Urban Advanced Mobility Dependability: A Model-Based Quantification on Vehicular Ad Hoc Networks with Virtual Machine Migration.

In the rapidly evolving urban advanced mobility (UAM) sphere, Vehicular Ad Hoc Networks (VANETs) are crucial for robust communication and operational efficiency in future urban environments. This paper quantifies VANETs to improve their reliability and availability, essential for integrating UAM into urban infrastructures. It proposes a novel Stochastic Petri Nets (SPN) method for evaluating VANET-based Vehicle Communication and Control (VCC) architectures, crucial given the dynamic demands of UAM. The SPN model, incorporating virtual machine (VM) migration and Edge Computing, addresses VANET integration challenges with Edge Computing. It uses stochastic elements to mirror VANET scenarios, enhancing network robustness and dependability, vital for the operational integrity of UAM. Case studies using this model offer insights into system availability and reliability, guiding VANET optimizations for UAM. The paper also applies a Design of Experiments (DoE) approach for a sensitivity analysis of SPN components, identifying key parameters affecting system availability. This is critical for refining the model for UAM efficiency. This research is significant for monitoring UAM systems in future cities, presenting a cost-effective framework over traditional methods and advancing VANET reliability and availability in urban mobility contexts.

Evaluating Link Lifetime Prediction to Support Computational Offloading Decision in VANETs.

In urban mobility, Vehicular Ad Hoc Networks (VANETs) provide a variety of intelligent applications. By enhancing automobile traffic management, these technologies enable advancements in safety and help decrease the frequency of accidents. The transportation system can now follow the development and growth of cities without sacrificing the quality and organisation of its services thanks to safety apps that include collision alerts, real-time traffic information, and safe driving applications, among others. Applications can occasionally demand a lot of computing power, making their processing impractical for cars with limited onboard processing capacity. Offloading of computation is encouraged by such a restriction. However, because vehicle mobility operations are dynamic, communication times (also known as link lifetimes) between nodes are frequently short. VANET applications and processes are impacted by such communication delays (e.g., the offloading decision when using the Computational Offloading technique). Making an accurate prediction of the link lifespan between vehicles is therefore challenging. The effectiveness of the communication time estimation is currently constrained by the link lifespan prediction methods used in the computational offloading process. This work investigates five machine learning (ML) algorithms to predict the link lifetime between nodes in VANETs in different scenarios. We propose the procedures required to carry out the link lifetime prediction method using existing ML techniques. The tactic creates datasets with the features the models need to learn and be trained. The SVR and XGBoost algorithms that were selected as part of the assessment process were trained. To make the prediction using the trained models, we modified the lifespan prediction function from an offloading approach. To determine the viability of applying link lifespan predictions from the models trained in the road and urban scenarios, we conducted a performance study. The findings indicate that compared to the conventional prediction strategy described in the literature, the suggested link lifetime prediction via regression approaches decreases prediction error rates. An offloading method from the literature is extended by the selected SVR. The task loss and recovery rates might be significantly reduced using the SVR. XGBoost outperformed its ML competitors in task recovery or drop rate by 70% to 80% in an assessed hypothesis compared to an offloading choice technique in the literature. With greater offloading rates from an application on the VANET, this effort is intended to give better efficiency in estimating this data using machine learning in various vehicular settings.

Model-Driven Impact Quantification of Energy Resource Redundancy and Server Rejuvenation on the Dependability of Medical Sensor Networks in Smart Hospitals.

The spread of the Coronavirus (COVID-19) pandemic across countries all over the world urges governments to revolutionize the traditional medical hospitals/centers to provide sustainable and trustworthy medical services to patients under the pressure of the huge overload on the computing systems of wireless sensor networks (WSNs) for medical monitoring as well as treatment services of medical professionals. Uncertain malfunctions in any part of the medical computing infrastructure, from its power system in a remote area to the local computing systems at a smart hospital, can cause critical failures in medical monitoring services, which could lead to a fatal loss of human life in the worst case. Therefore, early design in the medical computing infrastructure's power and computing systems needs to carefully consider the dependability characteristics, including the reliability and availability of the WSNs in smart hospitals under an uncertain outage of any part of the energy resources or failures of computing servers, especially due to software aging. In that regard, we propose reliability and availability models adopting stochastic Petri net (SPN) to quantify the impact of energy resources and server rejuvenation on the dependability of medical sensor networks. Three different availability models (A, B, and C) are developed in accordance with various operational configurations of a smart hospital's computing infrastructure to assimilate the impact of energy resource redundancy and server rejuvenation techniques for high availability. Moreover, a comprehensive sensitivity analysis is performed to investigate the components that impose the greatest impact on the system availability. The analysis results indicate different impacts of the considered configurations on the WSN's operational availability in smart hospitals, particularly 99.40%, 99.53%, and 99.64% for the configurations A, B, and C, respectively. This result highlights the difference of 21 h of downtime per year when comparing the worst with the best case. This study can help leverage the early design of smart hospitals considering its wireless medical sensor networks' dependability in quality of service to cope with overloading medical services in world-wide virus pandemics.

