Reflectance Index Research Articles

Synthesis and characterization of zinc cobalt sulphide nanofilms for optoelectronic applications

Zinc-cobalt sulphide (ZnxCo1−xS) thin films were fabricated on glass substrates using the chemical bath deposition (CBD) technique. In this study, the films were grown employing solutions of zinc acetate, cobalt sulphate, and thioacetamide as the respective sources of zinc, cobalt, and sulphur. The synthesized films were investigated for their potential use in optoelectronic device applications. Optical characterization revealed that the films exhibited a direct transition with an energy gap ranging from 3.350 to 3.360 ​eV. As the zinc concentrations were changing, the absorbance of the films were decreasing uniformly along the wavelength spectra. The films exhibit a low extinction coefficient (0.0–0.23) that may be due to the internal reflections within the material as concentrations changes. Variations in the thickness, reflectance and refractive index with zinc concentrations were also discussed. The electrical resistivity was found to decrease from 7.12×108 to 5.94×108 (Ω.cm) with zinc concentrations. According to the crystallography spectrum, ZnxCo1−xS has a polycrystalline structure with various distinct peaks at different orientations. Scanning electron microscopy shows that surface morphology of ZnxCo1−xS films has a well-defined nanoparticles of different shapes and sizes which are uniformly distributed and are significantly transformed as a function of concentrations. The results demonstrate that the chemically deposited thin films can be engineered for a range of optoelectronic applications.

Integrating Hyperspectral Reflectance-Based Phenotyping and SSR Marker-Based Genotyping for Assessing the Salt Tolerance of Wheat Genotypes under Real Field Conditions.

Wheat breeding programs are currently focusing on using non-destructive and cost-effective hyperspectral sensing tools to expeditiously and accurately phenotype large collections of genotypes. This approach is expected to accelerate the development of the abiotic stress tolerance of genotypes in breeding programs. This study aimed to assess salt tolerance in wheat genotypes using non-destructive canopy spectral reflectance measurements as an alternative to direct laborious and time-consuming phenological selection criteria. Eight wheat genotypes and sixteen F8 RILs were tested under 150 mM NaCl in real field conditions for two years. Fourteen spectral reflectance indices (SRIs) were calculated from the spectral data, including vegetation SRIs and water SRIs. The effectiveness of these indices in assessing salt tolerance was compared with four morpho-physiological traits using genetic parameters, SSR markers, the Mantel test, hierarchical clustering heatmaps, stepwise multiple linear regression, and principal component analysis (PCA). The results showed significant differences (p ≤ 0.001) among RILs/cultivars for both traits and SRIs. The heritability, genetic gain, and genotypic and phenotypic coefficients of variability for most SRIs were comparable to those of measured traits. The SRIs effectively differentiated between salt-tolerant and sensitive genotypes and exhibited strong correlations with SSR markers (R2 = 0.56-0.89), similar to the measured traits and allelic data of 34 SSRs. A strong correlation (r = 0.27, p < 0.0001) was found between the similarity coefficients of SRIs and SSR data, which was higher than that between measured traits and SSR data (r = 0.20, p < 0.0003) based on the Mantel test. The PCA indicated that all vegetation SRIs and most water SRIs were grouped with measured traits in a positive direction and effectively identified the salt-tolerant RILs/cultivars. The PLSR models, which were based on all SRIs, accurately and robustly estimated the various morpho-physiological traits compared to using individual SRIs. The study suggests that various SRIs can be integrated with PLSR in wheat breeding programs as a cost-effective and non-destructive tool for phenotyping and screening large wheat populations for salt tolerance in a short time frame. This approach can replace the need for traditional morpho-physiological traits and accelerate the development of salt-tolerant wheat genotypes.

