Color Bands Research Articles

The colours of the ocean using multispectral satellite imagery to estimate sea surface temperature and salinity in global coastal areas, the gulf of Mexico and the UK

Understanding and monitoring sea surface salinity (SSS) and temperature (SST) is vital for assessing ocean health. Interconnections among the ocean, atmosphere, seabed, and land create a complex environment with diverse spatial and temporal scales. Climate change exacerbates marine heatwaves, eutrophication, and acidification, impacting biodiversity and coastal communities. Satellite-derived ocean colour data provides enhanced spatial coverage and resolution compared to traditional methods, enabling the estimation of SST and SSS. This study presents a methodology for extracting SST and SSS using machine learning algorithms trained with in-situ and multispectral satellite data. A global neural network model was developed, leveraging spectral bands and metadata to predict these parameters. The model incorporated Shapley values to evaluate feature importance, offering insight into the contributions of specific bands and environmental factors. The global model achieved an R2 of 0.83 for temperature and 0.65 for salinity. In the Gulf of Mexico case study, the model demonstrated a root mean square error (RMSE) of 0.83°C for test cases and 1.69°C for validation cases for SST, outperforming traditional methods in dynamic coastal environments. Feature importance analysis identified the critical roles of infrared bands in SST prediction and blue/green colour bands in SSS estimation. This approach addresses the “black box” nature of machine learning models by providing insights into the relative importance of spectral bands and metadata. Key factors such as solar azimuth angle and specific spectral bands were highlighted, demonstrating the potential of machine learning to enhance ocean property estimation, particularly in complex coastal regions.

Smart Trolley System Using OpenCV

The shopping atmosphere of the malls and commercial places nowadays is confronted by problems such as long queues at checkout counters and inefficiencies in tracking purchased items. This paper introduces the “Smart Trolley System using OpenCV,” a frontier of computer vision and the IOT present in a retail. Through a unique configuration of the web cameras and IoT devices, it is now possible to track shoppers via colour bands, rejuvenating inventory management and curtailing checkout queues. This system revamps the shopping experience with a new approach of offering convenience and saving time in the crowded malls. It deals with the important issues of checkout queues, manual item tracking, limited automation, exit management, and real-time data integration. The study analyses various technologies such as pre-trained neural networks, Arduino, Xbee, and barcode scanners, and RFID technology to improve the shopping experience and customer satisfaction.

HPTLC detection, isolation-optimization, structural confirmation of in-house natural sweetener (Glycyrrhizin) from successive root extract of Glycyrrhiza glabra Linn.

Glycyrrhizin (GL) is a triterpenoid saponin that is commonly isolated from root of Glycyrrhiza glabra L. an indigenous medicinal and culinary herb. The present study focused on detecting and optimizing isolation method to isolate GL from successive root extracts of G. glabra L. in its purest form with high yield. The study exploited cold percolation extraction and variety of chromatographic methods such as HPTLC, HPLC and gradient column chromatography. The comparative HPTLC densitometry identified GL as a single grey color band at RF 0.36 under UV-254nm in successive extracts. The optimized extraction-isolation method isolated the in-house GL from 70% aqueous ethanol extracts using standardized eluent, i.e., ethyl acetate-methanol (7:3, v/v) and confirmed by Co-TLC. The compound obtained as colorless non sticky solid, with a yield 0.4%. Various spectroscopic tools characterized isolated GL along with HPLC peak detection, this confirmed its identity by matching with reported literature. The proposed extraction–isolation method could be utilized to enhance the efficacy of herbal pharmaceuticals, development of advanced Ayurvedic formulations and uplifting the quality of food products, and cosmeceuticals contain GL as active constituents. Additionally, this work established a simple, fast, and cost-effective HPTLC method that may also be applied to detect GL to establish genuineness of such raw drugs, identification of Ayurvedic dosages, and studying such triterpenoid saponins in other medicinal species belonging Glycyrrhiza for routine quality control.

