Synaptic memristors based on BaTiO3 thin films irradiated by swift heavy ions for neuromorphic computing.

Swift heavy ion (SHI) irradiation is an effective method for modulating the properties of thin oxide films by introducing defects, strains, and structural transformations. Here, we applied 516 MeV Xe31+ irradiation to BaTiO3 (BTO) thin films grown on Nb:SrTiO3 substrates to induce the generation of tracks and nanohillocks. Memristors with BTO films irradiated at a fluence of 5 × 1010 ions cm-2 displayed excellent retention and endurance characteristics. Moreover, the memristors exhibited highly stable synaptic plasticity functions such as excitatory/inhibitory post-synaptic currents (E/IPSC) and paired-pulse facilitation/depression (PPF/D). The memristors achieved a discrimination accuracy of 92.5% on given handwritten digit data by an artificial neural network with supervised learning. These results verify that the judicious application of SHI irradiation on thin oxide films is a viable strategy for exploring neuromorphic computation.

Predictive models for delay in medical decision-making among older patients with acute ischemic stroke: a comparative study using logistic regression analysis and lightGBM algorithm.

OBJECTIVE: To explore the factors affecting delayed medical decision-making in older patients with acute ischemic stroke (AIS) using logistic regression analysis and the Light Gradient Boosting Machine (LightGBM) algorithm, and compare the two predictive models. METHODS: A cross-sectional study was conducted among 309 older patients aged ≥ 60 who underwent AIS. Demographic characteristics, stroke onset characteristics, previous stroke knowledge level, health literacy, and social network were recorded. These data were separately inputted into logistic regression analysis and the LightGBM algorithm to build the predictive models for delay in medical decision-making among older patients with AIS. Five parameters of Accuracy, Recall, F1 Score, AUC and Precision were compared between the two models. RESULTS: The medical decision-making delay rate in older patients with AIS was 74.76%. The factors affecting medical decision-making delay, identified through logistic regression and LightGBM algorithm, were as follows: stroke severity, stroke recognition, previous stroke knowledge, health literacy, social network (common factors), mode of onset (logistic regression model only), and reaction from others (LightGBM algorithm only). The LightGBM model demonstrated the more superior performance, achieving the higher AUC of 0.909. CONCLUSIONS: This study used advanced LightGBM algorithm to enable early identification of delay in medical decision-making groups in the older patients with AIS. The identified influencing factors can provide critical insights for the development of early prevention and intervention strategies to reduce delay in medical decisions-making among older patients with AIS and promote patients' health. The LightGBM algorithm is the optimal model for predicting the delay in medical decision-making among older patients with AIS.

Behavior of selected organophosphate flame retardants (OPFRs) and their influence on rhizospheric microorganisms after short-term exposure in integrated vertical-flow constructed wetlands (IVCWs).

Considering its ubiquitous occurrence and potential adverse effects of organophosphorus flame retardant (OPFR), it is urgent to explore the efficient treatment for OPFRs wastewater. Thus, integrated vertical-flow constructed wetlands (IVCWs) were set up to comparatively evaluate their nitrogen removal capacity under tidal flow operations and to investigate environmental behavior and rhizosphere microbial responses after short-term exposure to three OPFRs. The results show that IVCWs have an excellent TN removal rate (628.13 ± 110.63 mg m-2 d-1) and moderate mitigation efficiencies (48.37 ± 9.52 to 82.28 ± 7.48%) for target OPFRs when treating low-C/N ratio wastewater. Moreover, the sorption of selected OPFRs to soil (28.85-308.41 ng g-1, dry weight (dw)), igneous rock (659.85-970.80 ng g-1 dw) and zeolite (1045.60-1351.70 ng g-1 dw) and accumulation in tissues of C. alternifolius (0-289.68 ng g-1 dw) and P. australis (0.56-108.22 ng g-1 dw) showed a hydrophobicity-specific feature. Namely, the highly hydrophobic compound tricresyl phosphate (TCrP) partitioned preferentially to sediment, and the chlorinated analytes were more easily taken up and then translocated into the plant body. Simultaneously, further mass balance analysis revealed the fate of OPFRs in IVCW components. A total of 53.25% of the highly hydrophobic TCrP inflow mass settled in sediment, while tris (2-chloroethyl) phosphate (TCEP) and tris (1-chloro-2-propyl) phosphate (TCPP) were more liable to discharge (35.33-50.89%) and other pathways (38.77-39.87%). Furthermore, the abundance of aerobic denitrifying bacteria (AD) in rhizosphere soil (2.25-5.12%), jointly with the prevalence of nitrobacteria (NOBs, 1.84-13.60%) and denitrifying bacteria (DNBs, 5.84-7.89%) in sublayer matrices, was responsible for superior TN removal. Additionally, the rhizosphere microbial richness, diversity and nitrogen-related microorganisms were clearly influenced by the presence of OPFRs. Notably, the genera Pseudomonas and Sphingobium might be the functional microorganisms for mixture OPFRs biodegradation.