Low-light Environments Research Articles

Key Parts of Transmission Line Detection Using Improved YOLO v3

Unmanned Aerial Vehicle (UAV) inspection has become one of main methods for current transmission line inspection, but there are still some shortcomings such as slow detection speed, low efficiency, and inability for low light environment. To address these issues, this paper proposes a deep learning detection model based on You Only Look Once (YOLO) v3. On the one hand, the neural network structure is simplified, that is the three feature maps of YOLO v3 are pruned into two to meet specific detection requirements. Meanwhile, the K-means++ clustering method is used to calculate the anchor value of the data set to improve the detection accuracy. On the other hand, 1000 sets of power tower and insulator data sets are collected, which are inverted and scaled to expand the data set, and are fully optimized by adding different illumination and viewing angles. The experimental results show that this model using improved YOLO v3 can effectively improve the detection accuracy by 6.0%, flops by 8.4%, and the detection speed by about 6.0%.

Physics-Based Noise Modeling for Extreme Low-Light Photography.

Enhancing the visibility in extreme low-light environments is a challenging task. Under nearly lightless condition, existing image denoising methods could easily break down due to significantly low SNR. In this paper, we systematically study the noise statistics in the imaging pipeline of CMOS photosensors, and formulate a comprehensive noise model that can accurately characterize the real noise structures. Our novel model considers the noise sources caused by digital camera electronics which are largely overlooked by existing methods yet have significant influence on raw measurement in the dark. It provides a way to decouple the intricate noise structure into different statistical distributions with physical interpretations. Moreover, our noise model can be used to synthesize realistic training data for learning-based low-light denoising algorithms. In this regard, although promising results have been shown recently with deep convolutional neural networks, the success heavily depends on abundant noisy-clean image pairs for training, which are tremendously difficult to obtain in practice. Generalizing their trained models to images from new devices is also problematic. Extensive experiments on multiple low-light denoising datasets - including a newly collected one in this work covering various devices - show that a deep neural network trained with our proposed noise formation model can reach surprisingly-high accuracy. The results are on par with or sometimes even outperform training with paired real data, opening a new door to real-world extreme low-light photography.

Modified Perceptual Cycle Generative Adversarial Network-Based Image Enhancement for Improving Accuracy of Low Light Image Segmentation

In recent years, the importance of the semantic segmentation field has been increasingly emphasized because autonomous vehicle and artificial intelligence (AI)-based robot technology are being researched extensively; and methods for accurately recognizing objects are required. Previous state-of-the-art segmentation methods have been proven to be effective for databases obtained during daytime. However, in extremely low light or nighttime environments, the shape and color information of objects are very small or disappear due to an insufficient amount of external light, which makes it difficult to train the segmentation network and significantly degrades performance. In our previous work, segmentation performance in a low light environment was improved using the enhancement-based segmentation method. However, low light images could not be restored precisely and segmentation performance improvement was limited because only per-pixel loss functions were used when training the enhancement network. To overcome these drawbacks, we propose a low light image segmentation method based on a modified perceptual cycle generative adversarial network (CycleGAN). Perceptual image enhancement was performed using our network, which significantly improved segmentation performance. Unlike the existing perceptual loss, the Euclidean distance of the feature maps extracted from the pretrained segmentation network was used. In our experiments, we used low light databases generated from two famous road scene open databases, which are Cambridge-driving Labeled Video Database (CamVid) and Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI), and confirmed that our proposed method shows better segmentation performance in extremely low light environments than the existing state-of-the art methods.

Photosynthetic and Morphological Acclimation to High and Low Light Environments in Petasites japonicus subsp. giganteus

Within each species, leaf traits such as light-saturated photosynthetic rate or dark respiration rate acclimate to local light environment. Comparing only static physiological traits, however, may not be sufficient to evaluate the effects of such acclimation in the shade because the light environment changes diurnally. We investigated leaf photosynthetic and morphological acclimation for a perennial herb, butterbur (Petasites japonicus (Siebold et Zucc.) Maxim. subsp. giganteus (G.Nicholson) Kitam.) (Asteraceae), in both a well-lit clearing and a shaded understory of a temperate forest. Diurnal changes in light intensity incident on the leaves were also measured on a sunny day and an overcast day. Leaves in the clearing were more folded and upright, whereas leaves in the understory were flatter. Leaf mass per area (LMA) was approximately twofold higher in the clearing than in the understory, while light-saturated photosynthetic rate and dark respiration rate per unit mass of leaf were similar between the sites. Consequently, both light-saturated photosynthetic rate and dark respiration rate per unit area of leaf were approximately twofold higher in the clearing than in the understory, consistent with previous studies on different species. Using this experimental dataset, we performed a simulation in which sun and shade leaves were hypothetically exchanged to investigate whether such plasticity increased carbon gain at each local environment. As expected, in the clearing, the locally acclimated sun leaves gained more carbon than the hypothetically transferred shade leaves. By contrast, in the understory, the daily net carbon gain was similar between the simulated sun and shade leaves on the sunny day due to the frequent sunflecks. Lower LMA and lower photosynthetic capacity in the understory reduced leaf construction cost per area rather than maximizing net daily carbon gain. These results indicate that information on static photosynthetic parameters may not be sufficient to evaluate shade acclimation in forest understories.

