Light Environment Research Articles

Indoor lighting environment space design simulation system based on optical imaging and intelligent manufacturing

Indoor lighting environment space design simulation system based on optical imaging and intelligent manufacturing

Light-regulated chloroplast morphodynamics in a single-celled dinoflagellate

Photosynthetic algae play a significant role in oceanic carbon capture. However, their performance is constantly challenged by fluctuations in environmental light conditions. While phototaxis is a common strategy to cope with such fluctuations, nonmotile species must adopt alternative mechanisms to avoid light-induced damage. Here, we show that the nonmotile, single-celled marine dinoflagellate Pyrocystis lunula contains a chloroplast network that undergoes strong deformation in response to strong light. By exposing cells to various physiologically relevant light conditions and applying temporal illumination sequences, we find that the light-induced network morphodynamics follows dynamic rules similar to temporal low-pass filtering. We develop a mathematical formalism to model the light-regulated behavior, exposing the relevant timescales of the morphodynamic response. Moreover, confocal microscopy reveals that the unusual reticulated morphology exhibits properties similar to auxetic metamaterials, facilitating the rapid and drastic deformation necessary for the light-avoidance motion, confined by the cell wall. This mechanism reduces the effective chloroplast area under high light conditions, minimizing light absorption and preventing photodamage. Our findings demonstrate that the intricate connection between the chloroplasts topologically complex structure and active dynamics enables the dinoflagellate's dynamic adaptation to changing light environments, thereby supporting essential life-sustaining processes.

Green light enhances the phytochemical preservation of lettuce during postharvest cold storage.

The postharvest lighting environment is a main factor that influences quality preservation for harvested biomass. The objective of this study was to evaluate postharvest changes in bioactive compounds of lettuce with different storage light spectra. The effects of green LEDs with peaks at 500 nm and 530 nm, white LEDs (400-700 nm), and dark storage were evaluated, where light intensity (10 μmol m-2 s-1) and photoperiod (12 h per day) were constant with air temperature at 5°C over the 14 d treatment period. Lettuce stored with 500 nm and 530 nm green LEDs exhibited 1474.5% and 1451.8% (approximately 15.7 and 15.5 times) higher antioxidant activity, respectively, compared to dark storage. Significant improvements in total phenolic content, and 67.5% and 64.8% increases in total soluble solids with 530 nm and 500 nm green LEDs over dark storage were discerned. Exposure to 530 nm green LEDs led to 128.2% (approximately 2.28 times) higher anthocyanin content, a 26.2% increase in carotenoids, and a 95% rise in flavonoid content compared to dark storage. Increases of 26.4% and 16.0% in chlorophyll a content in lettuce stored under 500 nm and 530 nm green LEDs, respectively, and 65.6% and 46.6% rises in the Chlorophyll a/b ratio were observed. Compared to dark storage, green LEDs (500 nm) resulted in a 13.5% higher total chlorophyll content. Findings underscore the positive impact of green LEDs on the nutritional quality of lettuce, providing insight for postharvest practices.

Far-Red Light Inhibits Soybean Biomass and Yield by Modulating Plant Photosynthesis

Alterations in the light environment can significantly influence soybean morphology and yield formation; however, the effects and mechanisms of different light qualities on these aspects require further investigation. Consequently, we selected soybean cultivars with marked differences in light sensitivity as test materials, conducted experiments with red, blue, and green light qualities against a blue light background, and analyzed parameters related to leaf photosynthetic capacity, chlorophyll fluorescence, morphological characteristics, biomass, and yield variations following different light quality treatments. The results showed that following far-red light treatment, soybean plants exhibited significant shade avoidance syndrome, internode elongation, increased plant height, and a marked reduction in both root and leaf biomass, as well as total biomass. Furthermore, there was a substantial reduction in photosynthetic capacity. This indicated that far-red light exerts an inhibitory effect on soybean growth and yield formation. Red light has basically no regulatory effect on plant morphology and yield, while green light has a yield-increasing effect, but there was a cultivar effect. This study not only enhances our understanding of the mechanisms through which light quality regulates plant photosynthesis but also lays a scientific foundation for future crop light environment management and for the further exploration of light quality’s regulatory potential on crop growth.

Changes in Secondary Structure upon Pr to Pfr Transition in Cyanobacterial Phytochrome Cph1 detected by DNP NMR.

