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We analyzed the characteristics of main karstic/non-karst reaches of the Lijiang River to uncover the causes behind different flood behaviors by providing a better understanding of the flood formation. Having 63 years of rainfall-runoff data and applying the HEC-HMS model, geo/hydrological features were investigated. The available reservoir capacity of karts (ARCK) was included through soil moisture accounting loss data to assess its impact. In particular, the expected instantaneous peak discharge rates/times were found largely imbalanced with generated unit hydrographs. Moreover, significant gaps among the floods’ features for different subbasins in terms of required peak modifications (2–4 times larger for mid-upstream, respectively) were mainly associated with the unique karst structure and initial condition due to various ARCK in rainy/dry seasons. Besides, notable dissimilarities between the wedge/prism storage volumes and the hydrograph’s wave traveling/receding time were observed owing to the geomorphological conditions. Although the contribution rates of drivers in karst flood formation cannot be quantitively modeled, based on our results the ARCK emerged to play a substantial role on the forecasted results, comparatively. Our results suggest that since ARCK varies, taking it into account (as initial abstraction) results in a more reliable estimation. This was underpinned by the results in which the unmodified simulations had a qualified rate of 52% accuracy on average and increased to 67.5% after the ARCK inclusion. This work adds to the body of evidence illustrating that in karst hydrology, ignoring the situational circumstances in modeling might lead to inaccuracies in flood forecasting for such dynamic watersheds. HIGHLIGHTS Hydrological models inaccurately forecast flood features in karst basins.; The seasonality of available karst reservoir capacity drives flood peaks.; Initial conditions must be considered in model calibration for karstic areas.;

As the single sensor is not competent for the variation of the total amount and components of DOM in a large range, according to the cross-sensitivity between fiber SPR sensors with different structures, a measuring method of DOM components is proposed by combining the deep learning algorithm with the fiber SPR sensors based on the regulation of metal film thickness. We exploit an improved cooperative particle swarm optimization algorithm (ICPSO) aiming at the problem of particle diversity loss caused by premature convergence of particles which not only considers the optimization information of single particles, the global particles and particles in the groups, but also considers the proportion of shared information. Then, the ICPSO is used to optimize the weights and thresholds of back propagation neural network (BPNN) to establish ICPSO-BP network, so as to construct three classifiers consists of ICPSO-BP (wave length), ICPSO-BP (spectral width), ICPSO-BP (light intensity). By comprehensive training of the resonance wavelength, spectrum width and light intensity of SPR effect for the measured water, five DOM components (tyrosine protein, tryptophan protein, fulvic acid, dissolved microbial metabolites and humic acid) and their concentrations in four water samples, namely, Inner canal (A), Hongze lake (B), Park lake (C) and Campus lake (D), have been effectively predicted. The prediction accuracy is more than 80%, among them, the highest prediction rate of tryptophan protein and its concentration in Hongze lake (B) which can reach 86%. Therefore, the dynamic range of SPR measurement is effectively expanded and better measurement accuracy and sensitivity are maintained, which verifies the feasibility of the proposed method in DOM measuring and provide a new idea for DOM component testing.

Textile reinforced concrete (TRC) was prepared from high-alumina cement mixed with short-cut basalt fiber or steel fiber for the objective to analyze the mechanical performance and deterioration mechanism at high temperature. The results show that the high-temperature resistance of TRC matrix concrete can be improved by adding steel fibers and basalt fibers, but the compressive strength of concrete decreases with the increase of temperature in a quadratic parabolic law, while the flexural strength decreases in a linear law. The addition of basalt fiber significantly improves the bending bearing capacity of the TRC plates. In comparison, the addition of steel fiber significantly improves the flexural strength of TRC thin-plates above 600°C. The scanning electron microscopy indicates that when the temperature surpasses 600°C, the addition of basalt fiber and the steel fiber surface are significantly damaged by high-temperature deterioration. The deterioration of the mechanical performance of TRC thin-plates at high temperatures is caused mainly by changes in the chemical composition of the concrete matrix, deterioration of the fiber and damage of the bonding surface. The results obtained in this study can provide the theoretical support for the design and application of TRC in a high-temperature environment.

Adequate thermal management to remove and dissipate the heat produced by the LED is one of the main challenges in designing LED applications. In view of the above problems, this paper analyzed a heat sink as a heat exchanger for the LED array via the experiment combined with the numerical simulation. The results show that the heat sink is necessary for the LED array to guarantee reliable and safe operation. Moreover, the influence of the height of heat sink on the heat transfer of the LED array is also analyzed, and the optimized height of the heat sink for the 20W LED array is 20 mm. Considering the heat transfer and the manufacturing cost, increasing the heat sink area blindly is not the best way to reduce the LED junction temperature, and more specific work should be considered.