Light Variations Research Articles

Unconstrained Face Recognition Using Infrared Images

Recently, face recognition (FR) has become an important research topic due to increase in video surveillance. However, the surveillance images may have vague non-frontal faces, especially with the unidentifiable face pose or unconstrained environment such as bad illumination and dark environment. As a result, most FR algorithms would not show good performance when they are applied on these images. On the contrary, it is common at surveillance field that only Single Sample per Person (SSPP) is available for identification. In order to resolve such issues, visible spectrum infrared images were used which can work in entirely dark condition without having any light variations. Furthermore, to effectively improve FR for both the low-quality SSPP and unidentifiable pose problem, an approach to synthesize 3D face modeling and pose variations is proposed in this paper. A 2D frontal face image is used to generate a 3D face model. Then several virtual face test images with different poses are synthesized from this model. A well-known Surveillance Camera’s Face (SCface) database is utilized to evaluate the proposed algorithm by using PCA, LDA, KPCA, KFA, RSLDA, LRPP-GRR, deep KNN and DLIB deep learning. The effectiveness of the proposed method is verified through simulations, where increase in average recognition rates up to 10%, 27.69%, 14.62%, 25.38%, 57.46%, 57.43, 37.69% and 63.28%, respectively, for SCface database as observed.

Lip-Reading Advancements: A 3D Convolutional Neural Network/Long Short-Term Memory Fusion for Precise Word Recognition

Lip reading, the art of deciphering spoken words from the visual cues of lip movements, has garnered significant interest for its potential applications in diverse fields, including assistive technologies, human–computer interaction, and security systems. With the rapid advancements in technology and the increasing emphasis on non-verbal communication methods, the significance of lip reading has expanded beyond its traditional boundaries. These technological advancements have led to the generation of large-scale and complex datasets, necessitating the use of cutting-edge deep learning tools that are adept at handling such intricacies. In this study, we propose an innovative approach combining 3D Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to tackle the challenging task of word recognition from lip movements. Our research leverages a meticulously curated dataset, named MobLip, encompassing various speech patterns, speakers, and environmental conditions. The synergy between the spatial information extracted by 3D CNNs and the temporal dynamics captured by LSTMs yields impressive results, achieving an accuracy rate of up to 87.5%, showcasing robustness to lighting variations and speaker diversity. Comparative experiments demonstrate our model’s superiority over existing lip-reading approaches, underlining its potential for real-world deployment. Furthermore, we discuss ethical considerations and propose avenues for future research, such as multimodal integration with audio data and expanded language support. In conclusion, our 3D CNN-LSTM architecture presents a promising solution to the complex problem of word recognition from lip movements, contributing to the advancement of communication technology and opening doors to innovative applications in an increasingly visual world.

Metagenomic exploration of Andaman region of the Indian Ocean

Ocean microbiome is crucial for global biogeochemical cycles and primary productivity. Despite numerous studies investigating the global ocean microbiomes, the microbiome composition of the Andaman region of the Indian Ocean remains largely unexplored. While this region harbors pristine biological diversity, the escalating anthropogenic activities along coastal habitats exert an influence on the microbial ecology and impact the aquatic ecosystems. We investigated the microbiome composition in the coastal waters of the Andaman Islands by 16S rRNA gene amplicon and metagenomic shotgun sequencing approaches and compared it with the Tara Oceans Consortium. In the coastal waters of the Andaman Islands, a significantly higher abundance and diversity of Synechococcus species was observed with a higher abundance of photosynthesis pigment-related genes to adapt to variable light conditions and nutrition. In contrast, Prochlorococcus species showed higher abundance in open ocean water samples of the Indian Ocean region, with a relatively limited functional diversity. A higher abundance of antibiotic-resistance genes was also noted in the coastal waters region. We also updated the ocean microbiome gene catalog with 93,172 unique genes from the Andaman coastal water microbiome. This study provides valuable insights into the Indian Ocean microbiome and supplements the global marine microbial ecosystem studies.

