73 research outputs found

    User Engagement with Mobile Technologies: A Multi-Dimensional Conceptualization of Technology Use

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    Our study conceptualizes user engagement – a form of technology use targeting the emerging ubiquitous mobile technology generation such as mobile health (mHealth) and social network applications. User engagement manifests in three dimensions, including behavioral, cognitive, and emotional engagement. We validated the measures (in both objective and subjective forms) for the three-dimension user engagement in two different mobile technology contexts, i.e., an e-nursing mobile application and a question-and-answer social network application. We further delineated the relationships among the three dimensions: 1) prior behavioral engagement contributed to both emotional and cognitive engagement, 2) emotional engagement lead to post behavioral engagement, and 3) emotional engagement, compared with prior behavioral engagement and cognitive engagement, exerted a stronger influence predicting post behavioral engagement. Our study enriches both technology use and engagement literature

    Effects of Nonaerated Circulation Water Velocity on Nutrient Release from Aquaculture Pond Sediments

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    Sustaining good water quality in aquaculture ponds is vital. Without an aerator, the dissolved oxygen in ponds comes primarily from mass transfer at the water-ambient atmosphere interface. As sediment can seriously affect water quality, this study used indoor experiments to examine the nutrient (nitrogen and phosphorus) release mechanisms and fluxes from sediment in aquaculture ponds with moving water but no aeration. The results showed that the ammonia nitrogen (NH3-N) concentration in the overlying water was inversely proportional to flow velocity and that a higher flow velocity tended to result in a lower concentration in the overlying water, a steeper vertical gradient of concentration within the bed sediments, and a faster release rate from the sediments. The sediment disturbed by flowing water released more nitrate nitrogen (NO3-N) and nitrite nitrogen (NO2-N) into the overlying water and NO2-N could become oxidized into NO3-N. In still water, NO3-N was released gradually and some anaerobic NO3-N was nitrified into NO2-N. Phosphorus release from the sediments was controlled by the adsorption-desorption balance, with the phosphorus concentration in the overlying water dropping gradually to a steady value from its initial maximum. The relationship between NH3-N release flux and flow rate is described by a cubic function

    Water–Soil–Vegetation Dynamic Interactions in Changing Climate

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    Previous studies of land degradation, topsoil erosion, and hydrologic alteration typically focus on these subjects individually, missing important interrelationships among these important aspects of the Earth\u27s system. However, an understanding of water–soil–vegetation dynamic interactions is needed to develop practical and effective solutions to sustain the globe\u27s eco-environment and grassland agriculture, which depends on grasses, legumes, and other fodder or soil-building crops. This special issue is intended to be a platform for a discussion of the relevant scientific findings based on experimental and/or modeling studies. Its 12 peer-reviewed articles present data, novel analysis/modeling approaches, and convincing results of water–soil–vegetation interactions under historical and future climates. Two of the articles examine how lake/pond water quality is related to human activity and climate. Overall, these articles can serve as important references for future studies to further advance our understanding of how water, soil, and vegetation interactively affect the health and productivity of the Earth\u27s ecosystem. © 2017 by the authors

    Graph Prompt Learning: A Comprehensive Survey and Beyond

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    Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration with graph data, a cornerstone in our interconnected world, remains nascent. This paper presents a pioneering survey on the emerging domain of graph prompts in AGI, addressing key challenges and opportunities in harnessing graph data for AGI applications. Despite substantial advancements in AGI across natural language processing and computer vision, the application to graph data is relatively underexplored. This survey critically evaluates the current landscape of AGI in handling graph data, highlighting the distinct challenges in cross-modality, cross-domain, and cross-task applications specific to graphs. Our work is the first to propose a unified framework for understanding graph prompt learning, offering clarity on prompt tokens, token structures, and insertion patterns in the graph domain. We delve into the intrinsic properties of graph prompts, exploring their flexibility, expressiveness, and interplay with existing graph models. A comprehensive taxonomy categorizes over 100 works in this field, aligning them with pre-training tasks across node-level, edge-level, and graph-level objectives. Additionally, we present, ProG, a Python library, and an accompanying website, to support and advance research in graph prompting. The survey culminates in a discussion of current challenges and future directions, offering a roadmap for research in graph prompting within AGI. Through this comprehensive analysis, we aim to catalyze further exploration and practical applications of AGI in graph data, underlining its potential to reshape AGI fields and beyond. ProG and the website can be accessed by \url{https://github.com/WxxShirley/Awesome-Graph-Prompt}, and \url{https://github.com/sheldonresearch/ProG}, respectively

    Spatiotemporal Distribution of Eutrophication in Lake Tai as Affected by Wind

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    One common hypothesis is that wind can affect concentrations of nutrients (i.e., nitrogen and phosphorus) and chlorophyll-a (Chl-a) in shallow lakes. However, the tests of this hypothesis have yet to be conclusive in existing literature. The objective of this study was to use long-term data to examine how wind direction and wind speed affect the spatiotemporal variations of total nitrogen (TN), total phosphorus (TP) and Chl-a in Lake Tai, a typical shallow lake located in east China. The results indicated that the concentrations of nutrients and Chl-a tended to decrease from the northwest to the southeast of Lake Tai, with the highest concentrations in the two leeward bays (namely Meiliang Bay and Zhushan Bay) in the northwestern part of the lake. In addition to possible artificial reasons (e.g., wastewater discharge), the prevalent southeastward winds in warm seasons (i.e., spring and summer) and northwestward winds in cool seasons (i.e., fall and winter) might be the major natural factor for such a northwest-southeast decreasing spatial pattern. For the lake as a whole, the concentrations of TN, TP and Chl-a were highest for a wind speed between 2.1 and 3.2 m·s-1, which can be attributed to the idea that the wind-induced drifting and mixing effects might be dominant in the bays while the wind-induced drifting and resuspension effects could be more important in the other parts of the lake. Given that the water depth of the bays was relatively larger than that of the other parts, the drifting and mixing effects were likely dominant in the bays, as indicated by the negative relationships between the ratios of wind speed to lake depth, which can be a surrogate for the vertical distribution of wind-induced shear stress and the TN, TP and Chl-a concentration. Moreover, the decreasing temporal trend of wind speed in combination with the ongoing anthropogenic activities will likely increase the challenge for dealing with the eutrophication problem of Lake Tai. © 2017 by the authors

