Lattice distortion inducing exciton splitting and coherent quantum beating in CsPbI3 perovskite quantum dots

Anisotropic exchange-splitting in semiconductor quantum dots (QDs) results in bright-exciton fine-structure-splitting (FSS) important for quantum information processing. Direct measurement of FSS usually requires single/few QDs at liquid-helium temperatures, because of its sensitivity to QD size and shape, whereas measuring and controlling FSS at an ensemble-level seem to be impossible unless all the dots are made to be nearly the same. Here we report strong bright-exciton FSS up to 1.6 meV in solution-processed CsPbI3 perovskite QDs, manifested as quantum beats in ensemble-level transient absorption at liquid-nitrogen to room temperatures. The splitting is robust to QD size and shape heterogeneity, and increases with decreasing temperature, pointing towards a mechanism associated with orthorhombic distortion of perovskite lattice. Effective-mass-approximation calculations reveal an intrinsic "fine-structure gap" that agrees well with the observed FSS. This gap stems from an avoided crossing of bright-excitons confined in orthorhombically-distorted QDs that are bounded by the pseudocubic {100} family of planes

Prognostic value of inflammatory markers for in-hospital mortality in intensive care patients with acute ischemic stroke: a retrospective observational study based on MIMIC-IV

BackgroundAcute ischemic stroke (AIS) is a primary cause of death and disability worldwide. Four markers that can be readily determined from peripheral blood, namely, the systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and total bilirubin, were measured in this study. We examined the relationship between the SII and in-hospital mortality after AIS and evaluated which of the above four indicators was most accurate for predicting in-hospital mortality after AIS.MethodsWe selected patients from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database who were aged >18 years and who were diagnosed with AIS on admission. We collected the patients’ baseline characteristics, including various clinical and laboratory data. To investigate the relationship between the SII and in-hospital mortality in patients with AIS, we employed the generalized additive model (GAM). Differences in in-hospital mortality between the groups were summarized by the Kaplan–Meier survival analysis and the log-rank test. The receiver operating characteristic (ROC) curve analysis was used to assess the accuracy of the four indicators (SII, NLR, PLR, and total bilirubin) for predicting in-hospital mortality in patients with AIS.ResultsThe study included 463 patients, and the in-hospital mortality rate was 12.31%. The GAM analysis showed a positive correlation between the SII and in-hospital mortality in patients with AIS, but the correlation was not linear. Unadjusted Cox regression identified a link between a high SII and an increased probability of in-hospital mortality. We also found that patients with an SII of >1,232 (Q2 group) had a considerably higher chance of in-hospital mortality than those with a low SII (Q1 group). The Kaplan–Meier analysis demonstrated that patients with an elevated SII had a significantly lower chance of surviving their hospital stay than those with a low SII. According to the results of the ROC curve analysis, the in-hospital mortality of patients with AIS predicted by the SII had an area under the ROC curve of 0.65, which revealed that the SII had a better discriminative ability than the NLR, PLR, and total bilirubin.ConclusionThe in-hospital mortality of patients with AIS and the SII were positively correlated, but not linearly. A high SII was associated with a worse prognosis in patients with AIS. The SII had a modest level of discrimination for forecasting in-hospital mortality. The SII was slightly better than the NLR and significantly better than the PLR and total bilirubin for predicting in-hospital mortality in patients with AIS

Analysis on hot briquetting mechanism of biomass fuel pellets

Under the carbon peaking and carbon neutrality strategy, biomass has attracted much attention due to its characteristics of regeneration, low pollution and zero carbon emissions. The imperfects of biomass, such as the loose structure and low energy density, can be effectively solved by briquetting, and the resulted fuel pellets can be used as a substitute for fossil fuels, which is of great significance for the construction of new energy system. In the paper, the influencing factors of the hot briquetting process of biomass were summarized, and the evolution behavior and binding mechanism of biomass particles during the hot briquetting process were analyzed and discussed. Biomass briquetting process mainly includes cold briquetting and hot briquetting. Compared with cold briquetting process of biomass, hot briquetting with lower energy consumption can produce the biomass fuel pellets with higher quality. The moisture content (4%−15%) of the raw biomass has greater influence, the briquetting temperature (70−150 ℃) has relatively smaller effect on the density of the fuel pellets, and the briquetting pressure (60−130 MPa) and the particle size ( < 2.5 mm) of the raw material show the different impact on the density of the fuel pellets from different biomass. During the hot process, cellulose mainly plays the role of supporting skeleton, hemicellulose and lignin play the role of binder. In the microcosmic process of hot briquetting process, the inertia movement and subsequent viscoelastic-plastic deformation of the biomass particles occur and the mechanical interlock is formed between the particles. The brittle particles are broken and the natural viscous components are released, and thus, the bridge linkage between the particles is formed under the integrated effects of moisture, temperature and pressure. Mechanical interlocking and bridging reduce the distance between biomass molecules and promote the generation of intermolecular forces. Based on the above-mentioned mechanism of the hot briquetting of biomass, the quality of the resulted fuel pellets can be improved by biomass component adjustment, biomass blending or hydrothermal pretreatment. In the future, the molecular dynamics simulation method will be used to investigate the biomass briquetting process to obtain the molecular bonding mechanism of biomass components, which is conducive to further exploring the hot briquetting mechanism of biomass, and provide important guiding significance for the preparation of fuel pellets and molding materials from biomass

OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System

Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to take humans out of the loop when building artificial intelligence (AI) applications, scant literature has focused on how AutoML works well in open-environment scenarios such as the process of training and updating large models, industrial supply chains or the industrial metaverse, where people often face open-loop problems during the search process: they must continuously collect data, update data and models, satisfy the requirements of the development and deployment environment, support massive devices, modify evaluation metrics, etc. Addressing the open-environment issue with pure data-driven approaches requires considerable data, computing resources, and effort from dedicated data engineers, making current AutoML systems and platforms inefficient and computationally intractable. Human-computer interaction is a practical and feasible way to tackle the problem of open-environment AI. In this paper, we introduce OmniForce, a human-centered AutoML (HAML) system that yields both human-assisted ML and ML-assisted human techniques, to put an AutoML system into practice and build adaptive AI in open-environment scenarios. Specifically, we present OmniForce in terms of ML version management; pipeline-driven development and deployment collaborations; a flexible search strategy framework; and widely provisioned and crowdsourced application algorithms, including large models. Furthermore, the (large) models constructed by OmniForce can be automatically turned into remote services in a few minutes; this process is dubbed model as a service (MaaS). Experimental results obtained in multiple search spaces and real-world use cases demonstrate the efficacy and efficiency of OmniForce