Changes in Tropical Clouds and Atmospheric Circulation Associated with Rapid Adjustment Induced by Increased Atmospheric CO2 A Multiscale Modeling Framework Study

The radiative heating increase due to increased CO2 concentration is the primary source for the rapid adjustment of atmospheric circulation and clouds. In this study, we investigate the rapid adjustment resulting from doubling of CO2 and its physical mechanism using a multiscale modeling framework (MMF). The MMF includes an advanced higher-order turbulence closure in its cloud-resolving model component and simulates realistic shallow and deep cloud climatology and boundary layer turbulence. The rapid adjustment over the tropics is characterized by 1) reduced ascent and descent strengths over the ocean, 2) increased lower tropospheric stability (LTS) over the subsidence region, 3) shoaling of planetary boundary layers over the ocean, 4) increased deep convection over lands and shift of cloud coverage from the ocean to lands, and 5) reduced sensible (SH) and latent heat (LH) fluxes over the oceanic deep convective regions. Unlike conventional general circulation models and another MMF, a reduction in the global-mean shortwave cloud radiative cooling is not simulated, due to the increase in low clouds at lower altitudes over the ocean, resulting from reduced cloud-top entrainment due to strengthened inversion. Changes in regional circulation play a key role in cloud changes and shift of cloud coverage to lands. Weaker energy transport resulting from water vapor and cloud CO2 masking effects reduces the upward motion and convective clouds in the oceanic regions. The ocean-land transports are linked to the partitioning of surface SH and LH fluxes that increases humidity over lands and enhances deep convection over the tropical lands

Best Practices to Increase Efficacy of Graduate School Admissions Communications at Clark University

Within the period of time that a graduate student deposits and subsequently arrives at their academic institution, receiving timely information is important for their preparation. This process has been deemed by the Deans of the Enterprise Schools at Clark University as one that needs further investigation. As such, this Capstone looks at the array of communication that goes out to each graduate student during this four-month period. The purpose of examining this communication is to analyze its effectiveness in engaging students. To analyze the effectiveness of this communication, surveys were distributed to current students in these schools to gather data surrounding their experience after applying to Clark. In addition to looking at Clark University’s current process, we conducted an analysis of trends and best practices from colleges and universities across the country. Based on findings from this research and our firsthand interviews of the aforementioned Deans and involved staff members, we have provided recommendations to improve this process. Ultimately, in order to improve student engagement our group has created recommendations that could improve some of the challenges in engaging and retaining students during this period of time

InterFace:Adjustable Angular Margin Inter-class Loss for Deep Face Recognition

In the field of face recognition, it is always a hot research topic to improve the loss solution to make the face features extracted by the network have greater discriminative power. Research works in recent years has improved the discriminative power of the face model by normalizing softmax to the cosine space step by step and then adding a fixed penalty margin to reduce the intra-class distance to increase the inter-class distance. Although a great deal of previous work has been done to optimize the boundary penalty to improve the discriminative power of the model, adding a fixed margin penalty to the depth feature and the corresponding weight is not consistent with the pattern of data in the real scenario. To address this issue, in this paper, we propose a novel loss function, InterFace, releasing the constraint of adding a margin penalty only between the depth feature and the corresponding weight to push the separability of classes by adding corresponding margin penalties between the depth features and all weights. To illustrate the advantages of InterFace over a fixed penalty margin, we explained geometrically and comparisons on a set of mainstream benchmarks. From a wider perspective, our InterFace has advanced the state-of-the-art face recognition performance on five out of thirteen mainstream benchmarks. All training codes, pre-trained models, and training logs, are publicly released \footnote{$https://github.com/iamsangmeng/InterFace$}.Comment: arXiv admin note: text overlap with arXiv:2109.09416 by other author

Differences in the Hydrological Cycle and Sensitivity Between Multiscale Modeling Frameworks with and Without a HigherOrder Turbulence Closure