    Explosion risk assessment model for underground mine atmosphere

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    In the coal mining industry, explosions or mine fires present the most hazardous safety threats for coal miners or mine rescue members. Hence, the determination of the mine atmosphere explosibility and its evolution are critical for the success of mine rescues or controlling the severity of a mine accident. However, although there are numbers of methods which can be used to identify the explosibility, none of them could well indicate the change to the explosion risk time evolution. The reason is that the underground sealed atmospheric compositions are so complicated and their dynamical changes are also affected by various influence factors. There is no one method that could well handle all such considerations. Therefore, accurately knowing the mine atmospheric status is still a complicated problem for mining engineers. Method of analyzing the explosion safety margin for an underground sealed atmosphere is urgently desired. This article is going to propose a series of theoretical explosion risk assessment models to fully analyze the evolution of explosion risk in an underground mine atmosphere. Models are based on characteristics of the Coward explosibility diagram with combining mathematical analyzing approaches to address following problems: (1) for an "not-explosive" atmosphere, judging the evolution of explosion risk and estimating the change-of-state time span from "not-explosive" to "explosive" and (2) for an "explosive" atmosphere, estimating the "critical" time span of moving out of explosive zone and stating the best risk mitigation strategy. Such research efforts could not only help mine operators understand the explosibility risk of a sealed mine atmosphere but also provide a useful tool to wisely control explosive atmosphere away from any dangers. In order to demonstrate research findings, case studies for derived models are shown and are also used to instruct readers how to apply them. The results provide useful information for effectively controlling an explosive underground sealed atmosphere

    Validation of the children international IgA nephropathy prediction tool based on data in Southwest China

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    BackgroundImmunoglobulin A nephropathy (IgAN) is one of the most common kidney diseases leading to renal injury. Of pediatric cases, 25%–30% progress into end-stage kidney disease (ESKD) in 20–25 years. Therefore, predicting and intervening in IgAN at an early stage is crucial. The purpose of this study was to validate the availability of an international predictive tool for childhood IgAN in a cohort of children with IgAN treated at a regional medical centre.MethodsAn external validation cohort of children with IgAN from medical centers in Southwest China was formed to validate the predictive performance of the two full models with and without race differences by comparing four measures: area under the curve (AUC), the regression coefficient of linear prediction (PI), survival analysis curves for different risk groups, and R2D.ResultsA total of 210 Chinese children, including 129 males, with an overall mean age of 9.43 ± 2.71 years, were incorporated from this regional medical center. In total, 11.43% (24/210) of patients achieved an outcome with a GFR decrease of more than 30% or reached ESKD. The AUC of the full model with race was 0.685 (95% CI: 0.570–0.800) and the AUC of the full model without race was 0.640 (95% CI: 0.517–0.764). The PI of the full model with race and without race was 0.816 (SE = 0.006, P < 0.001) and 0.751 (SE = 0.005, P < 0.001), respectively. The results of the survival curve analysis suggested the two models could not well distinguish between the low-risk and high-risk groups (P = 0.359 and P = 0.452), respectively, no matter the race difference. The evaluation of model fit for the full model with race was 66.5% and without race was 56.2%.ConclusionsThe international IgAN prediction tool has risk factors chosen based on adult data, and the validation cohort did not fully align with the derivation cohort in terms of demographic characteristics, clinical baseline levels, and pathological presentation, so the tool may not be highly applicable to children. We need to build IgAN prediction models that are more applicable to Chinese children based on their particular data

    Tandem autologous hematopoietic stem cell transplantation for treatment of adult T-cell lymphoblastic lymphoma: a multiple center prospective study in China

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    T-cell lymphoblastic lymphoma (T-LBL) is a highly aggressive form of lymphoma with poor clinical outcomes and lacks of a standard treatment regimen. In this study, we assessed the safety and efficacy of tandem autologous hematopoietic stem cell transplantation (auto-HSCT) strategy for adult T-LBL and evaluated prognostic factors affecting survival. 181 Newly-diagnosed adult T-LBL patients were enrolled, 89 patients were treated with chemotherapy alone, 46 patients were allocated to single auto-HSCT group, 46 patients were treated with tandem auto-HSCT. The median follow-up time was 37 months, the 3-year progression/relapse rate of the tandem auto-HSCT group was significantly lower than that of the single auto-HSCT group and chemotherapy group (26.5% vs 53.1% and 54.8%). The 3-year PFS and OS rate of the tandem auto-HSCT group (73.5% and 76.3%) were significantly higher than those of the single auto-HSCT group (46.9% and 58.3%) and the chemotherapy group (45.1% and 57.1%). In the tandem auto-HSCT group, age and disease status after the first transplantation impacted the OS and PFS. Multivariate analysis identified that disease status after the first transplantation was the only independent prognostic factor for patients treated with tandem-HSCT. In addition, diagnostic models of the initial CD8+CD28+/CD8+CD28- T cell ratio in predicting the disease status were found to be significant. Taken together, tandem auto-HSCT can be considered an optimal strategy for adult T-LBL patients (ChiCTR-ONN-16008480)
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