IN-SYNC. VIII. Primordial Disk Frequencies in NGC 1333, IC 348, and the Orion A Molecular Cloud

In this paper, we address two issues related to primordial disk evolution in three clusters (NGC 1333, IC 348, and Orion A) observed by the INfrared Spectra of Young Nebulous Clusters (IN-SYNC) project. First, in each cluster, averaged over the spread of age, we investigate how disk lifetime is dependent on stellar mass. The general relation in IC 348 and Orion A is that primordial disks around intermediate mass stars (2--5$M_{\odot}$) evolve faster than those around loss mass stars (0.1--1$M_{\odot}$), which is consistent with previous results. However, considering only low mass stars, we do not find a significant dependence of disk frequency on stellar mass. These results can help to better constrain theories on gas giant planet formation timescales. Secondly, in the Orion A molecular cloud, in the mass range of 0.35--0.7$M_{\odot}$, we provide the most robust evidence to date for disk evolution within a single cluster exhibiting modest age spread. By using surface gravity as an age indicator and employing 4.5 $\mu m$ excess as a primordial disk diagnostic, we observe a trend of decreasing disk frequency for older stars. The detection of intra-cluster disk evolution in NGC 1333 and IC 348 is tentative, since the slight decrease of disk frequency for older stars is a less than 1-$\sigma$ effect.Comment: 25 pages, 26 figures; submitted for publication (ApJ

Prevalence, risk factors, consequences, and prevention and management of patient aggression and violence against physicians in hospitals:A systematic review

Most reviews have examined workplace violence rather heterogeneously without explicit regard to a professional group or particular source of violence (from colleagues/leaders vs. from patients and their relatives/friends). This study reviews the literature regarding the prevalence, risk factors, consequences, and prevention and management of aggression and violence by patients (and their relatives/friends) against physicians in hospitals. A total of 104 studies were included by searching five databases. The prevalence of aggression and violence was higher in developing countries and against younger physicians. The risk factors for the occurrence of aggression and violence were present at multiple levels (i.e., patients, patient-physician interactions, hospitals, and society). However, knowledge on how risk factors at different levels interact is absent. Although research on risk factors acknowledged multiple levels, research on consequences was mainly focused on the individual level (i.e., work functioning, psychological well-being and health) with less attention to the team and organizational level. While some prevention models took into account the risk factors of aggression and violence in different contexts, there is still limited knowledge on how to establish a well-aligned and comprehensive intervention strategy that considers risk factors and consequences at different levels.</p

Uncertainty in the impact of the COVID-19 pandemic on air quality in Hong Kong, China

Strict social distancing rules are being implemented to stop the spread of COVID-19 pandemic in many cities globally, causing a sudden and extreme change in the transport activities. This offers a unique opportunity to assess the effect of anthropogenic activities on air quality and provides a valuable reference to the policymakers in developing air quality control measures and projecting their effectiveness. In this study, we evaluated the effect of the COVID-19 lockdown on the roadside and ambient air quality in Hong Kong, China, by comparing the air quality monitoring data collected in January-April 2020 with those in 2017-2019. The results showed that the roadside and ambient NO2, PM10, PM2.5, CO and SO2 were generally reduced in 2020 when comparing with the historical data in 2017-2019, while O3 was increased. However, the reductions during COVID-19 period (i.e., February-April) were not always higher than that during pre-COVID-19 period (i.e., January). In addition, there were large seasonal variations in the monthly mean pollutant concentrations in every year. This study implies that one air pollution control measure may not generate obvious immediate improvements in the air quality monitoring data and its effectiveness should be evaluated carefully to eliminate the effect of seasonal variations

The detection of age groups by dynamic gait outcomes using machine learning approaches

