Numerical study of vapor bubble effect on flow and heat transfer in microchannel

Flow boiling in a microchannel is characterized by nucleation and dynamic behavior of vapor bubbles in the channel. In the present study, the effect of vapor bubble on fluid flow and heat transfer in a microchannel is investigated via lattice Boltzmann (LB) modeling. With respect to boiling flow in a single microchannel, the bubble nucleation, growth, and departure are simulated by using an improved hybrid LB model. Relating bubble behavior with fluid flow and boiling heat transfer provides some insight into the relevant fundamental physics on flow boiling in the microchannel. It is found that the bubble growth before its departure from the wall induces an obvious resistance to the fluid flow. The processes of nucleation and motion of different bubbles interact, leading to an alternate, either enhanced or weakened, effect of bubble behavior on the flow boiling. (C) 2011 Elsevier Masson SAS. All rights reserved.</p

Learning Gaussian Mixture Representations for Tensor Time Series Forecasting

Tensor time series (TTS) data, a generalization of one-dimensional time series on a high-dimensional space, is ubiquitous in real-world scenarios, especially in monitoring systems involving multi-source spatio-temporal data (e.g., transportation demands and air pollutants). Compared to modeling time series or multivariate time series, which has received much attention and achieved tremendous progress in recent years, tensor time series has been paid less effort. Properly coping with the tensor time series is a much more challenging task, due to its high-dimensional and complex inner structure. In this paper, we develop a novel TTS forecasting framework, which seeks to individually model each heterogeneity component implied in the time, the location, and the source variables. We name this framework as GMRL, short for Gaussian Mixture Representation Learning. Experiment results on two real-world TTS datasets verify the superiority of our approach compared with the state-of-the-art baselines.Comment: 9 pages, 5 figures, published to IJCAI 202

Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting

With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the intricate spatio-temporal traffic patterns. In recent years, numerous neural networks with complicated architectures have been proposed to address this issue. However, the advancements in network architectures have encountered diminishing performance gains. In this study, we present a novel component called spatio-temporal adaptive embedding that can yield outstanding results with vanilla transformers. Our proposed Spatio-Temporal Adaptive Embedding transformer (STAEformer) achieves state-of-the-art performance on five real-world traffic forecasting datasets. Further experiments demonstrate that spatio-temporal adaptive embedding plays a crucial role in traffic forecasting by effectively capturing intrinsic spatio-temporal relations and chronological information in traffic time series.Comment: Accepted as CIKM2023 Short Pape

Physical Therapy Management Of A Manual Laborer With Chronic Rotator Cuff Tendinopathy: A Case Report

Background: Tendinopathy is characterized by tendon thickening, localized pain and chronic degeneration reflective of failed healing. 38% of manual laborers who participate in daily moderate to heavy lifting will experience Rotator Cuff Tendinopathy(RCT). There is a lack of research investigating the PT management of manual laborers who have RCT, but must continue to participate in harmful activities to fulfill occupational responsibilities. Purpose: The purpose of this case report was to describe the PT management of a patient with rotator cuff tendinopathy who, due to work requirements continued to participate in activities detrimental to the health of the supraspinatus and function of the shoulder girdle.https://dune.une.edu/pt_studcrposter/1036/thumbnail.jp

Patterns of deep fine root and water utilization amongst trees, shrubs and herbs in subtropical pine plantations with seasonal droughts

IntroductionSeasonal droughts will become more severe and frequent under the context of global climate change, this would result in significant variations in the root distribution and water utilization patterns of plants. However, research on the determining factors of deep fine root and water utilization is limited.MethodsWe measured the fine root biomass and water utilization of trees, shrubs and herbs, and soil properties, light transmission, and community structure parameters in subtropical pine plantations with seasonal droughts.Results and DiscussionWe found that the proportion of deep fine roots (below 1 m depth) is only 0.2-5.1%, but that of deep soil water utilization can reach 20.9-38.6% during the dry season. Trees improve deep soil water capture capacity by enhancing their dominance in occupying deep soil volume, and enhance their deep resource foraging by increasing their branching capacity of absorptive roots. Shrubs and herbs showed different strategies for deep water competition: shrubs tend to exhibit a “conservative” strategy and tend to increase individual competitiveness, while herbs exhibited an “opportunistic” strategy and tend to increase variety and quantity to adapt to competitions.ConclusionOur results improve our understanding of different deep fine root distribution and water use strategies between overstory trees and understory vegetations, and emphasize the importance of deep fine root in drought resistance as well as the roles of deep soil water utilization in shaping community assembly

The Galactic extinction and reddening from the South Galactic Cap U-band Sky Survey: u band galaxy number counts and u−ru-r color distribution

We study the integral Galactic extinction and reddening based on the galaxy catalog of the South Galactic Cap U-band Sky Survey (SCUSS), where $u$ band galaxy number counts and $u-r$ color distribution are used to derive the Galactic extinction and reddening respectively. We compare these independent statistical measurements with the reddening map of \citet{Schlegel1998}(SFD) and find that both the extinction and reddening from the number counts and color distribution are in good agreement with the SFD results at low extinction regions ($E(B-V)^{SFD}<0.12$ mag). However, for high extinction regions ($E(B-V)^{SFD}>0.12$ mag), the SFD map overestimates the Galactic reddening systematically, which can be approximated by a linear relation $\Delta E(B-V)= 0.43[E(B-V)^{SFD}-0.12$]. By combing the results of galaxy number counts and color distribution together, we find that the shape of the Galactic extinction curve is in good agreement with the standard $R_V=3.1$ extinction law of \cite{ODonnell1994}

The genomic and bulked segregant analysis of \u3ci\u3eCurcuma alismatifolia\u3c/i\u3e revealed its diverse bract pigmentation

Compared with most flowers where the showy part comprises specialized leaves (petals) directly subtending the reproductive structures, most Zingiberaceae species produce showy ‘‘flowers’’ through modifications of leaves (bracts) subtending the true flowers throughout an inflorescence. Curcuma alismatifolia, belonging to the Zingiberaceae family, a plant species originating from Southeast Asia, has become increasingly popular in the flower market worldwide because of its varied and esthetically pleasing bracts produced in different cultivars. Here, we present the chromosome-scale genome assembly of C. alismatifolia ‘‘Chiang Mai Pink’’ and explore the underlying mechanisms of bract pigmentation. Comparative genomic analysis revealed C. alismatifolia contains a residual signal of wholegenome duplication. Duplicated genes, including pigment-related genes, exhibit functional and structural differentiation resulting in diverse bract colors among C. alismatifolia cultivars. In addition, we identified the key genes that produce different colored bracts in C. alismatifolia, such as F3\u275’H, DFR, ANS and several transcription factors for anthocyanin synthesis, as well as chlH and CAO in the chlorophyll synthesis pathway by conducting transcriptomic analysis, bulked segregant analysis using both DNA and RNA data, and population genomic analysis. This work provides data for understanding the mechanism of bract pigmentation and will accelerate breeding in developing novel cultivars with richly colored bracts in C. alismatifolia and related species. It is also important to understand the variation in the evolution of the Zingiberaceae family