Intrinsic kinetic model with variable light intensity of the emerging pollutants degradation in a photocatalytic differential reactor with immobilized TIO2: Experiments and CFD

AbstractHeterogeneous photocatalysis is an alternative to mineralizing emerging pollutants. The present study focuses on the kinetic model of salicylic acid photocatalytic degradation in an aqueous solution as a function of pollutant concentration and light intensity. The intrinsic kinetic model parameters were determined using a differential photocatalytic reactor with immobilized TiO2 based on experiments and numerical simulations. Five degradation experiments with different light intensities were performed for a turbulent flow rate of 4.7 L/min and salicylic acid concentration of 20 mg/L. Light intensities used for the kinetic experiments were obtained by computational fluid dynamics (CFD) simulation using the radiation transfer equation that was pre‐validated. The TiO2 immobilized method presents low leaching of the catalyst and good degradation efficiency. The intrinsic kinetic model showed to be first‐order both for the pollutant concentration and for the light intensity and can be applied in simulation to optimize and design photocatalytic reactors.

Photogrammetric Point-Cloud Replicability When Documenting Forensic Archaeological Scenes under Variable Lighting Conditions

Forensic archaeological scenes involving human skeletal remains in wooded environments can be challenging to documentutilizing close-range photogrammetry (CRP) due to the complex nature of outdoor scenes. Previous research has demonstrated that changinglighting conditions can negatively affect three-dimensional (3D) model quality. The purpose of this research was therefore to test theimpact of variable lighting on the replicability of 3D point clouds using CRP in a wooded environment. One scattered scene was createdusing a composite human skeleton and several clothing items. The scene was photographed three times during one day to capture changinglighting conditions: at 9:45 am (Model 1), at noon (Model 2), and at 2 pm (Model 3). Photographs were collected freehand from multipleview angles using a Sony α7 III camera with a fixed wide-anglelens, and the models were processed using Agisoft Metashape Professional.All three models achieved a total scale bar error of less than 1 mm and therefore met the accepted standards for crime scene mappingbest practices. The dense point clouds were then analyzed using CloudCompare to assess point-cloud replicability between modelpairs. The Multiscale Model to Model Cloud Comparison (M3C2) tool was used to calculate signed distances between point-cloud pairs.Also, histograms that display these point-to-pointdeviations were generated for each comparison, and a Gaussian distribution was fittedto each histogram. Deviations between point clouds were minimal, indicating that CRP-generated point clouds are replicable under changinglighting conditions, as well as other environmental variables, such as mild wind conditions and complex ground surfaces. Thus, it isrecommended that forensic archaeologists incorporate CRP into their documentation protocol.

Modeling quasar proximity zones in a realistic cosmological environment with a self-consistent light curve

ABSTRACT We study quasar proximity zones in a simulation that includes a self-consistent quasar formation model and realistic intergalactic medium (IGM) environments. The quasar host halo is 1013 M⊙ at z = 6, more massive than typical halos studied in previous work. Between 6 < z < 7.5, the quasar luminosity varies rapidly, with a mean magnitude of MUV, mean = −24.8 and the fluctuation reaching up to two orders of magnitude. Using this light curve to post-process the dense environment around the quasar, we find that the proximity zone size (Rp) ranges between 0.5 and 5 pMpc. We show that the light curve variability causes a similar degree of scatter in Rp as does the density fluctuation, both of which result in a standard deviation of ∼0.3 pMpc. The Rp traces the light curve fluctuations closely but with a time delay of ∼104 yr, breaking the correspondence between the Rp and the contemporaneous MUV. This also indicates that we can only infer quasar activity within the past ∼104 yr instead of the integrated lifetime from Rp in the later part of cosmic reionization. Compared with the variable light curve, a constant light curve underestimates the Rp by 13 per cent at the dim end (MUV ∼ −23.5), and overestimates the Rp by 30 per cent at the bright end (MUV ∼ −26). By calculating the Rp generated by a number of quasars, we show that variable light curves predict a wider Rp distribution than lightbulb models, and readily explain the extremely small Rp values that have been observed.