Phytochromes perceive subtle changes in the light environment and convert them into biological signals by photoconversion between the red-light absorbing (Pr) and the far-red-absorbing (Pfr) states. In the primitive bacteriophytochromes this includes refolding of a tongue-like hairpin loop close to the chromophore, one strand of an antiparallelb-sheetbeing replaced byα-helix. However,the strand sequence in thecyanobacterial phytochrome Cph1is different from that of previously investigated bacteriophytochromes and has a higherb-sheetpropensity. Weconfirm here the transition experimentally and estimate minimum helix length usingdynamic nuclear polarisation (DNP) magic angle spinning NMR. Sample conditions were optimized for protein DNP NMR studies at high field, yielding Boltzmann enhancementseBof 19 at an NMR field of 18.801 T. Selective labelling of Trp, Ile, Arg, and Val residues with13C and15N enabledfiltering for pairs of labelled amino acids by the 3D CANCOCA technique to identify signals of the motif483Ile-Val-Arg485(IVR) present in both sheet and helix. Those signals were assigned for the Pfr state of the protein. Based on the chemical shift pattern, we confirm for Cph1 the formation of a helix covering the IVR motif.

Lightweight enhanced YOLOv8n underwater object detection network for low light environments.

In response to the challenges of target misidentification, missed detection, and other issues arising from severe light attenuation, low visibility, and complex environments in current underwater target detection, we propose a lightweight low-light underwater target detection network, named PDSC-YOLOv8n. Firstly, we enhance the input images using the improved Pro MSRCR algorithm for data augmentation. Secondly, we replace the traditional convolutions in the backbone and neck networks of YOLOv8n with Ghost and GSConv modules respectively to achieve lightweight network modeling. Additionally, we integrate the improved DCNv3 module into the C2f module of the backbone network to enhance the capability of target feature extraction. Furthermore, we introduce the GAM into the SPPF and incorporate the CBAM attention mechanism into the last layer of the backbone network to enhance the model's perceptual and generalization capabilities. Finally, we optimize the training process of the model using WIoUv3 as the loss function. The model is successfully deployed on an embedded platform, achieving real-time detection of underwater targets on the embedded platform. We first conduct experiments on the RUOD underwater dataset. Meanwhile, we also utilized the Pascal VOC2012 dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on RUOD dataset were 79.6% and 58.2%, respectively, and the PDSC -YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 can reach 86.1% and 60.8%. The number of parameters of the model is reduced by about 15.5%, the detection accuracy is improved by 6.5%. The original YOLOv8n algorithm was 73% and 53.2% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The PDSC-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 were 78.5% and 57%, respectively. The superior performance of PDSC-YOLOv8n indicates its effectiveness in the field of underwater target detection.

Metagenomic inference of microbial community composition and function in the weathering crust aquifer of a temperate glacier

Bacterial, fungal, and algal communities that colonize aquatic systems on glacial ice surfaces mediate biogeochemical reactions that alter meltwater composition and affect meltwater production and storage. In this study, we sought to improve understanding of microbial communities inhabiting the shallow aquifer that forms seasonally within the ice surface of a glacier’s ablation zone (i.e., the weathering crust aquifer). Using a metagenomic approach, we compared gene contents of microbial assemblages in the weathering crust aquifer (WCA) of the Matanuska Glacier (Alaska, USA) to those recovered from supraglacial features and englacial ice. High abundances of Pseudomonadota, Cyanobacteriota, Actinomycetota, and Bacteroidota were observed across all samples, while taxa in class Gammaproteobacteria were found at significantly higher abundances in the weathering crust aquifer. The weathering crust aquifer samples also contained higher abundances of Dothideomycetes and Microbotryomyetes; fungal classes commonly observed in snow and other icy ecosystems. Phylogenetic analysis of 18S rRNA and rbcL gene sequences indicated high abundances of algae in the WCA that are closely related (> 98% and > 93% identity, respectively) to taxa of Ancylonema (Streptophyta) and Ochromonas (Ochrophyta) reported from glacial ice surfaces in Svalbard and Antarctic sea ice. Many functional gene categories (e.g., homeostasis, cellular regulation, and stress responses) were enriched in samples from the weathering crust aquifer compared to those from proximal englacial and supraglacial habitats, providing evidence for ecological specialization in the communities. The identification of phagotrophic phytoflagellate taxa and genes involved in mixotrophy implies that combined phototrophic and heterotrophic production may assist with persistence in the low light, low energy, and ephemeral conditions of the weathering crust environment. The compositional and functional differences we have documented indicate distinct microbial distributions and functional processes occur in the weathering crust aquifer environment, and we discuss how deciphering these nuances is essential for developing a more complete understanding of ecosystem biogeochemistry in supraglacial hydrological systems.