J-PLUS: Bayesian object classification with a strum of BANNJOS

Context. With its 12 optical filters, the Javalambre-Photometric Local Universe Survey (J-PLUS) provides an unprecedented multicolor view of the local Universe. The third data release (DR3) covers 3192 deg2 and contains 47.4 million objects. However, the classification algorithms currently implemented in the J-PLUS pipeline are deterministic and based solely on the morphology of the sources. Aims. Our goal is to classify the sources identified in the J-PLUS DR3 images as stars, quasi-stellar objects (QSOs), or galaxies. For this task, we present BANNJOS, a machine learning pipeline that utilizes Bayesian neural networks to provide the full probability distribution function (PDF) of the classification. Methods. BANNJOS has been trained on photometric, astrometric, and morphological data from J-PLUS DR3, Gaia DR3, and CatWISE2020, using over 1.2 million objects with spectroscopic classification from SDSS DR18, LAMOST DR9, the DESI Early Data Release, and Gaia DR3. Results were validated on a test set of about 1.4 × 105 objects and cross-checked against theoretical model predictions. Results. BANNJOS outperforms all previous classifiers in terms of accuracy, precision, and completeness across the entire magnitude range. It delivers over 95% accuracy for objects brighter than r = 21.5 mag and ~ 90% accuracy for those up to r = 22 mag, where J-PLUS completeness is ≲ 25%. BANNJOS is also the first object classifier to provide the full PDF of the classification, enabling precise object selection for high purity or completeness, and for identifying objects with complex features, such as active galactic nuclei with resolved host galaxies. Conclusions. BANNJOS effectively classified J-PLUS sources into around 20 million galaxies, one million QSOs, and 26 million stars, with full PDFs for each, which allow for later refinement of the sample. The upcoming J-PAS survey, with its 56 color bands, will further enhance BANNJOS’s ability to detail the nature of each source.

Au@Pt nanoparticles-based signal-enhanced lateral flow immunoassay for ultrasensitive naked-eye detection of SARS-CoV-2.

With SARS-CoV-2 N protein as a model target, a signal-enhanced LFIA based on Au@Pt nanoparticles (NPs) as labelsis proposed. This Au@Pt NPs combined the distinguished localized surface plasma resonance (LSPR) effect of Au NPs and the ultrahigh peroxidase-like catalytic activity of Pt NPs. Au@Pt NPs could trigger substrate chromogenic reaction, generating acolor signal orders of magnitude darker than their intrinsic color. In the detection, after the coloration of the strips, 3,3',5,5'-tetramethylbenzidine (TMB) and H2O2 were added, and adark blue chelate (OxTMB) was produced soon, enhancing the band color significantly. After the signal amplification, the naked-eye detection limit for N protein reached 40pg/mL. The detection sensitivity enhanced more than 1000 times than that without signal amplification. Compared with mainstream LFIA requiring complex readout instruments, theAu@Pt-based LFIA achieved a comparable sensitivity usingnaked eyesdetection. This point is crucial, especially for unprofessional users or low-resource areas. Hence, this signal-enhanced LFIA may serve as a sensitive, cost-effective, and user-friendly detection method. It can shorten the testing window period and help identify early infections.

Super‐Sensitive Multi‐Modal Optical Manometer Based on Huge Pressure‐Induced Spectral Red‐Shift and Broadening of Mn2+ Emission Band in Green‐Emitting Zn2GeO4 Phosphors