Rice yield prediction through drone-derived vegetation indices: A case study in Tamil Nadu, India

Precision farming has been revolutionized by advancements in drone technology and remote sensing, enabling high accuracy in real-time crop monitoring and yield prediction. To explore the potential of drone-based remote sensing for predicting the rice yield by the assessment of vegetation indices were generated and analyzed to identify the most sensitive indices for predicting Laef Area Index (LAI), chlorophyll content, and biomass. The experiment was conducted in two seasons, Kuruvai (July - November 2023) and Navarai (December 2023 - March 2024). In Kuruvai 2023, a positive correlation was observed between vegetation indices, Wide Dynamic Range Vegetation Index (WDRVI), Modified Chlorophyll and Reflectance Index (MCARI) and Modified Soil Adjusted Vegetation Index (MSAVI) with ground truth biophysical parameters, while Navarai season Perpendicular Vegetation Index (PVI), Modified Chlorophyll and Reflectance Index (MCARI) and Green Normalized Difference Vegetation Index (GNDVI) exhibited the highest positive correlation. The multiple linear regression analysis revealed that a combined model incorporating LAI, SPAD chlorophyll, and biomass registered the highest R2 values of 0.792 and 0.800 for the Kuruvai and Navarai seasons. The predicted yield was positively correlated with the real-time yield with R2 values of 0.819 and 0.803 for both seasons. This study underscores the potential of drone-based VIs for precise yield prediction, offering a scalable and non-destructive method to enhance agricultural productivity and support decision-making in precision farming. Future research should focus on refining these models for broader applications across crops and agro-climatic conditions.

Stomatal and Non-Stomatal Leaf Responses during Two Sequential Water Stress Cycles in Young Coffea canephora Plants

Understanding the dynamics of physiological changes involved in the acclimation responses of plants after their exposure to repeated cycles of water stress is crucial to selecting resilient genotypes for regions with recurrent drought episodes. Under such background, we tried to respond to questions as: (1) Are there differences in the stomatal-related and non-stomatal responses during water stress cycles in different clones of Coffea canephora Pierre ex A. Froehner? (2) Do these C. canephora clones show a different response in each of the two sequential water stress events? (3) Is one previous drought stress event sufficient to induce a kind of “memory” in C. canephora? Seven-month-old plants of two clones (’3V’ and ‘A1’, previously characterized as deeper and lesser deep root growth, respectively) were maintained well-watered (WW) or fully withholding the irrigation, inducing soil water stress (WS) until the soil matric water potential (Ψmsoil) reached ≅ −0.5 MPa (−500 kPa) at a soil depth of 500 mm. Two sequential drought events (drought-1 and drought-2) attained this Ψmsoil after 19 days and were followed by soil rewatering until a complete recovery of leaf net CO2 assimilation rate (Anet) during the recovery-1 and recovery-2 events. The leaf gas exchange, chlorophyll a fluorescence, and leaf reflectance parameters were measured in six-day frequency, while the leaf anatomy was examined only at the end of the second drought cycle. In both drought events, the WS plants showed reduction in stomatal conductance and leaf transpiration. The reduction in internal CO2 diffusion was observed in the second drought cycle, expressed by increased thickness of spongy parenchyma in both clones. Those stomatal and anatomical traits impacted decreasing the Anet in both drought events. The ‘3V’ was less influenced by water stress than the ‘A1’ genotype in Anet, effective quantum yield in PSII photochemistry, photochemical quenching, linear electron transport rate, and photochemical reflectance index during the drought-1, but during the drought-2 event such an advantage disappeared. Such physiological genotype differences were supported by the medium xylem vessel area diminished only in ‘3V’ under WS. In both drought cycles, the recovery of all observed stomatal and non-stomatal responses was usually complete after 12 days of rewatering. The absence of photochemical impacts, namely in the maximum quantum yield of primary photochemical reactions, photosynthetic performance index, and density of reaction centers capable of QA reduction during the drought-2 event, might result from an acclimation response of the clones to WS. In the second drought cycle, the plants showed some improved responses to stress, suggesting “memory” effects as drought acclimation at a recurrent drought.

Species-specific functional trait responses in two species coexisting along a shore-to-inland dune gradient.

