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Metasurfaces are opening promising flexibilities to reshape the wavefront of electromagnetic waves. Notable optical phenomena are observed with the tailored surface plasmon, which is excited by metallic components in the visible spectrum. However, metamaterial or metasurface devices utilizing metallic materials encounter the challenge of low transmission efficiency, particularly within the visible spectrum. This study proposes a multilayer design strategy to enhance their transmission efficiency. By incorporating additional metal layers for improvements in the transmission efficiency and dielectric layers as spacers, cavities are formed along the propagation direction, enabling the modulation of transmittance and reflection through a process mimicking destructive interference. An analytical model simplified with the assumption of deep-subwavelength-thick metal layers is proposed to predict the structural parameters with optimized transmittance. Numerical studies employing the rigorous coupled wave analysis method confirmed that the additional metal layers significantly improve the transmittance. The introduction of the extra metal and dielectric layers enhances the transmission efficiency in specific spectral regions, maintaining a controllable passband and transmittance. The results indicate that the precise control over the layers' thicknesses facilitates the modulation of peak-to-valley ratios and the creation of comb-like filters, which can be further refined through controlled random variation in the thickness. Furthermore, when the thickness of the silver layer followed an arithmetic sequence, a multilayer structure with a transmittance of approximately 80% covering the entire visible spectrum could be achieved. Significantly, the polarization extinction ratio and the phase delay of the incident beams could still be modulated by adjusting the geometrical structure and parameters of the multilayer metamaterial for diversified functionalities. [ABSTRACT FROM AUTHOR]

Monitoring soil salinity with remote sensing is difficult, but knowing the link between saline soil surface spectra, soil water, and salt transport processes might help in modeling for soil salinity monitoring. In this study, we used an indoor soil column experiment, an unmanned aerial vehicle multispectral sensor camera, and a soil moisture sensor to study the water and salt transport process in the soil column under different water addition conditions and investigate the relationship between the soil water and salt transport process and the spectral reflectance of the image on the soil surface. The observation results of the soil column show that the soil water and salt transportation process conforms to the basic transportation law of "salt moves together with water, and when water evaporates, salt is retained in the soil weight". The salt accumulation phenomenon increases the image spectral reflectance of the surface layer of the soil column, while soil temperature has no effect on the reflectance. As the water percolates down, water and salt accumulate at the bottom of the soil column. The salinity index decreases instantly after the addition of brine and then tends to increase slowly. The experimental results indicate that this work can capture the relationship between the water and salt transport process and remote sensing spectra, which can provide theoretical basis and reference for soil water salinity monitoring. [ABSTRACT FROM AUTHOR]

In recent years, the estimation of biometric parameters from facial visuals, including images and videos, has emerged as a prominent area of research. However, the robustness of deep learning-based models is challenged, particularly in the presence of changing illumination conditions. To overcome these limitations and unlock new opportunities, thermal imagery has arisen as a viable alternative. Nevertheless, the limited availability of datasets containing thermal data and the small amount of annotations on them limits the exploration of this spectrum. Motivated by this gap, this paper introduces the Label-EURECOM Visible and Thermal (LVT) Face Dataset for face biometrics. This pioneering dataset includes paired visible and thermal images and videos from 52 subjects along with metadata of 22 soft biometrics and health parameters. Due to the reduced number of existing datasets in this domain, the LVT Face Dataset aims to facilitate further research and advancements in the utilization of thermal imagery for diverse eHealth applications and soft biometric estimation. Moreover, we present the first comparative study between visible and thermal spectra as input images for soft biometric estimation, namely gender age and weight, from face images on our collected dataset. [ABSTRACT FROM AUTHOR]

Abstract In recent years, the estimation of biometric parameters from facial visuals, including images and videos, has emerged as a prominent area of research. However, the robustness of deep learning-based models is challenged, particularly in the presence of changing illumination conditions. To overcome these limitations and unlock new opportunities, thermal imagery has arisen as a viable alternative. Nevertheless, the limited availability of datasets containing thermal data and the small amount of annotations on them limits the exploration of this spectrum. Motivated by this gap, this paper introduces the Label-EURECOM Visible and Thermal (LVT) Face Dataset for face biometrics. This pioneering dataset includes paired visible and thermal images and videos from 52 subjects along with metadata of 22 soft biometrics and health parameters. Due to the reduced number of existing datasets in this domain, the LVT Face Dataset aims to facilitate further research and advancements in the utilization of thermal imagery for diverse eHealth applications and soft biometric estimation. Moreover, we present the first comparative study between visible and thermal spectra as input images for soft biometric estimation, namely gender age and weight, from face images on our collected dataset.

The magneto-optical properties of atmospheric air molecules describe how air molecules interact with both magnetic fields and light. Studying these properties is crucial for understanding atmospheric dynamics, enhancing remote sensing for environmental monitoring, developing new materials for sensors and optical devices, refining medical imaging techniques like Magnetic Resonance Imaging, and advancing fundamental scientific knowledge with potential practical applications. The objective of this study is to investigate the magnetic optical properties of air within a weak magnetic field ranging from 0.122 T to 0.986 T using experimental methods. The analysis focuses on the variations in transmittance across the visible spectrum with changes in magnetic field intensity. Results indicate a decrease in transmittance with increasing wavelength, demonstrating a direct correlation between transmittance and magnetic field strength. The magneto-optical properties, specifically transmittance, exhibit a decreasing trend with increasing wavelength, with minimum and maximum transmittance values recorded at 460 nm and 664.755 nm, respectively. Additionally, the transmittance of air spectrum is directly influenced by the applied magnetic field. Moreover, the intensity ratio associated with Raman spectra shift decreases with increasing Raman spectra shift, with higher intensity ratios observed in the presence of a magnetic field compared to non-magnetic conditions. Furthermore, the magnetooptical response tends to shift towards higher wavelengths with increasing magnetic field strength.