Cucumber Leaf Segmentation Based on Bilayer Convolutional Network

When monitoring crop growth using top-down images of the plant canopies, leaves in agricultural fields appear very dense and significantly overlap each other. Moreover, the image can be affected by external conditions such as background environment and light intensity, impacting the effectiveness of image segmentation. To address the challenge of segmenting dense and overlapping plant leaves under natural lighting conditions, this study employed a Bilayer Convolutional Network (BCNet) method for accurate leaf segmentation across various lighting environments. The major contributions of this study are as follows: (1) Utilized Fully Convolutional Object Detection (FCOS) for plant leaf detection, incorporating ResNet-50 with the Convolutional Block Attention Module (CBAM) and Feature Pyramid Network (FPN) to enhance Region of Interest (RoI) feature extraction from canopy top-view images. (2) Extracted the sub-region of the RoI based on the position of the detection box, using this region as input for the BCNet, ensuring precise segmentation. (3) Utilized instance segmentation of canopy top-view images using BCNet, improving segmentation accuracy. (4) Applied the Varifocal Loss Function to improve the classification loss function in FCOS, leading to better performance metrics. The experimental results on cucumber canopy top-view images captured in glass greenhouse and plastic greenhouse environments show that our method is highly effective. For cucumber leaves at different growth stages and under various lighting conditions, the Precision, Recall and Average Precision (AP) metrics for object recognition are 97%, 94% and 96.57%, respectively. For instance segmentation, the Precision, Recall and Average Precision (AP) metrics are 87%, 83% and 84.71%, respectively. Our algorithm outperforms commonly used deep learning algorithms such as Faster R-CNN, Mask R-CNN, YOLOv4 and PANet, showcasing its superior capability in complex agricultural settings. The results of this study demonstrate the potential of our method for accurate recognition and segmentation of highly overlapping leaves in diverse agricultural environments, significantly contributing to the application of deep learning algorithms in smart agriculture.

Multi-Scale Feature Fusion Enhancement for Underwater Object Detection

Underwater object detection (UOD) presents substantial challenges due to the complex visual conditions and the physical properties of light in underwater environments. Small aquatic creatures often congregate in large groups, further complicating the task. To address these challenges, we develop Aqua-DETR, a tailored end-to-end framework for UOD. Our method includes an align-split network to enhance multi-scale feature interaction and fusion for small object identification and a distinction enhancement module using various attention mechanisms to improve ambiguous object identification. Experimental results on four challenging datasets demonstrate that Aqua-DETR outperforms most existing state-of-the-art methods in the UOD task, validating its effectiveness and robustness.

A unified Jacobi-Ritz-spectral BEM for vibro-acoustic behavior of spherical shell

In order to solve the vibration and acoustic characteristics of spherical shell in light fluid environment, based on Jacobi-Ritz-spectral BEM, a unified analysis formula for acoustic vibration of spherical shell under arbitrary boundary conditions is established. Based on the First-order shear deformation theory (FSDT) and domain decomposition method (DDM), the theoretical model of spherical shell structure is established. The improved Jacobi polynomial is innovatively used to construct the displacement tolerance function of the spherical shell. Based on the spectral Kirchhoff-Helmholtz integral formula, the theoretical model of the acoustic fluid outside the spherical shell is established. The special form of Jacobi polynomial Chebyshev polynomial is used to describe the excitation sound pressure on the spherical shell segment, so as to ensure that the generalized coordinates of the structure can be perfectly matched with the acoustic boundary element nodes. In addition, the integration along the meridian of the shell is used to control the movement of the fluid, which simplifies the surface integration. The CHIEF method is used to solve the problem of non-unique solution of acoustic variables of rotary structure. Compared with the published literature, numerical simulation results and experimental results, the proposed method has higher calculation accuracy. In addition, based on this method, the influence of boundary conditions, geometric dimensions and other factors on the acoustic and vibration characteristics of spherical shells is discussed, which accumulates data for analyzing the acoustic and vibration behavior of spherical shells.