Comparative Evaluation of Color Correction as Image Preprocessing for Olive Identification under Natural Light Using Cell Phones

Integrating deep learning for crop monitoring presents opportunities and challenges, particularly in object detection under varying environmental conditions. This study investigates the efficacy of image preprocessing methods for olive identification using mobile cameras under natural light. The research is grounded in the broader context of enhancing object detection accuracy in variable lighting, which is crucial for practical applications in precision agriculture. The study primarily employs the YOLOv7 object detection model and compares various color correction techniques, including histogram equalization (HE), adaptive histogram equalization (AHE), and color correction using the ColorChecker. Additionally, the research examines the role of data augmentation methods, such as image and bounding box rotation, in conjunction with these preprocessing techniques. The findings reveal that while all preprocessing methods improve detection performance compared to non-processed images, AHE is particularly effective in dealing with natural lighting variability. The study also demonstrates that image rotation augmentation consistently enhances model accuracy across different preprocessing methods. These results contribute significantly to agricultural technology, highlighting the importance of tailored image preprocessing in object detection models. The conclusions drawn from this research offer valuable insights for optimizing deep learning applications in agriculture, particularly in scenarios with inconsistent environmental conditions.

Imaging privacy threats from an ambient light sensor.

Embedded sensors in smart devices pose privacy risks, often unintentionally leaking user information. We investigate how combining an ambient light sensor with a device display can capture an image of touch interaction without a camera. By displaying a known video sequence, we use the light sensor to capture reflected light intensity variations partially blocked by the touching hand, formulating an inverse problem similar to single-pixel imaging. Because of the sensors' heavy quantization and low sensitivity, we propose an inversion algorithm involving an ℓp-norm dequantizer and a deep denoiser as natural image priors, to reconstruct images from the screen's perspective. We demonstrate touch interactions and eavesdropping hand gestures on an off-the-shelf Android tablet. Despite limitations in resolution and speed, we aim to raise awareness of potential security/privacy threats induced by the combination of passive and active components in smart devices and promote the development of ways to mitigate them.

Revisiting the quasi-periodic oscillations in blazar PG 1553＋113 with multi-wavebands data

The blazar PG 1553＋113 is hypothesized to harbor a supermassive black hole binary system, a scenario that aligns with its observed physical characteristics. In this study, we re-examine the authenticity of the periodicity of PG 1553＋113 by conducting a comprehensive analysis of multi-wavebands periodic light variations, using the updated light curve data of more than 15 years. We used two methods to search for the light curves data of this blazar in γ-ray, X-ray, optical and radio bands. The multi-wavebands analysis approach enables a thorough verification of the observed periodic patterns. The result of γ-ray detection showed a quasi-periodic oscillation (QPO) of 2.16 years, which verified the results given by Ackermann et al. (2015). And the optical band shows a QPO of 2.24 years. We analyzed the correlation among γ-ray, optical and radio bands, and we found that there is a strong correlation among them, and the emission of different bands coming from the same region (the same electron group). Finally, we estimated the black hole mass of PG 1553＋113 to be M≃4.3×109M⨀ based on the binary black hole model.

Cyanorhodopsin-II represents a yellow-absorbing proton-pumping rhodopsin clade within cyanobacteria.

Microbial rhodopsins are prevalent in many cyanobacterial groups as a light-energy-harvesting system in addition to the photosynthetic system. It has been suggested that this dual system allows efficient capture of sunlight energy using complementary ranges of absorption wavelengths. However, the diversity of cyanobacterial rhodopsins, particularly in accumulated metagenomic data, remains underexplored. Here, we used a metagenomic mining approach, which led to the identification of a novel rhodopsin clade unique to cyanobacteria, cyanorhodopsin-II (CyR-II). CyR-IIs function as light-driven outward H+ pumps. CyR-IIs, together with previously identified cyanorhodopsins (CyRs) and cyanobacterial halorhodopsins (CyHRs), constitute cyanobacterial ion-pumping rhodopsins (CyipRs), a phylogenetically distinct family of rhodopsins. The CyR-II clade is further divided into two subclades, YCyR-II and GCyR-II, based on their specific absorption wavelength. YCyR-II absorbed yellow light (λmax = 570nm), whereas GCyR-II absorbed green light (λmax = 550nm). X-ray crystallography and mutational analysis revealed that the difference in absorption wavelengths is attributable to slight changes in the side chain structure near the retinal chromophore. The evolutionary trajectory of cyanobacterial rhodopsins suggests that the function and light-absorbing range of these rhodopsins have been adapted to a wide range of habitats with variable light and environmental conditions. Collectively, these findings shed light on the importance of rhodopsins in the evolution and environmental adaptation of cyanobacteria.