AbstractTo overcome the small sensitivities of luminescent manometers, Mn2+‐activated Zn2GeO4 green‐emitting phosphors with admirable manometric characteristics are synthesized. The structural stability of the studied samples is theoretically and experimentally investigated. Moreover, as pressure elevates, a huge spectral red‐shift and a broadening of the emission band are observed in studied samples, contributing to color‐tunable luminescence, i.e., from green to yellow, and ultimately to orange–red. Specifically, when pressure increases to 6.76 GPa, the emission band centroid shifts from 535.9 to 634.9 nm, leading to a superior sensitivity of dλ/dp = 21.3 nm/GPa. Whereas, in the same operating pressure range, the full width at half maximum (FWHM) of the emission band rises from 32.3 to 102.9 nm, resulting in unprecedentedly high sensitivity of dFWHM/dp = 17.0 nm/GPa. Furthermore, through analyzing the pressure‐dependent color coordinates, a maximum sensitivity of 29.43% GPa−1 is achieved, when x‐coordinate is adopted. Additionally, the applicability of the developed manometer is confirmed by a simple uniaxial pressure experiments to showcase its practical use in industrial and daily‐life purposes. Notably, the designed sensor exhibits the highest pressure sensitivities reported to date in different modes, namely, emission band centroid, FWHM and color coordinate, making supersensitive multimodal optical manometry a reality.

Fluorescent sphalerite rich in tungsten, copper, gallium, silver, and other elements from the Cordilleran-style, polymetallic veins of Philipsburg, Montana

Sphalerite from the central, high-sulfidation zone (enargite-stable) of the Philipsburg polymetallic mining district, southwest Montana, displays unusually bright fluorescence (red, orange, yellow, blue, purple, green) under longwave UV light (365 nm). LA-ICP-MS analysis reveals the fluorescent sphalerite has very low Fe (average < 100 ppm) and variable content of other trace elements that correlate to luminescence color banding. Mean/maximum content (in ppm) in fluorescent sphalerite for selected elements are 5.7/7900 Ag, 107/11800 As, 1400/4730 Cd, 917/30400 Cu, 381/5000 Ga, 32/696 Ge, 119/2130 In, 230/8190 Mn, 43/3000 Pb, 16/1700 Sb, and 89/1980 W. This study is the first to document elevated tungsten content (>10 ppm) in sphalerite. Copper is closely correlated with Ga, consistent with the coupled substitution: Cu+ + Ga3+ = 2Zn2+. Similar coupled substitution reactions can be written for Ag+, In3+, As3+, Sb3+, Bi3+, and Ge4+. However, the brightest red fluorescent bands are most closely related to the unexpected presence of W. Sphalerite with high Cu and Ga but lacking W fluoresces yellow and shows a single Raman peak at 349 cm−1 corresponding to pure sphalerite. In contrast, red-fluorescent sphalerite shows the presence of a second peak at 427 cm−1 that increases in intensity with increased W content. We propose that tungsten enters the sphalerite lattice as W6+ via a substitution such as W6+ + 4Cu+ = 5Zn2+ and that this substitution creates lattice strain that results in the anomalous fluorescence and Raman signals. Sphalerite bands with low concentrations of Cu and Ga fluoresce blue or green. Vivid blue fluorescence is displayed by sphalerite with high Cd (>1000 ppm) but low concentrations of all other trace elements. Sphalerite from the low-sulfidation peripheral mines of the Philipsburg district contains high Fe (>10,000 ppm) and does not fluoresce. Nonetheless, this sphalerite is also highly enriched in trace metals, including Ag (mean 2480/max 8660 ppm), Cu (1610/3440), Mn (7020/8100), and Sb (1960/6390). The results of this study underscore the importance of including tungsten in the list of analytes in future studies of trace elements in sphalerite. In addition, a hand-held UV lamp may be a rapid and cost-effective method to screen sphalerite of variable composition in outcrop or drill core. It may be a useful exploration tool to vector towards a high-sulfidation zone of a zoned porphyry or epithermal deposit, when it is present.