The Effects of FR and UVA Irradiation Timing on Multi-Omics of Purple Lettuce in Plant Factories

The synergistic application of far-red (FR) and ultraviolet A (UVA) irradiation presents a promising approach for enhancing growth and the enrichment of secondary metabolites in plants. However, prolonged exposure to these combined light qualities imposes significant stress on plants, hindering their development. Therefore, an initial period of FR irradiation to promote plant growth, followed by a subsequent period of UVA irradiation to enhance the accumulation of plant quality, constitutes a viable strategy. Our study, focusing on purple lettuce, aims to elucidate the response mechanisms of the lettuce leaf under standard white light in commercial production, with the addition of different durations of FR and UVA irradiation, and to explore the complex dynamic changes at the multi-omics level. The results indicate that the duration of FR exposure is crucial in determining biomass-related phenotypes such as fresh weight, while the duration of UVA exposure significantly influences the accumulation of phenotypic markers like anthocyanins. At the transcriptional level, the most extensive transcriptional regulation was observed when FR was applied throughout the entire growth period, and UVA was applied eight days before harvest, significantly impacting pathways such as MAPK signaling cascades, plant hormone signal transduction, photosynthetic processes, and the biosynthesis of secondary metabolites. Metabolomic analysis corroborated the transcriptomic findings, with particular emphasis on antioxidant activity, photoprotection, and defense mechanisms. Our comprehensive analysis suggests that short-term UVA irradiation prior to harvest, based on full growth period FR irradiation, is feasible. The combined application of FR and UVA irradiation fine-tunes plant growth, developmental trajectories, and stress responses by modulating light signals, hormonal signals, and secondary metabolic pathways. These findings not only reveal the adaptive mechanisms of plants to fluctuating light environments but also provide a scientific basis for optimizing light management strategies in controlled plant production systems and precision agriculture.

An Investigation of Indoor Environment Quality on Occupants’ Thermal Responses, Health, and Productivity: A Study Based on Physiological Data in Occupied Office Space

This study explores the impact of Indoor Environment Quality (IEQ) on the health and productivity of office workers in an office building in Fujisawa, Kanagawa, Japan. Previous studies have shown that IEQ can affect the physiological responses of occupants, such as of skin temperature, heart rate, and metabolic rate, which are indicators of health and productivity. However, most studies took place in controlled laboratory environments, which may not accurately represent real-life experiences. The study collected subjective and objective data from actual occupied office space, including on perceptions of IEQ, health, and productivity, and measurements of IEQ parameters such as on the thermal environment, light environment, indoor air quality, and acoustics. The study used correlation and linear regression methods to examine the relationship between IEQ, physiological data, and subjective responses to health and productivity. The stable thermal environment and low physical intensity of office work may contribute to the weak correlation between physiological data, thermal responses, and health–productivity variables. The results of this study can provide insights into how IEQ affects the psychological responses, well-being, and performance of office workers in real-world settings.

Impact of RAW vs. RGB images on low-light object detection in autonomous driving

Abstract. In the field of autonomous driving, object detection in low-light conditions presents a critical challenge. Traditional detection algorithms, reliant on RGB imagery, often suffer from diminished performance under inadequate illumination. In contrast, RAW images, which retain a higher level of intrinsic image information, have the potential to enhance detection accuracy. Therefore, the paper aims to assess whether models trained on RAW images exhibit enhanced object detection performance in low-light scenarios. To this end, experiments are conducted utilizing the Low-light Object Detection (LOD) dataset, which includes paired RAW and RGB images, with variations in lighting simulated through different exposure times. Two distinct datasets are constructed: one consisting of RAW-normal and RAW-dark images, and another comprising RGB-normal and RGB-dark images. The models evaluated in this research include YOLOv8, Faster R-CNN (Region-based Convolutional Neural Network), and EfficientDet, selected to ensure robustness and generalizability of the findings. The results demonstrate that models trained on RAW images significantly outperform those trained on RGB images, thus exhibiting higher accuracy both in general and under low-light conditions. The preservation of detailed image information in RAW format facilitates enhanced feature extraction, thereby improving detection accuracy and resilience under low-light conditions. These findings provide novel insights into advancing object detection methodologies for autonomous driving systems operating in challenging lighting environments.