Prevalence of gait impairments increases with age and is associated with mobility decline, fall risk and loss of independence. For geriatric patients, the risk of having gait disorders is even higher. Consequently, gait assessment in the clinics has become increasingly important. The purpose of the present study was to classify healthy young-middle aged, older adults and geriatric patients based on dynamic gait outcomes. Classification performance of three supervised machine learning methods was compared. From trunk 3D-accelerations of 239 subjects obtained during walking, 23 dynamic gait outcomes were calculated. Kernel Principal Component Analysis (KPCA) was applied for dimensionality reduction of the data for Support Vector Machine (SVM) classification. Random Forest (RF) and Artificial Neural Network (ANN) were applied to the 23 gait outcomes without prior data reduction. Classification accuracy of SVM was 89%, RF accuracy was 73%, and ANN accuracy was 90%. Gait outcomes that significantly contributed to classification included: Root Mean Square (Anterior-Posterior, Vertical), Cross Entropy (Medio-Lateral, Vertical), Lyapunov Exponent (Vertical), step regularity (Vertical) and gait speed. ANN is preferable due to the automated data reduction and significant gait outcome identification. For clinicians, these gait outcomes could be used for diagnosing subjects with mobility disabilities, fall risk and to monitor interventions. (This work was supported by Keep Control project, funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 721577.

Zwicky Transient Facility constraints on the optical emission from the nearby repeating FRB 180916.J0158+65

The discovery rate of fast radio bursts (FRBs) is increasing dramatically thanks to new radio facilities. Meanwhile, wide-field instruments such as the 47 deg$^2$ Zwicky Transient Facility (ZTF) survey the optical sky to study transient and variable sources. We present serendipitous ZTF observations of the CHIME repeating source FRB 180916.J0158+65, that was localized to a spiral galaxy 149 Mpc away and is the first FRB suggesting periodic modulation in its activity. While 147 ZTF exposures corresponded to expected high-activity periods of this FRB, no single ZTF exposure was at the same time as a CHIME detection. No $>3\sigma$ optical source was found at the FRB location in 683 ZTF exposures, totalling 5.69 hours of integration time. We combined ZTF upper limits and expected repetitions from FRB 180916.J0158+65 in a statistical framework using a Weibull distribution, agnostic of periodic modulation priors. The analysis yielded a constraint on the ratio between the optical and radio fluences of $\eta \lesssim 200$, corresponding to an optical energy $E_{\rm opt} \lesssim 3 \times 10^{46}$ erg for a fiducial 10 Jy ms FRB (90% confidence). A deeper (but less statistically robust) constraint of $\eta \lesssim 3$ can be placed assuming a rate of $r(>5$ Jy ms)= hr$^{-1}$ and $1.2\pm 1.1$ FRB occurring during exposures taken in high-activity windows. The constraint can be improved with shorter per-image exposures and longer integration time, or observing FRBs at higher Galactic latitudes. This work demonstrated how current surveys can statistically constrain multi-wavelength counterparts to FRBs even without deliberately scheduled simultaneous radio observation.Comment: Accepted for publication in ApJL, 9 pages, 4 figures, 1 tabl

Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers:A Step toward Better Management of Neurological Disorders

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43-99% sensitivity and 48-98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making

Climate-Friendly Purchasing: An Exploratory Factor Analysis

Climate change includes long-term shifts in global temperatures, shrinking ice caps, and rising sea levels. Human activities are the main cause of such changes (Lynas et al., 2021) Individuals can mitigate the effects of climate change through their purchasing choices (Jakučionytė-Skodienė et al., 2022). To understand the strategies people use, we examined the factor structure of the Climate-Friendly Purchasing Choices domain from the Climate Change Action Inventory (CCAI; Barchard et al., 2022)

Hypermatrix factors for string and membrane junctions

The adjoint representations of the Lie algebras of the classical groups SU(n), SO(n), and Sp(n) are, respectively, tensor, antisymmetric, and symmetric products of two vector spaces, and hence are matrix representations. We consider the analogous products of three vector spaces and study when they appear as summands in Lie algebra decompositions. The Z3-grading of the exceptional Lie algebras provide such summands and provides representations of classical groups on hypermatrices. The main natural application is a formal study of three-junctions of strings and membranes. Generalizations are also considered.Comment: 25 pages, 4 figures, presentation improved, minor correction