Enhanced geological and radioactive mapping of Gabal Abu Ashayir-Gabal El Bakriyah area, central Eastern Desert, Egypt, using remotely sensing data

AbstractThe primary goal of this work is to integrate airborne gamma-ray spectrometry with multispectral data from ASTER and Landsat-8 for mapping of the geology, radiometric data, and types of alterations of Gabal Abu Ashayir-Gabal El Bakriyah area, central Eastern Desert, Egypt. Utilizing various image processing techniques like color band composites (CBC), principal component analysis (PCA), band ratios (BR), and minimum noise fraction (MNF), it is possible to map the hydrothermal alterations zones and identify the lithological units under investigation. By identifying the various lithological units and alteration types, these results are validated and verified in the field. These methods’ outputs are combined to create an accurate geological map of the research region and tracing the propylitic, phyllic and iron oxy-hydroxides alterations. These rock units are ophiolitic mélange (oldest), arc metavolcanics, arc granitoids, younger gabbro, late-collision granites (monzogranites, syenogranites and alkali feldspar granites) and Nubian Sandstone (Taref formation and Quseir Formation) youngest, Furthermore, the radioactive anomalies are primarily associated with ferrugination zones (iron oxy-hydroxides alteration) at Gabal El Bakriyah monzogranite and alkali feldspar granite. The radioactive minerals are uranothorite, thorite and fergusonite. The opaque minerals that encountered at the rock units of Gabal Abu Ashayir-Gabal El Bakriyah are chromite, ilmenite, magnetite, chalcopyrite and chalcocite, whereas the non-opaque minerals comprises titanite, rutile, fluorite, garnet, zircon, apatite and barite.

Development of a µPAD for the point-of-care testing of serum glutamic oxaloacetic transaminase (SGOT).

A wax-patterned paper analytical device (µPAD) has been developed for point-of-care colourimetric testing of serum glutamic oxaloacetic transaminase (SGOT). The detection method was based on the transamination reaction of aspartate with α-ketoglutarate, leading to the formation of oxaloacetate which reacts with the reagent Fast Blue BB salt and forms a cavern pink colour. The intensity of the cavern pink colour grows as the concentration of SGOT increases. UV-visible spectroscopy was utilized to optimize reaction conditions, and the optimized reagents were dropped onto the wax-patterned paper. The coloured PADs, after the addition of SGOT, have been photographed, and a colour band has been generated to correlate the SGOT concentration visually. The images were used to calculate the intensity values using ImageJ software, which inturn was used to calculate the SGOT concentration. The PADs were also tested with serum samples, and SGOT spiked serum samples. The PAD could detect the SGOT concentration ranging from 5 to 200 U/L. The analysis yielded highly accurate results with less than 6% relative error compared to the clinical sample. This colourimetric test demonstrated exceptional selectivity in the presence of other biomolecules in the blood serum, with a detection limit of 2.77 U/L and a limit of quantification of 9.25 U/L. Additionally, a plasma separation membrane was integrated with the PAD to directly test SGOT from finger-prick blood samples.

Comparing the effects of marking techniques on the survival of Piping Plover chicks

AbstractThe use of unique markers in ornithology has a long history and is predicated on the assumption that markers have negligible effects on behavior and survival. The assumption that marks are harmless is particularly important with imperiled species. We studied the effects of two different marking schemes on Piping Plovers (Charadrius melodus), a small shorebird protected under the U.S. Endangered Species Act that is intensively monitored and managed. We used two marking schemes, (1) color bands and (2) uniquely engraved flags during breeding seasons from 2013 to 2023 to determine whether the injuries were causing additive mortality and thus actually limiting populations. We estimated the effect of perceived limping and injury (e.g., swelling, laceration, etc.) on chick and hatch-year survival. We detected injuries in some years and associated with both marking schemes (range 0.0–6.0%, average = 2.7% of marked chicks each year). Interval survival for banded chicks was like that of flagged chicks ($$\beta $$ β = − 0.55, 95% BCI: − 1.30–0.33, f = 0.92). Mean survival to fledging, however, was higher in years where flags were used ($$\overline{\phi }=$$ ϕ ¯ = 0.55 ± 0.14) than when bands were used ($$\overline{\phi }=$$ ϕ ¯ = 0.34 ± 0.14), but we surmise that this difference was partly conflated with negative density-dependent factors ($$\beta $$ β = − 0.49, 95% BCI: − 0.73 to − 0.25, f = 1.00) and predation. Our results show that pre-fledge survival of birds with uniquely coded flags was similar to that of birds receiving color bands. There was also no evidence that injured birds had a significantly lower hatch-year survival than those that were not injured. However, the relatively high (up to 6%) injury rate in some years remains a concern. Injury and survival rates need to be considered and evaluated when deciding on whether to mark individuals. Ultimately, wildlife practitioners should strive to use the best methods for information gathering and management, without negatively impacting the species.