Pupillary and Anterior Chamber Changes Following Upper Eyelid Blepharoplasty.

This study investigated the preoperative and postoperative pupillary and anterior chamber (AC) characteristics of patients undergoing upper eyelid blepharoplasty. This prospective, cross-sectional study examined 32 eyes from 20 dermatochalasis patients. Following a detailed ophthalmological examination, quantitative pupillometry and Scheimpflug corneal topography were used to evaluate the pupil and AC characteristics of eyes on the day of operation and postoperative days 1 and 7. Static and dynamic pupillometry characteristics, including scotopic, mesopic, low photopic, and high photopic pupil diameter (PD), resting diameter, amplitude, latency, duration, and velocity of pupil contraction, and latency, duration, and velocity of pupil dilation, were measured. Additionally, AC volume, depth, and angle parameters were analyzed. There were significant differences between scotopic, mesopic, and resting PD and the amplitude of pupil contraction. Pairwise comparisons showed that postoperative day 1 scotopic, mesopic, and resting PD and amplitude of pupil contraction measurements were significantly higher than preoperative measurements (p = 0.008, p < 0.001, p = 0.006, and p = 0.033, respectively). Additionally, scotopic and resting PD were significantly lower on postoperative day 7 compared with postoperative day 1 (p = 0.001 and p = 0.041, respectively). However, there were no significant differences in AC parameters. This study revealed that static and dynamic pupil measurements changed following blepharoplasty, with postoperative increased PD occurring particularly under low-light conditions. Therefore, low-light environments should be avoided following blepharoplasty, and patients with angle-closure risk factors should be closely monitored in the early postoperative period.

Blue Light Exposure Effects on Sleep Attributes in a 72-Hour Military Exercise

In the digital age, the military is developing cutting edge technologies (e.g., heads-up and mixed reality displays, night vision devices, etc.) to maximize situational awareness and effectiveness. Effects of light exposure from screen-based systems on our Soldier-operators has not been fully considered. Some literature concludes that evening blue light exposure preceding bedtime can result in increased alertness and may negatively impact sleep, producing negative moodstates and sleepiness upon wakening. The current study looks at blue light exposure and subsequent sleep of forty-six Soldiers during a company-wide field exercise utilizing wrist-worn actigraphy and light sensors. Observations indicate periods of blue light exposure during evening field operations were followed by sleep intervals with significant decrements in sleep quality. Future studies will isolate the effects of military-specific blue light emitting device exposure in high contrast, low ambient light environments on sleep quality, providing specific guidance and interventions to reduce negative impacts on operations.

Screening of key genes responsible for Pennisetum setaceum 'Rubrum' leaf color using transcriptome sequencing.

Pennisetum setaceum ‘Rubrum’ is an ornamental grass plant that produces purple leaves in high-light environments and light purple or green leaves in low-light environments, the latter of which greatly reduces its aesthetic appeal. Therefore, we aimed to identify the key genes associated with leaf coloration and elucidate the molecular mechanisms involved in the color changes in P. setaceum ‘Rubrum’ leaves. We performed transcriptome sequencing of P. setaceum ‘Rubrum’ leaves before and after shading. A total of 19,043 differentially expressed genes were identified, and the numbers of upregulated and downregulated genes at T1 stage, when compared with their expression at the T0 stage, were 10,761 and 8,642, respectively. The possible pathways that determine P. setaceum ‘Rubrum’ leaf color included flavonoid biosynthesis, flavone and flavonol biosynthesis, and carotenoid biosynthesis. There were 31 differentially expressed genes related to chlorophyll metabolism, of which 21 were related to chlorophyll biosynthesis and 10 to chlorophyll degradation, as well as three transcription factors that may be involved in the regulation of chlorophyll degradation. There were 31 key enzyme genes involved in anthocyanin synthesis and accumulation in P. setaceum ‘Rubrum’ leaves, with four transcription factors that may be involved in the regulation of anthocyanin metabolism. The transcriptome data were verified and confirmed reliable by real-time fluorescence quantitative PCR analysis. These findings provide a genetic basis for improving leaf color in P. setaceum ‘Rubrum.’

