ABSTRACT
The Coronavirus crisis has forced hospitals globally to develop new virtual service portals and systems to: 1) triage, diagnose, and manage new patients virtually for every clinical specialty at home as their symptoms emerge, avoiding COVID-19 exposure to patient or physicians, and hospitalization, as much as possible, and 2) discharge, track, and support recovered patients via homecare and virtual visits to free as many critical care beds as possible. This paper focuses on simulating and modeling an episode of care with innovative initial patient contact and triage processes using the Colored Petri Net (CPN) formalism to help optimize workflow, patient throughput, and overall system efficacy. The two patient triage programs under consideration are a health system in Australia and an orthopedic surgical program in the US. We describe our model for the US program. Our presented results establish a desired stratification of patients through a virtual musculoskeletal triage. © 2022 IEEE Computer Society. All rights reserved.
ABSTRACT
The implementation of the urban underground logistics system (ULS) can effectively mitigate the contradiction between the surging logistics demand and the increased negativity of urban logistics. The widespread implementation of ULS still suffers from a lack of research into its operation in the marketplace, although the research on ULS system technology and network design appears to be sufficient. A new supply chain for logistics service based on ULS (ULS-SSC) was proposed, as ULS embedded in the urban logistics system could lead to the evolution of the role of supply chain participants. This article analyzed the organizational structure and operation characteristics of ULS-SSC and designed a top-down ULS-SSC operation process model based on the designed functional structure and subsystems relationship using the hierarchical colored Petri net (HCPN). The simulation results show that the integrated information management platform based on ULS can integrate urban logistics service supply chain resources and operate effectively under the two main service modes designed. The high-time delay intermediate links can be upgraded by system optimization, and the links with initial pickup and terminal distribution can be improved through outsourcing and supply chain collaboration. The findings provide new insights into the feasibility of the operation of ULS in the market and help stimulate the implementation of ULS.
ABSTRACT
This work is part of a learning environment that has virtual laboratories that are designed for distant practical work (Tele-PW). In these environments, Tele-PW is performed in two modes: individual and/or collaborative. In this paper, we are concentrating on the tele-collaborative distant practical work model. The work, presented in this paper, proposes an artificial agent called Synchronizer Coordinator Agent (SCA) to synchronize and coordinate the activities of a cognitive process in order to build and maintain a shared conception of a distant practical work between a set of learners. This agent provides certain features such as managing groups of learners, coordinating tasks, shared workspace among members of the Working Group. It is also responsible for the synchronization of workspace agents when they want to manipulate shared virtual objects simultaneously. We have chosen Petri nets to illustrate the principle of granting access to shared objects in the case of simultaneous requests. Experimental results show the effectiveness, of the artificial agent within any tele-collaborative/tele-cooperative learning situation. Several situations describe the geographical/time dispersion of learners and tutors in our system are considered during the system design phase.
ABSTRACT
Pandemic management requires that scientists rapidly formulate and analyse epidemiological models in order to forecast the spread of disease and the effects of mitigation strategies. Scientists must modify existing models and create novel ones in light of new biological data and policy changes such as social distancing and vaccination. Traditional scientific modelling workflows detach the structure of a model-its submodels and their interactions-from its implementation in software. Consequently, incorporating local changes to model components may require global edits to the code base through a manual, time-intensive and error-prone process. We propose a compositional modelling framework that uses high-level algebraic structures to capture domain-specific scientific knowledge and bridge the gap between how scientists think about models and the code that implements them. These algebraic structures, grounded in applied category theory, simplify and expedite modelling tasks such as model specification, stratification, analysis and calibration. With their structure made explicit, models also become easier to communicate, criticize and refine in light of stakeholder feedback. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
Subject(s)
Pandemics , SoftwareABSTRACT
This paper treat the design of the sequence organization and then the optimization of a discrete event system (DES) modelled by Temporal Petri Net (T-PN) comprising a set of specifications corresponding to time intervals to activate or access another event. A Petri net is a well-known model that describes distributed systems. It is commonly used to describe various aspects of distributed systems, such as choice and synchronization. This paper focuses on the organizing problems in the hospitalization domain during the Covid-19 pandemic. We advocate the use of a real time approach based on TemporalPN and mathematical modeling to help drive the healthcare system in the face of occurrence of this type of giving many patients currently, which requires rethinking the predictive decision. The proposed solution permits to optimize the time to find all empty rooms using PN Temporal and the Dijkstra approach. © 2022, Springer Nature Switzerland AG.
ABSTRACT
The traditional teaching model fails to support the modern requirements, which seek to support the diversity of each student in terms of skills, inclinations, and educational level. The adaptive educational hypermedia systems are a learning model that is both adaptive and personalized, and it is gaining popularity (AEHSs). These tools can be used by students to present, navigate, provide feedback, and assess. However, even in these systems, internal differentiation should include a wide range of practices, personalized forms of learning process organization, and high-quality education, taking into account each students’ diverse educational needs and capabilities, interests, unique experiences, personal rhythms of integration of educational functions, and personal learning style. So, the learning style, the cognitive background, and the student’s interest are decisive factors for the structure of a next-generation learning system. To overcome the issues of the traditional learning systems, this paper proposes an AEHS based on Variational Bayesian Petri Net (VBPNet). It is an intelligent system that can, by overcoming the significant limitations of the Petri Nets, depict any complex procedure with great precision and without considerable computing power requirements. The proposed VBPNet was used as an AEHS implementation algorithm to classify students based on their skills and real educational needs.
