Varying Lighting Conditions Research Articles

A New Automatic Watercolour Painting Algorithm Based on Dual Stream Image Segmentation Model with Colour Space Estimation

Image processing plays a crucial role in automatic watercolor painting by manipulating the digital image to achieve the desired watercolor effect. segmentation in automatic watercolor painting algorithms is essential for region-based processing, color mixing and blending, capturing brushwork and texture, and providing artistic control over the final result. It allows for more realistic and expressive watercolor-like paintings by processing different image regions individually and applying appropriate effects to each segment. Hence, this paper proposed an effective Dual Stream Exception Maximization (DSEM) for automatic image segmentation. DSEM combines both color and texture information to segment an image into meaningful regions. This approach begins by converting the image from the RGB color space to a perceptually-based color space, such as CIELAB, to account for variations in lighting conditions and human perception of color. With the color space conversion, DSEM extracts relevant features from the image. Color features are computed based on the values of the color channels in the chosen color space, capturing the nuances of color distribution within the image. Simultaneously, texture features are derived by computing statistical measures such as local variance or co-occurrence matrices, capturing the textural characteristics of the image. Finally, the model is applied over the deep learning model for the classification of the color space in the painting. Simulation analysis is performed compared with conventional segmentation techniques such a CNN and RNN. The comparative analysis states that the proposed DSEM exhibits superior performance compared to conventional techniques in terms of color space estimation, texture analysis and region merging. The performance of classification with DSEM is ~12% higher than the conventional techniques.

LIDN: A novel light invariant image dehazing network

With the increasing demand for efficient image dehazing solutions in computer vision algorithms, particularly for autonomous systems, a research focus on low-inference time yet high-performance dehazing techniques has emerged. Existing dehazing methods predominantly rely on either daytime or nighttime haze models, limiting their effectiveness in handling haziness under varying lighting conditions. To address this limitation, this research paper introduces the Light Invariant Dehazing Network (LIDN), an end-to-end image dehazing network consisting of four sub-modules: Feature Extractor, Deep Global Atmospheric Light Estimator, Medium Transmission Extractor, and Encoder-Decoder. The proposed model, trained using Quadruplet loss, effectively reduces artifacts and produces sharper dehazed images. Extensive experiments conducted under diverse lighting conditions demonstrate the superior performance of the proposed LIDN model compared to state-of-the-art daytime and nighttime dehazing approaches. Remarkably, the proposed model achieves these exceptional results with a runtime of only 0.24s per image, making it highly efficient than existing dehazing algorithms.

SU-Net: pose estimation network for non-cooperative spacecraft on-orbit

The estimation of spacecraft pose is crucial in numerous space missions, including rendezvous and docking, debris removal, and on-orbit maintenance. Estimating the pose of space objects is significantly more challenging than that of objects on Earth, primarily due to the widely varying lighting conditions, low resolution, and limited amount of data available in space images. Our main proposal is a new deep learning neural network architecture, which can effectively extract orbiting spacecraft features from images captured by inverse synthetic aperture radar (ISAR) for pose estimation of non-cooperative on orbit spacecraft. Specifically, our model enhances spacecraft imaging by improving image contrast, reducing noise, and using transfer learning to mitigate data sparsity issues via a pre-trained model. To address sparse features in spacecraft imaging, we propose a dense residual U-Net network that employs dense residual block to reduce feature loss during downsampling. Additionally, we introduce a multi-head self-attention block to capture more global information and improve the model’s accuracy. The resulting tightly interlinked architecture, named as SU-Net, delivers strong performance gains on pose estimation by spacecraft ISAR imaging. Experimental results show that we achieve the state of the art results, and the absolute error of our model is 0.128^{circ } to 0.4491^{circ }, the mean error is about 0.282^{circ }, and the standard deviation is about 0.065^{circ }. The code are released at https://github.com/Tombs98/SU-Net.

VPBR: An Automatic and Low-Cost Vision-Based Biophysical Properties Recognition Pipeline for Pumpkin.