Coastal dunes are characterised by strong gradients of abiotic stress, typically increasing in severity from inland areas towards the shoreline. Thus, dune gradients represent unique opportunities to study intraspecific responses to environmental changes and to investigate which factors drive community change. This study aims to examine functional trait variation in two coexisting species in response to environmental changes along a dune gradient in NW Spain. Trait convergence was also investigated and compared between both ends of the gradient. We measured functional leaf traits related to plant efficiency in the use of light, water and nutrients, also possible stressors (salt content and pH) and availability of limiting resources (water and nutrients) in the soil. Most soil variables showed changes following a non-directional gradient. Differences in soil variables were site specific and depended on growth of the study species. Structural and functional traits depended on species and/or plant position on the gradient, except for effective quantum yield of PSII and leaf δ15N. The pattern of variation was mostly directional for reflectance indices related to leaf physiology. Multivariate analyses showed significant interspecific differences in the set of traits they exhibited along positions in the gradient. Species also differed in the combination of traits selected under given environmental conditions. Coexisting species display a specific set of traits that reflects different strategies to environmental stress. Our study highlights the overly simplistic nature of some previous studies that assume dune gradients are monotonically directional, without considering that these gradients may be differentially modified by species activity.

Assessing plant pigmentation impacts: A novel approach integrating UAV and multispectral data to analyze atrazine metabolite effects from soil contamination

The objective of this study was to evaluate the levels of desethylatrazine (DEA), a hydrophilic metabolite of atrazine, and its impact on plant health. This was achieved by utilizing multispectral imagery captured by Unmanned Aerial Vehicles (UAVs) in combination with ground-measured data to assess photosynthetic pigment levels in Green Cos lettuce following atrazine application in agricultural soil. Strong correlations were found between DEA levels and chlorophyll a, chlorophyll b, and anthocyanin levels in lettuce (R² > 0.70), while the correlation with carotenoid levels was weaker (R² = 0.55). This disruption to the pigments could interfere with photosynthesis, potentially hindering the plant's growth and development, and ultimately leading to a reduction in yield. The Anthocyanin Reflectance Index (ARI) demonstrated a robust positive correlation with DEA, whereas the Normalized Difference Red Edge (NDRE), Leaf Chlorophyll Index (LCI), and Normalized Difference Vegetation Index (NDVI) displayed pronounced negative correlations. Incorporating ARI, LCI, and NDRE, with or without NDVI, provided the most accurate prediction of DEA levels, with an R² exceeding 0.96. NDRE emerged as the most efficient index for forecasting chlorophyll a and chlorophyll b levels. Modified Chlorophyll Absorption in Reflectance Index (MCARI) demonstrated the best fit for carotenoids, while ARI performed exceptionally well in describing actual measurements of anthocyanins (R² = 0.90). The best-performing VI models, developed from the selection of effective single variables, exhibited the best fit to actual pigment measurements (R² > 0.83). These findings underscore the role of UAV-derived multispectral imagery in assessing DEA levels and improving environmental monitoring, aiding in better planning for agriculture and environmental remediation to enhance ecosystem health and resilience.

Assessing the Capacity of High-resolution Commercial Satellite Imagery for Grapevine Downy Mildew Detection and Surveillance in New York state.

Grapevine downy mildew (GDM), caused by the oomycete Plasmopara viticola, can cause 100% yield loss and vine death under conducive conditions. High resolution multispectral satellite platforms offer the opportunity to track rapidly spreading diseases like GDM over large, heterogeneous fields. Here, we investigate the capacity of PlanetScope (3 m) and SkySat (50 cm) imagery for season-long GDM detection and surveillance. A team of trained scouts rated GDM severity and incidence at a research vineyard in Geneva, NY, USA from June to August of 2020, 2021, and 2022. Satellite imagery acquired within 72 hours of scouting was processed to extract single-band reflectance and vegetation indices (VIs). Random forest models trained on spectral bands and VIs from both image datasets could classify areas of high and low GDM incidence and severity with maximum accuracies of 0.85 (SkySat) and 0.92 (PlanetScope). However, we did not observe significant differences between VIs of high and low damage classes until late July-early August. We identified cloud cover, image co-registration, and low spectral resolution as key challenges to operationalizing satellite-based GDM surveillance. This work establishes the capacity of spaceborne multispectral sensors to detect late-stage GDM and outlines steps towards incorporating satellite remote sensing in grapevine disease surveillance systems.

