Prominent gaps exist between science and practice in the field of nature-based solutions (NBS) worldwide, with relatively well formulated concepts but less clear application procedures. China ...urgently needs to address this gap because many so called NBS practices advance rapidly nowadays, including river landscapes. Advocating planning as a bridging procedure in China’s top down governance system, this study introduces NBS planning for the Jialing River in Wusheng County to address three challenges: how to transform the riverfront planning from specialized to holistic, how to effectively communicate NBS in planning, and how to incorporate both scientific results and local wisdom into NBS decision-making. A planning scope was negotiated to incorporate holistic solutions. Five NBS paradigms were identified for better communication, and then spatially allocated with specific design guidelines and governance strategies. Our pilot study calls for reflection on the communication of NBS to the public, and alternative models of NBS implementations customized to different government regimes.
In urban stochastic transportation networks, there are specific links that hold great importance. Disruptions or failures in these critical links can lead to reduced connectivity within the road ...network. Under this circumstance, this manuscript proposed a novel identification of critical links mathematical optimization model based on the optimal reliable path with consideration of link correlations under demand uncertainty. The method presented in this paper offers a solution to bypass the necessity of conducting a full scan of the entire road network. Due to the non-additive and non-linear properties of the proposed model, a modified heuristic algorithm based on K-shortest algorithm and inequality technical is presented. The numerical experiments are conducted to show that improve a certain road link may not necessarily improve the overall traffic conditions. Moreover, the results indicate that if the travel time reliability is not considered, it will bring errors to the identification of key links.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Organizations that compete for attention in the marketplace face a strategic decision: whether to target a specialized niche or diversify to reach a broader market. Previous research has extensively ...analyzed the specialization dilemma faced by for-profit firms. We extend the analysis to knowledge-sharing groups in the marketplace of ideas. Using data on over 1,500 technology groups collected from an online event-organizing platform over a fifteen-year period, we measure the effect of topical focus, rarity, novelty, and technical exclusivity on audience growth, retention, and sustained engagement. We find that knowledge-sharing groups benefit marginally by specializing in rare topics but not in new topics. The strongest predictor of growth and survival is whether the group is associated with technically sophisticated topics, regardless of the breadth of focus, even though technical topics are less widely accessible. We conclude that what matters is not how specialized the organization, but how the organization is specialized.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In the original publication, the given names and surnames of the authors were swapped in one of the references and hence incorrectly cited as Blal et al. 2017 in the article.
Entropy waves play an important role in the production of indirect combustion noise and thermoacoustic instability. The characteristics of entropy waves from swirling flames have not been ...systematically investigated. Here, a premixed methane/air swirl burner was built with upstream acoustic excitation from a loudspeaker. A joint infrared imaging and tunable diode laser absorption spectroscopy (TDLAS) thermometry measurement was carried out to investigate the entropy waves generated in this burner. The infrared imaging technique provides qualitative images of the distribution, propagation and dissipation of entropy waves while the TDLAS technique provides quantitative measurement of temperature fluctuation. Time resolved measurements of the swirling flame bulk velocity, CH* chemiluminescence, gas temperature and infrared images were simultaneously obtained, demonstrating that entropy waves were generated from the premixed swirling flame under external acoustic excitation. Entropy waves were shown to be greatly influenced by the amplitude and frequency of acoustic waves. They also showed dissipation as the entropy waves propagate downstream according to the attenuated temperature fluctuation and infrared radiation intensities. Simultaneous high speed infrared imaging and particle image velocimetry measurements showed that the temperature non-uniformities arise from engulfment and mixing through periodic vortex roll-up.
This study proposes an effective wind speed forecasting model combining a data processing strategy, neural network predictor, and parameter optimization method. (a) Variational mode decomposition ...(VMD) is adopted to decompose the wind speed data into multiple subseries where each subseries contains unique local characteristics, and all the subseries are converted into two-dimensional samples. (b) A gated recurrent unit (GRU) is sequentially modeled based on the obtained samples and makes the predictions for future wind speed. (c) The grid search with rolling cross-validation (GSRCV) is designed to simultaneously optimize the key parameters of VMD and GRU. To evaluate the effectiveness of the proposed VMD-GRU-GSRCV model, comparative experiments based on hourly wind speed data collected from the National Renewable Energy Laboratory are implemented. Numerical results show that the root mean square error, mean absolute error, mean absolute percentage error, and symmetric mean absolute percentage error of this proposed model reach 0.2047, 0.1435, 3.77%, and 3.74%, respectively, which outperform the benchmark predictions using popular parameter optimization methods, data processing techniques, and hybrid neural network forecasting models.
