Realizing strong photon blockade at exceptional points in the weak coupling regime

We theoretically prove that it is possible to realize strong photon blockade at n-order exceptional points (EPn) in a two-level quantum emitter (QE)–cavity quantum electrodynamics (QED) system even if the emitter–cavity coupling strength is weak. When the single-mode cavity is gain, we show that the ultrastrong single-photon blockade (1 PB) emerges at two-order exceptional points (EP2), avoiding the strong non-linearity of the system. In addition, we first give the pseudo-Hermitian condition for the non-Hermitian cavity QED system and find that the third-order exceptional points (EP3) can be predicted under certain constraints of the parameters. For this case, the pronounced 1 PB at EP3 will be triggered. Furthermore, we also consider the usual EP2-enhanced 1 PB existing in the system with or without the dipole–dipole interaction (DDI) under the pseudo-Hermitian condition. A striking feature is that the system without DDI can realize more obvious 1 PB at EP2 than the case of with DDI. What is important is that both EP2 and EP3 will appear in the weak coupling regime. Our proposal sheds new light on strong EP-engineered photon blockade in the weak coupling regime, providing a unique platform for making high-quality single-photon sources

Comparaison d'algorithmes symboliques et connexionnistes pour corréler l'âge d'enfants en santé avec des paramètres neuromoteurs Sigma-Lognormaux

RÉSUMÉ: L'évaluation de la maturité du contrôle moteur est importante pour aider les médecins à diagnostiquer un retard ou une instabilité du développement moteur chez les enfants. Mais il est toujours difficile de concevoir des méthodes d'évaluation qui soient pratiques à mettre en œuvre et qui fournissent en même temps des évaluations précises. L'objectif de cette étude est de développer un algorithme efficace pour prédire la maturité du contrôle moteur basé sur la Théorie Cinématique des mouvements humains rapides. Pour cette étude, nous avons utilisé les enregistrements de 513 enfants (âgés de 5,5 à 13 ans) qui ont effectué des traits écrits sur une tablette électronique. Deux types de tests ont été investigués : le test du trait simple et le test du triangle. Dans un premier temps, les paramètres Sigma-Lognormaux ont été extraits des traces enregistrées et ont été utilisés comme entrée de notre modèle prédictif. Plusieurs modèles différents ont été comparés (régression linéaire, réseau de neurones, méthode des k plus proches voisins, forêt d'arbres décisionnels, amplification du gradient, machine à vecteurs de support). Ces différents modèles ont donné de bons résultats compte tenu de la variabilité intra-enfant des traits manuscrits et de la variabilité inter-enfant de la maturité du contrôle moteur. Le meilleur résultat a été obtenu avec les modèles basés sur les réseaux neuronaux : coefficient de détermination (R2) : 0,548 ; erreur absolue moyenne (MAE) : 0,937. Les mo-dèles de régression linéaire donnent également de bons résultats (R2 : 0,476 MAE : 1,032). Plus de 90% des prédictions ont une erreur relative inférieure à 20%. Nous avons constaté que l'utilisation des paramètres du test de trait simple n'est pas optimale ; les résultats sont meilleurs lorsqu'on utilise les paramètres du test de tracé triangulaire. En conclusion, notre étude montre que le modèle Sigma-Lognormal propose une nouvelle possibilité pour esti-mer la maturité du contrôle moteur. Cela nous donne une approche totalement nouvelle en complément des méthodes traditionnelles utilisées actuellement. Cette méthode est rapide et confortable pour l'utilisateur. Puisqu'il suffit aux enfants de faire des traits d'écriture sur une tablette électronique avec un stylo, il devrait être plus pratique pour les médecins d'utiliser ce test dans un contexte clinique pour les pré-pistages avec moins d'intervention professionnelle par rapport aux méthodes traditionnelles avec les questionnaires, c'est un moyen rapide et bon marché. ABSTRACT: The evaluation of motor control maturity is important to help physicians diagnose delayed or unstable motor development in children. But it has traditionally been difficult to design assessment methods that are practical to implement and provide accurate evaluations at the same time. The aim of this study is to develop an effective algorithm to predict motor control maturity based on Kinematic Theory of rapid human movements. For this study, we used recordings from 513 children (5.5 to 13 years old) who made written strokes on an electronic tablet. Two types of tests were used : a single stroke and a triangle drawing test. First, Sigma-Lognormal parameters were extracted from recorded movements and used as the input to our predictive model. Multiple different models were compared (linear regression, neural networks, K-nearest neighbors regression, random forest, gradient boosting regression, support vector regression). These different models performed well considering the within children variability in handwritten strokes and the between-children variability in motor control maturity. The top score is obtained with the neural network based models : coefficient of determination (R2) : 0.548 ; mean absolute error (MAE) : 0.937. Linear regression models also performed well (R2 : 0.476 MAE : 1.032). More than 90% of the predictions have a relative error of less than 20%. We found that using simple stroke parameters alone is not optimal ; the results were better when using parameters from the triangle test or a mix of features from both tests. In conclusion, our study shows that the Sigma-Lognormal model proposes a new possibility to estimate the motor control maturity. This gives us a completely new approach to complement the traditional methods currently used. This method is fast and comfortable for the user. Since it only requires for children to make handwriting strokes on an electronic tablet with a pen, it is expected to be more convenient for doctors to use this test in a clinical context for pre-screening with less professional intervention compared to traditional methods with questionnaires, it is a quick and cheap way

