Time-varying propagation model and dynamic-feedback-phase correction for multiplexed orbital angular momentum beams in atmospheric turbulence.

Freespace optical (FSO) communication in an outdoor setting is complicated by atmospheric turbulence (AT). A time-varying (TV) multiplexed orbital angular momentum (OAM) propagation model to consider AT under transverse-wind conditions is formulated for the first time, and optimized dynamic correction periods for various TV AT situations are found to improve the transmission efficiency. The TV nature of AT has until now been neglected from modeling of OAM propagation models, but it is shown to be important. First, according to the Taylor frozen-turbulence hypothesis, a series of AT phase screens influenced by transverse wind are introduced into the conventional angular-spectrum propagation analysis method to model both the temporal and spatial propagation characteristics of multiplexed OAM beams. Our model shows that while in weak TV AT, the power standard deviation of lower-order modes is usually smaller than that of higher-order modes, the phenomena in strong TV AT are qualitatively different. Moreover, after analyzing the effective time of each OAM phase correction, optimized dynamic correction periods for a dynamic feedback communication link are obtained. An optimized result shows that, under the moderate TV AT, both a system BER within the forward-error-correction limit and a low iterative computation volume with 6% of the real-time correction could be achieved with a correction period of 0.18 s. The research emphasizes the significance of establishing a TV propagation model for exploring the effect of TV AT on multiplexed OAM beams and proposing an optimized phase-correction mechanism to mitigate performance degradation caused by TV AT, ultimately enhancing overall transmission efficiency.

Development and Validation of Machine Learning-Based Models for Prediction of Intensive Care Unit Admission and In-Hospital Mortality in Patients with Acute Exacerbations of Chronic Obstructive Pulmonary Disease.

Background: This present work focused on predicting prognostic outcome of inpatients developing acute exacerbation of chronic obstructive pulmonary disease (AECOPD), and enhancing patient monitoring and treatment by using objective clinical indicators. Methods: The present retrospective study enrolled 322 AECOPD patients. Registry data downloaded based on COPD Pay-for-Performance Program database from January 2012 to December 2018 were used to check whether the enrolled patients were eligible. Our primary and secondary outcomes were ICU admission and in-hospital mortality, respectively. The best feature subset was chosen by recursive feature elimination. Moreover, seven machine learning (ML) models were trained for forecasting ICU admission among AECOPD patients, and the model with the most excellent performance was used. Results: According to our findings, random forest (RF) model showed superb discrimination performance, and the values of area under curve (AUC) were 0.973 and 0.828 in training and test cohorts, separately. Additionally, according to decision curve analysis, the net benefit of RF model was higher when differentiating patients with a high risk of ICU admission at a <0.55 threshold probability. Moreover, the ML-based prediction model was also constructed to predict in-hospital mortality, and it showed excellent calibration and discrimination capacities. Conclusion: The ML model was highly accurate in assessing the ICU admission and in-hospital mortality risk for AECOPD cases. Maintenance of model interpretability helped effectively provide accurate and lucid risk prediction of different individuals.

Thermal acclimation uncovers a simple genetic basis of adaptation to high temperature in a cosmopolitan pest.

Understanding a population's fitness heterogeneity and genetic basis of thermal adaptation is essential for predicting the responses to global warming. We examined the thermotolerance and genetic adaptation of Plutella xylostella to exposure to hot temperatures. The population fitness parameters of the hot-acclimated DBM strains varied in the thermal environments. Using genome scanning and transcription profiling, we find a number of genes potentially involved in thermal adaptation of DBM. Editing two ABCG transporter genes, PxWhite and PxABCG, confirmed their role in altering cuticle permeability and influencing thermal responses. Our results demonstrate that SNP mutations in genes and changes in gene expression can allow DBM to rapidly adapt to thermal environment. ABCG transporter genes play an important role in thermal adaptation of DBM. This work improves our understanding of genetic adaptation mechanisms of insects to thermal stress and our capacity to predict the effects of rising global temperatures on ectotherms.

Metavalently bonded tellurides: the essence of improved thermoelectric performance in elemental Te.

Elemental Te is important for semiconductor applications including thermoelectric energy conversion. Introducing dopants such as As, Sb, and Bi has been proven critical for improving its thermoelectric performance. However, the remarkably low solubility of these elements in Te raises questions about the mechanism with which these dopants can improve the thermoelectric properties. Indeed, these dopants overwhelmingly form precipitates rather than dissolve in the Te lattice. To distinguish the role of doping and precipitation on the properties, we have developed a correlative method to locally determine the structure-property relationship for an individual matrix or precipitate. We reveal that the conspicuous enhancement of electrical conductivity and power factor of bulk Te stems from the dopant-induced metavalently bonded telluride precipitates. These precipitates form electrically beneficial interfaces with the Te matrix. A quantum-mechanical-derived map uncovers more candidates for advancing Te thermoelectrics. This unconventional doping scenario adds another recipe to the design options for thermoelectrics and opens interesting pathways for microstructure design.