In this study, data-driven deep learning methods were applied in order to model and predict the treatment of real municipal wastewater using anaerobic membrane bioreactors (AnMBRs). Based on the ...one-year operating data of two AnMBRs, six parameters related to the experimental conditions (temperature of reactor, temperature of environment, temperature of influent, influent pH, influent COD, and flux) and eight parameters for wastewater treatment evaluation (effluent pH, effluent COD, COD removal efficiency, biogas composition (CH4, N2, and CO2), biogas production rate, and oxidation-reduction potential) were selected to establish the data sets. Three deep learning network structures were proposed to analyze and reproduce the relationship between the input parameters and output evaluation parameters. The statistical analysis showed that deep learning closely agrees with the AnMBR experimental results. The prediction accuracy rate of the proposed densely connected convolutional network (DenseNet) can reach up to 97.44%, and the single calculation time can be reduced to within 1 s, suggesting the high performance of AnMBR treatment prediction with deep learning methods.
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•Deep learning was applied to predict the treatment of municipal wastewater by AnMBRs.•Multiple deep learning network structures were proposed, optimized, and compared.•The prediction accuracy of the deep learning methods could reach up to 97.44%.•Multiple parameters for evaluating AnMBR treatment were collected and analyzed.•The deep learning approach agreed with the AnMBR experimental results.
Water management in the catalyst layers (CLs) of proton-exchange membrane fuel cells is crucial for its commercialization and popularization. However, the high experimental or computational cost in ...obtaining water distribution and diffusion remains a bottleneck in the existing experimental methods and simulation algorithms, and further mechanistic exploration at the nanoscale is necessary. Herein, we integrate, for the first time, molecular dynamics simulation with our customized analysis framework based on a multiattribute point cloud dataset and an advanced deep learning network. This was achieved through our workflow that generates simulated transport data of water molecules in the CLs as the training and test dataset. Deep learning framework models the multibody solid–liquid system of CLs on a molecular scale and completes the mapping from the Pt/C substrate structure and Nafion aggregates to the density distribution and diffusion coefficient of water molecules. The prediction results are comprehensively analyzed and error evaluated, which reveals the highly anisotropic interaction landscape between 50,000 pairs of interacting nanoparticles and explains the structure and water transport property relationship in the hydrated Nafion film on the molecular scale. Compared to the conventional methods, the proposed deep learning framework shows computational cost efficiency, accuracy, and good visual display. Further, it has a generality potential to model macro- and microscopic mass transport in different components of fuel cells. Our framework is expected to make real-time predictions of the distribution and diffusion of water molecules in CLs as well as establish statistical significance in the structural optimization and design of CLs and other components of fuel cells.
To better conserve the ecology of the wild
range, we studied the rhizosphere microenvironment of
in Beijing's Yunmeng Mountain National Forest Park.
rhizosphere soil physicochemical properties and ...enzyme activities changed significantly with temporal and elevational gradients. The correlations between soil water content (SWC), electrical conductivity (EC), organic matter content (OM), total nitrogen content (TN), catalase activity (CAT), sucrose-converting enzyme activity (INV), and urease activity (URE) were significant and positive in the flowering and deciduous periods. The alpha diversity of the rhizosphere bacterial community was significantly higher in the flowering period than in the deciduous period, and the effect of elevation was insignificant. The diversity of the
rhizosphere bacterial community changed significantly with the change in the growing period. A network analysis of the correlations revealed stronger linkages between the rhizosphere bacterial communities in the deciduous period than in the flowering period.
was the dominant genus in both periods, but its relative abundance decreased in the deciduous period. Changes in the relative abundance of
may be the main factor influencing the changes in the
rhizosphere bacterial community. Moreover, the
rhizosphere bacterial community and soil characteristics were significantly correlated. Additionally, the influence of soil physicochemical properties on the rhizosphere bacterial community was larger than that of enzyme activity on the bacterial community. We mainly analyzed the change patterns in the rhizosphere soil properties and rhizosphere bacterial diversity of
during temporal and spatial variation, laying the foundation for further understanding of the ecology of wild
.
Deterministic optimization has been successfully applied to a series of design problems of square thin-walled energy absorption tubes and to a certain extent has fulfilled great expectations for the ...application of such structures in subway vehicles. However, most studies have not considered the uncertainty of parameters or the correlation of uncertainty parameters, leaving little or no tolerance and resulting in over-conservative structural design. This research proposes a multi-objective uncertain method with an ellipsoid-based model to address the effects of parametric uncertainties of a centrally symmetrical square tube with diaphragms (CSSTD) on design optimization, in which the ellipsoid model is adopted to describe the related uncertainty parameters. The nonlinear interval number programming method coupled with a reliability-based possibility degree of interval (RPDI) model is introduced to handle the transformation of uncertain optimization problems. Simultaneously, local-densifying technology is adopted to enhance the local accuracy of the approximate model. Finally, the outer layer of the micro multi-objective genetic algorithm (
μ
MOGA) combined with the inner layer of the intergeneration projection genetic algorithm (IP-GA) is applied to solve the Pareto optimal solution set of the transformed deterministic optimization. The optimization results indicate that the proposed multi-objective uncertain optimization with an ellipsoid-based model not only guarantees the crashworthiness of the CSSTD, but also improves the design robustness, which means that the proposed method can provide insightful information for crashworthiness design of subways.
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