Analysis of Bathymetry Accuracy Using Sentinel 2 Satellite on Different Characteristics Waters in Bali Island

Bathymetry surveys today are often carried out using the echo-sounding method, but this method has disadvantages, such as requiring a lot of time and being quite expensive. Along with the development of technology, some alternative methods can be used to visualize bathymetry, such as remote sensing. Remote Sensing uses satellite imagery in the operation, while the technique to acquire bathymetry is called Satellite-Derived Bathymetry (SDB). This method uses an optical satellite with several color bands or multispectral images. In this research, a satellite used to map ocean depth is Sentinel-2. The SDB technique used in this research is the Lyzenga Algorithm. The Lyzenga algorithm uses multilinear logarithms in its operation and can be used using three optical image channels (blue, green, and red channels). Supported by the SDB algorithm, an analysis of research locations was carried out at several points in the waters of Bali Island due to the diversity of water characteristics such as sea depth and wave height. From several analysis results of different characteristic waters in Bali Island, We can see that many parameters impact the result of the Satellite to visualize bathymetry. The Satellite's optimal result for reading the bathymetry depth is approximately 30 meters. But in reality, some cases can interfere with the accuracy of Satellite visualizing bathymetry within this depth. Breaking waves, high water sedimentation, and some objects that could guide the Satellite to misread them as elevation.

Real-time chlorophyll-a forecasting using machine learning framework with dimension reduction and hyperspectral data

Since water is an essential resource in various fields, it requires constant monitoring. Chlorophyll-a concentration is a crucial indicator of water quality and can be used to monitor water quality. In this study, we developed methods to forecast chlorophyll-a concentrations in real-time using hyperspectral data on IoT platform and various machine learning algorithms. Compared to regular cameras that record information only in the three broad color bands of red, green, and blue, the hyperspectral images of drinking water sources record the data in dozens or even hundreds of distinct small wavelength bands, providing each pixel in an image with a full spectrum. Different machine learning algorithms have been developed using hyperspectral data and field observations of water quality and weather conditions. Previous studies have predicted chlorophyll concentrations using either partial least squares (PLS), which is a dimensionality reduction method, or machine learning. In contrast, our study employed the PLS technique as a preprocessing step to diminish the dimensionality of the hyperspectral data, followed by the application of the machine learning techniques with optimized hyperparameters to improve the precision of the predictions, thereby introducing a real-time mechanism for chlorophyll-a prediction. Consequently, a machine learning algorithm with R2 values of 0.9 or above and sufficiently small RMSE was developed for real-time chlorophyll-a forecasting. Real-time chlorophyll-a forecasting using LightGBM has the best performance, with a mean R2 of 0.963 and a mean RMSE of 2.679. This paper is expected to have applications in algal bloom early detection on monitoring systems.

An Fe3O4-Au heterodimer nanoparticle-based lateral flow assay for rapid and simultaneous detection of multiple influenza virus nucleic acids.