Pumpkins are a nutritious and globally enjoyed fruit for their rich and earthy flavor. The biophysical properties of pumpkins play an important role in determining their yield. However, manual in-field techniques for monitoring these properties can be time-consuming and labor-intensive. To address this, this research introduces a novel approach that feeds high-resolution pumpkin images to train a mathematical model to automate the measurement of each pumpkin's biophysical properties. Color correction was performed on the dataset using a color-checker panel to minimize the impact of varying light conditions on the RGB images. A segmentation model was then trained to effectively recognize two fundamental components of each pumpkin: the fruit and vine. Real-life measurements of various biophysical properties, including fruit length, fruit width, stem length, stem width and fruit peel color, were computed and compared with manual measurements. The experimental results on 10 different pumpkin samples revealed that the framework obtained a small average mean absolute percentage error (MAPE) of 2.5% compared to the manual method, highlighting the potential of this approach as a faster and more efficient alternative to conventional techniques for monitoring the biophysical properties of pumpkins.

Boosting visual servoing performance through RGB-based methods

Purpose This paper aims to evaluate and compare the performance of different computer vision algorithms in the context of visual servoing for augmented robot perception and autonomy. Design/methodology/approach The authors evaluated and compared three different approaches: a feature-based approach, a hybrid approach and a machine-learning-based approach. To evaluate the performance of the approaches, experiments were conducted in a simulated environment using the PyBullet physics simulator. The experiments included different levels of complexity, including different numbers of distractors, varying lighting conditions and highly varied object geometry. Findings The experimental results showed that the machine-learning-based approach outperformed the other two approaches in terms of accuracy and robustness. The approach could detect and locate objects in complex scenes with high accuracy, even in the presence of distractors and varying lighting conditions. The hybrid approach showed promising results but was less robust to changes in lighting and object appearance. The feature-based approach performed well in simple scenes but struggled in more complex ones. Originality/value This paper sheds light on the superiority of a hybrid algorithm that incorporates a deep neural network in a feature detector for image-based visual servoing, which demonstrates stronger robustness in object detection and location against distractors and lighting conditions.

O2RNet: Occluder-occludee relational network for robust apple detection in clustered orchard environments

Automated apple harvesting has attracted significant research interest in recent years because of its great potential to address the issues of labor shortage and rising labor costs. One key challenge to automated harvesting is accurate and robust apple detection, due to complex orchard environments that involve varying lighting conditions, fruit clustering and foliage/branch occlusions. Apples are often grown in clusters on trees, which may be mis-identified as a single apple and thus causes issues in fruit localization for subsequent robotic harvesting operations. In this paper, we present the development of a novel deep learning-based apple detection framework, called the Occluder-Occludee Relational Network (O2RNet), for robust detection of apples in clustered situations. A comprehensive dataset of RGB images were collected for two varieties of apples under different lighting conditions (overcast, direct lighting, and back lighting) with varying degrees of apple occlusions, and the images were annotated and made available to the public. A novel occlusion-aware network was developed for apple detection, in which a feature expansion structure is incorporated into the convolutional neural networks to extract additional features generated by the original network for occluded apples. Comprehensive evaluations of the developed O2RNet were performed using the collected images, which outperformed 12 other state-of-the-art models with a higher accuracy of 94% and a higher F1-score of 0.88 on apple detection. O2RNet provides an enhanced method for robust detection of clustered apples, which is critical to accurate fruit localization for robotic harvesting.

Effect of mixed light emitting diode spectrum on antioxidants content and antioxidant activity of red lettuce grown in a closed soilless system