A double-band perfect absorber is proposed based on M o S 2 metamaterial. The proposed absorber is composed of a monolayer molybdenum disulfide, a crystalline silicon layer, and a silver substrate. Utilizing the critical coupling principle of guided resonance, efficient optical absorption of molybdenum disulfide was achieved. Specifically, at the resonance wavelengths ( λ 1 = 610.431 nm , λ 2 = 654.484 nm ), two perfect absorption peaks of 99.42% and 99.31% were attained based on finite-difference time-domain (FDTD) simulations. Through detailed analysis of field distributions and spectral responses, the interactions between electromagnetic waves and this perfect absorber structure are examined. Based on the simulation results, the structure's geometric parameters wield control over the resonance wavelength range of M o S 2 , holding pivotal practical implications for bolstering its absorption potency and selectivity. Moreover, the innovative approach of employing critical coupling to amplify light-silica interactions holds promise for application across various atomic-scale thin materials. Additionally, the absorber's sensing capabilities are revealing many metrics. The highest quality factor, sensitivity, and figure of merit (FOM) of the sensors are recorded as 111.3, 178.4 nm/RIU, and 30.3 R I U - 1 , respectively. Hence, our findings potentially pave the way for enhancing light-matter interactions within monolayer transition-metal dichalcogenides, offering promise for applications in multiband absorbers and sensors. [ABSTRACT FROM AUTHOR]

This paper explores a data augmentation approach for images of rigid bodies, particularly focusing on electrical equipment and analogous industrial objects. By leveraging manufacturer-provided datasheets containing precise equipment dimensions, we employed straightforward algorithms to generate synthetic images, permitting the expansion of the training dataset from a potentially unlimited viewpoint. In scenarios lacking genuine target images, we conducted a case study using two well-known detectors, representing two machine-learning paradigms: the Viola–Jones (VJ) and You Only Look Once (YOLO) detectors, trained exclusively on datasets featuring synthetic images as the positive examples of the target equipment, namely lightning rods and potential transformers. Performances of both detectors were assessed using real images in both visible and infrared spectra. YOLO consistently demonstrates F 1 scores below 26% in both spectra, while VJ's scores lie in the interval from 38% to 61%. This performance discrepancy is discussed in view of paradigms' strengths and weaknesses, whereas the relatively high scores of at least one detector are taken as empirical evidence in favor of the proposed data augmentation approach. [ABSTRACT FROM AUTHOR]

Photonic crystals have garnered significant attention in the field of photonics for their exceptional ability to control the flow of light. In this study, we conduct a comprehensive evaluation of 10 materials, namely Lead Sulfide (PbS), Gallium Arsenide (GaAs), Magnesium Fluoride (MgF2), Silicon Dioxide (SiO2), Germanium Dioxide (GeO2), Magnesium Oxide (MgO), Aluminum Oxide (Al2O3), Zinc Oxide (ZnO), Mercury Sulfide (HgS), and Zinc Sulfide (ZnS), in combination with titanium dioxide (TiO2), to determine the material configuration that yields the highest reflection coefficient. Our results reveal that the combination of SiO2–TiO2 emerges as the optimal choice, as it has the maximum reflection coefficient of 0.99508 in comparison with the other evaluated materials. These findings serve as a solid foundation for further exploration, including the investigation of additional materials and the impact of geometric variations on the reflection coefficient. [ABSTRACT FROM AUTHOR]

In the cultivation of maize, the leaf area index (LAI) serves as an important metric to determine the development of the plant. Unmanned aerial vehicles (UAVs) that capture RGB images, along with random forest regression (RFR), can be used to indirectly measure LAI through vegetative indices. Research using these techniques is at an early stage, especially in the context of maize for silage. Therefore, this study aimed to evaluate which vegetative indices have the strongest correlations with maize LAI and to compare two regression methods. RFR, ridge regression (RR), support vector machine (SVM), and multiple linear regression (MLR) regressions were performed in Python for comparison using images obtained in an area cultivated with maize for silage. The results showed that the RGB spectral indices showed saturation when the LAI reached 3 m2 m−2, with the VEG (vegetable index), COM (combination), ExGR (red–green excess), and TGI (triangular greenness index) indices selected for modeling. In terms of regression, RFR showed superior performance with an R2 value of 0.981 and a root mean square error (RMSE) of 0.138 m2 m−2. Therefore, it can be concluded that RFR using RGB indices is a good way to indirectly obtain the LAI. [ABSTRACT FROM AUTHOR]

Optical phase control is essential for optical beam steering applications. The silicon nitride thermo-optic modulator generally suffers from high electrical power consumption. Microresonator and multipass structures could reduce the electrical power consumption of silicon nitride thermo-optic modulators, with the drawback of a narrow operating bandwidth and high insertion loss. We demonstrate a single-pass silicon nitride thermo-optic phase modulator at 532 nm with low insertion loss and low power consumption, achieving a π phase shift power consumption down to 0.63 mW in a Mach–Zehnder switch. The rise and fall time are around 1.07 ms and 0.67 ms, respectively. [ABSTRACT FROM AUTHOR]

Metasurfaces are opening promising flexibilities to reshape the wavefront of electromagnetic waves. Notable optical phenomena are observed with the tailored surface plasmon, which is excited by metallic components in the visible spectrum. However, metamaterial or metasurface devices utilizing metallic materials encounter the challenge of low transmission efficiency, particularly within the visible spectrum. This study proposes a multilayer design strategy to enhance their transmission efficiency. By incorporating additional metal layers for improvements in the transmission efficiency and dielectric layers as spacers, cavities are formed along the propagation direction, enabling the modulation of transmittance and reflection through a process mimicking destructive interference. An analytical model simplified with the assumption of deep-subwavelength-thick metal layers is proposed to predict the structural parameters with optimized transmittance. Numerical studies employing the rigorous coupled wave analysis method confirmed that the additional metal layers significantly improve the transmittance. The introduction of the extra metal and dielectric layers enhances the transmission efficiency in specific spectral regions, maintaining a controllable passband and transmittance. The results indicate that the precise control over the layers’ thicknesses facilitates the modulation of peak-to-valley ratios and the creation of comb-like filters, which can be further refined through controlled random variation in the thickness. Furthermore, when the thickness of the silver layer followed an arithmetic sequence, a multilayer structure with a transmittance of approximately 80% covering the entire visible spectrum could be achieved. Significantly, the polarization extinction ratio and the phase delay of the incident beams could still be modulated by adjusting the geometrical structure and parameters of the multilayer metamaterial for diversified functionalities.

