Robust Transmissions in Wireless Powered Multi-Relay Networks with Chance Interference Constraints

In this paper, we consider a wireless powered multi-relay network in which a multi-antenna hybrid access point underlaying a cellular system transmits information to distant receivers. Multiple relays capable of energy harvesting are deployed in the network to assist the information transmission. The hybrid access point can wirelessly supply energy to the relays, achieving multi-user gains from signal and energy cooperation. We propose a joint optimization for signal beamforming of the hybrid access point as well as wireless energy harvesting and collaborative beamforming strategies of the relays. The objective is to maximize network throughput subject to probabilistic interference constraints at the cellular user equipment. We formulate the throughput maximization with both the time-switching and power-splitting schemes, which impose very different couplings between the operating parameters for wireless power and information transfer. Although the optimization problems are inherently non-convex, they share similar structural properties that can be leveraged for efficient algorithm design. In particular, by exploiting monotonicity in the throughput, we maximize it iteratively via customized polyblock approximation with reduced complexity. The numerical results show that the proposed algorithms can achieve close to optimal performance in terms of the energy efficiency and throughput.Comment: 14 pages, 8 figure

FedCPC: An Effective Federated Contrastive Learning Method for Privacy Preserving Early-Stage Alzheimer's Speech Detection

The early-stage Alzheimer's disease (AD) detection has been considered an important field of medical studies. Like traditional machine learning methods, speech-based automatic detection also suffers from data privacy risks because the data of specific patients are exclusive to each medical institution. A common practice is to use federated learning to protect the patients' data privacy. However, its distributed learning process also causes performance reduction. To alleviate this problem while protecting user privacy, we propose a federated contrastive pre-training (FedCPC) performed before federated training for AD speech detection, which can learn a better representation from raw data and enables different clients to share data in the pre-training and training stages. Experimental results demonstrate that the proposed methods can achieve satisfactory performance while preserving data privacy.Comment: accepted in IEEE-ASRU202

Development of Drive Control Strategy for Front-and-Rear-Motor-Drive Electric Vehicle (FRMDEV)

In order to achieve both high-efficiency drive and low-jerk mode switch in FRMDEVs, a drive control strategy is proposed, consisting of top-layer torque distribution aimed at optimal efficiency and low-layer coordination control improving mode-switch jerk. First, with the use of the off-line particle swarm optimization algorithm (PSOA), the optimal switching boundary between single-motor-drive mode (SMDM) and dual-motor drive mode (DMDM) was modelled and a real-time torque distribution model based on the radial basis function (RBF) was created to achieve the optimal torque distribution. Then, referring to the dynamic characteristics of mode switch tested on a dual-motor test bench, a torque coordination strategy by controlling the variation rate of the torque distribution coefficient during the mode-switch process was developed. Finally, based on a hardware-in-loop (HIL) test platform and an FRMDEV, the proposed drive control strategy was verified. The test results show that both drive economy and comfort were improved significantly by the use of the developed drive control strategy

A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care

The COVID-19 pandemic has posed a heavy burden to the healthcare system worldwide and caused huge social disruption and economic loss. Many deep learning models have been proposed to conduct clinical predictive tasks such as mortality prediction for COVID-19 patients in intensive care units using Electronic Health Record (EHR) data. Despite their initial success in certain clinical applications, there is currently a lack of benchmarking results to achieve a fair comparison so that we can select the optimal model for clinical use. Furthermore, there is a discrepancy between the formulation of traditional prediction tasks and real-world clinical practice in intensive care. To fill these gaps, we propose two clinical prediction tasks, Outcome-specific length-of-stay prediction and Early mortality prediction for COVID-19 patients in intensive care units. The two tasks are adapted from the naive length-of-stay and mortality prediction tasks to accommodate the clinical practice for COVID-19 patients. We propose fair, detailed, open-source data-preprocessing pipelines and evaluate 17 state-of-the-art predictive models on two tasks, including 5 machine learning models, 6 basic deep learning models and 6 deep learning predictive models specifically designed for EHR data. We provide benchmarking results using data from two real-world COVID-19 EHR datasets. One dataset is publicly available without needing any inquiry and another dataset can be accessed on request. We provide fair, reproducible benchmarking results for two tasks. We deploy all experiment results and models on an online platform. We also allow clinicians and researchers to upload their data to the platform and get quick prediction results using our trained models. We hope our efforts can further facilitate deep learning and machine learning research for COVID-19 predictive modeling.Comment: Junyi Gao, Yinghao Zhu and Wenqing Wang contributed equall