BackgroundLight spectra have been demonstrated to result in different levels of comfort or stress, which affect plant growth and the availability of health-promoting compounds in ways that sometimes contradict one another. To determine the optimal light conditions, it is necessary to weigh the vegetable’s mass against the amount of nutrients it contains, as vegetables tend to grow poorly in environments where nutrient synthesis is optimal. This study investigates the effects of varying light conditions on the growth of red lettuce and its occurring nutrients in terms of productivities, which were determined by multiplying the total weight of the harvested vegetables by their nutrient content, particularly phenolics. Three different light-emitting diode (LED) spectral mixes, including blue, green, and red, which were all supplemented by white, denoted as BW, GW, and RW, respectively, as well as the standard white as the control, were equipped in grow tents with soilless cultivation systems for such purposes.ResultsResults demonstrated that the biomass and fiber content did not differ substantially across treatments. This could be due to the use of a modest amount of broad-spectrum white LEDs, which could help retain the lettuce’s core qualities. However, the concentrations of total phenolics and antioxidant capacity in lettuce grown with the BW treatment were the highest (1.3 and 1.4-fold higher than those obtained from the control, respectively), with chlorogenic acid accumulation (8.4 ± 1.5 mg g− 1 DW) being particularly notable. Meanwhile, the study observed a high glutathione reductase (GR) activity in the plant achieved from the RW treatment, which in this study was deemed the poorest treatment in terms of phenolics accumulation.ConclusionIn this study, the BW treatment provided the most efficient mixed light spectrum to stimulate phenolics productivity in red lettuce without a significant detrimental effect on other key properties.

Skin Detection System Using Infrared Optoelectronic Technology and Its Application in Facial Recognition

Facial recognition technology has made significant progress. However, variable lighting conditions can affect its performance. Considering the need to scan facial skin for recognition purposes, this study proposes a miniature optoelectronic acquisition system for skin in the near-infrared range. The system utilizes the C11708MA photodetector from Hamamatsu Photonics’ MS series as the probe for spectral data acquisition. Other hardware components are designed accordingly. A three-stage amplification buffer circuit is employed as the front-end acquisition and preprocessing circuit. The AD7671 chip from Analog Devices Inc. is selected as the AD converter, and the communication module utilizes the CY7C68013 chip from Cypress’ EZ-USB FX2 series. The control and transmission module employs the EP2C5T144C8N FPGA chip from ALTERA’s Cyclone II generation. In order to address the power supply requirements of the CY7C68013 USB chip (3.3 V), the FPGA core (1.2 V), and the AD7671 and front-end preprocessing circuit (5 V), AMS1117 voltage regulator chips are designed for stable 5 V–1.2 V and 5 V–3.3 V power supplies. In the experiments, wavelength calibration and spectral preprocessing are performed on the system prior to data processing. Near-infrared reflectance spectra of different skin conditions (melanoma, vitiligo) are compared with normal skin. The results demonstrate the accurate assessment capability of the designed infrared optoelectronic skin detection system. Facial skin data obtained from the system are used to generate facial images, and the recognition performance of different detection systems is compared in an algorithmic environment, thereby demonstrating the promising application prospects of the infrared optoelectronic skin detection system in the field of facial recognition.

Physiological and Biochemical Traits of Needles Imply That Understory Light Conditions in the Growing Season May Be Favorable to Pinus koraiensis Trees

Light is often considered the primary factor leading to the regeneration failure of Korean pines (Pinus koraiensis) under the forest canopy. However, studies on the effect of light on Korean pines mainly focus on the use of an artificial sunshade net to control shade; field studies on the canopy are extremely scarce, and the current experimental results are contradictory. For a deeper understanding of the relationship between light conditions and understory Korean pine trees, the conditions of low, middle, high and full light (control) under the forest were tested at 18 years of age. The photosynthetic pigments, chlorophyll fluorescence, non-structural carbohydrate metabolism, antioxidant enzyme activity, and nutrient concentrations of current-year needles from Korean pine trees were measured. From June to September, light intensity and quality decreased under full light, but following leaf fall, understory light conditions improved slightly. As the light conditions improved, the photosynthetic pigments in the needles decreased, but Car/Chl were highest in the needles under full light. All light conditions had a positive correlation with glucose concentrations and Rubisco activity. Full-light needles had the highest APX activity, DPPH scavenging capacity, and proline concentration, as well as higher NPQ and lower Fv/Fm readings. This indicated that full-light Korean pine trees were stressed and inhibited photosynthesis to some extent, while the understory light environment may alleviate stress. The conservative strategy of storing more starch and using less glucose in understory Korean pine trees may be one of the reasons for the observed differences in growth rates among Korean pine trees under varying light conditions. Overall, this study implies that understory light during the growing season is not always unfavorable to 18-year-old Korean pine trees; this means that 18-year-old Korean pine trees still have shade tolerance to some extent and are capable of living under a canopy of deciduous trees.