Monitoring soil salinity with remote sensing is difficult, but knowing the link between saline soil surface spectra, soil water, and salt transport processes might help in modeling for soil salinity monitoring. In this study, we used an indoor soil column experiment, an unmanned aerial vehicle multispectral sensor camera, and a soil moisture sensor to study the water and salt transport process in the soil column under different water addition conditions and investigate the relationship between the soil water and salt transport process and the spectral reflectance of the image on the soil surface. The observation results of the soil column show that the soil water and salt transportation process conforms to the basic transportation law of “salt moves together with water, and when water evaporates, salt is retained in the soil weight”. The salt accumulation phenomenon increases the image spectral reflectance of the surface layer of the soil column, while soil temperature has no effect on the reflectance. As the water percolates down, water and salt accumulate at the bottom of the soil column. The salinity index decreases instantly after the addition of brine and then tends to increase slowly. The experimental results indicate that this work can capture the relationship between the water and salt transport process and remote sensing spectra, which can provide theoretical basis and reference for soil water salinity monitoring.

Genetic engineering and directed evolution are effective methods for addressing the low yield and poor industrialization level of microbial target products. The current research focus is on how to efficiently and rapidly screen beneficial mutants from constructed large-scale mutation libraries. Traditional screening methods such as plate screening and well-plate screening are severely limited in their development and application due to their low efficiency and high costs. In the past decade, microfluidic technology has become an important high-throughput screening technology due to its fast speed, low cost, high automation, and high screening throughput, and it has developed rapidly. Droplet-based microfluidic high-throughput screening has been widely used in various fields, such as strain/enzyme activity screening, pathogen detection, single-cell analysis, drug discovery, and chemical synthesis, and has been widely applied in industries such as those involving materials, food, chemicals, textiles, and biomedicine. In particular, in the field of enzyme research, droplet-based microfluidic high-throughput screening has shown excellent performance in discovering enzymes with new functions as well as improved catalytic efficiency or stability, acid-base tolerance, etc. Currently, droplet-based microfluidic high-throughput screening technology has achieved the high-throughput screening of enzymes such as glycosidase, lipase, peroxidase, protease, amylase, oxidase, and transaminase as well as the high-throughput detection of products such as riboflavin, coumarin, 3-dehydroquinate, lactic acid, and ethanol. This article reviews the application of droplet-based microfluidics in high-throughput screening, with a focus on high-throughput screening strategies based on UV, visible, and fluorescence spectroscopy, including labeled optical signal detection screening, as well as label-free electrochemical detection, mass spectrometry, Raman spectroscopy, nuclear magnetic resonance, etc. Furthermore, the research progress and development trends of droplet-based microfluidic technology in enzyme modification and strain screening are also introduced. [ABSTRACT FROM AUTHOR]

Jumping spiders (Araneae: Salticidae) possess a visual system that is exceptional among arthropods, mainly due to its unmatched acuity. Although vision of these spiders has been studied for decades, some of its main aspects remain unclear. One such aspect is the light spectrum visible to salticids, which is surprisingly heterogenous and involves various underlying physiological mechanisms. The results of the behavioral experiments with the regal jumping spider (Phidippus regius) presented in this study provide evidence that these vision-dependent spiders are able to capture their prey even under red (longwave) light, which is usually not perceived by spiders. Broader use of this experimental design in studies of spider vision is discussed. [ABSTRACT FROM AUTHOR]

Electronic absorption spectra were calculated in the visible region for clusters of the pigment Yellow 3 that comprise one, two, and four molecules. The geometry was optimized by the PBEh-3c and B3LYP-D4/def2-SVPD methods. The results obtained by the B3LYP-D4/def2-SVPD method correlate best with the experimental data. The spectral characteristics were calculated by the TD-DFT and sTD-DFT methods with the PBE0 functional and the def2-SVPD basis set. By analyzing the natural transition orbitals (NTOs) and changing the electron density during the formation of excited states of the studied clusters, it was shown that the main contribution to the spectral lines in the visible range is made by the density transfer from the aromatic rings to the nitro group and the conjugated bond system in the center of the molecule. In this case, for the crystalline state of matter, all excited states are delocalized, and the main contribution to the intermolecular transfer of the electron density is made by the formation of excitons. [ABSTRACT FROM AUTHOR]

Purpose High-contrast tissue characterization of thermal ablation has been desired to evaluate therapeutic outcomes accurately. This paper presents a photoacoustic (PA) characterization of thermal tissue ablation in the visible spectrum, in which higher light absorbance can produce spectral contrast starker than in the near-infrared range. Methods Ex vivo experiments were performed to measure visible PA spectra (480–700 nm) from fresh porcine liver tissues that received a thermal dose in a range of cumulative equivalent minutes at 43°C (CEM43). The local hemoglobin lobe area between 510–600 nm and whole-spectral area under the curve were evaluated to represent the transition of hemoglobin into methemoglobin (MetHb) in the target tissue. Results The thermal process below an estimated therapeutic CEM43 threshold (80–340 minutes) presented a progressive elevation of the PA spectrum and an eventual loss of local hemoglobin peaks in the visible spectrum, closer to the MetHb spectrum. Interestingly, an excessive CEM43 produced a substantial drop in the PA spectrum. In the spectral analysis, the visible spectrum yielded 13.9–34.1 times higher PA sensitivity and 1.42 times higher contrast change than at a near-infrared wavelength. Conclusion This novel method of PA tissue characterization in the visible spectrum could be a potential modality to evaluate various thermal therapeutic modalities at high-contrast resolution.

