with low success rates. Current PSS design approaches have been dispersed in literature until a recent systematic literature review by the authors proposed a comprehensive generic process model to enable the inclusion of sustainability considerations in the early stages. This paper takes the generic process model as a starting point and instantiates it to the context of two manufacturing companies, seeking to (i) explore the instantiation process from a generic to company-specific process models to support manufacturing companies; and (ii) reflect upon and prepare an evaluation of the generic process model concerning eight predefined criteria. The study reveals a number of potential areas for improving both generic and instantiated process models.

“Digital Triplet” is a concept proposed to support manufacturing system engineers by combining the knowledge and know-how of expert engineers with digital technology. This study proposes a method to represent engineering processes executed by engineers in a reusable manner based on Digital Triplet. Specifically, we develop a procedure to create a "log-level description" that records an engineering process as it is and a “generalized process model” that generalizes multiple log-level descriptions, using a process modeling language named “PD3” (Process Modeling Language for Digital Triplet). In a case study, expert engineers first executed engineering processes to improve production speed on a simplified production line. Next, log-level descriptions and a generalized process model were created for the engineering processes by the proposed procedure. Furthermore, an experiment was conducted on novice engineers using the generalized process model, and the results suggested that the model is effective for education. Currently, creating log-level descriptions or generalized process models is a labor-intensive task as it is done manually based on the subjective judgment of the person representing the processes. However, we showed the application of "process mining" techniques has the potential to partially automate the task of creating the models.

In this paper a methodology for improving mechatronic development processes is presented. The methodology has been developed in the German joint research project MIKADO funded by the German Federal Ministry of Education and Research (BMBF). It describes a comprehensive procedure for process improvement based on a set of reference process models and supported by specific software applications. In a first step the procedure specifies how to generate process models based on an existing process or for planning a new one. In the following analysis phase optimisation potentials in the generated process models are identified. This phase is supported by a set of predefined reference process models that indicate - from a mechatronic view point - at which time during the development and how multidisciplinary coordination activities have to be carried out. A comparison of the generated process models with these reference process models and the application of questionnaires reveals the optimisation potentials that are subsequently used as a basis for improving the generated process model. The improved process model is then used for process control in a workflow management system (WfMS). The methodology has been evaluated against industrial requirements by two medium sized enterprises in the micro-production domain and in the solar energy systems domain. In Section 14.2 the problem statement will be presented. In Section 14.3 the current state of the art in model-based process improvement approaches is presented and the need for the development of new approaches is motivated. Section 14.4 describes the improvement process, the reference process models and the results of the evaluation. Finally Section 14.5 presents the conclusion drawn from the evaluation and future research topics.

In this paper we describe workflow analysis for web publishing based on a general stage-activity process model. Using a case study approach, existing web publishing methods used by major Hong Kong ICP companies have been evaluated. Based on the evaluation results and our observations, issues and problems of the existing methods are identified. A general process model, called the stage-activity model, is proposed which facilitates the development of a framework for automating the Web publishing process, incorporating workflow management. The results and outcome of this research contribute to a clear understanding of what challenges ICPs currently face and a solution to building an automating web publishing system. The framework can also be used to evaluate and select web publishing tools in the market.

This paper presents a comprehensive literature review, focusing on the emerging intersection of chatbot technology and the generation of process models. As an evolving field of study, the integration of interactive chatbots into process model generation represents a promising approach, blending advancements in artificial intelligence in general, and natural language processing in particular, with process management methods. This review systematically examines the existing literature across multiple disciplines, identifying and analyzing studies that touch upon the individual components of this nascent topic: chatbot technology, process model generation, and their synergistic potential. Despite the scarcity of direct research aimed at using chatbots for process model generation, this review synthesizes relevant findings from related domains, such as natural language processing applications in process modeling, and the broader impact of chatbot interfaces in various domains. Through this analysis, we aim to map the current landscape of research, highlight significant gaps, and suggest potential pathways for future investigations. This paper not only aggregates existing knowledge, but also assesses the applicability and implications of current technologies and theories when generating process models with the assistance of interactive chatbots. The outcome is a foundational compendium for researchers and practitioners interested in exploring this innovative intersection, providing a springboard for future research and development in this promising area.

This research builds on the intersection of a web-based (online) multi-interactive multi-tabletop collaborative environment (so-called M-ITCL) and process mining process discovery algorithms applied on the collaborative interaction data (event logs) previously collected from an authentic learning classroom. The main focus of the study was to investigate which process mining algorithm could lead to generation of process models that differentiate (replay) the events correctly with 100% level of fitness, precision, generalisation and simplicity. The results showed that alpha algorithm resulted in the generation of process models with good simplicity but with poor precision and generalisation. Heuristic algorithm resulted in the generation of process models with good precision but with poor generalisation and simplicity. Fuzzy algorithm resulted in generation of rather simple process models with good precision and generalisation. Moreover, the models/graphs generated through fuzzy algorithm could differentiate all of the cases correctly with 100% level of fitness as a validation measure.