Fruit Detection and Counting in Apple Orchards Based on Improved Yolov7 and Multi-Object Tracking Methods.

With the increasing popularity of online fruit sales, accurately predicting fruit yields has become crucial for optimizing logistics and storage strategies. However, existing manual vision-based systems and sensor methods have proven inadequate for solving the complex problem of fruit yield counting, as they struggle with issues such as crop overlap and variable lighting conditions. Recently CNN-based object detection models have emerged as a promising solution in the field of computer vision, but their effectiveness is limited in agricultural scenarios due to challenges such as occlusion and dissimilarity among the same fruits. To address this issue, we propose a novel variant model that combines the self-attentive mechanism of Vision Transform, a non-CNN network architecture, with Yolov7, a state-of-the-art object detection model. Our model utilizes two attention mechanisms, CBAM and CA, and is trained and tested on a dataset of apple images. In order to enable fruit counting across video frames in complex environments, we incorporate two multi-objective tracking methods based on Kalman filtering and motion trajectory prediction, namely SORT, and Cascade-SORT. Our results show that the Yolov7-CA model achieved a 91.3% mAP and 0.85 F1 score, representing a 4% improvement in mAP and 0.02 improvement in F1 score compared to using Yolov7 alone. Furthermore, three multi-object tracking methods demonstrated a significant improvement in MAE for inter-frame counting across all three test videos, with an 0.642 improvement over using yolov7 alone achieved using our multi-object tracking method. These findings suggest that our proposed model has the potential to improve fruit yield assessment methods and could have implications for decision-making in the fruit industry.

Development of an Experimental Phytotron and its Application in the Research on the Energy-ecological Efficiency of Indoor Plant Lighting

The study of indoor plant lighting is noted to be an interdisciplinary research area. Thus, there is a need to integrate various technical sciences and practical methods in growing plants under artificial conditions. Nowadays, it is reasonable to treat energy-ecological efficiency of indoor plant lighting as a complex scientific direction that takes into account substance flow forming the energy and environmental indicators of an artificial agroecosystem. (Research purpose) To develop an experimental laboratory phytotron intended for research on the energy-ecology efficiency of indoor plant lighting, and to conduct its biological testing. (Materials and methods) From the standpoint of the logical-semantic approach, the term "energy-ecological efficiency" was interpreted as a conjunction of its components - energy efficiency and environmental compatibility. The paper outlines the theoretical fundamentals of the energy-ecological efficiency approach to indoor plant lighting, which determine the experimental research features. The requirements for the techniques of conducting experiments are formulated. The phytotron design and its operation principles are described. Biological testing was carried out on Blagovest F1 tomato plants (Lycopersicum Esculentum Mill.) in the seedling phase of plant development. The plant response to changes in the lighting factors was determined: photoperiod (normal and extended), spectrum (a blue-enhanced spectrum and a control one), and irradiance level (low, medium, and high). (Results and discussion) Under the varied lighting conditions, energy-ecology efficiency varied from 0.075 grams per mole (with the control spectrum, an extended photoperiod and high irradiance) to 0.138 grams per mole (with the blue-enhanced spectrum, a normal photoperiod and medium irradiance). (Conclusions) An experimental laboratory phytotron was developed to study the energy-ecology efficiency of indoor plant lighting. The ability to control irradiance was achieved depending on the mass of plants. The phytotron unit provides easy access to plants, it is compact, ergonomic and has improved functional capacity.

