The roadside unit (RSU) is one of the fundamental components in a vehicular ad hoc network (VANET), where a vehicle communicates in infrastructure mode. The RSU has multiple functions, including the sharing of emergency messages and the updating of vehicles about the traffic situation. Deploying and managing a static RSU (sRSU) requires considerable capital and operating expenditures (CAPEX and OPEX), leading to RSUs that are sparsely distributed, continuous handovers amongst RSUs, and, more importantly, frequent RSU interruptions. At present, researchers remain focused on multiple parameters in the sRSU to improve the vehicle-to-infrastructure (V2I) communication; however, in this research, the mobile RSU (mRSU), an emerging concept for sixth-generation (6G) edge computing vehicular ad hoc networks (VANETs), is proposed to improve the connectivity and efficiency of communication among V2I. In addition to this, the mRSU can serve as a computing resource for edge computing applications. This paper proposes a novel energy-efficient reservation technique for edge computing in 6G VANETs that provides an energy-efficient, reservation-based, cost-effective solution by introducing the concept of the mRSU. The simulation outcomes demonstrate that the mRSU exhibits superior performance compared to the sRSU in multiple aspects. The mRSU surpasses the sRSU with a packet delivery ratio improvement of 7.7%, a throughput increase of 5.1%, a reduction in end-to-end delay by 4.4%, and a decrease in hop count by 8.7%. The results are generated across diverse propagation models, employing realistic urban scenarios with varying packet sizes and numbers of vehicles. However, it is important to note that the enhanced performance parameters and improved connectivity with more nodes lead to a significant increase in energy consumption by 2%.

The difficulties of continuously monitoring cardiac patients in general hospital wards are still present because of the manual charting system and the slow clinical reaction to worsening physiological state. This paper outlines an edge- and fog-based Internet of Things (IoT) healthcare system to acquire, process, and prioritize the vital signs of patients in real time to minimize the alert latency and increase the time of clinical interventions. Wearable 12-lead ECG sensors transmit physiological measurements, such as heart rate, blood pressure, and oxygen saturation, to an intelligent edge service, where preprocessing, triage by threshold, and machine learning ECG classification are performed, and selective synchronization of physiological data with a cloud backend and data delivery to the clinician are made possible by a mobile application. The proposed architecture combines a ribbon-like streaming scheme, Flask-based gateway services, and Firebase Firestore to coordinate scalable mob/cloud with the help of multi-client data dissemination. To encompass borderline clinical deterioration, which is often unnoticed by conventional threshold systems, physiological parameters are classified into normal, alarming, emergency, and a new state, average. The Pan–Tompkins++ peak detector algorithm and multiple edge-resident classifiers, such as random forest, XGBoost, decision tree, naive Bayes, K-nearest neighbor, and support vector machine, are used to analyze the ECG waveforms. Experimental analysis of PhysioNet datasets and tests in real wards prove that the ensemble models can reach the highest possible ECG classification precision of 91.96 percent and snapshot-driven mobile alerts can decrease routine patient evaluation time by several minutes, to an average of 15.23 ± 2.71 s. These results suggest that edge-centric IoT systems can be appropriate in latency-critical hospital settings and that fog-based coordination is useful in next-generation smart healthcare systems.

A vision of 6G aims to automate versatile services by eliminating the complexity of human effort for Industry 5.0 applications. This results in an intelligent environment with cognitive and collaborative capabilities of AI conversational orchestration that enable a variety of applications across smart Autonomous Vehicle (AV) networks. In this article, an innovative framework for AI conversational orchestration is proposed by enabling on-the-fly virtual infrastructure service orchestration for Anything-as-a-Service (XaaS) to automate a network service paradigm. The proposed framework will potentially contribute to the growth of 6G conversational orchestration by enabling on-the-fly automation of cloud and network services. The orchestration aspect of the 6G vision is not limited to cognitive collaborative communications, but also extends to context-aware personalized infrastructure for 6G automation. The experimental results of the implemented proof-of-concept framework are presented. These experiments not only affirm the technical capabilities of this framework, but also push into several Industry 5.0 applications.

Distributed Simulation has still to be adopted significantly by the wider simulation community. Reasons for this might be that distributed simulation applications are difficult to develop and access to multiple computing resources are required. Cloud computing offers low-cost on-demand computing resources. Developing applications that can use cloud computing can be also complex, particularly those that can run on different clouds. Cloud-based Distributed Simulation (CBDS) is potentially attractive, as it may solve the computing resources issue as well as other cloud benefits, such as convenient network access. However, as possibly shown by the lack of sustainable approaches in the literature, the combination of cloud and distributed simulation may be far too complex to develop a general approach. E-Infrastructures have emerged as large-scale distributed systems that support high-performance computing in various scientific fields. Workflow Management Systems (WMS) have been created to simplify the use of these e-Infrastructures. There are many examples of where both technologies have been extended to use cloud computing. This article therefore presents our investigation into the potential of using these technologies for CBDS in the above context and the WORkflow architecture for cLoud-based Distributed Simulation (WORLDS), our contribution to CBDS. We present an implementation of WORLDS using the CloudSME Simulation Platform that combines the WS-PGRADE/gUSE WMS with the CloudBroker Platform as a Service. The approach is demonstrated with a case study using an agent-based distributed simulation of an Emergency Medical Service in REPAST and the Portico HLA RTI on the Amazon EC2 cloud.

The goal of healthcare organizations is to provide efficient and effective patient care while ensuring patient satisfaction. One significant challenge in achieving this goal is managing patient flow and reducing hospital wait times. Almost 550 randomly selected patients were part of the study. Information was obtained from respondents by using pretested questionnaire and further descriptive statistics were used for analysis. In this study, the application of business process engineering (BPE) was explored as a solution to increasing patient flow and reducing waiting times in hospitals. The study examined the present patient flow in public hospitals, which was a crucial component of the research, it also identified constrictions and inefficiencies in the current process and showed how the use of different methodologies helped in eradicating the problem. The results of the conducted study revealed that the implementation of BPE methodologies can significantly improve patient flow and reduce waiting times in hospitals, leading to increased patient satisfaction and improved operational efficiency. According to these results, using BPE techniques significantly reduced patient wait times from an average of 118 minutes to 19.29 minutes. The findings of this study have important implications for healthcare organizations looking to improve patient flow and reduce waiting times and highlight the potential of BPE as a valuable tool for healthcare process improvement.

Isocitrate Dehydrogenase (IDH) mutation is a significant genetic alteration that is found in brain tumors. Its diagnosis is vital for the prognosis of low-grade Glioma and secondary grade Glioma patients. Physicians used invasive methods to diagnose the Gliomas, which was an unsafe method but now advanced magnetic resonance imaging techniques are being used for tumor visualization and treatments. Classical machine learning and deep learning methods have been used by some studies for the problem of IDH mutation status detection using magnetic resonance images. Recent studies have used concatenation of deep and handcrafted features to achieve superior performance. This study has used the concatenations of the deep features extracted through pre-trained convolution neural networks (CNNs) for the task of IDH mutation status detection using magnetic resonance images. To select the pre-trained CNNs, five top accuracies on the ImageNet dataset were considered. Magnetic resonance images were acquired from The Cancer Genome Atlas Glioblastoma Multiforme and The Cancer Genome Atlas Low-Grade Glioma. All experiments (performed using features extracted from each CNN and their concatenation) were compared with each other and state-of-the-art. The proposed technique achieved 99% accuracy while being efficient in terms of data and computational resources.