In the cultivation of maize, the leaf area index (LAI) serves as an important metric to determine the development of the plant. Unmanned aerial vehicles (UAVs) that capture RGB images, along with random forest regression (RFR), can be used to indirectly measure LAI through vegetative indices. Research using these techniques is at an early stage, especially in the context of maize for silage. Therefore, this study aimed to evaluate which vegetative indices have the strongest correlations with maize LAI and to compare two regression methods. RFR, ridge regression (RR), support vector machine (SVM), and multiple linear regression (MLR) regressions were performed in Python for comparison using images obtained in an area cultivated with maize for silage. The results showed that the RGB spectral indices showed saturation when the LAI reached 3 m2 m−2, with the VEG (vegetable index), COM (combination), ExGR (red–green excess), and TGI (triangular greenness index) indices selected for modeling. In terms of regression, RFR showed superior performance with an R2 value of 0.981 and a root mean square error (RMSE) of 0.138 m2 m−2. Therefore, it can be concluded that RFR using RGB indices is a good way to indirectly obtain the LAI.

The unit cell structure design and mathematical simulation models of a wideband Metamaterial Light Absorber (MLA) operating in visible range (450 – 750 THz) based on a tungsten metal, based on a Stepped Impedance Resonator (SIR) model is presented. The MLA is being analyzed for electron mobility to achieve significant light photon conversion to use this technology in solar cells for energy harvesting applications. A Metamaterial Light Absorber based on a three-layered metallic semiconductor optical thin film structure is proposed. The top metallic layer is made by Tungsten. Semiconductor layer is developed by silicon and the whole structure is developed over a glass substrate and the same procedure is validated using the Finite Element Method (FEM).The design was subjected to a number of parametric analysis in order to obtain the optimal physical dimension. The various analysis has also much elucidated the process of absorption. Based on the simulation model, the proposed MLA has two peak absorptions of 100 and 99% at 567 THz and 645 THz respectively. The positive light absorption is more than 75% from the frequency range of 545 to 680 THz and produced positive absorption over the whole visible spectrum (450 – 750 THz).The predicted electric field distributions indicate an increased wideband absorption is induced by the stimulation of local and propagating surface electrons and guiding mode resonances. The proposed MLA based on tungsten SIR has the potential to be used in solar cells, solar energy harvesters, solar thermo-PVs, etc. [ABSTRACT FROM AUTHOR]

When the Galton whistle was introduced in the 1870s, it was the first demonstration many had encountered of the phenomenon that nonhumans sometimes exceed humans in sensory range, for example perceiving ultraviolet light and ultrasonic signals. While some empirical research had explored this possibility beforehand, this area of perceptual research progressed slowly. A horror short story by Ambrose Bierce in 1893, "The Damned Thing," used the concept of superior nonhuman sensory range as a twist ending, seemingly anticipating scientific discoveries to come or at least understanding the implications of the early findings well in advance of the field. This article analyzes Bierce's possible sources, with Bierce representing the general educated nonscientist and providing insights into the spread of this concept into public and scientific awareness. [ABSTRACT FROM AUTHOR]

Responsible for food production and industry inputs, agriculture needs to adapt to worldwide increasing demands and environmental requirements. In this scenario, black oat has gained environmental and economic importance since it can be used in no-tillage systems, green manure, or animal feed supplementation. Despite its importance, few studies have been conducted to introduce more accurate and technological applications. Plant height (H) correlates with biomass production, which is related to yield. Similarly, productivity status can be estimated from vegetation indices (VIs). The use of unmanned aerial vehicles (UAV) for imaging enables greater spatial and temporal resolutions from which to derive information such as H and VI. However, faster and more accurate methodologies are necessary for the application of this technology. This study intended to obtain high-quality digital surface models (DSMs) and orthoimages from UAV-based RGB images via a direct-to-process means; that is, without the use of ground control points or image pre-processing. DSMs and orthoimages were used to derive H (HDSM) and VIs (VIRGB), which were used for H and dry biomass (DB) modeling. Results showed that HDSM presented a strong correlation with actual plant height (HREF) (R2 = 0.85). Modeling biomass based on HDSM demonstrated better performance for data collected up until and including the grain filling (R2 = 0.84) and flowering (R2 = 0.82) stages. Biomass modeling based on VIRGB performed better for data collected up until and including the booting stage (R2 = 0.80). The best results for biomass estimation were obtained by combining HDSM and VIRGB, with data collected up until and including the grain filling stage (R2 = 0.86). Therefore, the presented methodology has permitted the generation of trustworthy models for estimating the H and DB of black oats. [ABSTRACT FROM AUTHOR]

Optical phase control is essential for optical beam steering applications. The silicon nitride thermo-optic modulator generally suffers from high electrical power consumption. Microresonator and multipass structures could reduce the electrical power consumption of silicon nitride thermo-optic modulators, with the drawback of a narrow operating bandwidth and high insertion loss. We demonstrate a single-pass silicon nitride thermo-optic phase modulator at 532 nm with low insertion loss and low power consumption, achieving a π phase shift power consumption down to 0.63 mW in a Mach–Zehnder switch. The rise and fall time are around 1.07 ms and 0.67 ms, respectively.

Genetic engineering and directed evolution are effective methods for addressing the low yield and poor industrialization level of microbial target products. The current research focus is on how to efficiently and rapidly screen beneficial mutants from constructed large-scale mutation libraries. Traditional screening methods such as plate screening and well-plate screening are severely limited in their development and application due to their low efficiency and high costs. In the past decade, microfluidic technology has become an important high-throughput screening technology due to its fast speed, low cost, high automation, and high screening throughput, and it has developed rapidly. Droplet-based microfluidic high-throughput screening has been widely used in various fields, such as strain/enzyme activity screening, pathogen detection, single-cell analysis, drug discovery, and chemical synthesis, and has been widely applied in industries such as those involving materials, food, chemicals, textiles, and biomedicine. In particular, in the field of enzyme research, droplet-based microfluidic high-throughput screening has shown excellent performance in discovering enzymes with new functions as well as improved catalytic efficiency or stability, acid-base tolerance, etc. Currently, droplet-based microfluidic high-throughput screening technology has achieved the high-throughput screening of enzymes such as glycosidase, lipase, peroxidase, protease, amylase, oxidase, and transaminase as well as the high-throughput detection of products such as riboflavin, coumarin, 3-dehydroquinate, lactic acid, and ethanol. This article reviews the application of droplet-based microfluidics in high-throughput screening, with a focus on high-throughput screening strategies based on UV, visible, and fluorescence spectroscopy, including labeled optical signal detection screening, as well as label-free electrochemical detection, mass spectrometry, Raman spectroscopy, nuclear magnetic resonance, etc. Furthermore, the research progress and development trends of droplet-based microfluidic technology in enzyme modification and strain screening are also introduced.