Enhanced actuation performance of multiple stimuli responsive PDMS-based bilayer actuators by adding ionic liquid

Soft actuators are in high demand for applications such as sensors, soft robotics, biomimetic devices, and actuators that can respond to different energies have enormous advantages. The soft actuators that can respond to diverse energy sources are greatly advantageous. In this study, we developed a PDMS ion gel/graphene oxide and reduced graphene oxide bilayer actuator, with its performance elevated by infusing ionic liquid into PDMS. The fabricated bilayer actuator exhibits rapid response and significant deformation even under minimal energy stimuli. Specifically, it can achieve bend angles of 43°, 53°, and 63°within 2 s when exposed to voltage, light, and humidity variations, respectively. Building on this technology, we engineered a mechanical gripper that adeptly holding an object 52 times heavier than the total mass of the actuators used, showcasing the actuator’s commendable practical application potential. Moreover, the actuator is robust, maintaining its deformation prowess even after 1000 operational cycles.

Improving RGB-infrared object detection with cascade alignment-guided transformer

The integration of multispectral data in object detection, especially visible and infrared images, has been the subject of considerable attention recently. Complementary information from visible (RGB) and infrared (IR) images can ameliorate the challenges posed by variable lighting conditions, rendering them an invaluable resource in many fields, including RGB-IR object detection, RGB-IR semantic segmentation, and RGB-IR crowd counting. However, existing methods still suffer from weak misalignment and fusion imprecision problems. These two problems present significant challenges for accurate object detection. In this paper, our primary focus is to solve the above problems in RGB-IR object detection tasks. Specifically, we first propose a Translation-Scale-Rotation Alignment (TSRA) module to align two modality features from region proposals. Based on the aligned region features, we introduce a Complementary Fusion Transformer (CFT) module to capture the complementary features. These two modules can be coupled in an unified Region of Interest (RoI) detection head called Cascade Alignment-Guided Transformer (CAGT) to obtain the robust fused features. Finally, based on CAGT, a region feature alignment and fusion detector called CAGTDet is constructed for RGB-IR object detection. Through comprehensive experiments on the aerial DroneVehicle dataset, our method effectively mitigates the impact of these two issues, resulting in robust detection results. Moreover, to evaluate the generalization of our method, we also perform experiments on the nature images sampled from the KAIST multispectral pedestrian dataset. The results show that our method surpasses other state-of-the-art methods.

Intelligent classifier for various degrees of coffee roasts using smart multispectral vision system

AbstractThis study proposes an innovative deep learning model for use in a multispectral vision system comprising a complementary metal‐oxide semiconductor image sensor and a spectrometer. To ensure accurate color recognition, the deep learning model includes an embedded adaptive automatic color temperature correction engine. By using this color temperature correction engine, the multispectral vision system can intelligently compensate for lighting and chromatic variations. To evaluate the performance of the system, we created a nine‐dimensional data set using the IT8.7/2 color target. We then used this data set to train the deep learning model. Our deep learning model outperformed other lightweight deep learning models in experiments, making it suitable for deployment on edge devices and embedded systems. We tested the ability of the multispectral vision system to classify adulterated coffee beans into their respective classes. The overall accuracy rate was more than 99.3%, indicating that out proposed multispectral vision system is effective in identifying color differences. Considering its capabilities in agricultural screening, we suggest incorporating our adaptive automatic multispectral vision system into agricultural machines for the realization of Agriculture 4.0.

Investigating the Effects of Full-Spectrum LED Lighting on Strawberry Traits Using Correlation Analysis and Time-Series Prediction.

In crop cultivation, particularly in controlled environmental agriculture, light quality is one of the most critical factors affecting crop growth and harvest. Many scholars have studied the effects of light quality on strawberry traits, but they have used relatively simple light components and considered only a small number of light qualities and traits in each experiment, and the results were not complete or objective. In order to comprehensively investigate the effects of different light qualities from 350 nm to 1000 nm on strawberry traits to better predict the future growth trend of strawberries under different light qualities, we proposed a new approach. We introduced Spearman's rank correlation coefficient to handle complex light quality variations and multiple traits, preprocessed the cultivation data through the CEEDMAN method, and predicted them using the Informer network. We took 500 strawberry plants as samples and cultivated them in 72 groups of dynamically changing light qualities. Then, we recorded the growth changes and formed training and testing sets. Finally, we discussed the correlation between light quality and plant trait changes in consistency with current studies, and the proposed prediction model achieved the best performance in the prediction task of nine plant traits compared with the comparison models. Thus, the validity of the proposed method and model was demonstrated.