Relationship of Non-Invasive Arterial Stiffness Parameters with 10-Year Atherosclerotic Cardiovascular Disease Risk Score in Post-COVID-19 Patients-The Results of a Cross-Sectional Study.

This study evaluated the relationship of non-invasive arterial stiffness parameters with an individual 10-year risk of fatal and non-fatal atherosclerotic cardiovascular disease (ASCVD) events in the cohort post-coronavirus disease 2019 (COVID-19). The study group included 203 convalescents aged 60.0 (55.0-63.0) and 115 (56.7%) women. The ASCVD risk was assessed as low to moderate to very high based on medical history (for 62 participants with pre-existing ASCVD/diabetes/chronic kidney disease in the entire cohort) or calculated in percentages using the Systemic Coronary Risk Evaluation 2 (SCORE2) algorithm based on age, sex, smoking status, systolic blood pressure (BP), and non-high-density lipoprotein cholesterol (for 141 healthy participants). The stiffness index (SI) and reflection index (RI) measured by photoplethysmography, as well as pulse pressure (PP), calculated as the difference between systolic and diastolic BP, were markers of arterial stiffness. Stiffness parameters increased significantly with the increase in ASCVD risk in the entire cohort. In 30 (14.8%) patients in the low- to moderate-risk group, the median SI was 8.07 m/s (7.10-8.73), RI 51.40% (39.40-65.60), and PP 45.50 mmHg (40.00-57.00); in 111 (54.7%) patients in the high-risk group, the median SI was 8.70 m/s (7.40-10.03), RI 57.20% (43.65-68.40), and PP 54.00 mmHg (46.00-60.75); and in 62 (30.5%) patients in the very-high-risk group, the median was SI 9.27 m/s (7.57-10.44), RI 59.00% (50.40-72.40), and PP 60.00 mmHg (51.00-67.00). In healthy participants, the SI ≤ 9.0 m/s (sensitivity of 92.31%, area under the curve [AUC] 0.686, p < 0.001) based on the receiver operating characteristics was the most sensitive variable for discriminating low to moderate risk, and PP > 56.0 mmHg (sensitivity of 74.36%, AUC 0.736, p < 0.001) was used for discriminating very high risk. In multivariate logistic regression, younger age, female sex, PP ≤ 50 mmHg, SI ≤ 9.0 m/s, and triglycerides < 150 mg/dL had the best relationship with low to moderate SCORE2 risk. In turn, older age, currently smoking, PP > 56.0 mmHg, RI > 68.6%, and diastolic BP ≥ 90 mmHg were related to very high SCORE2 risk. In conclusion, arterial stiffness is significantly related to ASCVD risk in post-COVID-19 patients and can be helpful as a single risk marker in everyday practice. Cut-off points for arterial stiffness parameters determined based on SCORE2 may help make individual decisions about implementing lifestyle changes or pharmacological treatment of ASCVD risk factors.

Assessing soil CO2 emission on eucalyptus species using UAV-based reflectance and vegetation indices

Eucalyptus species play an important role in the global carbon cycle, especially in reducing the greenhouse effect as well as storing atmospheric CO₂. Thus, assessing the amount of CO₂ released by the soil in forest areas can generate important information for environmental monitoring. This study aims to verify the relation between soil carbon dioxide (CO₂) flux (FCO₂), spectral bands, and vegetation indices (VIs) derived from a UAV-based multispectral camera over an area of eucalyptus species. Multispectral imageries (green, red-edge, and near-infrared) from the Parrot Sequoia sensor, derived vegetation indices, and the FCO₂ data from a LI-COR 8100 analyzer, combined with soil moisture and temperature data, were collected and related. The vegetation indices ATSAVI (Adjusted Transformed Soil-Adjusted VI), GSAVI (Green Soil Adjusted Vegetation Index), and SAVI (Soil-Adjusted Vegetation Index), which use soil correction factors, exhibited a strong negative correlation with FCO₂ for the species E. camaldulensis, E. saligna, and E. urophylla species. A Multivariate Analysis of Variance showed significance (p < 0.01) for the species factor, which indicates that there are differences when considering all variables simultaneously. The results achieved in this study show a specific correlation between the data of soil CO₂ emission and the eucalypt species, providing a distinction of values between the species in the statistical data.