In general, remote sensing studies assessing cover crop growth are species nonspecific, use imagery from satellites or modified unmanned aerial vehicles (UAVs), and rely on multispectral vegetation indexes (VIs). However, using RGB imagery and visible-spectrum VIs from commercial off-the-shelf (COTS) UAVs to assess species specific cover crop growth is limited in the current scientific literature. Thus, this study evaluated RGB imagery and visible-spectrum VIs from COTS UAVs for suitability to estimate concentration (%) and content (kg ha−1) based cereal rye (CR) biomass, carbon (C), nitrogen (N), phosphorus (P), potassium (K), and sulfur (S). UAV surveys were conducted at two fields in Indiana and evaluated five visible-spectrum VIs—Visible Atmospherically Resistant Index (VARI), Green Leaf Index (GLI), Modified Green Red Vegetation Index (MGRVI), Red Green Blue Vegetation Index (RGBVI), and Excess of Greenness (ExG). This study utilized simple linear regression (VI only) and stepwise multiple regression (VI with weather and geographic data) to produce individual models for estimating CR biomass, C, N, P, K, and S concentration and content. The goodness-of-fit statistics were generated using repeated K-fold cross-validation to compare individual model performance. In general, the models developed using simple linear regression were inferior to those developed using the multiple stepwise regression method. Furthermore, for models developed using the multiple stepwise regression method all five VIs performed similarly when estimating concentration-based CR variables; however, when estimating content-based CR variables the models developed with GLI, MGRVI, and RGBVI performed similarly explaining 74–81% of the variation in CR data, and outperformed VARI and ExG. However, on an individual field basis, MGRVI consistently outperformed GLI and RGBVI for all CR characteristics. This study demonstrates the potential to utilize COTS UAVs for estimating in-field CR characteristics; however, the models generated in this study need further development to expand geographic scope and incorporate additional abiotic factors. [ABSTRACT FROM AUTHOR]

Optical metamaterials give birth to the control and regulation of light. However, because of strong energy dissipation and fabrication difficulty in meta-atoms, low-loss isotropic three dimensional negative index metamaterials (NIMs) in the visible spectrum has long been regarded as an extremely challenging. Here, we report an ultralow loss isotropic metamaterials for visible light and its inverse Doppler effect. The ball-thorn-shaped metaclusters with symmetrical structure consisting of the dielectric and its surface dispersed super-thin silver layer was proposed, the surface plasma resonance is formed by discrete silver layer with a thickness of two or three atomic layers. We invented a unique technique for preparing ultralow loss isotropic clusters and three-dimensional large block samples. The negative refractive index and the inverse Doppler effect of green and red light is measured by the prism method for the first time. The discrete super-thin silver layer produced by the photoreduction method greatly reduces the generation of loss and break through noble metal high energy losses of traditional optical frequency metamaterial, the metaclusters unfold bottleneck of the nano-assemble visible light metamaterials, opening a door for disorder assembling ultralow loss isotropic three-dimensional large block NIMs devices of arbitrary shape.

In this paper, we report on the tunable dispersion characteristics of TE and TM modes in a ternary PPC comprised of graphene, silicon and polymer as nanolayers. By employing TMM, it is investigated that the structure shows novel ideas of tunability with the variations in the incident angle and polymer thickness in which Bloch waves are affected. The polymer-based 1D TPC exhibits two forbidden gaps, while the focus of illustration is on the wideband located in higher wavelength region for both TE and TM polarizations. With increase in the incident angle, the two PBGs show a blueshift in both cases of TE and TM modes, whereas with increase in the polymer thickness, the PBGs of TE waves show a reverse effect, viz. redshift. Further, the enlarged forbidden gap slightly increases in width for TE polarization, whereas in the case of TM polarization, it is decreasing drastically. The shifting in the position towards low wavelength for upper bandedge is more effective in the TM mode as compared to that of the TE mode. By increasing the polymer thickness, the gap also increases for TE mode. The 3D reflection spectra for s and p waves show that, a PBG of width 40 nm in the wavelength range 555.6–595.6 nm, is common in the enlarged PBG for both TE and TM modes, and so the TPC bears the features of ODR in the visible region. Such novel features of tunablity can be useful in designing optical devices in the visible region, including broadband reflectors, narrowband ODRs, and low-loss perfect reflectors for solar cells. [ABSTRACT FROM AUTHOR]

In this paper, we propose a visible broadband metamaterial absorber (MA) based on nickel metal. The MA is composed of three layers: a nickel-based pattern array on the top, silicon dioxide in the middle layer, and a nickel film at the bottom. The performance of the MA was simulated by the finite-different time-domain method. The average absorption of the designed absorber reaches 95.76% in the whole visible spectrum of 380 – 750 nm. The absorption rates at 500 and 637 nm are 96.63% and 99.93%, respectively. In addition, the MA proposed is insensitive to polarization in the whole visible region. Then we investigate the mechanism of the high absorption rate and the influence of the structural parameters on the absorption spectrum. Propagating surface plasmon resonance and localized surface plasmon resonance are generated at resonant wavelengths of 500 and 637 nm, respectively. This is theoretically consistent with the parametric study. The proposed MA shows extensive application prospects, such as for solar cells. [ABSTRACT FROM AUTHOR]

