Showing more kindness to disabled tourists because of understanding: The interaction among the perceived level of information, affection and altruistic behavior intention

It is the common responsibility of the whole society to provide friendly and appropriate help for disabled tourists.However, the altruistic behavior of ordinary tourists towards disabled tourists has been ignored in academia, and few studies have focused on the internal factors that affect the altruistic behavior of ordinary tourists. To fill in these gaps, planning to investigate 500 respondents, based on The Tri-component attitude model, this study, applying SEM, will examine the effect of ordinary tourists\u27 perceived level of information about the travel barriers faced by physical disabled tourists on their empathy and discomfort, which positively affected their altruistic behavior intention to physical disabledtourists. Moreover, the contents of ordinary tourists\u27 perceived level of information and information source will be explored through 10 in-depth interviews. Theoretical and practical implications of this research were discussed as well

SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detection

This paper provides a novel framework for single-domain generalized object detection (i.e., Single-DGOD), where we are interested in learning and maintaining the semantic structures of self-augmented compound cross-domain samples to enhance the model's generalization ability. Different from DGOD trained on multiple source domains, Single-DGOD is far more challenging to generalize well to multiple target domains with only one single source domain. Existing methods mostly adopt a similar treatment from DGOD to learn domain-invariant features by decoupling or compressing the semantic space. However, there may have two potential limitations: 1) pseudo attribute-label correlation, due to extremely scarce single-domain data; and 2) the semantic structural information is usually ignored, i.e., we found the affinities of instance-level semantic relations in samples are crucial to model generalization. In this paper, we introduce Semantic Reasoning with Compound Domains (SRCD) for Single-DGOD. Specifically, our SRCD contains two main components, namely, the texture-based self-augmentation (TBSA) module, and the local-global semantic reasoning (LGSR) module. TBSA aims to eliminate the effects of irrelevant attributes associated with labels, such as light, shadow, color, etc., at the image level by a light-yet-efficient self-augmentation. Moreover, LGSR is used to further model the semantic relationships on instance features to uncover and maintain the intrinsic semantic structures. Extensive experiments on multiple benchmarks demonstrate the effectiveness of the proposed SRCD.Comment: 10 pages, 5 figure

Simulation and experimental on the quick-freezing of diced mango by dry ice spray

In order to improve the quality of quick-frozen diced mango, a cylindrical quick-frozen container with dry ice spray is designed, the temperature field and velocity field of diced mango sprayed by dry ice in quick-freezing tank are simulated by COMSOL Multiphysics. The effects of different inlet velocities (0.15, 0.20, 0.25, 0.30, 0.35 and 0.40m/s), on the quick-freezing process of diced mango are studied. The results show that with the increase of the inlet velocity of dry ice, the time for diced mango to meet the requirements of quick freezing is continuously shortened, and the outlet solid fraction fluctuates within a certain range. When the inlet velocity is 0.25m/s, the inlet radius is 15mm and the size of diced mango is 10mm, the quick-freezing effect is the best. By the experimental verification, the average errors of surface temperature and core temperature of diced mango to meet the requirements of quick freezing are 3.9% and 3.8% respectively. The results lay a foundation for the popularization and application of dry ice spray quick frozen diced mango

Study on the substitutability of nighttime light data for SDG indicators: a case study of Yunnan Province

Introdution: One crucial method to attain Sustainable Development Goals (SDGs) involves timely adjustment of development policies, promoting the realization of SDGs through a time-series assessment of the degree of accomplishment. In practical applications, data acquisition is a significant constraint in evaluating the SDGs, not only in China but across the globe. Hence, expanding data channels and exploring the feasibility of various data sources for sustainable development assessment are effective strategies to tackle the challenge of data acquisition.Methods: In light of this issue, this study selected Nighttime Light Data, a remote sensing data source closely linked to human social activities, as an alternative data source. Using Yunnan Province as an example, 16 localized indicators of social, economic, and environmental types were chosen. These indicators were then subjected to a correlation analysis with the Total Nighttime Light Index (TNLI). The relationships between different types of indicators and TNLI were analyzed at both temporal and spatial scales, thus identifying the indicators for which TNLI could serve as a suitable substitute measure.Results: The study indicates that when the SDG indicators are classified into economic, social and environmental categories, the total value of nighttime light presents a significant correlation and substitutability with economic indicators; significantly correlated with some social indicators, it can reveal the weak links in the development of underdeveloped areas; it is not significantly correlated with environmental indicators, while a trend correlation exists, which can provide some reference values.Discussion: This study has demonstrated the feasibility of using Nighttime Light Data for sustainable development assessment. It provides a novel evaluation method for countries that, despite a lack of resources for conducting sustainable development assessments, have a greater need for such assessments due to their lower economic development. Furthermore, a multitude of assessment methods can be developed based on Nighttime Light Data

Ginsenoside Rg3 Attenuates Lipopolysaccharide-Induced Acute Lung Injury via MerTK-Dependent Activation of the PI3K/AKT/mTOR Pathway

