Neuropsychiatric Disorders and Frailty in Older Adults over the Spectrum of Cancer: A Narrative Review

Background: The interplay between different neuropsychiatric conditions, beyond dementia, in the presence of a diagnosis of cancer in older adults may mediate patients’ fitness and cancer-related outcomes. Here, we aimed to investigate the presence of depression, sleep disturbances, anxiety, attitude, motivation, and support in older adults receiving a diagnosis of cancer and the dimension of frailty in order to understand the magnitude of the problem. Methods: This review provides an update of the state of the art based on references from searches of PubMed between 2000 and June 2021. Results: The evidence obtained underscored the tight association between frailty and unfavorable clinical outcomes in older adults with cancer. Given the intrinsic correlation of neuropsychiatric disorders with frailty in the realm of cancer survivorship, the evidence showed they might have a correlation with unfavorable clinical outcomes, late-life geriatric syndromes and higher degree of frailty. Conclusions: The identification of common vulnerabilities among neuropsychiatric disorders, frailty, and cancer may hold promise to unmask similar shared pathways, potentially intercepting targeted new interventions over the spectrum of cancer with the delivery of better pathways of care for older adults with cancer.

SAR Target Recognition Based on Joint Representation of Multimode Representations

For the problems of feature extraction and decision making in synthetic aperture radar (SAR) image target recognition, a method based on multimode clustering and decision fusion is proposed. The bidimensional variational mode decomposition (BVMD) is used to decompose the SAR image to obtain multiple modes, which provide multilevel descriptions of the target characteristics. Clustering is performed based on the intrinsic correlation of multiple modes, and several subsets with different modes are selected. Based on the joint sparse representation (JSR), each mode subset is classified, and the corresponding reconstruction error vector is obtained. The linear weighted fusion is employed to fuse the results from different mode subsets. Finally, a decision is made based on the fused results. Experiments are carried out based on the MSTAR dataset. The results show the effectiveness of the method under the standard operating condition (SOC) and robustness under extended operating conditions (EOCs).

Infinitesimal Periodic Deformations and Quadrics

We describe a correspondence between the infinitesimal deformations of a periodic bar-and-joint framework and periodic arrangements of quadrics. This intrinsic correlation provides useful geometric characteristics. A direct consequence is a method for detecting auxetic deformations, identified by a pattern consisting of homothetic ellipsoids. Examples include frameworks with higher crystallographic symmetry.

Green Credit, Green Reputation and Corporate Financial Performance: Evidence From China

Using green credit to guide the direction of funds is very important for improving the current social environment and enhancing economic development quality. Through its differential pricing features, evidence suggests that green credit has limited the flow of capital to highly polluting and high emission ("two high") industries, allowing more capital to flow to green industries and improving the quality of environmental and economic development. The article combines green credit and the performance of listed banks in a theoretical and empirical study to explore the intrinsic correlation between them and to find the intrinsic motivation for banks to implement green credit, which effectively improves social welfare and promotes sustainable economic development. The article first reviews the current status of green credit research and theories related to green development in China and other countries, and then analyses the dynamics of green credit development, value creation, as well as the mechanisms by which green credit improves the financial performance of listed banks. Finally, the article explores the impact of green credit on the financial performance of listed banks through empirical analysis. Here we have established a panel data model to sort out and analyze the relevant data of 19 listed Banks in China from 2008 to 2017 to study the impact of green credit on listed banks' financial performance. This study has shown that the green credit ratio, as an indicator of the amount of green credit implemented by listed banks, will positively impact financial performance. But the impact of the current period and the one-period lag is more significant, while the effect of the two-period lag is not significant. The second major finding was that when green reputation is used as an indicator to measure the quality of green credit implementation of listed banks, listed banks' financial performance can be significantly improved. Besides, this study has also found that green credit implementation generally has different impacts on different types of banks.

Appearance of Thermal Time

In this paper a viewpoint that time is an informational and thermal entity is presented. We consider a model for a simple relaxation process for which a relationship among event, time and temperature is mathematically formulated. It is then explicitly illustrated that temperature and time are statistically inferred through measurement of events. The probability distribution of the events thus provides an intrinsic correlation between temperature and time, which can relevantly be expressed in terms of the Fisher information metric. The two-dimensional differential geometry of temperature and time then leads us to a finding of a simple equation for the scalar curvature, $$R = -1$$ R = - 1 , in this case of relaxation process. This basic equation, in turn, may be regarded as characterizing a nonequilibrium dynamical process and having a solution given by the Fisher information metric. The time can then be interpreted so as to appear in a thermal way.

Unraveling intrinsic correlation effects with angle-resolved photoemission spectroscopy

Interaction effects can change materials properties in intriguing ways, and they have, in general, a huge impact on electronic spectra. In particular, satellites in photoemission spectra are pure many-body effects, and their study is of increasing interest in both experiment and theory. However, the intrinsic spectral function is only a part of a measured spectrum, and it is notoriously difficult to extract this information, even for simple metals. Our joint experimental and theoretical study of the prototypical simple metal aluminum demonstrates how intrinsic satellite spectra can be extracted from measured data using angular resolution in photoemission. A nondispersing satellite is detected and explained by electron–electron interactions and the thermal motion of the atoms. Additional nondispersing intensity comes from the inelastic scattering of the outgoing photoelectron. The ideal intrinsic spectral function, instead, has satellites that disperse both in energy and in shape. Theory and the information extracted from experiment describe these features with very good agreement.

Physics Based Multi-Fidelity Data Fusion for Efficient Characterization of Mode Shape Variation Under Uncertainties

Efficient prediction of mode shape variation under uncertainties is important for design and control. While Monte Carlo simulation (MCS) is straightforward, it is computationally expensive and not feasible for complex structures with high dimensionalities. To address this issue, in this study we develop a multi-fidelity data fusion approach with an enhanced Gaussian process (GP) architecture to evaluate mode shape variation. Since the process to acquire high-fidelity data from full-scale physical model usually is costly, we involve an order-reduced model to rapidly generate a relatively large amount of low-fidelity data. Combining these with a small amount of high-fidelity data altogether, we can establish a Gaussian process meta-model and use it for efficient model shape prediction. This enhanced meta-model allows one to capture the intrinsic correlation of model shape amplitudes at different locations by incorporating a multi-response strategy. Comprehensive case studies are performed for methodology validation.