Comparing Symbolic and Connectionist Algorithms for Correlating the Age of Healthy Children with Sigma-Lognormal Neuromuscular Parameters

It is important to accurately evaluate the motor control maturity to help physicians diagnose delayed or abnormal motor development in children. Traditionally, it has been challenging to design assessment methods that are practical and accurate at the same time. This study aims to develop an effective algorithm to predict motor control maturity based on the Kinematic Theory of rapid human movements. We used handwritten pen strokes made on an electronic tablet by 513 children (5.5 to 13 years of age). We considered two types of movements: a single stroke and a triangle drawing test. For the analysis, Sigma-Lognormal parameters were extracted from recordings and used in predictive models. We compared multiple models, including linear regression, deep learning, K-nearest neighbors regression, random forest, gradient boosting regression, and support vector regression. These models performed well considering the within-children variability in handwritten strokes and the between-children variability in motor control maturity. The best score was obtained using the neural network model: coefficient of determination (R2): 0.548; mean absolute error (MAE): 0.937. We found simple stroke parameters alone to be sub-optimal; the results were better when using parameters from the triangle test. In conclusion, our study demonstrates that the Sigma-Lognormal model offers new possibilities for estimating the motor control maturity. Our method is fast and comfortable for the children, as it only requires performing handwriting strokes on an electronic tablet. This simple and user-friendly test is expected to be more convenient for doctors in a clinical context

Adverse events of sorafenib in hepatocellular carcinoma treatment.

Sorafenib is an oral multikinase inhibitor approved by the US Food and Drug Administration for treatment of the patients with surgically unresectable hepatocellular carcinoma (HCC). Sorafenib mitigates angiogenesis by targeting vascular endothelial growth factor receptors and platelet-derived growth factor receptors in endothelial cells and pericytes. Moreover, it suppresses cell proliferation via blockage of B-RAF and RAF1 of the mitogen-activated protein kinase pathway in tumor cells. Sorafenib has been the standard molecular targeted medication in the treatment of advanced-stage HCC patients ineligible for potentially curative interventional (radiofrequency or microwave ablation) or palliative trans-arterial chemoembolization (TACE) therapies for over a decade. However, it only increases overall survival by less than 3 months, and systemic exposure to sorafenib causes clinically significant toxicities (about 50% of patients). Given the high frequency and severity of these toxicities, sorafenib dose must be often reduced or discontinued altogether. In this review, we discussed the mechanism of sorafenib-associated adverse events and their management during HCC treatment