Sensitive, convenient and rapid detection and subtyping of influenza viruses are crucial for timely treatment and management of infected people. Compared with antigen detection, nucleic acid detection has higher specificity and can shorten the detection window. Hence, in this work, we improved the lateral flow assay (LFA, one of the most promising user-friendly and on-site methods) to achieve detection and subtyping of H1N1, H3N2 and H9N2 influenza virus nucleic acids. Firstly, the antigen-antibody recognition mode was transformed into a nucleic acid hybridization reaction. Secondly, Fe3O4-Au heterodimer nanoparticles were prepared to replace frequently used Au nanoparticles to obtain better coloration. Thirdly, four lines were arranged on the LFA strip, which were three test (T) lines and one control (C) line. Three T lines were respectively sprayed by the DNA sequences complementary to one end of H1N1, H3N2 and H9N2 influenza virus nucleic acids, while Fe3O4-Au nanoparticles were respectively coupled with the DNA sequences complementary to the other end of H1N1, H3N2 and H9N2 nucleic acids to construct three kinds of probes. The C line was sprayed by the complementary sequences to the DNAs on all three kinds of probes. In the detection, by hybridization reaction, the probes were combined with their target nucleic acids which were captured by the corresponding T lines to form color bands. Finally, according to the position of the color bands and their grey intensity, simultaneous qualitative and semi-quantitative detection of the three influenza virus nucleic acids was realized. The detection results showed that this multi-channel LFA had good specificity, and there was no significant cross reactivity among the three subtypes of influenza viruses. The simultaneous detection achieved comparable detection limits with individual detections. Therefore, this multi-channel LFA had good application potential for sensitive and rapid detection and subtyping of influenza viruses.

Hyperspectral imaging coupled with deep learning model for visualization and detection of early bruises on apples

Early bruise on apples caused by external impacts during the transportation process is commonly difficult to be detected on the apple surface, limiting the application of traditional machine vision methods in determining fruit quality. In recent years, hyperspectral imaging (HSI) has emerged as a promising technology for identifying early bruise of fruits due to its efficient and nondestructive detection. In this study, HSI data in the shortwave infrared range were collected at 2-hour and 6-hour intervals after mechanical damage. The combination of the successive projections algorithm (SPA) and principal component analysis (PCA) was used to select three key feature bands, namely, 1074 nm, 1269 nm and 1441 nm. Pseudo color transformation and band ratio algorithm were then employed to improve the contrast between damaged and healthy apple tissues for image enhancement. The fast and precise YOLOv5 (FP-YOLOv5) model achieved effective identification of apple bruises, with a high recognition rate of 95 % and a fast detection speed at 130 fps. Overall, the proposed framework based on band selection and image enhancement exhibits better performance in the detection of early apple bruises, providing useful insights for HSI combined with a deep learning model in the grading evaluation of fruit quality.

Preparation of CO2‐based waterborne polyurethane/cellulose nanocrystal composite films and research of their radiative cooling properties

AbstractConventional colored radiative cooling (ColPRC) has a single color and limited reflection of visible light bands, which affects its radiative cooling effect, and the introduction of multiple photonic crystal structures can improve its radiative cooling performance. Therefore, in this study, a composite film was established by cross‐linking cellulose nanocrystals (CNC) with CO2‐based waterborne polyurethane (WPU) to obtain multistage structural colors. X‐ray diffraction pattern, thermogravimetric analysis, scanning electron microscopy, spectral analysis, and so on were used to examine the structure and properties of the films. The results showed that with the increase of CNC content, the color shifted blue, the crystallinity increased, the flame retardancy increased, but the thermal stability decreased; the addition of WPU assists in resisting moisture; the reflectivity of the multistage structured films is improved in the VIS–NIR range compared with the monochromatic films (an average increase of 5%), which helps to reduce solar irradiation in the visible wavelengths; high emissivity (≈90%) in the atmospheric window, which facilitates deep‐space heat exchange. The maximum temperature difference of the multistage structural color film can reach 6°C under strong solar irradiation. This research can be applied in photovoltaic devices or outdoor thermal management to provide new ways to address human energy efficiency.

