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In order to improve the problem of low image quality caused by insufficient illumination, a low-light image enhancement method with robustness is proposed, which can effectively handle extremely dark images while achieving good results for scenes with insufficient local illumination. First, we expose the images to different degrees to form a multi-exposure image sequence; then, we introduce global-based luminance weights and contrast-based gradient weights to fuse the multi-exposure image sequence; finally, we use a bootstrap filter to suppress the noise that may occur during the image processing. We employ pertinent assessment criteria, such as the Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), the Average Gradient (AG), and the Figure Definition (FD), to assess how well the method enhances. Experimental results show that PSNR (31.32) and SSIM (0.74) are the highest in pretty dark scenes compared to most conventional algorithms such as MF, BIMEF, LECARM, etc. Similarly, in processing uneven illumination such as "moonlit night" images, the AG (10.21) and the FD (14.54) are at maximum. In addition, other evaluation metrics such as Shannon (SH) are optimal in the above scenarios. In addition, we apply the algorithm in this paper to the online monitoring images of electric power equipment, which can improve the image lightness while recovering the detail information. The algorithm has strong robustness in extremely dark images and natural low-light images, and the enhanced images have minimal distortion and best appearance in different low-light scenes. [ABSTRACT FROM AUTHOR]

Optimizing the train plan for high-speed rail systems should consider both the passengers' demands and enterprise's benefits. The choice of the departure time period is the most important factor affecting the passenger demand distribution. In this paper, the optimization problem of a train operation plan based on time period preference is studied for a high-speed rail corridor. First, according to the travel process of the passengers, the extended service network for a high-speed rail system is established. The main factors that influence the passengers' travel choices are analyzed, and the departure time period preference, stop time and flexible pricing strategy based on the time period preference are put forward. The generalized travel cost function, including the convenience, ticket fare and stop time costs, is constructed, and a two-level programming model is established based on the function. The upper-level planning model is formulated as a mixed 0–1 programming problem that aims at maximizing the revenue of the railway enterprise. It is mainly constrained by passenger travel demand and solved by improved genetic algorithms. The lower-level model is a user equilibrium (UE) model. The Frank–Wolfe algorithm is used to allocate multiple groups of OD (origin and destination) passenger flows to each train so that the generalized travel expenses of all the passengers with the same OD are minimized and equal. Finally, the train operation plan is solved based on the Lan-xi (Lanzhou–Xi'an) high-speed rail data, and the validity of both the model and algorithm is verified. [ABSTRACT FROM AUTHOR]

A surge chamber is a common pressure reduction facility in a hydropower plant. Owing to large flow inertia in the upstream headrace tunnel and downstream tailrace tunnel, a hydropower plant with upstream and downstream surge chambers (HPUDSC) was adopted. This paper aimed to investigate the operational stability and nonlinear dynamic behavior of a HPUDSC. Firstly, a nonlinear dynamic model of the HPUDSC system was built. Subsequently, the operational stability and nonlinear dynamic behavior of the HPUDSC system were studied based on Hopf bifurcation theory and numerical simulation. Finally, the influencing factors of stability of the HPUDSC system were investigated. The results indicated the nonlinear HPUDSC system occurred at subcritical Hopf bifurcation, and the stability domain was located above the bifurcation curve, which provided a basis for the tuning of the governor parameters during operation. The dominant factors of stability and dynamic behavior of the HPUDSC system were flow inertia and head loss of the headrace tunnel and the area of the upstream surge chamber. Either increasing the head loss of the headrace tunnel and area of the upstream surge chamber or decreasing the flow inertia of the headrace tunnel could improve the operational stability of the HPUDSC. The proposed conclusions are of crucial engineering value for the stable operation of a HPUDSC. [ABSTRACT FROM AUTHOR]

In bacteria, small non-coding RNAs (sRNAs) are critical regulators of cellular adaptation to changes in metabolism, physiology, or the external environment. In the last decade, more than 2000 of sRNA families have been reported in the Rfam database and have been shown to exert various regulatory functions in bacterial transcription and translation. However, little is known about sRNAs and their functions in Mesorhizobium. Here, we predicted putative sRNAs in the intergenic regions (IGRs) of M. huakuii 7653R by genome-wide comparisons with four related Mesorhizobial strains. The expression and transcribed regions of candidate sRNAs were analyzed using a set of high-throughput RNA deep sequencing data. In all, 39 candidate sRNAs were found, with 5 located in the symbiotic megaplasmids and 34 in the chromosome of M. huakuii 7653R. Of these, 24 were annotated as functional sRNAs in the Rfam database and 15 were recognized as putative novel sRNAs. The expression of nine selected sRNAs was confirmed by Northern blotting, and most of the nine selected sRNAs were highly expressed in 28 dpi nodules and under symbiosis-mimicking conditions. For those putative novel sRNAs, functional categorizations of their target genes were performed by analyzing the enriched GO terms. In addition, MH_s15 was shown to be an abundant and conserved sRNA.

Organisms have been shifting their timing of life history events (phenology) in response to changes in the emergence of resources induced by climate change. Yet understanding these patterns at large scales and across long time series is often challenging. Here we used the US weather surveillance radar network to collect data on the timing of communal swallow and martin roosts and evaluate the scale of phenological shifts and its potential association with temperature. The discrete morning departures of these aggregated aerial insectivores from ground‐based roosting locations are detected by radars around sunrise. For the first time, we applied a machine learning algorithm to automatically detect and track these large‐scale behaviors. We used 21 years of data from 12 weather surveillance radar stations in the Great Lakes region to quantify the phenology in roosting behavior of aerial insectivores at three spatial levels: local roost cluster, radar station, and across the Great Lakes region. We show that their peak roosting activity timing has advanced by 2.26 days per decade at the regional scale. Similar signals of advancement were found at the station scale, but not at the local roost cluster scale. Air temperature trends in the Great Lakes region during the active roosting period were predictive of later stages of roosting phenology trends (75% and 90% passage dates). Our study represents one of the longest‐term broad‐scale phenology examinations of avian aerial insectivore species responding to environmental change and provides a stepping stone for examining potential phenological mismatches across trophic levels at broad spatial scales. [ABSTRACT FROM AUTHOR]