Behavior recognition algorithm based on a dual-stream residual convolutional neural network

Abstract In the process of behavior recognition, the recognition operation may be carried out in various environments such as sunny, cloudy, and night. Since traditional recognition algorithms are judged by identifying the pixels of the image, the intensity of the light will affect the image. The brightness and contrast of the display thus interfere with the recognition results. Therefore, traditional algorithms are easily affected by the lighting environment around the recognition object. To improve the accuracy and recognition rate of the behavior recognition algorithm in different lighting environments, a convolutional neural network (CNN) algorithm using a dual-stream method of time flow and spatial flow is studied here. First, we collect behavioral action data sets and preprocess the data. The core of the behavior recognition algorithm of the dual-stream residual CNN is to use the time stream and the spatial stream to fuse behavioral features and eliminate meaningless data features. After processing Perform feature selection on the data, select the acoustic wave and light-sensing features of the data, and finally, use the extracted features to classify and identify using the two-stream residual CNN and the traditional behavior recognition method. The behavior recognition algorithm based on the dual-stream residual CNN was tested on the data of four groups of people. For the behavioral feature map with a data volume of 50, the behavior recognition algorithm of the dual-stream residual CNN was effective in various environments under different lighting conditions. The recognition accuracy can reach 83.5%, which is 12.3% higher than the traditional. The behavior recognition algorithm of the dual-stream residual CNN takes 17.25 s less than the conventional recognition algorithm. It is concluded that behavior recognition based on dual-stream residual CNNs can indeed improve the recognition accuracy and recognition speed in environments with different lighting conditions than traditional behavior recognition.

Qualitative Mechanisms of Perceived Indoor Environmental Quality on Anxiety Symptoms in University

The indoor environment is widely acknowledged as a non-pharmacological tool for regulating residents’ mental health. In dormitory environments with relatively high residential density, the mental health of university students requires particular attention. This study surveyed 445 students from a northern Chinese university and used structural equation modeling (SEM) to analyze the impact of perceived indoor environmental quality (IEQ)—including thermal, lighting, acoustics, indoor air quality, and overcrowding—on self-reported anxiety symptoms. The results indicated the following: (1) students’ perceptions of dormitory IEQ significantly affected anxiety symptoms, explaining 40% of the variance; (2) anxiety symptoms associated with the IEQ were mainly characterized by anxiety and panic (r = 0.91, p < 0.001); (3) subjective perceptions of the acoustic environment (r = −0.55, p < 0.001) and indoor air quality (r = −0.15, p < 0.05) were key predictors of anxiety, while thermal environment, lighting environment, and overcrowding were not significant. The findings enrich the IEQ system and provide directions for optimizing the dormitory indoor environment from the perspective of student mental health, with implications for other types of residential buildings.

White Light Generation and Stability Analysis of High-Power Blue LDs with Remote YAG Phosphors

This paper presents a comprehensive analysis of white light generation and the associated aging dynamics using high-power blue laser diodes (LDs) combined with transmissive single crystal remote YAG phosphors. By systematically varying input currents (ranging from 0.6 A to 3 A) and phosphor thicknesses (250 μm and 500 μm), this study elucidates the optical and electrical characteristics of LD-phosphor systems under diverse operating conditions. The results highlight the system’s potential for stable and efficient white light generation, making it suitable for high-power lighting applications. Experimental setups included both single LDs and a 4 × 2 LD array. For the single LD, a peak optical output of 4.16 W was achieved at 3 A, corresponding to an initial luminous flux of approximately 700 Lm and a correlated color temperature (CCT) of 4653 K, with minimal color temperature shift observed during a 60 min aging process. The 4 × 2 LD array demonstrated consistent white light output across varying phosphor thicknesses, with maximum luminous fluxes of 1857 Lm at 1.4 A and 2622 Lm at 1.6 A for phosphor thicknesses of 250 μm and 500 μm, respectively. Importantly, the phosphor exhibited excellent thermal stability throughout the aging process, with the CCT maintained within a range of 4600 K to 5500 K. These findings underscore the reliability and applicability of LD-based white light systems in demanding high-power lighting environments, offering a promising alternative to conventional light sources for automotive, industrial, and specialized lighting applications.

LEAFusion: An Infrared and Visible Light Image Fusion Network Resilient to Harsh Light Environment Interference Based on Harsh Light Environment Aware

LEAFusion: An Infrared and Visible Light Image Fusion Network Resilient to Harsh Light Environment Interference Based on Harsh Light Environment Aware

Evolution of Large Eyes in Stromboidea (Gastropoda): Impact of Photic Environment and Life History Traits.