Current conventional global climate models (GCMs) produce a weak increase in globalmean precipitation with anthropogenic warming in comparison with the lower tropospheric moisture increases. The motive of this study is to understand the differences in the hydrological sensitivity between two multiscale modeling frameworks (MMFs) that arise from the different treatments of turbulence and low clouds in order to aid to the understanding of the model spread among conventional GCMs. We compare the hydrological sensitivity and its energetic constraint from MMFs with (SPCAMIPHOC) or without (SPCAM) an advanced higherorder turbulence closure. SPCAMIPHOC simulates higher global hydrological sensitivity for the slow response but lower sensitivity for the fast response than SPCAM. Their differences are comparable to the spreads of conventional GCMs. The higher sensitivity in SPCAMIPHOC is associated with the higher ratio of the changes in latent heating to those in net atmospheric radiative cooling, which is further related to a stronger decrease in the Bowen ratio with warming than in SPCAM. The higher sensitivity of cloud radiative cooling resulting from the lack of low clouds in SPCAM is another major factor in contributing to the lower precipitation sensitivity. The two MMFs differ greatly in the hydrological sensitivity over the tropical lands, where the simulated sensitivity of surface sensible heat fluxes to surface warming and CO2 increase in SPCAMIPHOC is weaker than in SPCAM. The difference in divergences of dry static energy flux simulated by the two MMFs also contributes to the difference in land precipitation sensitivity between the two models

The impact of adult children’s support on the psychological health of rural older adult people in China

BackgroundFamily old-age care is dominant in Chinese rural society, and children’s support is an important force in family old-age care. However, the migration of a large number of young and middle-aged rural laborers has undermined the traditional arrangements for old-age care in rural areas and affected the psychological health of the older adult.Methods2014 China Longitudinal Aging Social Survey targets Chinese citizens aged 60 or older and covers 28 provinces in mainland China. In this paper, the database of the CLASS was selected for empirical analysis to explore the impact of children’s support on the depression level and loneliness of rural older adults through multiple linear regression, and was divided into two groups according to children’s migration to analyze heterogeneity.ResultsChildren’s financial support facilitates the maintenance of mental health among rural older adults. Children’s support promotes mental health among rural older adults, but this association does not exist among older adults without children’s migration. Individual characteristics of older people have a greater impact on mental health.DiscussionOur study firstly compares the differences of children’s migration status between children’s support and mental health among the older adult in rural China. In order to improve the mental health of the older adult, it is necessary to create a favorable atmosphere of love and respect for the older adult, improve the social security system in rural areas, and give full play to the strengths of the social forces, so as to ensure that the older adult have a sense of worthiness and enjoyment in their old age

Multivariate Deep Learning Classification of Alzheimer’s Disease Based on Hierarchical Partner Matching Independent Component Analysis

Machine learning and pattern recognition have been widely investigated in order to look for the biomarkers of Alzheimer’s disease (AD). However, most existing methods extract features by seed-based correlation, which not only requires prior information but also ignores the relationship between resting state functional magnetic resonance imaging (rs-fMRI) voxels. In this study, we proposed a deep learning classification framework with multivariate data-driven based feature extraction for automatic diagnosis of AD. Specifically, a three-level hierarchical partner matching independent components analysis (3LHPM-ICA) approach was proposed first in order to address the issues in spatial individual ICA, including the uncertainty of the numbers of components, the randomness of initial values, and the correspondence of ICs of multiple subjects, resulting in stable and reliable ICs which were applied as the intrinsic brain functional connectivity (FC) features. Second, Granger causality (GC) was utilized to infer directional interaction between the ICs that were identified by the 3LHPM-ICA method and extract the effective connectivity features. Finally, a deep learning classification framework was developed to distinguish AD from controls by fusing the functional and effective connectivities. A resting state fMRI dataset containing 34 AD patients and 34 normal controls (NCs) was applied to the multivariate deep learning platform, leading to a classification accuracy of 95.59%, with a sensitivity of 97.06% and a specificity of 94.12% with leave-one-out cross validation (LOOCV). The experimental results demonstrated that the measures of neural connectivities of ICA and GC followed by deep learning classification represented the most powerful methods of distinguishing AD clinical data from NCs, and these aberrant brain connectivities might serve as robust brain biomarkers for AD. This approach also allows for expansion of the methodology to classify other psychiatric disorders

Facile Fabrication of a Hierarchical Superhydrophobic Coating with Aluminate Coupling Agent Modified Kaolin

A superhydrophobic coating was fabricated from the dispersion of unmodified kaolin particles and aluminate coupling agent in anhydrous ethanol. Through surface modification, water contact angle of the coating prepared by modified kaolin particles increased dramatically from 0° to 152°, and the sliding angle decreased from 90° to 3°. Scanning electron microscopy was used to examine the surface morphology. A structure composed of micro-nano hierarchical component, combined with the surface modification by aluminate coupling agent which reduced the surface energy greatly, was found to be responsible for the superhydrophobicity. The method adopted is relatively simple, facile, and cost-effective and can potentially be applied to large water-repellent surface coatings