Time-domain model for spherical wave reflection in a flat surface with absorber character – Application to the SOPRA measurement method

This paper presents a developed time-domain model for the reflection of spherical waves in an absorber-like surface. The model is made to enable evaluation of measurement methods for assessing the sound absorptive properties of traffic noise barriers in direct sound field, it is thus called the Direct Field Absorption (DFA) model. In this study, the DFA model is applied to the in-situ SOPRA method for quick sound reflection index measurements on road noise barriers. The DFA model results in an impulse response (IR), based on a theoretically derived impedance of a certain absorber. The DFA IR is entered to the SOPRA formula to calculate the reflection index, RIQ, of the absorber, which is subsequently compared to the measured RIQ of a wall fitted with the absorber in question. If the results are similar, it is reasonable to assume that the SOPRA measurement results are valid. But if there are any significant differences between the DFA and SOPRA sound reflection indices, possible reasons for them should be examined.The first results are encouraging, showing that the DFA model can be a valuable tool to evaluate the results of reflection measurements. Furthermore, the DFA model could be useful for estimating the sound absorptive performance at lower frequencies of a noise barrier limited to its spatial extent in width and height, i.e., when it is physically impossible to measure. Further studies are necessary though, since the conclusions of this paper are based on only one kind of absorber.

First-Principles insights to probe structural and opto-electronic properties of [formula omitted] (Y=Mg, Sr) halide perovskites with variety of DFT methods

This study reports the structural, electronic, optical, phonon, thermodynamic and thermoelectric properties of AgYF3 (X=Mg, Sr) for photovoltaic and energy applications. We performed first principles calculations using full potential linearized augmented plane wave, FP-LAPW method implemented in Wien2k. The generalized gradient approximations of Perdew–Burke–Ernzerhof PBE-GGA, and PBE revised for solids, PBEsol, is employed for structural optimization of these lead free halide perovskites. The Birch–Murnaghan energy volume curve fitting comprehend the structural stability. The optimized lattice constant of AgMgF3 and AgSrF3 obtained with PBE-GGA(PBEsol) is 3.99(3.92)Å and 4.42(4.65)Å. The stability is further tested with the help of formation energy and positive phonon dispersion curves calculations. For the calculations of explicit electronic and optical properties, we also employed Tran–Blaha modified Beck–Johnson (TB-mBJ) and Strongly Constrained but Appropriately Normed, SCAN, exchange and correlations functionals. The electronic band gap of AgMgF3 computed with PBEsol, TB-mBJ and SCAN is 1.96 eV, 5.25 eV and 2.59 eV exhibiting M-Γ indirect band gap. The band gap energy of AgSrF3 is 2.06 eV, 6.42 eV and 2.70 eV with PBEsol, TB-mBJ and SCAN. The indirect band gap nature of AgSrF3 is confirmed by PBEsol and TB-mBJ while it anticipated direct band gap behavior with meta-GGA SCAN. The different optical parameters like dielectric constant, optical conductivity, energy loss function, absorption, reflectivity and refractive index are calculated to assess optical activity of both perovskites. Comprehensive electronic and optical analysis advocates the utility of AgMgF3 and AgSrF3 for different applications is solar technology and optoelectronic devices.

Machine learning-driven assessment of biochemical qualities in tomato and mandarin using RGB and hyperspectral sensors as nondestructive technologies.

Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non-destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R2 = 0.84 with car of tomato fruits. The SRIs such as RSI 710,600, and R730,650 showed the greatest R2 values with respect to Chl a (R2 = 0.80) for mandarin fruits while the GI had the greatest R2 with Chl a (R2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R2 of 0.993 with an RMSE of 0.149 for the training set, and an R2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits.