IoT Sensor Networks in Smart Buildings: A Performance Assessment Using Queuing Models.

Smart buildings in big cities are now equipped with an internet of things (IoT) infrastructure to constantly monitor different aspects of people's daily lives via IoT devices and sensor networks. The malfunction and low quality of service (QoS) of such devices and networks can severely cause property damage and perhaps loss of life. Therefore, it is important to quantify different metrics related to the operational performance of the systems that make up such computational architecture even in advance of the building construction. Previous studies used analytical models considering different aspects to assess the performance of building monitoring systems. However, some critical points are still missing in the literature, such as (i) analyzing the capacity of computational resources adequate to the data demand, (ii) representing the number of cores per machine, and (iii) the clustering of sensors by location. This work proposes a queuing network based message exchange architecture to evaluate the performance of an intelligent building infrastructure associated with multiple processing layers: edge and fog. We consider an architecture of a building that has several floors and several rooms in each of them, where all rooms are equipped with sensors and an edge device. A comprehensive sensitivity analysis of the model was performed using the Design of Experiments (DoE) method to identify bottlenecks in the proposal. A series of case studies were conducted based on the DoE results. The DoE results allowed us to conclude, for example, that the number of cores can have more impact on the response time than the number of nodes. Simulations of scenarios defined through DoE allow observing the behavior of the following metrics: average response time, resource utilization rate, flow rate, discard rate, and the number of messages in the system. Three scenarios were explored: (i) scenario A (varying the number of cores), (ii) scenario B (varying the number of fog nodes), and (iii) scenario C (varying the nodes and cores simultaneously). Depending on the number of resources (nodes or cores), the system can become so overloaded that no new requests are supported. The queuing network based message exchange architecture and the analyses carried out can help system designers optimize their computational architectures before building construction.

International multicenter observational study on assessment of ventilatory management during general anaesthesia for robotic surgery and its effects on postoperative pulmonary complication (AVATaR): study protocol and statistical analysis plan.

INTRODUCTION: Robotic-assisted surgery (RAS) has emerged as an alternative minimally invasive surgical option. Despite its growing applicability, the frequent need for pneumoperitoneum and Trendelenburg position could significantly affect respiratory mechanics during RAS. AVATaR is an international multicenter observational study aiming to assess the incidence of postoperative pulmonary complications (PPC), to characterise current practices of mechanical ventilation (MV) and to evaluate a possible association between ventilatory parameters and PPC in patients undergoing RAS. METHODS AND ANALYSIS: AVATaR is an observational study of surgical patients undergoing MV for general anaesthesia for RAS. The primary outcome is the incidence of PPC during the first five postoperative days. Secondary outcomes include practice of MV, effect of surgical positioning on MV, effect of MV on clinical outcome and intraoperative complications. ETHICS AND DISSEMINATION: This study was approved by the Institutional Review Board of the Hospital Israelita Albert Einstein. The study results will be published in peer-reviewed journals and disseminated at international conferences. TRIAL REGISTRATION NUMBER: NCT02989415; Pre-results.

Serum Tumor Markers Provide Refined Prognostication in Selecting Liver Transplantation Candidate for Hepatocellular Carcinoma Patients Beyond the Milan Criteria.

OBJECTIVE: To develop and validate a model to predict tumor recurrence after living donor liver transplantation (LDLT) (MoRAL) for hepatocellular carcinoma (HCC) beyond the Milan criteria (MC). BACKGROUND: Some subgroups of HCC exceeding the MC experience substantial benefit from LDLT. METHODS: This multicenter study included a total of 566 consecutive patients who underwent LDLT in Korea: the beyond-MC cohort (n = 205, the derivation [n = 92] and validation [n = 113] sets) and the within-MC cohort (n = 361). The primary endpoint was time-to-recurrence. RESULTS: Using multivariate Cox proportional hazard model, we derived the MoRAL score using serum levels of protein induced by vitamin K absence-II and alpha-fetoprotein, which provided a good discriminant function on time-to-recurrence (concordance index = 0.88). Concordance index was maintained similarly on both internal and external validations (mean 0.87 and 0.84, respectively). At cut off of 314.8 (75th percentile value), a low MoRAL score (≤314.8) was associated with significantly longer recurrence-free (versus > 314.8, HR = 5.29, P < 0.001) and overall survivals (HR = 2.59, P = 0.001) in the beyond-MC cohort. The 5-year recurrence-free and overall survival rates of beyond-MC patients with a low MoRAL score were as high as 66.3% and 82.6%, respectively. The within-MC patients with a high MoRAL score showed a higher risk of recurrence than beyond-MC patients with a low MoRAL score (HR = 2.56, P = 0.035). The MoRAL score was significantly correlated with explant histology. CONCLUSIONS: This new model using protein induced by vitamin K absence-II and alpha-fetoprotein provides refined prognostication. Among beyond-MC HCC patients, those with a MoRAL score ≤314.8 and without extrahepatic metastasis might be potential candidates for LDLT.