A novel Circular Ring Resonator (CRR) type single band metamaterial energy harvesting absorber which has the polarization insensitive property for solar cell application is designed, simulated and investigated to operate under the visible region 450THz - 750THz which is the more suitable operating range for a solar cell. The proposed Perfect Metamaterial Absorber (PMA) is designed with three layer based solar cell structure. The three layers are named as substrate, middle or dielectric layer and a top layer made by a metal conductor. The bottommost layer is considered as a substrate layer of 5 nm thickness, and it is developed by Silicon (Si). Dielectric layer is considered as a middle layer of 30 nm thickness and it is developed by a direct band gap semiconductor material called Gallium Arsenide (GaAs). A tungsten material with a thickness of 2.5 nm is applied at a top most conducting layer. It is discovered with a peak absorption rate of >75% is occurred spanning a wide frequency band of 538 THz to677 THz. In this proposed work, the parametric study of the overall of PMA is implemented in the range of 450–750 THz. At the resonance frequencies 560 THz and 670 THz, the absorber reacts with complete perfect absorption of 99.99% and 99% respectively. Also the absorber reacts with greater than 75%between 538THz - 677 THz frequency band. The tunable nature of PMA is occurred due to the top conducting material like Tungsten and dielectric semiconductor material like GaAs used in the middle layer of the design. The proposed unit cell absorber and all its analysis enable the solar cell application over the whole visible spectrum to achieve an absorption rate of greater than 75%. Various iterations are carried out and discussed to illustrate added features of the proposed work using (S21) and (S11) properties. The proposed work is determined to be the optimum absorber for a solar cell to produce high efficiency. [ABSTRACT FROM AUTHOR]

Abstract Ecosystem trait is a standardized description of biological features of a community, and it bridges individual plants and ecosystem. Conventionally most ecosystem trait data are collected from field survey and the generated data is hard to meet the requirements as set in the concept of ecosystem trait. To a great extent, remotely piloted aircraft systems (RPAS) remote sensing, which is capable of retrieving ecosystem traits across multiple scales, can overcome constraints in field plot survey. In this study, we selected alpine grassland ecosystem on the Tibetan Plateau (TP), which is under‐studied due to scarcity of field monitoring data, as the research target. A new data framework was proposed by integrating field plot and RPAS remote sensing data to map spatial patterns of ecosystem traits for the alpine grasslands. Across four landscapes on the TP, ecosystem traits of vegetation coverage (CVC), species number (CSN), individual number (CIN), above ground biomass (AGB), organic carbon content (OC%) and total nitrogen content (TN%) were retrieved. We also calculated Shannon's Diversity Index and Shannon's Evenness Index for each plot. The results showed that RPAS‐based high spatial resolution RGB image is capable of predicting both physical and chemical ecosystem traits for alpine grasslands on the TP. Remote sensing on physical traits are overall more efficient than on chemical traits, with the highest R2 of 0.86 and 0.48 for physical trait and chemical one, respectively. The bands of Red and Green contributed more to the prediction model than band of Blue did, and the spectral mean value played a greater role than the spectral standard deviation. Based on the retrieved results, a set of spatial patterns on ecosystem traits can be revealed. This study represents an advance on ecosystem trait study and can significantly improve our understanding on ecosystem functions of the alpine ecosystem on the TP.

Attached microalgal growth of Chlorella vulgaris on palm decanter cake (PDC) under irradiation with various visible monochromatic and polychromatic spectra to produce biodiesel was studied in this work. The results demonstrated that the white spectrum cultivation exhibited the highest microalgal density of 1.13 g/g along with 1.213 g/L day of microalgal productivity. Correspondingly, the biodiesel obtained was comprised mainly of C16 and C18 fatty acids, possessing a high cetane number and oxidation stability from the high saturated fatty acid content (70.38%), which was appealing in terms of most biodiesel production requirements. Nevertheless, the highest lipid content (14.341%) and lipid productivity (93.428 mg/L per day) were discovered with green spectrum cultivation. Blue and white spectra led to similar protein contents (34%) as well as carbohydrate contents (61%), corroborating PDC as a feasible carbon and nutrient source for growing microalgae. Lastly, the energy feasibilities of growing the attached microalgae under visible spectra were investigated, with the highest net energy ratio (NER) of 0.302 found for the yellow spectrum. This value outweighed that in many other works which have used suspended growth systems to produce microalgal fuel feedstock. The microalgal growth attached to PDC is deemed to be a suitable alternative cultivation mode for producing sustainable microalgal feedstock for the biofuel industry. [ABSTRACT FROM AUTHOR]

This study collected multidimensional feature data such as spectra, texture, and component contents of Polygonati Rhizoma from different origins and varieties (Polygonatum kingianum Coll. et Hemsl from Yunnan and Guizhou; Polygonatum cyrtonema Hua from Anhui and Jiangxi; Polygonatum sibiricum Red from Hunan). Multivariate statistical analysis was used to select 39 characteristic factors for distinguishing PR origins and 14 characteristic factors for discriminating PR varieties (VIP > 1 and P < 0.05). In addition, by combining multivariate statistical analysis with a deep belief network (DBN) classification algorithm, a novel artificial intelligence algorithm was developed and optimized. Compared to traditional discriminant analysis methods, the accuracy of this new approach was significantly improved, achieving a 100% discrimination rate for PR varieties and a 100% accuracy rate for tracing the origin of PR. This research provides a reference and data support for constructing intelligent algorithms based on multidimensional data fusion, to achieve food variety discrimination and origin tracing. • Non-destructive Quantitative analysis of PRs appearance based on computer vision. • Generation and modeling of multidimensional data for PRs characterization. • The MDF- DBN discriminated with 100% accuracy the variety and origin of PRs. • This study provides a new reference for origin traceability of other food products. [ABSTRACT FROM AUTHOR]