Light Quality Influence on Growth Performance and Physiological Activity of Coleus Cultivars

This study investigates the influence of different light qualities, including red, green, blue, purple, and white lights, on the growth, physiological activity, and ornamental characteristics of two Coleus cultivars. Emphasizing the importance of leveraging phenotypic plasticity in plants within controlled environments, using light quality is a practice prevalent in the ornamental industry. The research explores the varied responses of two Coleus cultivars to distinct light spectra. The key findings reveal the efficacy of red light in enhancing shoot and leaf parameters in C. ‘Highway Ruby’, while red and green light exhibit comparable effects on shoot width and leaf dimensions in C. ‘Wizard Jade’. White light-emitting diodes (LEDs), particularly with color temperatures of 4100 K and 6500 K, promote root length growth in the respective cultivars. Moreover, chlorophyll content and remote sensing vegetation indices, including chlorophyll content (SPAD units), the normalized difference vegetation index (NDVI), the modified chlorophyll absorption ratio index (MCARI), and the photochemical reflectance index (PRI), along with the chlorophyll fluorescence, were significantly affected by light qualities, with distinct responses observed between the cultivars. In summary, this study highlights the transformative potential of LED technology in optimizing the growth and ornamental quality of foliage plants like Coleus, setting a benchmark for light quality conditions. By leveraging LED technology, producers and nursery growers access enhanced energy efficiency and unparalleled versatility, paving the way for significant advancements in plant growth, color intensity, and two-tone variations. This presents a distinct advantage over conventional production methods, offering a more sustainable and economically viable approach for increased plant reproduction and growth development. Likewise, the specific benefits derived from this study provide invaluable insights, enabling growers to strategically develop ornamental varieties that thrive under optimized light conditions and exhibit heightened visual appeal and market desirability.

Comprehensive analysis of hyperspectral features for monitoring canopy maize leaf spot disease

Accurate quantification of hyperspectral features altered by plant disease infection is pivotal for effective disease management. However, the sensitivity of hyperspectral features to plant disease progression remains elusive, primarily because these features are often influenced by plant growth and environmental factors in addition to the specific disease. This study explores the sensitivity of biophysical and spectral features as indicators for maize adaptation to leaf spot disease. Using high-resolution UAV hyperspectral imaging, we captured maize adaptation dynamics over 30 days post-infection. We evaluated the sensitivity and importance of hyperspectral features for disease monitoring, including biophysical parameters retrieved by the PROSAIL model, and spectral features, including spectral reflectance, vegetation indices (VIs), and wavelet features (WFs). Our findings reveal that WFs first indicate disease response as early as 6 days after infection (DAI), followed by VIs at DAI 8, and variations in chlorophyll content (Cab) become apparent by DAI 10. The Cab, plant senescence reflectance index (PSRI), and normalized photosynthetic reflectance index (NPRI) are determined to be important features at the early stage of the disease. Our experimental results show that the different feature sets are complementary at the early and severe stages of the disease. Our classification models integrating Cab, VIs, and WFs showed higher overall accuracy than models using only spectral features or VIs, with a maximum improvement of 9.36 %. However, these feature sets are redundant in the mild and initial severe disease stages, where models using only spectral features achieve the highest overall accuracy of 86.21 %. This study underscores the novel insights by offering an understanding of plant responses to disease infection and enhancing early detection strategies.

Computational Investigation of Structural, Electronic and Optical Properties of Ternary Chalcogenide TLGaQ2 Monoclinic Compounds: a First Principle Study

Purpose: This paper aim to investigate and predict by simulation the impact of the substitution of Q-site atom (Q꞊S, Se) on the TIGaQ2 monoclinic compounds for the first time, along the three main polarizations of the incident wave directions [100], [010] and [001]. Theoretical Framework: This study focuses mainly on ternary chalcogenide compounds TlGaQ2 (Q= S, Se) from ABQ2 family to highlight the objective resources aimed at promising prospects offered for them. Design/Methodology/Approach: A series of ab-initio calculations based on the (PW+PP) method within the density functional theory DFT framework were carried out with CASTEP code for the simulation of their physical properties (structural, electronic-optical). The exchange correlation potential was treated within the generalized gradient approximation (GGA) implemented in the CASTEP code and expressed by the PBE functional. Findings: The equilibrium lattice parameters are in good agreement with the available experimental results. The calculated band structure shows that are direct bandgap semiconductor nature 1.92eV (1.41eV) for TlGaS2 and TlGaSe2 respectively with a great potential for photovoltaic solar cell absorber materials. A set of optical parameters were calculated including the complex dielectric function, the reflective index, reflectivity, the absorption coefficient and loss function. The optical properties obtained revealed very interesting optical properties exhibiting strong optical absorption in UV range up to (~2x105cm-1) for both compounds making them appropriate for photovoltaic solar-cell absorber materials. Furthermore, low reflectivity and energy loss function are shown within in the visible and ultraviolet energy range, allowing them to be promising materials in several optoelectronics applications likes photosensitive devices. Implications: All these findings results show a successful accurately prediction for chalcogenide materials behavior and will be helpful for future studies allowing a better understanding of their potential applications in modern photovoltaic technologies as well as within UV ranges for optoelectronics applications.

