Sustainable power consumption for variance-based integration model in cellular 6G-IoT system

With the emergence of the 5G network, the count of analysis papers associated with the 6G Internet of Things (IoT) has rapidly increased due to the rising attention of researchers in next-generation technology, 6G networks and IoT techniques. Owing to this, grasping the overall research topics and directions is a complex task. To mutually address the significant issues of 6G cellular IoT, i.e., information transmission, data aggregation and power supply, we proposed a variance-based integrating model for the 6G-IoT approach that considers energy, communication and computation (ECC). Initially, the base station (BS) charges huge IoT devices concurrently utilizing WPT in the downlink. After that, IoT devices gather the energy to perform the communication task and the computation task in the uplink in a similar spectrum. Also, the model integrates the optimization of transmit beams via the Improved Ant Colony Optimization (IACO) model to balance the system performance, power consumption and computational complexity. Further, this study exploited activated Remote Radio Units (RRUs) to improve the network performance and energy efficiency in the downlink model. The simulation outcomes evaluate the performance of the proposed work over the conventional models concerning error analysis. From the results, the MSE value in the IACO work is much lower, around 0.011, while the compared schemes achieved comparatively higher MSE values

PROMISE: a real-world clinical-genomic database to address knowledge gaps in prostate cancer

PURPOSE: Prostate cancer is a heterogeneous disease with variable clinical outcomes. Despite numerous recent approvals of novel therapies, castration-resistant prostate cancer remains lethal. A real-world clinical-genomic database is urgently needed to enhance our characterization of advanced prostate cancer and further enable precision oncology. METHODS: The Prostate Cancer Precision Medicine Multi-Institutional Collaborative Effort (PROMISE) is a consortium whose aims are to establish a repository of de-identified clinical and genomic patient data that are linked to patient outcomes. The consortium structure includes a (1) bio-informatics committee to standardize genomic data and provide quality control, (2) biostatistics committee to independently perform statistical analyses, (3) executive committee to review and select proposals of relevant questions for the consortium to address, (4) diversity/inclusion committee to address important clinical questions pertaining to racial disparities, and (5) patient advocacy committee to understand patient perspectives to improve patients\u27 quality of care. RESULTS: The PROMISE consortium was formed by 16 academic institutions in early 2020 and a secure RedCap database was created. The first patient record was entered into the database in April 2020 and over 1000 records have been entered as of early 2021. Data entry is proceeding as planned with the goal to have over 2500 patient records by the end of 2021. CONCLUSIONS: The PROMISE consortium provides a powerful clinical-genomic platform to interrogate and address data gaps that have arisen with increased genomic testing in the clinical management of prostate cancer. The dataset incorporates data from patient populations that are often underrepresented in clinical trials, generates new hypotheses to direct further research, and addresses important clinical questions that are otherwise difficult to investigate in prospective studies