Automatic identification and sorting of livestock organs in the meat processing industry could reduce costs and improve efficiency. Two hyperspectral sensors encompassing the visible (400–900 nm) and short-wave infrared (900–1700 nm) spectra were used to identify the organs by type. A total of 104 parenchymatous organs of cattle and sheep (heart, kidney, liver, and lung) were scanned in a multi-sensory system that encompassed both sensors along a conveyor belt. Spectral data were obtained and averaged following manual markup of three to eight regions of interest of each organ. Two methods were evaluated to classify organs: partial least squares discriminant analysis (PLS-DA) and random forest (RF). In addition, classification models were obtained with the smoothed reflectance and absorbance and the first and second derivatives of the spectra to assess if one was superior to the rest. The in-sample accuracy for the visible, short-wave infrared, and combination of both sensors was higher for PLS-DA compared to RF. The accuracy of the classification models was not significantly different between data pre-processing methods or between visible and short-wave infrared sensors. Hyperspectral sensors, particularly those in the visible spectrum, seem promising to identify organs from slaughtered animals which could be useful for the automation of quality and process control in the food supply chain, such as in abattoirs. [ABSTRACT FROM AUTHOR]

Artificial vision has wide-ranging applications in the food sector; it is easy to use, relatively low cost and allows to conduct rapid non-destructive analyses. The aim of this study was to use artificial vision techniques to control and model the coffee roasting process. Samples of Castillo variety coffee were used to construct the roasting curve, with captured images at different times. Physico-chemical determinations, such as colour, titratable acidity, pH, humidity and chlorogenic acids, and caffeine content, were investigated on the coffee beans. Data were processed by (i) Principal component analysis (PCA) to observe the aggrupation depending on the roasting time, and (ii) partial least squares (PLS) regression to correlate the values of the analytical determinations with the image information. The results allowed to construct robust regression models, where the colour coordinates (L*, a*), pH and titratable acidity presented excellent values in prediction (R2Pred 0.95, 0.91, 0.94 and 0.92). The proposed algorithms were capable to correlate the chemical composition of the beans at each roasting time with changes in the images, showing promising results in the modelling of the coffee roasting process.

Responsible for food production and industry inputs, agriculture needs to adapt to worldwide increasing demands and environmental requirements. In this scenario, black oat has gained environmental and economic importance since it can be used in no-tillage systems, green manure, or animal feed supplementation. Despite its importance, few studies have been conducted to introduce more accurate and technological applications. Plant height (H) correlates with biomass production, which is related to yield. Similarly, productivity status can be estimated from vegetation indices (VIs). The use of unmanned aerial vehicles (UAV) for imaging enables greater spatial and temporal resolutions from which to derive information such as H and VI. However, faster and more accurate methodologies are necessary for the application of this technology. This study intended to obtain high-quality digital surface models (DSMs) and orthoimages from UAV-based RGB images via a direct-to-process means; that is, without the use of ground control points or image pre-processing. DSMs and orthoimages were used to derive H (HDSM) and VIs (VIRGB), which were used for H and dry biomass (DB) modeling. Results showed that HDSM presented a strong correlation with actual plant height (HREF) (R2 = 0.85). Modeling biomass based on HDSM demonstrated better performance for data collected up until and including the grain filling (R2 = 0.84) and flowering (R2 = 0.82) stages. Biomass modeling based on VIRGB performed better for data collected up until and including the booting stage (R2 = 0.80). The best results for biomass estimation were obtained by combining HDSM and VIRGB, with data collected up until and including the grain filling stage (R2 = 0.86). Therefore, the presented methodology has permitted the generation of trustworthy models for estimating the H and DB of black oats.

Ecosystem trait is a standardized description of biological features of a community, and it bridges individual plants and ecosystem. Conventionally most ecosystem trait data are collected from field survey and the generated data is hard to meet the requirements as set in the concept of ecosystem trait. To a great extent, remotely piloted aircraft systems (RPAS) remote sensing, which is capable of retrieving ecosystem traits across multiple scales, can overcome constraints in field plot survey. In this study, we selected alpine grassland ecosystem on the Tibetan Plateau (TP), which is under‐studied due to scarcity of field monitoring data, as the research target. A new data framework was proposed by integrating field plot and RPAS remote sensing data to map spatial patterns of ecosystem traits for the alpine grasslands. Across four landscapes on the TP, ecosystem traits of vegetation coverage (CVC), species number (CSN), individual number (CIN), above ground biomass (AGB), organic carbon content (OC%) and total nitrogen content (TN%) were retrieved. We also calculated Shannon's Diversity Index and Shannon's Evenness Index for each plot. The results showed that RPAS‐based high spatial resolution RGB image is capable of predicting both physical and chemical ecosystem traits for alpine grasslands on the TP. Remote sensing on physical traits are overall more efficient than on chemical traits, with the highest R2 of 0.86 and 0.48 for physical trait and chemical one, respectively. The bands of Red and Green contributed more to the prediction model than band of Blue did, and the spectral mean value played a greater role than the spectral standard deviation. Based on the retrieved results, a set of spatial patterns on ecosystem traits can be revealed. This study represents an advance on ecosystem trait study and can significantly improve our understanding on ecosystem functions of the alpine ecosystem on the TP. [ABSTRACT FROM AUTHOR]

Brassicaceae sprouts are important sources of vitamins, phenolic compounds, minerals, glucosinolates, and isothiocyanates. LEDs illumination have been demonstrated to increase yield and the phytochemical content of young plants. In the present work, rocket, radish, and tatsoi seeds were germinated in darkness for 7 days at 20 °C and 90% RH. After harvesting, sprouts were stored for 5 days at 5 °C under different LEDs treatments: White, Blue, Green, Orange, and Red. Darkness was used as control. The respiration rate and the sulforaphane content were monitored as a reference of the primary and secondary metabolism changes to evaluate the influence of LEDs. The application of Blue and Green LEDs increased the CO2 emission by ~25–45% compared to Darkness while no C2H4 emission was detected. The biosynthesis of sulforaphane was also increased by ~15–25%, under different wavelengths, although a clear tendency was not found among species. The state of the art of this research field was reviewed to elucidate the knowledge on it. Conclusively, the primary and secondary metabolism of plants, specifically in sprouts, can be stimulated using postharvest LED lighting.