APPLICATION OF MACHINE LEARNING IN ESTIMATING ONTREE YIELD OF CITRUS FRUIT

Citrus is manually counted to estimate the yield. By using some innovative agricultural techniques yield and production can be increased. Numerous agricultural innovations have been introduced in recent years. Higher agricultural production, prediction, and reliable crop status information are more important than ever due to the expected growth of the human population. Agriculture has always been the foundation of human society. Current study was aimed to develop a reliable and meaningful information-gathering agricultural field based on image processing during 2020. Citrus yield can be increased in the initial stages by counting it with RGB and HSV-based images taken from an Android phone from various angles using machine learning techniques. Fertilizers such as potash, phosphorus, and nitrogen can then be utilized to boost yield. According to the findings, farmers can control and monitor citrus health production more efficiently and effectively by integrating machine learning with agriculture. The citrus calculation using the given technique compared with manually counted citrus, having difference of up to 5 to 10 citruses for a single plant per plot in a field. The proposed method produced excellent results under varying lighting conditions, leaf occlusion, and fruit overlap on photos taken at various distances from the orange trees.

Vision-Based White Radish Phenotypic Trait Measurement with Smartphone Imagery

White radish is a nutritious and delectable vegetable that is enjoyed globally. Conventional techniques for monitoring radish growth are arduous and time-consuming, encouraging the development of novel methods for quicker measurements and greater sampling density. This research introduces a mathematical model working on high-resolution images to measure radish’s biophysical properties automatically. A color calibration was performed on the dataset using a color checker panel to minimize the impact of varying light conditions on the RGB images. Subsequently, a Mask-RCNN model was trained to effectively segment different components of the radishes. The observations of the segmented results included leaf length, leaf width, root width, root length, leaf length to width, root length to width, root shoulder color, and root peel color. The automated real-life measurements of these observations were then conducted and compared with actual results. The validation results, based on a set of white radish samples, demonstrated the models’ effectiveness in utilizing images for quantifying phenotypic traits. The average accuracy of the automated method was confirmed to be 96.2% when compared to the manual method.

Photosynthetic Acclimation of Shade-Grown Soybean Seedlings to a High-Light Environment.

Soybean in relay intercropping is initially exposed to a shade environment, followed by exposure to full sunlight after the harvesting of primary crops, e.g., maize. Therefore, soybean's ability to acclimate to this changing light environment determines its growth and yield formation. However, the changes in soybean photosynthesis under such light alternations in relay intercropping are poorly understood. This study compared the photosynthetic acclimation of two soybean varieties with contrasting shade tolerance, i.e., Gongxuan1 (shade-tolerant) and C103 (shade-intolerant). The two soybean genotypes were grown in a greenhouse under full sunlight (HL) and 40% full sunlight (LL) conditions. Subsequently, after the fifth compound leaf expanded, half of the LL plants were transferred to a high-sunlight environment (LL-HL). Morphological traits were measured at 0 and 10 days, while chlorophyll content, gas exchange characteristics and chlorophyll fluorescence were assayed at 0, 2, 4, 7 and 10 days after transfer to an HL environment (LL-HL). Shade-intolerant C103 showed photoinhibition 10 days after transfer, and the net photosynthetic rate (Pn) did not completely recover to that under a high light level. On the day of transfer, the shade-intolerant variety, C103, exhibited a decrease in net photosynthetic rate (Pn), stomatal conductance (Gs) and transpiration rate (E) in the low-light (LL) and low-light-to-high-light (LL-HL) treatments. Additionally, intercellular CO2 concentration (Ci) increased in low light, suggesting that non-stomatal factors were the primary limitations to photosynthesis in C103 following the transfer. In contrast, the shade-tolerant variety, Gongxuan1, displayed a greater increase in Pn 7 days after transfer, with no difference observed between the HL and LL-HL treatments. Ten days after transfer, the shade-tolerant Gongxuan1 exhibited 24.1%, 10.9% and 20.9% higher biomass, leaf area and stem diameter than the intolerant C103. These findings suggest that Gongxuan1 possesses a higher capacity to adapt to variations in light conditions, making it a potential candidate for variety selection in intercropping systems.

IoT Enabled Real Time Appearance System using AI Camera and Deep Learning for Student Tracking

Internet of Things based Automatic Attendance Management systems that use Artificial Intelligent cameras and deep learning algorithms can suggestively advance the accuracy and proficiency of class presence following in schools, colleges as well as universities. This technology involves the use of cameras that are placed in classrooms or other areas where attendance needs to be monitored.The cameras are equipped with advanced deep learning algorithms that can detect and recognize students based on their unique facial features. These algorithms use machine learning techniques to analyse images and identify individual faces, even in varying lighting conditions and different angles.The data collected by the cameras is then transmitted to an Intenet of Things based platform, which stores and approach the attendance data in real time. This platform can also be used to generate reports and analytics on attendance, helping teachers and administrators make data driven decisions to improve student performance.