Light availability influences the intensity of nectar robbery and its effects on reproduction in a tropical shrub via multiple pathways.

The multiple exogenous pathways by which light availability affects plant reproduction (e.g., via influence on attraction of mutualists and antagonists) remain surprisingly understudied. The light environment experienced by a parent can also have transgenerational effects on offspring via these same pathways. We evaluated (a) the influence of light availability on floral traits in Odontonema cuspidatum, (b) the relative importance of the pathways by which light influences nectar robbery and reproductive output, and (c) the role of parental light environment in mediating these relationships. We conducted a reciprocal translocation experiment using clonally propagated ramets and field surveys of naturally occurring plants. Light availability influenced multiple floral traits, including flower number and nectar volume, which in turn influenced nectar robbery. But nectar robbery was also directly influenced by light availability, due to light effects on nectar robber foraging behavior or neighborhood floral context. Parental light environment mediated the link between light availability and nectar robber attraction, suggesting local adaptation to low-light environments in floral visitor attraction. However, we found no transgenerational effect on reproduction. Our findings demonstrate that exogenous pathways by which light influences plants (particularly through effects on floral antagonists) can complicate the positive relationship between light availability and plant reproduction. Our results are among the first to document effects of light on floral antagonists and clonal transgenerational effects on flower visitor attraction traits.

Self-luminescent cement-based composite materials: properties and mechanisms

Self-luminescent cement-based composite materials (SLCCM) have been considered as an innovatively functional civil engineering material for environmental protection pavement and smart pavement. In this study, luminescent cement-based composite materials (LCCM) were prepared by doping luminescent powder (LP) and reflective powder (RP) into cement-based materials. The mechanical properties and luminous properties, as well as the luminescence mechanism, were evaluated. The results indicate that the incensement of RP mesh improved the mechanical strength of the LCCM specimen, while the mechanical strengths, brightness and afterglow time of the LCCM first increase and then decrease with an increase in the mesh of the LP. The LP and RP reacted with cement hydration products to produce abundant C-S-H. However, when the particle size was too small, SrAl2O4 could hydrolyzes to produce SrAl4O7, furthermore, a large amount of aluminum ions in the LP are free, which accelerates the hydration reaction of Ca(OH)2 to produce C-S-H and AFt crystals. The self-hydration of LP and the coverage of hydration products make the luminescent properties of LCCM with fine particles decrease, while the coarse particles have the lowest luminescent properties due to the limited effective luminescent area in cement. The optimum excitation spectrum of the LCCM is found in the range between 350 and 450 nm, and the emission spectrum is yellowish–green with a peak value of 510–520 nm. The comprehensive evaluation results of LCCM show great feasibility of its utilization in road pavement, which is help to save energy, reduce ecological light pollution and improve the safety of road traffic in low lighting environment.

Seeing Pedestrian in the Dark via Multi-Task Feature Fusing-Sharing Learning for Imaging Sensors.

Pedestrian detection is an essential problem of computer vision, which has achieved tremendous success under controllable conditions using visible light imaging sensors in recent years. However, most of them do not consider low-light environments which are very common in real-world applications. In this paper, we propose a novel pedestrian detection algorithm using multi-task learning to address this challenge in low-light environments. Specifically, the proposed multi-task learning method is different from the most commonly used multi-task learning method—the parameter sharing mechanism—in deep learning. We design a novel multi-task learning method with feature-level fusion and a sharing mechanism. The proposed approach contains three parts: an image relighting subnetwork, a pedestrian detection subnetwork, and a feature-level multi-task fusion learning module. The image relighting subnetwork adjusts the low-light image quality for detection, the pedestrian detection subnetwork learns enhanced features for prediction, and the feature-level multi-task fusion learning module fuses and shares features among component networks for boosting image relighting and detection performance simultaneously. Experimental results show that the proposed approach consistently and significantly improves the performance of pedestrian detection on low-light images obtained by visible light imaging sensor.