Eyes within the marine gastropod superfamily Stromboidea range widely in size, from 0.2 to 2.3mm - the largest eyes known in any gastropod. Despite this interesting variation, the underlying evolutionary pressures remain unknown. Here, we use the wealth of material available in museum collections to explore the evolution of stromboid eye size and structure. Our results suggest that depth is a key light-limiting factor in stromboid eye evolution; here, increasing water depth is correlated with increasing aperture width relative to lens diameter, and therefore an increasing investment in sensitivity in dim light environments. In the major clade containing all large-eyed stromboid families, species observed active during the day and the night had wider eye apertures relative to lens sizes than species observed active during the day only, thereby prioritising sensitivity over resolution. Species with no consistent diel activity pattern also had smaller body sizes than exclusively day-active species, which may suggest that smaller animals are more vulnerable to shell-crushing predators, and avoid the higher predation pressure experienced by animals active during the day. Within the same major clade, ancestral state reconstruction suggests that absolute eye size increased above 1mm twice. The unresolved position of Varicospira, however, weakens this hypothesis and further work with additional markers is needed to confirm this result.

Simulation of light environments inside lunar holes and their associated subsurface caverns

Vertical holes of several tens of meters in diameter and depth have been discovered on the Moon, potentially serving as skylights into subsurface volcanic caverns resembling lava tubes. Because of their scientific importance and potential for use as future lunar bases, these lunar holes and caverns are expected to be targets of intensive exploration in the near future. Using numerical simulations, this study investigates the light environment within a directly and/or indirectly sunlit subsurface cavern accessed through a skylight hole. We specifically analyze the Mare Tranquillitatis Hole (MTH), one of the largest lunar vertical holes, situated near the Moon's nearside meridian. The floor and walls of the hole, and the walls and ceiling of its associated subsurface cavern are lit by reflected sunlight from other parts of the hole and cavern, such as the floors, depending on the solar elevation angle. Furthermore, this paper presents estimation results for camera imaging for a case in which solar elevation angle is 45° in the morning, which is appropriate timing for exploration. Assuming reflectance of 0.1 for the lunar hole and cavern surfaces, we estimate the incident energy onto each pixel of a camera as it descends to the hole's floor. We specifically simulate conditions for a camera with a 12-bit dynamic range, similar to the wide-angle optical navigation camera (ONC-W) onboard the Hayabusa 2 spacecraft. The results suggest that a camera with a fixed gain would struggle to capture both directly and indirectly sunlit areas simultaneously, without saturating bright areas and ensuring minimum incident energy resolution (say, a 10 digital number) for darker areas. To overcome this challenge, adjusting the camera gain based on the hole's and cavern's illumination conditions is necessary.Graphical abstract

Optimizing light environments in aquatics center to enhance the performance of Olympic champions and other elite swimmers: An experimental study

Several major international sports events in the next five years require new sports venues, with lighting environments being a crucial factor. Typically, the lighting systems in sports venues are primarily for illumination. However, since light has been indicated to affect human psychology and physiology, we investigated whether the light environments in sports buildings impact athletic performance. Additionally, swimming is the indoor sport with the most gold medals in the Olympics. Therefore, we conducted a within-subject, randomized crossover study in an aquatics center with fourteen elite swimmers, including the Olympic champions and Asian champions (6 female and 8 male; 19.3 ± 3.4 years of age). During the experiment, the elite swimmers were exposed to either a common correlated color temperature (CCT) light environment (controlled condition: 5780 K) or an experimental light environment (8512 K) in the aquatics center, with both having the same illuminance (1020 lx). The neurobehavioral and swimming test results showed that the Olympic champions and other elite swimmers had significantly faster responses, fewer lapses, greater arousal, less visual fatigue, quicker reactions at start, and higher swimming speeds in the experimental condition compared to the controlled condition (p < 0.05). Our findings suggest that the light environment in the aquatics center affects swimmers' non-visual performance and enhancing the CCT of the light environment could improve swimmers' performance. We propose the concepts of Sports Lighting Beyond Illumination and Sports Human-Centric Lighting to enhance athletes’ health and performance, and to improve the sports environment for a better experience for all participants.