Acute lung injury (ALI) is a common clinical disease with high morbidity in both humans and animals. Ginsenoside Rg3, a type of traditional Chinese medicine extracted from ginseng, is widely used to cure many inflammation-related diseases. However, the specific molecular mechanism of the effects of ginsenoside Rg3 on inflammation has rarely been reported. Thus, we established a mouse model of lipopolysaccharide (LPS)-induced ALI to investigate the immune protective effects of ginsenoside Rg3 and explore its molecular mechanism. In wild type (WT) mice, we found that ginsenoside Rg3 treatment significantly mitigated pathological damages and reduced myeloperoxidase (MPO) activity as well as the production of pro-inflammatory cytokines tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6); furthermore, the production of anti-inflammatory mediators interleukin-10 (IL-10) and transforming growth factor-β (TGF-β), polarization of M2 macrophages and expression levels of the phosphorylation of phosphatidylinositol 3-hydroxy kinase (PI3K), protein kinase B (PKB, also known as AKT), mammalian target of rapamycin (mTOR) and Mer receptor tyrosine kinase (MerTK) were promoted. However, there were no significant differences with regards to the pathological damage, MPO levels, inflammatory cytokine levels, and protein expression levels of the phosphorylation of PI3K, AKT and mTOR between the LPS treatment group and ginsenoside Rg3 group in MerTK-/- mice. Taken together, the present study demonstrated that ginsenoside Rg3 could attenuate LPS-induced ALI by decreasing the levels of pro-inflammatory mediators and increasing the production of anti-inflammatory cytokines. These processes were mediated through MerTK-dependent activation of its downstream the PI3K/AKT/mTOR pathway. These findings identified a new site of the specific anti-inflammatory mechanism of ginsenoside Rg3

AI of Brain and Cognitive Sciences: From the Perspective of First Principles

Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation. Despite these powerful applications, there are still many tasks in our daily life that are rather simple to humans but pose great challenges to AI. These include image and language understanding, few-shot learning, abstract concepts, and low-energy cost computing. Thus, learning from the brain is still a promising way that can shed light on the development of next-generation AI. The brain is arguably the only known intelligent machine in the universe, which is the product of evolution for animals surviving in the natural environment. At the behavior level, psychology and cognitive sciences have demonstrated that human and animal brains can execute very intelligent high-level cognitive functions. At the structure level, cognitive and computational neurosciences have unveiled that the brain has extremely complicated but elegant network forms to support its functions. Over years, people are gathering knowledge about the structure and functions of the brain, and this process is accelerating recently along with the initiation of giant brain projects worldwide. Here, we argue that the general principles of brain functions are the most valuable things to inspire the development of AI. These general principles are the standard rules of the brain extracting, representing, manipulating, and retrieving information, and here we call them the first principles of the brain. This paper collects six such first principles. They are attractor network, criticality, random network, sparse coding, relational memory, and perceptual learning. On each topic, we review its biological background, fundamental property, potential application to AI, and future development.Comment: 59 pages, 5 figures, review articl

Breast cancer is associated with coronary heart disease: a cross-sectional survey of NHANES 1999–2018

BackgroundUnderstanding the correlation between female breast cancer (BC) and the prevalence of coronary heart disease (CHD) is important for developing prevention strategies and reducing the burden of female social disease. This study aimed to evaluate the relationship between BC and CHD using data from the National Health and Nutrition Examination Survey (NHANES) database from 1999 to 2018.MethodsThe study cohort included 16,149 eligible non-pregnant female participants aged 20 years or older. Logistic regression was used to analyze the relationship between BC and CHD, excluding the interaction between covariates and BC through hierarchical subgroup analysis.ResultsThe study found that participants with BC had a 2.30 times greater risk of developing CHD compared to those without BC [95% confidence interval (CI): 2.29–2.31]. After adjusting for all included covariates, BC was still significantly associated with CHD risk (odds ratio: 1.11, 95% CI: 1.10–1.12). When participants were stratified by age, education level, and prevalence of hypertension, it was evident that participants with BC had a higher risk of developing CHD compared to those without BC, although the effect of BC on CHD varied across stratification.ConclusionsOur study demonstrates the close relationship between CHD and female BC. Therefore, it is necessary to screen patients with CHD for BC and monitor BC survivors for the long-term risk of developing CHD

Enhanced response of soil respiration to experimental warming upon thermokarst formation

As global temperatures continue to rise, a key uncertainty of terrestrial carbon (C)–climate feedback is the rate of C loss upon abrupt permafrost thaw. This type of thawing—termed thermokarst—may in turn accelerate or dampen the response of microbial degradation of soil organic matter and carbon dioxide (CO2) release to climate warming. However, such impacts have not yet been explored in experimental studies. Here, by experimentally warming three thermo-erosion gullies in an upland thermokarst site combined with incubating soils from five additional thermokarst-impacted sites on the Tibetan Plateau, we investigate how warming responses of soil CO2 release would change upon upland thermokarst formation. Our results show that warming-induced increase in soil CO2 release is ~5.5 times higher in thermokarst features than the adjacent non-thermokarst landforms. This larger warming response is associated with the lower substrate quality and higher abundance of microbial functional genes for recalcitrant C degradation in thermokarst-affected soils. Taken together, our study provides experimental evidence that warming-associated soil CO2 loss becomes stronger upon abrupt permafrost thaw, which could exacerbate the positive soil C–climate feedback in permafrost-affected regions