This research builds on the intersection of a web-based (online) multi-interactive multi-tabletop collaborative environment (so-called M-ITCL) and process mining process discovery algorithms applied on the collaborative interaction data (event logs) previously collected from an authentic learning classroom. The main focus of the study was to investigate which process mining algorithm could lead to generation of process models that differentiate (replay) the events correctly with 100% level of fitness, precision, generalisation and simplicity. The results showed that alpha algorithm resulted in the generation of process models with good simplicity but with poor precision and generalisation. Heuristic algorithm resulted in the generation of process models with good precision but with poor generalisation and simplicity. Fuzzy algorithm resulted in generation of rather simple process models with good precision and generalisation. Moreover, the models/graphs generated through fuzzy algorithm could differentiate all of the cases correctly with 100% level of fitness as a validation measure.

A very large majority of the current product development process models put forward in textbooks present a homogenous structure, what Ulrich & Eppinger [1] call the market-pull model, presented as a generic one, while other possible product development process models are merely seen as variants. This paper focuses on the task clarification and derived activities (mainly the systematic search for customer needs through market study and the supplementary development costs it entails) and investigates two alternative strategies that are not derived from the generic process model. The first alternative is the market-pull model without an extensive task clarification. The second is the application of the so-called expeditionary marketing strategy. With the help of simplified analytic modeling, the conditions for which these alternatives are as efficient as the generic process model are discussed. This advocates the development of more flexible process models.

This paper presents a novel transmission protocol for simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) that jointly exploits traditional time switching and mode splitting protocol. The proposed approach, namely time-switching-variable mode splitting (TVM), allows to dynamically allocate STAR-RIS resources (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i>., time slot duration, and number of reflecting and transmitting elements) based on variable environment conditions. Energy harvesting (EH) and information transfer (IT) phases alternate to each other in downlink according to a variable time frame structure, while the energy harvested in downlink is exploited for IT in uplink. The elements of the STAR-RIS are then dynamically switched from transmission to reflection in different time slots based on the demand of the network, allowing the transmission users (TUs) and the reflection users (RUs) to be connected to the base station (BS) simultaneously. The proposed approach is investigated under the hypothesis of a non-orthogonal multiple access (NOMA) network, and the Nakagami-<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> distributed channel model with attenuation loss. The analytical performance metrics are obtained considering dynamic time slots and link duration for both IT and EH downlink phases, as well as a variable number of transmission and reflection elements for each time slot. The assessment of the proposed TVM protocol is carried out by means of numerical and Monte Carlo (MC) simulation results of the joint outage probability, the sum throughput, and the age of information in case of multiple reflecting and transmitting users.

In this paper, we investigate the secrecy throughput for a full-duplex wireless powered communication network. A multi-antenna base station (BS) transmits energy towards nodes all the time and each node harvests energy prior to its transmission time slot. Nodes sequentially transmit their confidential information to the BS in presence of other nodes which are considered as potential eavesdroppers. We derive the secrecy rate and formulate the sum secrecy throughput optimization of all nodes. The optimization variables are the time slot duration and the BS beamforming during different transmission phases. The problem is non-convex and non-trivial. We propose a suboptimal approach in which the BS focuses its beamforming to blind the potential eavesdroppers (other nodes) during the information transmission phase, with which we then obtain the optimum beamforming in each time slot and its duration. We compare our algorithm with uniform time slot and uniform beamforming in different settings and demonstrate its superior performance.

Generating process models that reflect close behavioral resemblance to the actual process Standard Operating Procedure (SOP) in process mining can be challenging without taking the four quality criteria of process discovery into account. The four quality criteria, i.e. fitness, precision, generalization, and simplicity, should be well balanced in order to produce proper process models which are aligned to the real-life executions. This paper proposes a method to optimize process discovery quality criteria (PDQC) by implementing different thresholds and analyzing calculation results using Receiver Operating Characteristic (ROC) curve and Infrequent Inductive Miner algorithm. This paper sets up two experiments with different scenarios to measure the calculations of quality criteria and the quality of generated models. The experiments compare two SOPs to the process models discovered by Infrequent Inductive Miner algorithm; hence the SOPs serve as references to determine the generated models quality. The purpose of applying two different scenarios in the experiments is to discover how well the Infrequent Inductive Miner thresholds can produce predictive models under these two different scenarios circumstances. This paper has been successful in predicting the best-fit model in reference to the SOPs by optimizing the four quality criteria of process discovery using ROC thresholds settings and by using infrequent inductive miner algorithm for models generation, and also in improving the accuracy of models measurements. The accuracy rate of the prediction model from Experiment 1 is 83%, while Experiment 2 yields an accuracy rate of 88%. The most optimal threshold settings to generate the best model in this paper are threshold 0.4 in Experiment 1 and threshold 0.5 in Experiment 2.