Improving Soybean Gross Primary Productivity Modeling Using Solar-Induced Chlorophyll Fluorescence and the Photochemical Reflectance Index by Accounting for the Clearness Index

Solar-induced chlorophyll fluorescence (SIF) has been widely utilized to track the dynamics of gross primary productivity (GPP). It has been shown that the photochemical reflectance index (PRI), which may be utilized as an indicator of non-photochemical quenching (NPQ), improves SIF-based GPP estimation. However, the influence of weather conditions on GPP estimation using SIF and PRI has not been well explored. In this study, using an open-access dataset, we examined the impact of the clearness index (CI), which is associated with the proportional intensity of solar incident radiation and can represent weather conditions, on soybean GPP estimation using SIF and PRI. The midday PRI (xanthophyll de-epoxidation state) minus the early morning PRI (xanthophyll epoxidation state) yielded the corrected PRI (ΔPRI), which described the amplitude of xanthophyll pigment interconversion during the day. The observed canopy SIF at 760 nm (SIFTOC_760) was downscaled to the broadband photosystem-level SIF for photosystem II (SIFTOT_FULL_PSII). Our results show that GPP can be accurately estimated using a multi-linear model with SIFTOT_FULL_PSII and ΔPRI. The ratio of GPP measured using the eddy covariance (EC) method (GPPEC) to GPP estimated using SIFTOT_FULL_PSII and ΔPRI exhibited a non-linear correlation with the CI along both the half-hourly (R2 = 0.21) and daily scales (R2 = 0.25). The GPP estimates using SIFTOT_FULL_PSII and ΔPRI were significantly improved by the addition of the CI (for the half-hourly data, R2 improved from 0.64 to 0.71 and the RMSE decreased from 8.28 to 7.42 μmol•m−2•s−1; for the daily data, R2 improved from 0.71 to 0.81 and the RMSE decreased from 6.69 to 5.34 μmol•m−2•s−1). This was confirmed by the validation results. In addition, the GPP estimated using the Random Forest method was also largely improved by considering the influences of the CI. Therefore, our findings demonstrate that GPP can be well estimated using SIFTOT_FULL_PSII and ΔPRI, and it can be significantly enhanced by accounting for the CI. These results will be beneficial to vegetation GPP estimation using different remote sensing platforms, especially under various weather conditions.

Theoretical study of optoelectronic, elastic and mechanical properties of gallium modified barium titanate (Ba1-xGaxTiO3) perovskite ceramics by DFT

In this report, we study the gallium modified Barium titanate (Ga-BTO) Ba1-xGaxTiO3 with (x = 50 %) perovskite ceramics, which have not been synthesized experimentally to date. The density functional theory-based full potential linear augmented plane wave has been employed to determine the optoelectronic, elastic, and mechanical properties of pure and modified BaTiO3. Completely relaxed 2 × 2× 2 of primitive five atoms’ cells has been used for calculations. A large cut-off energy of 120 Ry has been used to eliminate the basics of plane wave-indicating wave functions. The influence of substitution of Ga on electronic structure and optical parameters such as reflectivity, refractivity, dielectric function, optical conductivity, extinction, and absorption coefficients are analyzed and elucidated with TDOS and PDOS. The Ga modified BTO manifests a forbidden energy gap of 1.84 eV. The static dielectric constant Ɛ1(o) has values of 8.8 and 100 for pure and modified compounds respectively. Ga dopant improved the polarizing power of BTO and for the doped BTO imaginary part, Ɛ2(ω)showed a prominent peak located at 3.9 eV. The optical properties show that perovskite compound substituted with Ga is a good absorber in the ultraviolet region and the modified material has a small value of reflectivity and static refractive index 12.2. The general profiles of optical spectra of modified BaTiO3 display highly desirable features like the prominent peaks of optical conductivity obtained at 4.2 and 5.8 eV for modified BTO that make it a promising candidate for optoelectronic and spintronics. Ga-modified BTO has higher ductility, modulus (169.96 GPa), and anisotropy (2.949) than pure BTO found by analyzing their elastic and mechanical properties.