Inorganic Salt Hydrate for Thermal Energy Storage

Using phase change materials (PCMs) for thermal energy storage has always been a hot topic within the research community due to their excellent performance on energy conservation such as energy efficiency in buildings, solar domestic hot water systems, textile industry, biomedical and food agroindustry. Several literatures have reported phase change materials concerning various aspects. Among these materials, salt hydrates are worthy of exploring due to their high-energy storage density, rational price, multiple sources and relatively good thermal conductivity. This paper reviews the present state of salt hydrates PCMs targeting classification, properties, defects, possible solutions as well as their idiographic features which are suitable for applications. In addition, new trends of future research are also indicated

Remote Sensing of Global Sea Surface pH Based on Massive Underway Data and Machine Learning

Seawater pH is a direct proxy of ocean acidification, and monitoring the global pH distribution and long-term series changes is critical to understanding the changes and responses of the marine ecology and environment under climate change. Owing to the lack of sufficient global-scale pH data and the complex relationship between seawater pH and related environmental variables, generating time-series products of satellite-derived global sea surface pH poses a great challenge. In this study, we solved the problem of the lack of sufficient data for pH algorithm development by using the massive underway sea surface carbon dioxide partial pressure (pCO(2)) dataset to structure a large data volume of near in situ pH based on carbonate calculation between underway pCO(2) and calculated total alkalinity from sea surface salinity and relevant parameters. The remote sensing inversion model of pH was then constructed through this massive pH training dataset and machine learning methods. After several tests of machine learning methods and groups of input parameters, we chose the random forest model with longitude, latitude, sea surface temperature (SST), chlorophyll a (Chla), and Mixed layer depth (MLD) as model inputs with the best performance of correlation coefficient (R-2 = 0.96) and root mean squared error (RMSE = 0.008) in the training set and R-2 = 0.83 (RMSE = 0.017) in the testing set. The sensitivity analysis of the error variation induced by the uncertainty of SST and Chla (SST <= +/- 0.5 degrees C and Chla <= +/- 20%; RMSESST <= 0.011 and RMSEChla <= 0.009) indicated that our sea surface pH model had good robustness. Monthly average global sea surface pH products from 2004 to 2019 with a spatial resolution of 0.25 degrees x 0.25 degrees were produced based on the satellite-derived SST and Chla products and modeled MLD dataset. The pH model and products were validated using another independent station-measured pH dataset from the Global Ocean Data Analysis Project (GLODAP), showing good performance. With the time-series pH products, refined interannual variability and seasonal variability were presented, and trends of pH decline were found globally. Our study provides a new method of directly using remote sensing to invert pH instead of indirect calculation based on the construction of massive underway calculated pH data, which would be made useful by comparing it with satellite-derived pCO(2) products to understand the carbonate system change and the ocean ecological environments responding to the global change

Synthesis and characterization of fluorinated carbon nanotubes for lithium primary batteries with high power density

The synthesis and characterization of fluorinated carbon nanotubes have been carried out under an inert gas containing fluorine. All of the samples have been characterized by x-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), solid-state nuclear magnetic resonance (13C and 19F ss-NMR) and transmission electron microscopy (TEM) techniques. The comparison of the effects of various experimental parameters on the structure of fluorinated materials allows the disclosure of the fluorination mechanism. It is shown that fluorine was intercalated into the outer part of the carbon nanotubes initially where graphene layers were coaxial within a distance of 0.60 nm. In contrast, the inner part of the carbon nanotubes was not intercalated. The electrochemical performance such as discharge capacity as a cathode for a primary lithium battery has also been investigated. The samples with a F/C ratio of 0.75 exhibited the best performance, namely high energy and power densities. The highest specific energy density and specific power density were 1147 Wh kg-1 and 8998 W kg-1, respectively, at a current density of 4 A g-1. ? 2013 IOP Publishing Ltd