The Effect of Obesity on Insulin Resistance in Terms of Cytokines and Hormones

With the increasing incidence and mortality of obesity, obesity-related health problems have become a worldwide priority. Clinical observation shows that obesity related to adipocyte differentiation is an important pathogenic factor of insulin resistance, and weight loss can reduce insulin resistance, indicating that obesity is related to insulin resistance. As the understanding of mechanism between obesity, cytokines, hormones and insulin resistance becomes clear, it is possible that these cytokines or hormones could be used in the use of biomarkers and the design of targeted therapies for insulin resistance. This review provides an overview of how obesity effect adipokines，hepatokines and inflammatory cytokines whose changes result in or exacerbate insulin resistance

Joint prediction of travel mode choice and purpose from travel surveys: A multitask deep learning approach

The prediction and behavioural analysis of travel mode choice and purpose are critical for transport planning and have attracted increasing interest in research. Traditionally, the prediction of travel mode choice and trip purpose has been tackled separately, which fail to fully leverage the shared information between travel mode and purpose. This study addresses this gap by proposing a multitask learning deep neural network framework (MTLDNN) to jointly predict mode choice and purpose. We empirically evaluate and validate this framework using the household travel survey data in Greater London, UK. The results show that this framework has significantly lower cross-entropy loss than multinomial logit models (MNL) and single-task-learning deep neural network models (STLDNN). On the other hand, the predictive accuracy of MTLDNN is similar to STLDNN and is significantly higher than MNL. Moreover, in terms of behaviour analysis, the substitution pattern and choice probability of MTLDNN regarding input variables largely agree with MNL and STLDNN. This work demonstrates that MTLDNN is efficient in utilising the information shared by travel mode choice and purpose, and is capable of producing behaviourally reasonable substitution patterns across travel modes. Future research would develop more advanced MTLDNN frameworks for travel behaviour analysis and generalise MTLDNN to other travel behaviour topics

MeshNet: Mesh Neural Network for 3D Shape Representation

Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics. Regarding the task of 3D shape representation, there have been extensive research efforts concentrating on how to represent 3D shapes well using volumetric grid, multi-view and point cloud. However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data. In this paper, we propose a mesh neural network, named MeshNet, to learn 3D shape representation from mesh data. In this method, face-unit and feature splitting are introduced, and a general architecture with available and effective blocks are proposed. In this way, MeshNet is able to solve the complexity and irregularity problem of mesh and conduct 3D shape representation well. We have applied the proposed MeshNet method in the applications of 3D shape classification and retrieval. Experimental results and comparisons with the state-of-the-art methods demonstrate that the proposed MeshNet can achieve satisfying 3D shape classification and retrieval performance, which indicates the effectiveness of the proposed method on 3D shape representation

Are business intelligence systems different to decision support systems and other business information systems?

A common view of information systems (IS) researchers is that business intelligence (BI) systems are essentially a type of decision support systems (DSS). This approach to knowledge implies that DSS theory can be transferred to BI systems in order to explain and predict their action. Further, some researchers feel that BI systems can also be adequately researched using general IS theory. This paper is the first from a project that is examining if BI systems have significant differences to operational IS and DSS. This first exploration is informed by a focus group of senior BI professionals. The study illuminates some differences between BI and other types of business IS and indicates that context could be significant for BI theorizing and that care is needed in transferring operational IS and DSS theory to BI systems research. In practice, these differences could be a source of project failure

Toward Real-World Light Field Super-Resolution

Deep learning has opened up new possibilities for light field super-resolution (SR), but existing methods trained on synthetic datasets with simple degradations (e.g., bicubic downsampling) suffer from poor performance when applied to complex real-world scenarios. To address this problem, we introduce LytroZoom, the first real-world light field SR dataset capturing paired low- and high-resolution light fields of diverse indoor and outdoor scenes using a Lytro ILLUM camera. Additionally, we propose the Omni-Frequency Projection Network (OFPNet), which decomposes the omni-frequency components and iteratively enhances them through frequency projection operations to address spatially variant degradation processes present in all frequency components. Experiments demonstrate that models trained on LytroZoom outperform those trained on synthetic datasets and are generalizable to diverse content and devices. Quantitative and qualitative evaluations verify the superiority of OFPNet. We believe this work will inspire future research in real-world light field SR.Comment: CVPRW 202

Dynamic Rheological Studies of Poly(p-phenyleneterephthalamide) and Carbon Nanotube Blends in Sulfuric Acid

We have studied the dynamic scanning of liquid-crystalline (LC) poly(p-phenyleneterephthalamide) sulfuric acid (PPTA-H2SO4) solution, and its blend with single-walled carbon nanotubes (SWNTs), by using a flat plate rotational rheometer. The effects of weight concentration and molecular weight of PPTA, as well as operating temperature, on dynamic viscoelasticity of the PPTA-H2SO4 LC solution system are discussed. The transition from a biphasic system to a single-phase LC occurs in the weight concentration range of SWNTs from 0.1% to 0.2%, in which complex viscosity reaches the maximum at 0.2 wt% and the minimum at 0.1 wt%, respectively, of SWNTs. With increasing SWNT weight concentration, the endothermic peak temperature increases from 73.6 to 79.9 °C. The PPTA/SWNT/H2SO4 solution is in its plateau zone and storage modulus (G′) is a dominant factor within the frequency (ω) range of 0.1–10 rad/s. As ω increases, the G′ rises slightly, in direct proportion to the ω. The loss modulus (G″) does not rise as a function of ω when ω < 1 s−1, then when ω > 1 s−1 G″ increases faster than G′, yet not in any proportion to the ω

Does Friend Support Matter? The Association between Gender Role Attitudes and School Bullying among Male Adolescents in China

This study investigated the association between gender role attitudes, perceived friend support, and school bullying among male adolescents from 11 schools in two cities in China. A total of 3172 Chinese adolescents between 12 and 20 years of age (48.80% girls and 51.20% boys) completed questionnaires that included measures of bullying, gender role attitudes, and perceived social support. In terms of outcome measures, the Chinese version of the Illinois Bully Scale (IBS), Attitudes toward Women Scale for Adolescents (AWSA), and Multidimensional Scale of Perceived Social Support (MSPSS) were used to assess bullying perpetration, gender role attitudes, and perceived friend support, respectively. Based on masculinity theories and the stress-buffering theory, the study found that male adolescents held more traditional gender role attitudes (t = 30.78, p < 0.001) and reported higher prevalence of bullying behaviors (36.02%) than girls (31.20%). In addition, boys’ bullying behaviors were significantly predicted by gender role attitudes through perceived friend support. That is, male youth with more conservative gender role attitudes reported less perceived friend support (adjusted OR = 1.055; SE = 0.013), which elevated their risks of bullying perpetration (adjusted OR = 2.082; SE = 0.302). These findings have critical implications for bullying intervention and prevention through gender equity education