Towards Flexible Time-to-event Modeling: Optimizing Neural Networks via Rank Regression

Time-to-event analysis, also known as survival analysis, aims to predict the time of occurrence of an event, given a set of features. One of the major challenges in this area is dealing with censored data, which can make learning algorithms more complex. Traditional methods such as Cox's proportional hazards model and the accelerated failure time (AFT) model have been popular in this field, but they often require assumptions such as proportional hazards and linearity. In particular, the AFT models often require pre-specified parametric distributional assumptions. To improve predictive performance and alleviate strict assumptions, there have been many deep learning approaches for hazard-based models in recent years. However, representation learning for AFT has not been widely explored in the neural network literature, despite its simplicity and interpretability in comparison to hazard-focused methods. In this work, we introduce the Deep AFT Rank-regression model for Time-to-event prediction (DART). This model uses an objective function based on Gehan's rank statistic, which is efficient and reliable for representation learning. On top of eliminating the requirement to establish a baseline event time distribution, DART retains the advantages of directly predicting event time in standard AFT models. The proposed method is a semiparametric approach to AFT modeling that does not impose any distributional assumptions on the survival time distribution. This also eliminates the need for additional hyperparameters or complex model architectures, unlike existing neural network-based AFT models. Through quantitative analysis on various benchmark datasets, we have shown that DART has significant potential for modeling high-throughput censored time-to-event data.Comment: Accepted at ECAI 202

An Energy-Efficient Multi-Level Sleep Strategy for Periodic Uplink Transmission in Industrial Private 5G Networks

This paper proposes an energy-efficient multi-level sleep mode control for periodic transmission (MSC-PUT) in private fifth-generation (5G) networks. In general, private 5G networks meet IIoT requirements but face rising energy consumption due to dense base station (BS) deployment, particularly impacting operating expenses (OPEX). An approach of BS sleep mode has been studied to reduce energy consumption, but there has been insufficient consideration for the periodic uplink transmission of industrial Internet of Things (IIoT) devices. Additionally, 5G New Reno’s synchronization signal interval limits the effectiveness of the deepest sleep mode in reducing BS energy consumption. By addressing this issue, the aim of this paper is to propose an energy-efficient multi-level sleep mode control for periodic uplink transmission to improve the energy efficiency of BSs. In advance, we develop an energy-efficient model that considers the trade-off between throughput impairment caused by increased latency and energy saving by sleep mode operation for IIoT’s periodic uplink transmission. Then, we propose an approach based on proximal policy optimization (PPO) to determine the deep sleep mode of BSs, considering throughput impairment and energy efficiency. Our simulation results verify the proposed MSC-PUT algorithm’s effectiveness in terms of throughput, energy saving, and energy efficiency. Specifically, we verify that our proposed MSC-PUT enhances energy efficiency by nearly 27.5% when compared to conventional multi-level sleep operation and consumes less energy at 75.21% of the energy consumed by the conventional method while incurring a throughput impairment of nearly 4.2%. Numerical results show that the proposed algorithm can significantly reduce the energy consumption of BSs accounting for periodic uplink transmission of IIoT devices

Solar Photoelectrochemical Synthesis of Electrolyte-free H2O2 Aqueous Solution without Needing Electrical Bias and H2

The conventional synthesis of hydrogen peroxide (H2O2) such as heterogeneous catalytic and electrochemical processes requires H-2 and O-2 as reagents, costly noble metals, and organic solvents, which are energy/waste-intensive and hazardous. An alternative method of photoelectrochemical (PEC) synthesis that needs only water and sunlight is environment-friendly but its practical application is limited due to the energy-demanding method for the separation of the synthesized H2O2 from the electrolytes. Herein, we demonstrated the direct synthesis of an electrolyte-free aqueous solution of pure H2O2 by developing a PEC system with solid polymer electrolyte (SPE) and engineered electrodes. Ruthenium catalyst-decorated TiO2 nanorods (RuOx/TNR: photoanode) and anthraquinone-anchored graphite rods (AQ/G: cathode) are placed in an anode compartment and a cathode compartment, respectively, while a middle compartment containing SPE is located between these compartments. Upon solar simulating irradiation (AM 1.5G, 100 mW cm(-2)), the photoanode generates H+ ions via water oxidation reaction (WOR) and the cathode generates HO2- ions via two-electron oxygen reduction reaction (ORR), while the SPE selectively transports H+ and HO2- into the middle compartment to form pure H2O2 solution. The combined system enabled continuous H2O2 synthesis over 100 h even under bias-free (0.0 V of cell voltage) conditions with the production of similar to 80 mM H2O2 (electrolyte-free) and a faradaic efficiency of similar to 90%, which is the highest concentration of pure H2O2 obtained using PEC systems. This study successfully demonstrates the proof-of-concept that might enable the production of a concentrated pure (electrolyte-free) aqueous solution of H2O2 using sunlight, water, and dioxygen only.11Nsciescopu