Unified Object Detector for Different Modalities Based on Vision Transformers

Traditional systems typically require different models for processing different modalities, such as one model for RGB images and another for depth images. Recent research has demonstrated that a single model for one modality can be adapted for another using cross-modality transfer learning. In this paper, we extend this approach by combining cross/inter-modality transfer learning with a vision transformer to develop a unified detector that achieves superior performance across diverse modalities. Our research envisions an application scenario for robotics, where the unified system seamlessly switches between RGB cameras and depth sensors in varying lighting conditions. Importantly, the system requires no model architecture or weight updates to enable this smooth transition. Specifically, the system uses a depth sensor in low light conditions (night time) and both an RGB camera and a depth sensor or RGB camera only in well-lit environments. We evaluate our unified model on the SUN RGB-D dataset and demonstrate that it achieves a similar or better performance in terms of the mAP50 compared to state-of-the-art methods in the SUNRGBD16 category and a comparable performance in point-cloud-only mode. We also introduce a novel inter-modality mixing method that enables our model to achieve significantly better results than previous methods. We provide our code, including training/inference logs and model checkpoints, to facilitate reproducibility and further research.

Effects of Environmental Conditions on the Individual Architectures and Photosynthetic Performances of Three Species in Drosera.

The aim of this study was to determine the environmental conditions, individual architectures, and photosynthetic efficiencies of three sundew species: Drosera rotundifolia, D. anglica, and D. intermedia, found in well-preserved peatlands and sandy lake shores in NW Poland. Morphological traits and chlorophyll a fluorescence (Fv/Fm) were measured in 581 individuals of Drosera. D. anglica occupies the best-lit and warmest habitats, and also those that are the most heavily hydrated and the richest in organic matter; its rosettes are larger under conditions of higher pH, less organic matter, and less well-lit habitats. D. intermedia occupies substrates with the highest pH but the lowest conductivity, the poorest level of organic matter, and the least hydration. It is highly variable in terms of individual architecture. D. rotundifolia occupies habitats that are the most diverse, and that are often poorly lit, with the lowest pH but the highest conductivity. It is the least variable in terms of individual architecture. The value of the Fv/Fm ratio in Drosera is low (0.616 ± 0.137). The highest photosynthetic efficiency is achieved by D. rotundifolia (0.677 ± 0.111). It is significant for all substrates, indicating its high phenotypic plasticity. The other species have lower and similar Fv/Fm values (D. intermedia, 0.571 ± 0.118; D. anglica, 0.543 ± 0.154). Due to its very low photosynthetic efficiency, D. anglica avoids competition by occupying highly hydrated habitats. D. intermedia has adapted to the occupation of highly variable habitats in terms of hydration, while D. rotundifolia is primarily adapted to variable light conditions.

Genome-Wide Association Study unveils ascorbate regulation by PAS/LOV PROTEIN during high light acclimation.

Varying light conditions elicit metabolic responses as part of acclimation with changes in ascorbate levels being an important component. Here, we adopted a genome-wide association-based approach to characterize the response in ascorbate levels on high light acclimation in a panel of 315 Arabidopsis (Arabidopsis thaliana) accessions. These studies revealed statistically significant SNPs for total and reduced ascorbate under high light conditions at a locus in chromosome 2. Ascorbate levels under high light and the region upstream and within PAS/LOV PROTEIN (PLP) were strongly associated. Intriguingly, subcellular localization analyses revealed that the PLPA and PLPB splice variants co-localized with VITAMIN C DEFECTIVE2 (VTC2) and VTC5 in both the cytosol and nucleus. Yeast 2-hybrid and Bimolecular fluorescence complementation analyses revealed that PLPA and PLPB interact with VTC2 and that blue light diminishes this interaction. Furthermore, PLPB knockout mutants were characterized by 1.5- to 1.7- fold elevations in their ascorbate levels, whereas knockout mutants of the cry2 cryptochromes displayed 1.2- to 1.3- fold elevations compared to WT. Our results collectively indicate that PLP plays a critical role in the elevation of ascorbate levels, which is a signature response of high light acclimation. The results strongly suggest that this is achieved via release of the inhibitory effect of PLP on VTC2 upon blue light illumination, as the VTC2-PLPB interaction is stronger under darkness. The conditional importance of the cryptochrome receptors under different environmental conditions suggests a complex hierarchy underpinning the environmental control of ascorbate levels. However, the data we present here clearly demonstrate that PLP dominates during high light acclimation.

