Game-o-Meta: Trusted Federated Learning Scheme for P2P Gaming Metaverse beyond 5G Networks.

The aim of the peer-to-peer (P2P) decentralized gaming industry has shifted towards realistic gaming environment (GE) support for game players (GPs). Recent innovations in the metaverse have motivated the gaming industry to look beyond augmented reality and virtual reality engines, which improve the reality of virtual game worlds. In gaming metaverses (GMs), GPs can play, socialize, and trade virtual objects in the GE. On game servers (GSs), the collected GM data are analyzed by artificial intelligence models to personalize the GE according to the GP. However, communication with GSs suffers from high-end latency, bandwidth concerns, and issues regarding the security and privacy of GP data, which pose a severe threat to the emerging GM landscape. Thus, we proposed a scheme, Game-o-Meta, that integrates federated learning in the GE, with GP data being trained on local devices only. We envisioned the GE over a sixth-generation tactile internet service to address the bandwidth and latency issues and assure real-time haptic control. In the GM, the GP's game tasks are collected and trained on the GS, and then a pre-trained model is downloaded by the GP, which is trained using local data. The proposed scheme was compared against traditional schemes based on parameters such as GP task offloading, GP avatar rendering latency, and GS availability. The results indicated the viability of the proposed scheme.

ABV-CoViD: An Ensemble Forecasting Model to Predict Availability of Beds and Ventilators for COVID-19 Like Pandemics.

Recently, healthcare stakeholders have orchestrated steps to strengthen and curb the COVID-19 wave. There has been a surge in vaccinations to curb the virus wave, but it is crucial to strengthen our healthcare resources to fight COVID-19 and like pandemics. Recent researchers have suggested effective forecasting models for COVID-19 transmission rate, spread, and the number of positive cases, but the focus on healthcare resources to meet the current spread is not discussed. Motivated from the gap, in this paper, we propose a scheme, ABV-CoViD (Availibility of Beds and Ventilators for COVID-19 patients), that forms an ensemble forecasting model to predict the availability of beds and ventilators (ABV) for the COVID-19 patients. The scheme considers a region-wise demarcation for the allotment of beds and ventilators (BV), termed resources, based on region-wise ABV and COVID-19 positive patients (inside the hospitals occupying the BV resource). We consider an integration of artificial neural network (ANN) and auto-regressive integrated neural network (ARIMA) model to address both the linear and non-linear dependencies. We also consider the effective wave spread of COVID-19 on external patients (not occupying the BV resources) through a [Formula: see text]- ARNN model, which gives us long-term complex dependencies of BV resources in the future time window. We have considered the COVID-19 healthcare dataset on 3 USA regions (Illinois, Michigan, and Indiana) for testing our ensemble forecasting scheme from January 2021 to May 2022. We evaluated our scheme in terms of statistical performance metrics and validated that ensemble methods have higher accuracy. In simulation, for linear modelling, we considered the [Formula: see text] model, and [Formula: see text] model for ANN modelling. We considered the [Formula: see text](12,6) forecasting. On a population of 2,93,90,897, the obtained mean absolute error (MAE) on average for 3 regions is 170.5514. The average root means square error (RMSE) of [Formula: see text]-ARNN is 333.18, with an accuracy of 98.876%, which shows the scheme's efficacy in ABV measurement over conventional and manual resource allocation schemes.

Cardiorespiratory Fitness, Body Fatness, and Submaximal Systolic Blood Pressure Among Young Adult Women.

OBJECTIVES: We examined the independent and combined associations of cardiorespiratory fitness (CRF), body fat (BF) percentage (BF%), and body mass index (BMI) with submaximal systolic blood pressure (SSBP) among young adult women. MATERIALS AND METHODS: Analyses included a sample of 211 normotensive women with a BMI between 20 and 35 kg/m(2); BF% was calculated using total BF measured from dual X-ray absorptiometry, CRF was assessed using a graded exercise test, and SSBP was measured at each stage. RESULTS: There was a significant direct association of SSBP with BF% and BMI, whereas an inverse association between SSBP and CRF when adjusted for the covariates. There was no significant association between SSBP and BF% across the stages 1-3 with a borderline significant association at stage 4 when further adjusted for CRF, whereas no association at any of the stages when adjusted for BMI. A borderline significant association between SSBP and BMI was found at stage 1 and significant association at stages 2-4 when additionally adjusted for CRF, whereas the association disappeared at stages 1-2 when adjusted for BF%. The inverse association between SSBP and CRF was eliminated at stages 3-4 when further adjusted for BF% with borderline significant association at stages 1-2. The associations remained significant at the stages 1-2 but not at stages 3-4 after adjusting for BMI. CONCLUSION: CRF, BF%, and BMI seem to have critical roles in determining SSBP with CRF and BF% being more potent at lower intensity exercise, whereas BMI was more strongly associated at higher intensity exercise.