On-Orbit Geometric Calibration of the HJ-2 A/B Satellites' Infrared Sensors

Abstract. With the advancement of China's satellite technology, the HuanJingJianZai-2 A/B (HJ-2 A/B) satellites, equipped with whisk-broom infrared sensors, represent a significant leap forward in environmental monitoring and Earth observation capabilities. This technological leap, however, introduces new challenges in calibration. The unique structure of the HJ-2 A/B infrared spectroradiometer (IRS) necessitates innovative calibration techniques, as traditional methods primarily focused on exterior orientation parameters (EOPs) and often overlooked the importance of interior orientation accuracy, which is essential for accurate multispectral band registration and color rendering. Addressing this gap, we introduce an innovative multi-focal-plane-array joint calibration method specifically designed for whisk-broom cameras. Our method involves selecting a master band from each focal plane array for accurate focal length calibration and deriving ground control points from image matching and altitude interpolation for comprehensive bundle adjustment. This adjustment refines EOPs and interior orientation parameters (IOPs), ensuring globally optimal EOPs and enhanced IOPs calibration stability. The application of our method to the HJ-2 A/B IRS yielded substantial improvements in georeferencing and band registration accuracies, surpassing traditional methods. This paper details the multi-focal-plane-array joint calibration method, describes the IRS and experimental setup, presents the experimental results, and concludes with the implications and potential applications of our findings.

Magnetic response contrast of Oligo-Miocene basaltic magmatism and structural control during its ascent at the northern tip of the Red Sea Rift

Oligo-Miocene volcanic activity has occurred along the northern Egyptian margin of the Red Sea Rift system, leading to the creation of small-volume monogenetic volcanoes. This research delves into the morphology and setting of these rift-related volcanics by combining aeromagnetic data, remote sensing, and field verification. Basaltic exposures in the study area were mapped using False Color Composite (FCC) and band ratios in Landsat 8 OLI data. In a map view, these mappable volcanic rocks appear as discrete semi-circular basaltic bodies and as discontinuous dikes. The basaltic bodies are aligned along ENE and NW-oriented zones, with most dikes orientated NW-SE. Multiple isolated high magnetic zones with NE and NW orientations were identified. Most basaltic outcrops coincide with high magnetic zones except for Gabal Abu Treifiya along the Cairo-Suez Road. The integration and combination of three layers of surface and subsurface structure patterns reveal an array of variance. The dominant trend of surface and deeper structural lineaments is NW-SE and NE-SW respectively. In contrast, shallow structural lineaments have NW-SE, NE-SW, E-W, and N–S trends. Long linear grabens result from the pairing of NW-SE normal faults. The basaltic occurrences such as Qattamiya and Abu Treifiya are located at the intersection of the NW-SE and E-W normal faults along the NW-SE linear grabens. The field investigation at both locations reveals a polyphase magmatic feeding system manifesting in the surface today as coherent and volcaniclastic rock units. This suggests a diverse range of eruptive styles through a complex feeder system that involves more than one volcanic cone. Though the two areas have good basaltic exposures, yet they have different magnetic responses. The severe alteration of Abu Treifiya's volcanic rocks into nonmagnetic minerals assemblage of zeolite, smectite, calcite, and hydrolysis of basaltic glass diminishes the expected high magnetic anomaly. This study contributes to our knowledge of Rift-related volcanism and the factors influencing the distribution of these volcanic materials.

Enhanced Visual Performance for In–Vehicle Reading Task Evaluated by Preferences, Emotions and Sustained Attention