Growth of cucumber seedlings in different varieties as affected by light environment

High-quality cucumber seedlings are a prerequisite for ensuring high yield of cucumbers. With the continuous increase of cucumber planting area in China, the demand for high-quality cucumber seedlings is also increasing. One of the important ways to improve the quality of cucumber seedlings is to improve the light environment by using artificial light source. In this study, three cucumber seedlings (cv. Jintong, cv. Yunv and cv. Xiazhiguang) were grown for 23 d under eight levels of daily light integral (DLI) at 4.3 mol/(m2·d), 8.6 mol/(m2·d), 10.1 mol/(m2·d), 13.0 mol/(m2·d), 5.8 mol/(m2·d), 11.5 mol/(m2·d), 14.4 mol/(m2·d) and 17.3 mol/(m2·d), respectively. The results showed that when DLI was 14.4 mol/(m2·d), the seedling height, stem diameter, total leaf area and shoot dry/fresh weight of all three cucumber cultivars reached the maximum, while hypocotyl length decreased with the increase of light intensity. When DLI was 14.4 mol/(m2·d), Jintong and Xiazhiguang had the highest health index, which were 49.29 and 28.56, respectively, while that of Yunv was 81.59 (DLI = 14.4 mol/(m2·d)). With the increase of DLI, the photosynthetic capacity of cucumber increases gradually. The highest net photosynthetic rate was shown at DLI of 14.4 mol/(m2·d), while the chlorophyll content of cucumber seedlings of all three cultivars were less affected by DLI. Jintong and Yunv had the highest chlorophyll content when DLI was 8.6 mol/(m2·d) because they were adapted to low-light environment. In conclusion, the DLI of 14.4 mol/(m2·d) for Jintong and Xiazhiguang, while that of 17.3 mol/(m2·d) for Yunv are suggested for the light environment design of factory-cultivated seedling for cucumber. Keywords: cucumber seedling, daily light integral (DLI), growth, photosynthesis DOI: 10.25165/j.ijabe.20201305.5566 Citation: Ji F, Wei S Q, Liu N, Xu L J, Yang P. Growth of cucumber seedlings in different varieties as affected by light environment. Int J Agric & Biol Eng, 2020; 13(5): 73–78.

The Effect of Fertilizer on Epipremnum Aureum in Phytoremediating Soil Contaminated with Crude Oil

Epipremnum aureum is a common ornamental foliage which is used mainly for indoor decoration. It demonstrates tolerance to low light environment and the ability to remove atmospheric chemicals such as gasoline and formaldehyde. A previous screening study showed the ability of Epipremnum aureum to remove crude oil from soil. Further to the screening study, this study examined the effect of fertilization on phytoremediation of crude oil-contaminated soil by Epipremnum aureum Epipremnum aureum was grown in pots with soil contaminated with 5%, 10% and 15% of crude oil by weight respectively, with and without the addition of fertilizer. In line with previous study, Epipremnum aureum showed the ability to remove crude oil contaminants. The plant demonstrated highest crude oil removal rate in pots with 15% contamination. Application of fertilizer enhanced the removal of crude oil in all experimental pots by Week 6, hence increasing the rate of crude oil decline over the experimental duration. It is deemed that application of fertilizer provides nutrients to promote plant growth and proliferation of roots which enhance rhizodegradation of crude oil in soil. This study extends on the existing knowledge that Epipremnum aureum can phytoremediate crude-oil contaminated soil by characterizing its tolerance to crude oil and its response to fertilization, which is crucial to its practical application in phytoremediation. Phytoremediation presents a promising yet cost-effective measure in treating contaminated land. Further study can examine the optimum fertilizer concentration for phytoremediation and the tolerance level of Epipremnum aureum to crude oil contamination.

Accuracy Assessment of Small Unmanned Aerial Vehicle for Traffic Accident Photogrammetry in the Extreme Operating Conditions of Kuwait

This study presents the first accuracy assessment of a low cost small unmanned aerial vehicle (sUAV) in reconstructing three dimensional (3D) models of traffic accidents at extreme operating environments. To date, previous studies have focused on the feasibility of adopting sUAVs in traffic accidents photogrammetry applications as well as the accuracy at normal operating conditions. In this study, 3D models of simulated accident scenes were reconstructed using a low-cost sUAV and cloud-based photogrammetry platform. Several experiments were carried out to evaluate the measurements accuracy at different flight altitudes during high temperature, low light, scattered rain and dusty high wind environments. Quantitative analyses are presented to highlight the precision range of the reconstructed traffic accident 3D model. Reported results range from highly accurate to fairly accurate represented by the root mean squared error (RMSE) range between 0.97 and 4.66 and a mean percentage absolute error (MAPE) between 1.03% and 20.2% at normal and extreme operating conditions, respectively. The findings offer an insight into the robustness and generalizability of UAV-based photogrammetry method for traffic accidents at extreme environments.