Some observational properties of the Herbig Be Star AS 310

The results of spectral and photometric studies of Herbig Be-type star AS 310 have been presented. Photometric observations in the BVRcIc bands performed in 2016–2021 showed that the amplitude of the seasonal brightness variations of the star changes smoothly in different years and reaches a maximum of approximately around ΔV ∼ 0.6 mag in 2018. The star is more often in a weak state of radiation, but from time to time an increase in its brightness is observed. With a decrease in brightness in the V-band, the star’s radiation in the blue color increases, while in the red part of the spectrum, it decreases. Searches for periodicity in the light variations did not reveal definite periods of variability. The emission components observed in the hydrogen and forbidden nitrogen lines are rather formed in the nebula where the star itself is immersed. The spectrum contains strong lines of neutral helium, without any signs of emission components. The spectral class corresponds to the combined spectrum of B3–5 V + B1.5–2 V.

High-precision fruit localization using active laser-camera scanning: Robust laser line extraction for 2D-3D transformation

Recent advancements in deep learning-based approaches have led to remarkable progress in fruit detection, enabling robust fruit identification in complex environments. However, much less progress has been made on fruit 3D localization, which is equally crucial for robotic harvesting. Complex fruit shape/orientation, fruit clustering, varying lighting conditions, and occlusions by leaves and branches have greatly restricted existing sensors from achieving accurate fruit localization in the natural orchard environment. In this paper, we report on the design of a novel localization technique, called Active Laser-Camera Scanning (ALACS), to achieve accurate and robust fruit 3D localization. The ALACS hardware setup comprises a red line laser, an RGB color camera, a linear motion slide, and an external RGB-D camera. Leveraging the principles of dynamic-targeting laser-triangulation, ALACS enables precise transformation of the projected 2D laser line from the surface of apples to the 3D positions. To facilitate laser pattern acquisitions, a Laser Line Extraction (LLE) method is proposed for robust and high-precision feature extraction on apples. Comprehensive evaluations of LLE demonstrated its ability to extract precise patterns under variable lighting and occlusion conditions. The ALACS system achieved average apple localization accuracies of 6.9 - 11.2 mm at distances ranging from 1.0 m to 1.6 m, compared to 21.5 mm by a commercial RealSense RGB-D camera, in an indoor experiment. Orchard evaluations demonstrated that ALACS has achieved a 95% fruit detachment rate versus a 71% rate by the RealSense camera. By overcoming the challenges of apple 3D localization, this research contributes to the advancement of robotic fruit harvesting technology.

Rewritable Photoluminescence and Structural Color Display for Dual-Responsive Optical Encryption.

Optical encryption using coloration and photoluminescent (PL) materials can provide highly secure data protection with direct and intuitive identification of encrypted information. Encryption capable of independently controlling wavelength-tunable coloration as well as variable light intensity PL is not adequately demonstrated yet. Herein, a rewritable PL and structural color (SC) display suitable for dual-responsive optical encryption developed with a stimuli-responsive SC of a block copolymer (BCP) photonic crystal (PC) with alternating in-plane lamellae, of which a variety of 3D and 2D perovskite nanocrystals is preferentially self-assembled with characteristic PL, is presented. The SC of a BCP PC is controlled in the visible range with different perovskite precursor doping times. The perovskite nanocrystals developed in the BCP PC are highly luminescent, with a PL quantum yield of ≈33.7%, yielding environmentally stable SC and PL dual-mode displays. The independently programmed SC and PL information is erasable and rewritable. Dual-responsive optical encryption is demonstrated, in which true Morse code information is deciphered only when the information encoded by SCs is properly combined with PL information. Numerous combinations of SC and PL realize high security level of data anticounterfeiting. This dual-mode encryption display offers novel optical encryption with high information security and anti-counterfeiting.

Time-of-day perception in paintings.