Photonic integrated circuits have been designed, simulated, and tested on TriPleX™ platform technology for implementing optical Haar Transform (HT) in the visible spectrum. Optical circuits contain the new building block (BB), designed using the multimode interference (MMI) structure, performing optical HT on a pair of the optical signals. The Outputs of the BB demonstrate the sum and subtraction of the input signals according to the HT operation. 1st-order and 2nd-order optical HT achieved for the green light input signals. Simulation and experimental results have successfully validated the designed photonic circuits' capability in implementing optical HT. [ABSTRACT FROM AUTHOR]

Visual and olfactory mediated cues play a major role in mate selection in different groups of insects. This study aimed to observe if there is any role of mate color (under different wavelengths) and/or cuticular hydrocarbons in mate selection of Aulacophora foveicollis. In this study, visual cues and olfactory cues showed a vital role for male A. foveicollis to find mates. Males preferred (81%) orange (similar body colour) coloured glass model than white (52%) and black (49%) under the visible spectrum; whereas under the UV spectrum white coloured model was more preferred (77%) than orange (60%) and black (48%) coloured. In contrast, unresponsive males were very high though white coloured model was still preferred (36.67%) than orange (24.44%) or black (22.22%) coloured under IR spectrum. It was also observed that body surface waxes can play alone an olfactory cue for male mate selection (attraction was 67%) from short distance. Under the visual spectrum, when both visual and olfactory cues were presented, attraction of males toward all orange (93%), white (74%), and black (71%) coloured glass bead model increased. Even under UV and IR spectra attraction increased when body surface extract of female was applied on the glass models though the preference was more towards white coloured model than orange as previous. Male A. foveicollis showed less preference towards black colour. These works provide evidence that both visuals and olfactory cues can act separately as well as synergistically to find mates for male insects. These findings will be helpful for sustainable pest management program. [ABSTRACT FROM AUTHOR]

In this paper, we present a computational analysis method for the study of silicon dioxide (SiO2) graphene-based and amorphous silicon (a-Si)-graphene based nano meta-atoms in the terahertz (THz) regime. The proposed meta-atoms were modeled using a SiO2 substrate, optimized using a-Si substrate, and “sandwiched” between graphene layers. The octagonal-shaped torus was geometrically designed as a 3D structure to achieve optimal electromagnetic wave absorption from separate atoms. The upper and lower tori had identical diagonal arms to capture the maximum number of photons with minimal absorption losses. Graphene was chosen as the upper and ground layer material to optimize the electrical and optical properties of our proposed nano metamaterial. Finally, the following four fractional bandwidths with absorption percentages were optimized from the simulation: 4 bands (225.7–254.74 THz, 99.8%; 356.64–401.1 THz, 97.28%; 525.4 THz, 99.19%; 656.34 THz, 94.94%). The multi-band fractional absorption characteristic in the THz regime highlighted our proposed nano meta-atom as a potential candidate for absorption in the infrared and visible spectrum.

Automatic identification and sorting of livestock organs in the meat processing industry could reduce costs and improve efficiency. Two hyperspectral sensors encompassing the visible (400–900 nm) and short-wave infrared (900–1700 nm) spectra were used to identify the organs by type. A total of 104 parenchymatous organs of cattle and sheep (heart, kidney, liver, and lung) were scanned in a multi-sensory system that encompassed both sensors along a conveyor belt. Spectral data were obtained and averaged following manual markup of three to eight regions of interest of each organ. Two methods were evaluated to classify organs: partial least squares discriminant analysis (PLS-DA) and random forest (RF). In addition, classification models were obtained with the smoothed reflectance and absorbance and the first and second derivatives of the spectra to assess if one was superior to the rest. The in-sample accuracy for the visible, short-wave infrared, and combination of both sensors was higher for PLS-DA compared to RF. The accuracy of the classification models was not significantly different between data pre-processing methods or between visible and short-wave infrared sensors. Hyperspectral sensors, particularly those in the visible spectrum, seem promising to identify organs from slaughtered animals which could be useful for the automation of quality and process control in the food supply chain, such as in abattoirs.

A simple, sensitive, accurate new analytical method based on colorimetric determination was developed and validated for the estimation of Flupirtine maleate in pure and pharmaceutical dosage forms. Flupirtine reacts with Sodium nitrite (NaNO2) and Hydrochloric acid (HCl) to form a yellow-colored compound and colored complex which can be estimated by colorimetrically. The color of the solutions has measured the absorbance at γmax 411 nm. Beers law was followed in the range of 10-50µg/ml respectively. The correlation coefficient was found to be 0.9998. LOD and LOQ were determined and found to be 0.103 µg/ml and 0.318 µg/ml respectively. The recovery studies were performed. The percent recovery was found to be 99.4 ± 1. The method was conducted and validated as per ICH guidelines. The proposed method was found to be precise, accurate and successfully validated for the routine analysis of Flupirtine maleate in bulk drug and in the formulation. [ABSTRACT FROM AUTHOR]

The visible spectrum is the most widely used modality for video media. Nonetheless, it is highly dependent on the lighting conditions. Hence, infrared (IR) imaging lower light sensitivity characterisation presents the untapped potential for robust automatic recognition systems. This is applicable to many applications including IR action recognition (AR), which is a relatively young field in IR. As such, in this study, the authors tackle IR and multimodal AR with the proposed utilisation of variational learning of Beta‐Liouville (BL) hidden Markov models (HMMs). Furthermore, to the best of the authors' knowledge, this is the first evaluation of the BL HMM in visible AR and in multimodal fusion for AR. They present the results of the proposed model on the infrared action recognition and the IOSB datasets. Experimental results demonstrate promising outcomes. The importance of using IR and multispectral fusion in AR is also highlighted by the results. [ABSTRACT FROM AUTHOR]