The proliferation of electric and hybrid vehicles has made it possible for people to read and work in a stationary vehicle for extended periods. However, the current commonly used in–vehicle lighting design is still centered around driving and driving safety. Following recommendations from the literature, a neutral white color band (4000 K–5000 K) with 50–100 lx at the vehicle table area is favored. Whether this lighting environment can meet the needs to enhance the reading performance in a modern vehicle was investigated in this presented study. Therefore, in total, 12 lighting settings were designed based on combinations of four illuminance levels (50 lx, 100 lx, 150 lx and 200 lx) and three correlated color temperatures (3000 K, 4000 K and 5000 K); we recruited 19 subjects (12 females, 7 males) and let study participants evaluate each condition based on electronic and paper reading. Next, subjective preferences, positive and negative emotions, feeling of fatigue and sustained attention were tested. We found that higher illuminance and higher CCT (Correlated Color Temperature) can significantly improve the performance of in–vehicle readers in most aspects following Kruithof’s law (p &lt; 0.05). Among them, we recommend the combination of 150 lx and 4000 K as the light parameters for in–vehicle reading as a new development guideline. In addition, we also discovered the inconsistency of people’s lighting preferences between in–vehicle spaces and conventional spaces. For indoor lighting, illuminance values up to 1000 lx are still favored. For an in–vehicle function, starting with 200 lx, the preference level and reading performance already declined. In comparison between electronic and paper reading, both were similarly evaluated. These results show that a neutral white light color should be chosen with a horizontal illuminance of maximal 150 lx for a reading light function independent of the reading device. Interdisciplinarily speaking, our findings can be applied in similar small spaces or transportation modes with gentle acceleration and deceleration such as small space hotel rooms, trains, airplanes or ships.

Two-stage image colorization via color codebook

Image colorization has a wide range of applications, but it remains a challenging task due to it is an inherently ill-posed problem with multi-modal uncertainty. The advancement of deep learning techniques has provided extensive avenues for addressing image colorization. However, current works mainly suffer from two problems: inaccurate colorization leading to biased color tones (e.g., cool or warm bias) and undersaturation of images. Existing Transformer-based methods can produce impressive results, but they often come with high training costs and may result in color overflow effects. In this paper, we propose a two-stage image colorization strategy based on a color codebook. Clustering methods in the three-dimensional CIE Lab color space is proposed to integrate brightness information so that the colors in the codebook can be lifelike. In the first stage, we treat the colorization task as a classification problem based on a color codebook, and a high-quality codebook is advantageous for enhancing color classification accuracy. In the second stage, different from the traditional Transformer-based method, a pyramid-type Transformer structure is used to extract rich image features to refine the colors, which can solve potential color bands, color errors and color overflow. In addition, the parameters and FLOPs are significantly smaller than other traditional Transformer-based methods. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches. On the ImageNet validation set, the achieved values are 4.60, 25.23, 0.19, and 39.82 in terms of FID, PSNR, LPIPS, and CF, respectively. On the COCO-Stuff validation set, the achieved values are 5.62, 25.15, 0.19, and 36.25 in terms of FID, PSNR, LPIPS, and CF, respectively. The codes are available at https://github.com/Tanghui2000/Two-stage_Image_Colorization_via_Color_Codebook.

Comparison Test Between Amrita Virtual Lab and Real Spectrometer on Refractive Index Using Blended Laboratory

Education in the 21st century is an era where learning is unconventional. Interactive learning in the 21st century can be done by conducting virtual or real laboratory activities, even by combining the two in one activity at once. Various innovations in virtual laboratories have spread to optical materials, especially refraction by using a virtual spectrometer. Conceptually, the refractive index is a measure of the bending ray of a light beam as it passes from one medium to another. The refractive index is given by measurement between the refractive index of air, the angle of the prism, and the angle of minimum deviation. The angle of the prism and the angle of minimum deviation can be measured with a spectrometer. The spectrometer is a scientific instrument used to separate and measure the spectral components of physical phenomena and can separate white light and measure individual narrow color bands. Other than an on-hand spectrometer, other tools that we can use to measure the angle of minimum deviation are by using a virtual spectrometer provided by several virtual labs. The study aimed to compare the result of refractive index between on on-hand spectrometer and a virtual lab. Here we report our study on spectrometer whether the virtual lab experiment yields the same results as the real lab. We compare both results of experimental data using data and graph analytics. The results of the study show that the difference in the index of refraction measured between the virtual lab and the real lab is about 0.2%. This shows that there is no significant difference between virtual lab and real lab.&#x0D; Keywords: amrita virtual lab, real spectrometer, refractive index, blended laboratory