State-of-the-Art Review: Effects of Using Cool Building Cladding Materials on Roofs

Cool roofs are roofing systems designed to reflect significant solar radiation, reducing heat absorption and subsequent cooling energy demands in buildings. This paper provides a comprehensive review of cool roof technologies, covering performance standards, material options, energy-saving potential, and hygrothermal considerations. The review examines provisions in current codes and standards, which specify minimum requirements for solar reflectance, thermal emittance, and solar reflectance index (SRI) values. These criteria often vary based on factors like roof slope, climate zone, and building type. Different cool roof materials are explored, including reflective paints and coatings that can be applied to existing roofs as cost-effective solutions. Several studies have demonstrated the energy performance benefits of cool roofs, showing significant reductions in cooling loads, indoor air temperatures, peak cooling demand, and overall cooling energy consumption compared to traditional roofs. However, hygrothermal performance must be evaluated, especially in cold climates, to optimize insulation levels and avoid moisture accumulation risks, as reduced heat absorption can alter moisture migration patterns within the building envelope. While cool roofs provide substantial energy savings in hot climates, further research is needed to validate modeling approaches against real-world studies, investigate the impact of seasonality and green spaces on cool roof efficacy and urban heat island mitigation, and explore energy-saving potential, moisture control, and condensation risks in cold and humid environments.

Environmental impact of the variations in solar reflectance index values of some selected dimension stones due to cyclic thermal shock

Natural stones are widely used environmentally appropriate building materials as dimension stone both for exterior cladding and floor coverings in urban areas. Depending on their solar reflectivity, these materials may absorb more solar radiation, especially with noontime summer sunlight exposure, which may cause both increasing cooling costs in commercial buildings and overheating of a city with the formation of urban heat islands. Solar reflectance index (SRI) is defined as the ability of a surface to reject solar heat which tends to change with physical, chemical and biological degradation processes by varying solar reflectance (SR) and thermal emissivity (TE) values. Weakening of the stone matrix structure by physical degradation mechanism, such as thermal shock (T-S) cycles mostly have significant impact on SRI. Within the scope of this study, SRI values were calculated before and after cyclic T-S by measuring the optical properties such as SR and TE values of 30 selected sedimentary and metamorphic originated carbonate-based natural stone types. The changes in surface properties such as color, roughness, and gloss, that may have an impact on the SRI values were also analyzed. In addition, a multivariate regression analysis was established to predict SRI value from such properties with a high degree of accuracy. Finally, it was found that, initial SRI values of 9 natural stone types (mostly metamorphic originated) have been increased considerably by cyclic T-S and tend to increase their ability to reduce urban temperatures by higher SRI values, one of the most important selection criteria in energy consumption for natural stones.

A digital volume pulse device-finger photo pulse plethysmography to trace the vascular integrity amongst the low back pain subjects with lumbar disc degenerative diseases diagnosed by MRI analysis.

Finger photo pulse plethysmography is a simple, inexpensive and non-invasive method for measurement of arterial stiffness. The objective is to assess the correlation of arterial stiffness in low back pain subjects with lumbar disc degeneration. Thirty-four back pain patients of both sexes in age group of 30-65 were included. Anthropometric measures like height, body weight, body mass index (BMI) were included. Stiffness index (SI) and reflection index (RI) were measured from the digital volume pulse waveform. There was a negative correlation between SI/RI and no correlation between SI and RI with BMI in both sexes. A significant correlation found between weight and BMI in both sexes. Arterial stiffness may not have any influence on disc degeneration. BMI showed some influence on disc degeneration and back pain.