ABSTRACT
Medical information systems (MIS) play a vital role in managing and scheduling medical resources to underpin healthcare services, which has become more critically important during major public health emergencies. During the Covid-19 pandemic, MIS is facing significant challenges to cope with the surge in demands of medical resources, resulting in more deaths and wider spreading of the disease. Our research examines how to allocate and utilize the medical resources across hospitals in a more accurate, and effective way to mitigate medical resource shortages and sustain the resource provisions. This paper mainly investigated the hospital's supply-and-demand problems for medical resources under major public health emergencies by analyzing the allocation of medical staff resources. Furthermore, a formal method based on the Colored Petri Nets (CPN) has been proposed to model and characterize the medical business process and resource scheduling tasks. The experiments demonstrate that our approach can correctly and efficiently complete the dynamical scheduling process for surging requests. © 2021 IEEE.
ABSTRACT
Correctness of networking protocols represents the principal requirement of cybersecurity. Correctness of protocols is established via the procedures of their verification. A classical communication system includes a pair of interacting systems. Recent developments of computing and communication grids for radio broadcasting, cellular networks, communication subsystems of supercomputers, specialized grids for numerical methods and networks on chips require verification of protocols for any number of devices. For analysis of computing and communication grid structures, a new class of infinite Petri nets has been introduced and studied for more than 10 years. Infinite Petri nets were also applied for simulating cellular automata. Rectangular, triangular and hexagonal grids on plane, hyper cube and hyper torus in multidimensional space have been considered. Composing and solving in parametric form infinite Diophantine systems of linear equations allowed us to prove the protocol properties for any grid size and any number of dimensions. Software generators of infinite Petri net models have been developed. Special classes of graphs, such as a graph of packet transmission directions and a graph of blockings, have been introduced and studied. Complex deadlocks have been revealed and classified. In the present paper, infinite Petri nets are divided into two following kinds: a single infinite construct and an infinite set of constructs of specified size (and number of dimensions). Finally, the paper discusses possible future work directions.
ABSTRACT
The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), responsible for the coronavirus disease of 2019 (COVID-19) pandemic, has affected and continues to affect millions of people across the world. Patients with essential arterial hypertension and renal complications are at particular risk of the fatal course of this infection. In our study, we have modeled the selected processes in a patient with essential hypertension and chronic kidney disease (CKD) suffering from COVID-19, emphasizing the function of the renin-angiotensin-aldosterone (RAA) system. The model has been built in the language of Petri nets theory. Using the systems approach, we have analyzed how COVID-19 may affect the studied organism, and we have checked whether the administration of selected anti-hypertensive drugs (angiotensin-converting enzyme inhibitors (ACEIs) and/or angiotensin receptor blockers (ARBs)) may impact the severity of the infection. Besides, we have assessed whether these drugs effectively lower blood pressure in the case of SARS-CoV-2 infection affecting essential hypertensive patients. Our research has shown that neither the ACEIs nor the ARBs worsens the course infection. However, when assessing the treatment of hypertension in the active SARS-CoV-2 infection, we have observed that ARBs might not effectively reduce blood pressure; they may even have the slightly opposite effect. On the other hand, we have confirmed the effectiveness of arterial hypertension treatment in patients receiving ACEIs. Moreover, we have found that the simultaneous use of ARBs and ACEIs averages the effects of taking both drugs, thus leading to only a slight decrease in blood pressure. We are a way from suggesting that ARBs in all hypertensive patients with COVID-19 are ineffective, but we have shown that research in this area should still be continued.
Subject(s)
COVID-19/complications , Essential Hypertension/complications , Renal Insufficiency, Chronic/complications , COVID-19/metabolism , COVID-19/physiopathology , Computer Simulation , Essential Hypertension/metabolism , Essential Hypertension/physiopathology , Humans , Models, Biological , Renal Insufficiency, Chronic/metabolism , Renal Insufficiency, Chronic/physiopathology , Renin-Angiotensin System , SARS-CoV-2/isolation & purification , SARS-CoV-2/physiologyABSTRACT
BACKGROUND: Emerging and re-emerging infectious diseases such as Zika, SARS, ncovid19 and Pertussis, pose a compelling challenge for epidemiologists due to their significant impact on global public health. In this context, computational models and computer simulations are one of the available research tools that epidemiologists can exploit to better understand the spreading characteristics of these diseases and to decide on vaccination policies, human interaction controls, and other social measures to counter, mitigate or simply delay the spread of the infectious diseases. Nevertheless, the construction of mathematical models for these diseases and their solutions remain a challenging tasks due to the fact that little effort has been devoted to the definition of a general framework easily accessible even by researchers without advanced modelling and mathematical skills. RESULTS: In this paper we describe a new general modeling framework to study epidemiological systems, whose novelties and strengths are: (1) the use of a graphical formalism to simplify the model creation phase; (2) the implementation of an R package providing a friendly interface to access the analysis techniques implemented in the framework; (3) a high level of portability and reproducibility granted by the containerization of all analysis techniques implemented in the framework; (4) a well-defined schema and related infrastructure to allow users to easily integrate their own analysis workflow in the framework. Then, the effectiveness of this framework is showed through a case of study in which we investigate the pertussis epidemiology in Italy. CONCLUSIONS: We propose a new general modeling framework for the analysis of epidemiological systems, which exploits Petri Net graphical formalism, R environment, and Docker containerization to derive a tool easily accessible by any researcher even without advanced mathematical and computational skills. Moreover, the framework was implemented following the guidelines defined by Reproducible Bioinformatics Project so it guarantees reproducible analysis and makes simple the developed of new user-defined workflows.