The coordination of OsbZIP72 and OsMYBS2 with reverse roles regulates the transcription of OsPsbS1 in rice.

Nonphotochemical quenching (NPQ), an intricate photoprotective process, plays fundamental roles in maintaining plant fitness. The PsbS protein is essential for the rapid induction of NPQ, and acts in a dose-dependent manner in leaves. However, little information is known on the transcriptional control of PsbS in land plants. Here we demonstrated that the expression of OsPsbS1 is directly upregulated by OsbZIP72 while repressed by OsMYBS2 in rice. We identified a new cis-element GACAGGTG in japonica OsPsbS1 promoter, to which OsbZIP72 could strongly bind and activate the expression of OsPsbS1. The new cis-element CTAATC confers specific binding for OsMYBS2 in japonica OsPsbS1 promoter. OsbZIP72 can be activated by SAPK1, and acts depending on the abscisic acid (ABA) signalling pathway. GF14A protein affects the repression activity of OsMYBS2 by regulating its nucleocytoplasmic shuttling, and Ser53 is necessary for OsMYBS2 to be retained in the cytoplasm. The inducibility of OsPsbS1 transcription under high light conditions in OsbZIP72 knockout lines was greatly impaired, while the repression of OsPsbS1 transcription under a low light environment in OsMYBS2 knockout lines was significantly alleviated. These results reveal cross-talk among NPQ processes, the ABA signalling pathway and abiotic stress signalling. The elaborate mechanisms may help enhance photoprotection and improve photosynthesis in rice.

Gaze in the Dark: Gaze Estimation in a Low-Light Environment with Generative Adversarial Networks.

In smart interactive environments, such as digital museums or digital exhibition halls, it is important to accurately understand the user’s intent to ensure successful and natural interaction with the exhibition. In the context of predicting user intent, gaze estimation technology has been considered one of the most effective indicators among recently developed interaction techniques (e.g., face orientation estimation, body tracking, and gesture recognition). Previous gaze estimation techniques, however, are known to be effective only in a controlled lab environment under normal lighting conditions. In this study, we propose a novel deep learning-based approach to achieve a successful gaze estimation under various low-light conditions, which is anticipated to be more practical for smart interaction scenarios. The proposed approach utilizes a generative adversarial network (GAN) to enhance users’ eye images captured under low-light conditions, thereby restoring missing information for gaze estimation. Afterward, the GAN-recovered images are fed into the convolutional neural network architecture as input data to estimate the direction of the user gaze. Our experimental results on the modified MPIIGaze dataset demonstrate that the proposed approach achieves an average performance improvement of 4.53%–8.9% under low and dark light conditions, which is a promising step toward further research.

Light regulates inorganic nitrogen uptake and storage, but not nitrate assimilation, by the red macroalga Hemineura frondosa (Rhodophyta)

ABSTRACT Macroalgal growth in temperate coastal ecosystems is primarily regulated by light and inorganic nitrogen availability. The effect of light (photon irradiance) on NO3 − and NH4 + uptake, and NO3 − assimilation, were studied in the red macroalga, Hemineura frondosa, which does not operate a carbon concentrating mechanism (non-CCM). Non-CCM macroalgae grow in low and high light environments but become increasingly dominant with depth, suggesting a mechanism for ‘preserving energy’ under low light levels. H. frondosa was acclimated to limiting (30 µmol photons m− 2 s− 1) and saturating (150 µmol photons m− 2 s–1) irradiances for 8 days. Then, NO3 − and NH4 + uptake rates were measured under limiting and saturating irradiances at six concentrations ranging from 2–64 μM. NO3 − uptake did not follow saturating uptake kinetics at both irradiances suggesting multiple uptake mechanisms. NH4 + uptake saturated at concentrations <32 µM under limiting but not under saturating irradiance. Saturating irradiance resulted in greater maximum uptake rates of both NO3 − and NH4 +. There was no evidence that irradiance regulated NO3 − reduction by nitrate reductase. Also illustrated is the importance of measuring nitrate reductase activity on fresh material, as freezing in liquid nitrogen and storage at −80°C for 7 days caused a 65% decline in activity. Photosynthetic pigments, soluble tissue nitrogen and % total tissue nitrogen were all higher in limiting irradiance. In this first study of the nitrogen physiology of a non-CCM seaweed, we show that light regulates NO3 − and NH4 + uptake but not NO3 − assimilation.

