Investigation of key factors to earthquake insurance take-up rates in Quebec and British Columbia households and prediction model building

Maintaining an adequate level of earthquake take-up rate could protect the insurance industry from systemic failure. Past research has shown that British Columbia and Quebec have significant differences in earthquake insurance take-up rate. This report investigates key factors from the structure (default options and various types) of the insurance plan and personal characteristics along with socioeconomic/demographic profiles that affect the demand for earthquake protection in the form of insurance. The report also provides a prediction model for earthquake insurance take-up rate. The results show an importance ranking of key factors of earthquake insurance take up, the most important three are annual expected loss ratio , “age” and “average household size”. An optimal prediction model constructed by random forest with 15 predictors provides 69.4% testing accuracy. An important finding is that there exist cognitive biases among participants. Possible explanations of this finding are discussed

What Can Online Doctor Reviews Tell Us? A Deep Learning Assisted Study of Telehealth Service

The present study develops a novel deep learning method which assists text mining of online doctor reviews to extract underlying sentiment scores. Those scores can be used to estimate a healthcare service quality model to investigate how the online doctor reviews impact the online doctor consultation demand. Based on the data from the largest online health platforms in China, our model results show that the underlying sentiment scores have statistically significant impacts on the demand of online doctor consultation. Theoretically, the present study constructs an innovative deep learning algorithm with a better performance than four widely used text mining methods, which can be applied to text mining of many online forums or social media texts. Empirically, our model results show what factors impact the health service quality and online doctor consultation demand, and following those factors, healthcare professionals can improve their service

Blockwise Rank Decoding Problem and LRPC Codes: Cryptosystems with Smaller Sizes

In this paper, we initiate the study of the Rank Decoding (RD) problem and LRPC codes with blockwise structures in rank-based cryptosystems. First, we introduce the blockwise errors ($\ell$-errors) where each error consists of $\ell$ blocks of coordinates with disjoint supports, and define the blockwise RD ($\ell$-RD) problem as a natural generalization of the RD problem whose solutions are $\ell$-errors (note that the standard RD problem is actually a special $\ell$-RD problem with $\ell=1$). We adapt the typical attacks on the RD problem to the $\ell$-RD problem, and find that the blockwise structures do not ease the problem too much: the $\ell$-RD problem is still exponentially hard for appropriate choices of $\ell>1$. Second, we introduce blockwise LRPC ($\ell$-LRPC) codes as generalizations of the standard LPRC codes whose parity-check matrices can be divided into $\ell$ sub-matrices with disjoint supports, i.e., the intersection of two subspaces generated by the entries of any two sub-matrices is a null space, and investigate the decoding algorithms for $\ell$-errors. We find that the gain of using $\ell$-errors in decoding capacity outweighs the complexity loss in solving the $\ell$-RD problem, which makes it possible to design more efficient rank-based cryptosystems with flexible choices of parameters. As an application, we show that the two rank-based cryptosystems submitted to the NIST PQC competition, namely, RQC and ROLLO, can be greatly improved by using the ideal variants of the $\ell$-RD problem and $\ell$-LRPC codes. Concretely, for 128-bit security, our RQC has total public key and ciphertext sizes of 2.5 KB, which is not only about 50% more compact than the original RQC, but also smaller than the NIST Round 4 code-based submissions HQC, BIKE, and Classic McEliece

Observation and simulation study on the rapid intensification mechanism of Typhoon “Mekkhala” (2006)