TSPAN4 is a prognostic and immune target in Glioblastoma multiforme

Background: Atherosclerosis can impact cancer progression due to the cholesterol and calcium metabolism, illustrating the links between atherosclerosis and cancer metastasis. Tetraspanin 4 (TSPAN4) may help understand migrasomes in diseases and provide novel targets for treatment.Methods: TSPAN4 expression in atherosclerosis Gene Expression Omnibus (EO) dataset and multiple omics data were explored, such as enriched pathways analysis, protein-protein interaction analysis, immune subtypes as well as diagnostic and prognostic value in pan-cancer. The relationship between Glioblastoma multiforme (GBM) and TSPAN4 was further investigated.Results: Compared to control, TSPAN4 expression was upregulated in foam cells from patients with atherosclerosis and survival analysis demonstrated high TSPAN4 expression contributes to poor prognosis. TSPAN4 expression differs significantly in immune subtypes of cancers, which can be a diagnostic and prognostic target of cancers due to the high accuracy. Overall survival analysis of subgroups demonstrated that higher TSPAN4 expression had a worse prognosis and the univariate analysis and multivariate analysis demonstrated age, TSPAN4 expression, WHO grade, IDH status and histological types were independent risk factors of Glioblastoma multiforme.Conclusion: The TSPAN4 expression was associated with atherosclerosis progression and pan-cancer, especially in Glioblastoma multiforme and GBMLGG. Therefore, TSPAN4 may serve as a potential biomarker and the crosstalk between atherosclerosis and tumor progression. The results are not fully validated and further studies are still needed to validate in vivo and in vitro

A comprehensive benchmark for COVID-19 predictive modeling using electronic health records in intensive care

The COVID-19 pandemic highlighted the need for predictive deep-learning models in health care. However, practical prediction task design, fair comparison, and model selection for clinical applications remain a challenge. To address this, we introduce and evaluate two new prediction tasks?outcome-specific length-of-stay and early-mortality prediction for COVID-19 patients in intensive care?which better reflect clinical realities. We developed evaluation metrics, model adaptation designs, and open-source data preprocessing pipelines for these tasks while also evaluating 18 predictive models, including clinical scoring methods and traditional machine-learning, basic deep-learning, and advanced deep-learning models, tailored for electronic health record (EHR) data. Benchmarking results from two real-world COVID-19 EHR datasets are provided, and all results and trained models have been released on an online platform for use by clinicians and researchers. Our efforts contribute to the advancement of deep-learning and machine-learning research in pandemic predictive modeling

Maintenance of estuarine water quality by mangroves occurs during flood periods: A case study of a subtropical mangrove wetland

Seasonal changes in water quality were measured in samples taken at various distances from shallow water across mudflat to mangroves during flood period and from mangroves across mudflat to shallow water during ebb period in a subtropical mangrove estuary (Zhangjiang Estuary, Fujian, China). The TN (total dissolved nitrogen), TP (total dissolved phosphorus), COD (chemical oxygen demand), and DOC (dissolved organic carbon) contents during the flood period were significantly higher than those during the ebb period. In contrast, the opposite was true for the POC (particulate organic carbon) content and transparency. The mangroves at Zhangjiang Estuary may trap nutrients at rates of 90.5 g N/m(2)/yr, 2.2 g TP/m(2)/yr, and 13.7 g C/m(2)/yr in the form of DOC, and export POC at a rate of 81.8 g/m(2)/yr. Our results support the hypothesis that the maintenance of estuarine water quality by mangroves occurs during flood periods. (C) 2010 Elsevier Ltd. All rights reserved.Natural Science Fund of China [40876046, 40376025]; Fujian Province Universit

Early human albumin administration is associated with reduced mortality in septic shock patients with acute respiratory distress syndrome: A retrospective study from the MIMIC-III database

Background: Sepsis-induced acute respiratory distress syndrome (ARDS) was associated with higher mortality. It is unclear whether albumin supplementation early in the course of ARDS can affect the prognostic outcomes of septic shock (SS) patients with ARDS.Methods: The MIMIC-III database was employed to identify SS patients with ARDS. The effect of early application (<24 h after ICU admission) of human albumin on 28-day mortality in SS patients with ARDS was explored. The propensity score matching was used to minimize the bias between the non-albumin and early albumin treatment groups.Results: The analysis for all eligible patients who received human albumin showed significantly lower 28-hospital mortality rates than the non-albumin group (37% versus 47%, p = 0.018). After propensity matching, the difference between the two groups also significantly (34.8% versus 48.1%, p = 0.031). Moreover, we found that the relationship between albumin use and reduced 28-day mortality was inconsistent across SOFA score subgroups (Pinteraction = 0.004, non-adjustment for multiple testing).Conclusion: Early human albumin administration in SS patients with ARDS was independently associated with a reduction of 28-day mortality. Furthermore, the benefit of human albumin treatment appeared to be more pronounced in patients with a SOFA score of ≤ 10