Human Height Estimation by Color Deep Learning and Depth 3D Conversion

In this study, an estimation method for human height is proposed using color and depth information. Color images are used for deep learning by mask R-CNN to detect a human body and a human head separately. If color images are not available for extracting the human body region due to low light environment, then the human body region is extracted by comparing between current frame in depth video and a pre-stored background depth image. The topmost point of the human head region is extracted as the top of the head and the bottommost point of the human body region as the bottom of the foot. The depth value of the head top-point is corrected to a pixel value that has high similarity to a neighboring pixel. The position of the body bottom-point is corrected by calculating a depth gradient between vertically adjacent pixels. Two head-top and foot-bottom points are converted into 3D real-world coordinates using depth information. Two real-world coordinates estimate human height by measuring a Euclidean distance. Estimation errors for human height are corrected as the average of accumulated heights. In experiment results, we achieve that the estimated errors of human height with a standing state are 0.7% and 2.2% when the human body region is extracted by mask R-CNN and the background depth image, respectively.

Learning-Based Dark and Blurred Underwater Image Restoration

Underwater image processing is a difficult subtopic in the field of computer vision due to the complex underwater environment. Since the light is absorbed and scattered, underwater images have many distortions such as underexposure, blurriness, and color cast. The poor quality hinders subsequent processing such as image classification, object detection, or segmentation. In this paper, we propose a method to collect underwater image pairs by placing two tanks in front of the camera. Due to the high-quality training data, the proposed restoration algorithm based on deep learning achieves inspiring results for underwater images taken in a low-light environment. The proposed method solves two of the most challenging problems for underwater image: darkness and fuzziness. The experimental results show that the proposed method surpasses most other methods.

The maximum carboxylation rate of Rubisco affects CO2 refixation in temperate broadleaved forest trees

Mesophyll resistance to CO2 diffusion (rm) and the maximum carboxylation rate of Rubisco (Vcmax) affect photosynthetic rates, and can potentially also influence the percentage of respiratory and photorespiratory CO2 being refixated (Pr) by mesophyll cells. Here we investigated how various leaf anatomical traits (e.g. leaf mass per area [LMA] and leaf dry matter content [LDMC]) influenced rm in leaves of mature forest trees. We further explored how rm and Vcmax in turn affected Pr, and if these traits varied among species and leaves along a light gradient. Photosynthetic CO2 response of leaves grown in high-, medium-, and low-light environments was measured, from Pinus sylvestris [Scots pine], Picea abies [Norway spruce], Quercus robur [English oak], and Betula pendula [Silver birch] in southern Sweden. A modified version of the Farquhar-von Caemmerer-Berry model was fitted to the leaf gas exchange data to estimate Vcmax, rm and Pr. We found that of all leaf traits measured, only LMA for Q. robur was significantly higher in leaves from high-light environments. When comparing species, both rm and LMA were significantly higher in the conifers, and rm had a negative correlation with Vcmax. We found that Pr was similar between different species and functional groups, with an average of 73.2% (and SD of ±10.4) across all species. There was a strong, positive correlation between Pr and Vcmax in broadleaves, and we hypothesise that this effect might derive from a higher CO2 drawdown near Rubisco in leaves with high Vcmax.

Study on the influence of an underground low-light environment on human safety behavior

Objective. This study aimed to research the impact that a coal mine lighting environment has on human safety behavior in an underground coal mine. Methods. We built a coal mine lighting simulation experiment system including both general lighting and local lighting divided into six different illumination gradients (0, 10, 30, 50, 75 and 100 lx) to test and analyze the effects of general illumination gradients on human fatigue, attention, reaction and eye–hand coordination with SPSS version 22.0. Results. Illuminance has a significant effect on human fatigue, attention, reaction ability and eye–hand coordination ability. Specifically, human fatigue is negatively correlated and the other indicators are positively correlated with the increase of illuminance. Notably, attention distribution ability seems to be most significantly influenced by illuminance according to this study, followed by human fatigue, reaction and eye–hand coordination ability. Conclusion. The results of the study indicate that illumination of the general lighting environment is expected to be controlled at a gradient of at least 50–75 lx or above in the coal mine environment where there is both general lighting and local lighting to reduce the incidence of accidents.