Ag(I) ions working as a hole-transfer mediator in photoelectrocatalytic water oxidation on WO3 film

Ag(I) is commonly employed as an electron scavenger to promote water oxidation. In addition to its straightforward role as an electron acceptor, Ag(I) can also capture holes to generate the high-valent silver species. Herein, we demonstrate photoelectrocatalytic (PEC) water oxidation and concurrent dioxygen evolution by the silver redox cycle where Ag(I) acts as a hole-transfer mediator. Ag(I) enhances the PEC performance of WO3 electrodes at 1.23 V vs. RHE with increasing O-2 evolution, while forming Ag(II) complexes ((AgNO3+)-N-II). Upon turning off both light and potential bias, the photocurrent immediately drops to zero, whereas O-2 evolution continues over similar to 10 h with gradual bleaching of the colored complexes. This phenomenon is observed neither in the Ag(I)-free PEC reactions nor in the photocatalytic (i.e., bias-free) reactions with Ag(I). This study finds that the role of Ag(I) is not limited as an electron scavenger and calls for more thorough studies on the effect of Ag(I).11Ysciescopu

Early prediction of need for invasive mechanical ventilation in the neonatal intensive care unit using artificial intelligence and electronic health records: a clinical study

Abstract Background Respiratory support is crucial for newborns with underdeveloped lung. The clinical outcomes of patients depend on the clinician’s ability to recognize the status underlying the presented symptoms and signs. With the increasing number of high-risk infants, artificial intelligence (AI) should be considered as a tool for personalized neonatal care. Continuous monitoring of vital signs is essential in cardiorespiratory care. In this study, we developed deep learning (DL) prediction models for rapid and accurate detection of mechanical ventilation requirements in neonates using electronic health records (EHR). Methods We utilized data from the neonatal intensive care unit in a single center, collected between March 3, 2012, and March 4, 2022, including 1,394 patient records used for model development, consisting of 505 and 889 patients with and without invasive mechanical ventilation (IMV) support, respectively. The proposed model architecture includes feature embedding using feature-wise fully connected (FC) layers, followed by three bidirectional long short-term memory (LSTM) layers. Results A mean gestational age (GA) was 36.61 ± 3.25 weeks, and the mean birth weight was 2,734.01 ± 784.98 g. The IMV group had lower GA, birth weight, and longer hospitalization duration than the non-IMV group (P < 0.05). Our proposed model, tested on a dataset from March 4, 2019, to March 4, 2022. The mean AUROC of our proposed model for IMV support prediction performance demonstrated 0.861 (95%CI, 0.853–0.869). It is superior to conventional approaches, such as newborn early warning score systems (NEWS), Random Forest, and eXtreme gradient boosting (XGBoost) with 0.611 (95%CI, 0.600–0.622), 0.837 (95%CI, 0.828–0.845), and 0.0.831 (95%CI, 0.821–0.845), respectively. The highest AUPRC value is shown in the proposed model at 0.327 (95%CI, 0.308–0.347). The proposed model performed more accurate predictions as gestational age decreased. Additionally, the model exhibited the lowest alarm rate while maintaining the same sensitivity level. Conclusion Deep learning approaches can help accurately standardize the prediction of invasive mechanical ventilation for neonatal patients and facilitate advanced neonatal care. The results of predictive, recall, and alarm performances of the proposed model outperformed the other models