The spectral shape, irradiance, direction, and diffuseness of daylight vary regularly throughout the day. The variations in illumination and their effect on the light reflected from objects may in turn provide visual information as to the time of day. We suggest that artists' color choices for paintings of outdoor scenes might convey this information and that therefore the time of day might be decoded from the colors of paintings. Here we investigate whether human viewers' estimates of the depicted time of day in paintings correlate with their image statistics, specifically chromaticity and luminance variations. We tested time-of-day perception in 17th- to 20th-century Western European paintings via two online rating experiments. In Experiment 1, viewers' ratings from seven time choices varied significantly and largely consistently across paintings but with some ambiguity between morning and evening depictions. Analysis of the relationship between image statistics and ratings revealed correlations with the perceived time of day: higher "morningness" ratings associated with higher brightness, contrast, and saturation and darker yellow/brighter blue hues; "eveningness" with lower brightness, contrast, and saturation and darker blue/brighter yellow hues. Multiple linear regressions of extracted principal components yielded a predictive model that explained 76% of the variance in time-of-day perception. In Experiment 2, viewers rated paintings as morning or evening only; rating distributions differed significantly across paintings, and image statistics predicted people's perceptions. These results suggest that artists used different color palettes and patterns to depict different times of day, and the human visual system holds consistent assumptions about the variation of natural light depicted in paintings.

Shaping Sustainable Bioplastics: Illuminating Chlorella sp. Growth with Light Variations and NPK Levels

this study delved into the exploration of a biodegradable alternative to synthetic plastics through the production of biodegradable bioplastics derived from microalgae. The investigation suggests that utilizing <i>Chlorella </i>sp. as a viable source for sustainable biomass in bioplastic creation holds promise due to the convenience of microalgae cultivation and its rapid growth rate. However, the large-scale cultivation of microalgae requires a considerable amount of nutrients, posing challenges to its economic viability. To address this hurdle, it becomes imperative to prioritize the enhancement of growth parameters for microalgae development. This study endeavors to identify the optimal NPK (nitrogen, phosphorus, and potassium) concentration and light spectrum for <i>Chlorella </i>sp. using a cost-effective NPK fertilizer medium. Various combinations of light spectra and NPK levels were examined to optimize growth conditions. Cultures of <i>Chlorella</i> sp. were subjected to red (660 nm), blue (460 nm), and white light (380 ~ 760 nm) which acted as experimental control over a span of 10 days. Among these, blue light yielded the highest optical density at 0.687, while red light exhibited the lowest optical density at 0.349. The findings underscore that the quantity of NPK fertilizer employed as a growth medium correlate directly with the observed cellular growth in <i>Chlorella</i> sp. cultures. The study also encompassed tensile strength and biodegradability assessments to characterize the resulting bioplastics. Tensile tests disclosed that bioplastics synthesized with sorbitol displayed a lower tensile strength of 0.106 MPa, in contrast to bioplastics containing both sorbitol and chitosan, which demonstrated a tensile strength of 0.167 MPa. In conclusion, both the appropriate light wavelengths and NPK nutrients emerge as pivotal factors influencing photosynthesis and the growth of photoautotrophic microalgae. The overarching objective of this research was achieved by successfully producing bioplastics using microalgae biomass residue cultivated under optimized parameters.

Cell size differences affect photosynthetic capacity in a Mesoamerican and an Andean genotype of Phaseolus vulgaris L.

The efficiency of CO2 flux in the leaf is hindered by a several structural and biochemical barriers which affect the overall net photosynthesis. However, the dearth of information about the genetic control of these features is limiting our ability for genetic manipulation. We performed a comparative analysis between three-week-old plants of a Mesoamerican and an Andean cultivar of Phaseolus vulgaris at variable light and CO2 levels. The Mesoamerican bean had higher photosynthetic rate, maximum rate of rubisco carboxylase activity and maximum rate of photosynthetic electron transport at light saturation conditions than its Andean counterpart. Leaf anatomy comparison between genotypes showed that the Mesoamerican bean had smaller cell sizes than the Andean bean. Smaller epidermal cells in the Mesoamerican bean resulted in higher stomata density and consequently higher stomatal conductance for water vapor and CO2 than in the Andean bean. Likewise, smaller palisade and spongy mesophyll cells in the Mesoamerican than in the Andean bean increased the cell surface area per unit of volume and consequently increased mesophyll conductance. Finally, smaller cells in the Mesoamerican also increased chlorophyll and protein content per unit of leaf area. In summary, we show that different cell sizes controls the overall net photosynthesis and could be used as a target for genetic manipulation to improve photosynthesis.