Based on Doppler Weather radar observations and numerical simulations applying the Weather Research and Forecasting (WRF) system, this study focused on the rapid intensification (RI) of Typhoon “Mekkhala” (2006) in the inshore area in 2020. The simulated track of the typhoon relatively matched with the observation, with a slight eastward bias compared to the observed track. During the phase of RI, there was a slight weakening of vertical wind shear between 200–500 hPa. The temporary decrease in vertical wind shear became a favorable factor for the intensification of the typhoon. In general, vertical wind shear of the lower atmosphere is the key to supporting the RI of Typhoon Mekkhala. In the middle troposphere, the southward component of the vertical wind shear suddenly increases, indicates that the inflow of southern wind to the core of the typhoon had strengthened. Thus, the strengthening of the moisture transport by enhanced southern wind, contributed to the intensification of the typhoon. During the intensification of the typhoon, the low-level vorticity was significantly enhanced, and the high vorticity values expanded from the lower to higher troposphere. The vertical distribution of vorticity transformed from symmetry to asymmetry. The development of secondary circulation on both sides of the typhoon is a dynamic factor for intensification

The Altered Reconfiguration Pattern of Brain Modular Architecture Regulates Cognitive Function in Cerebral Small Vessel Disease

Background: Cerebral small vessel disease (SVD) is a common cause of cognitive dysfunction. However, little is known whether the altered reconfiguration pattern of brain modular architecture regulates cognitive dysfunction in SVD.Methods: We recruited 25 cases of SVD without cognitive impairment (SVD-NCI) and 24 cases of SVD with mild cognitive impairment (SVD-MCI). According to the Framingham Stroke Risk Profile, healthy controls (HC) were divided into 17 subjects (HC-low risk) and 19 subjects (HC-high risk). All individuals underwent resting-state functional magnetic resonance imaging and cognitive assessments. Graph-theoretical analysis was used to explore alterations in the modular organization of functional brain networks. Multiple regression and mediation analyses were performed to investigate the relationship between MRI markers, network metrics and cognitive performance.Results: We identified four modules corresponding to the default mode network (DMN), executive control network (ECN), sensorimotor network and visual network. With increasing vascular risk factors, the inter- and intranetwork compensation of the ECN and a relatively reserved DMN itself were observed in individuals at high risk for SVD. With declining cognitive ability, SVD-MCI showed a disrupted ECN intranetwork and increased DMN connection. Furthermore, the intermodule connectivity of the right inferior frontal gyrus of the ECN mediated the relationship between periventricular white matter hyperintensities and visuospatial processing in SVD-MCI.Conclusions: The reconfiguration pattern of the modular architecture within/between the DMN and ECN advances our understanding of the neural underpinning in response to vascular risk and SVD burden. These observations may provide novel insight into the underlying neural mechanism of SVD-related cognitive impairment and may serve as a potential non-invasive biomarker to predict and monitor disease progression

Using microneedle array electrodes for non-invasive electrophysiological signal acquisition and sensory feedback evoking

Introduction: Bidirectional transmission of information is needed to realize a closed-loop human-machine interaction (HMI), where electrophysiological signals are recorded for man-machine control and electrical stimulations are used for machine-man feedback. As a neural interface (NI) connecting man and machine, electrodes play an important role in HMI and their characteristics are critical for information transmission.Methods: In this work, we fabricated a kind of microneedle array electrodes (MAEs) by using a magnetization-induced self-assembly method, where microneedles with a length of 500–600 μm and a tip diameter of ∼20 μm were constructed on flexible substrates. Part of the needle length could penetrate through the subjects’ stratum corneum and reach the epidermis, but not touch the dermis, establishing a safe and direct communication pathway between external electrical circuit and internal peripheral nervous system.Results: The MAEs showed significantly lower and more stable electrode-skin interface impedance than the metal-based flat array electrodes (FAEs) in various testing scenarios, demonstrating their promising impedance characteristics. With the stable microneedle structure, MAEs exhibited an average SNR of EMG that is more than 30% higher than FAEs, and a motion-intention classification accuracy that is 10% higher than FAEs. The successful sensation evoking demonstrated the feasibility of the MAE-based electrical stimulation for sensory feedback, where a variety of natural and intuitive feelings were generated in the subjects and thereafter objectively verified through EEG analysis.Discussion: This work confirms the application potential of MAEs working as an effective NI, in both electrophysiological recording and electrical stimulation, which may provide a technique support for the development of HMI