Effects of elevated ozone and carbon dioxide on the dynamic photosynthesis of Fagus crenata seedlings under variable light conditions

Ozone (O3) is an air pollutant that is toxic to trees. O3 reduces steady-state net photosynthetic rate (A), and the adverse effects of O3 are mitigated under elevated CO2 condition. However, the combined effects of O3 and elevated CO2 on dynamic photosynthesis under variable light conditions have not yet been clarified. In this study, we investigated the effects of O3 and elevated CO2 on dynamic photosynthesis in the leaves of Fagus crenata seedlings under variable light conditions. The seedlings were grown under four gas treatments comprising two levels of O3 concentration (lower and two times higher than the ambient O3 concentration) and two levels of CO2 concentration (ambient and 700 ppm). Although O3 significantly decreased steady-state A under ambient CO2 concentrations, no significant decrease was observed under elevated CO2 concentrations, indicating the mitigating effect of elevated CO2 on O3-induced adverse effects on steady-state A. During photosynthetic induction, the response of A to the change in photosynthetic photon flux density (PPFD) from 50 (low light) to 1000 μmol m−2 s−1 (high light) showed that the increase in A was slowed by O3 and accelerated by elevated CO2. Under fluctuating light condition of repeating low light for 4 min and high light for 1 min, A at end of each high light period gradually decreased in all treatments, and O3 and elevated CO2 accelerated the reduction of A. In contrast to steady-state A, no mitigating effect of elevated CO2 was observed for any parameters related to dynamic photosynthesis. We conclude that the combined effects of O3 and elevated CO2 on A of F. crenata are different under steady-state and variable light conditions, and the O3-induced decrease in leaf A may not be mitigated by elevated CO2 in the field under variable light conditions.

Fotokromik Senofilcon A Kontakt Lensin Total Göz Aberasyonlarına Etkisi

Purpose: To investigate the effect of newly designed photochromic Senofilcon-A lens(PCL) on optical aberrations of the eye (total (TA) and high-order-aberrations (HOA)) under ambient illumination conditions.
 Methods: In this retrospective study, 40 eyes of 20 volunteers were fitted with a non-photochromic Senofilcon-A contact lens on the right eye, a PCL on left eye. Subjects were evaluated in terms of best-corrected visual-acuity. Aberrometric measurements were obtained at baseline(lens-free), with activated and deactivated PCL in 3 mm, 6 mm and maximum pupil size. 
 Results: In lens-free situation there was no difference in terms of TA and HOAs(for 3mm, 6mm and max-pupil-size, TA; P= .456; P= .687; P= .899 and HOA; P = .226; P = .259; P = .442 respectively).In all pupil diameters, in the PCL group compared to the standard lens group, there was a slight increase in total aberrations independent of lens activation, which was not statistically significant(for 3mm,6mm and max-pupil-size, activated lens situation;P = .523; P = .622; P = .244 and deactivated lens situation; P = .785; P = .357; P = .201,respectively). Contrary to total aberrations, higher order aberrations were non-significantly slightly decreased in the PCL group(for 3mm,6mm and max-pupil-size, activated lens situation; P = .235; P = .661; P = .841 and deactivated lens situation;P = .245; P = .071; P = .231,respectively).
 Conclusion: The PCL produces similar TA and HOAs with the non-photochromic soft contact lens in both activated and deactivated situation. PCLs can be preferred for those who need uninterrupted vision in variable light conditions.