Color Data Research Articles

Evaluation of Ocean Color Algorithms to Retrieve Chlorophyll-a Concentration in the Mexican Pacific Ocean off the Baja California Peninsula, Mexico

Mathematical algorithms relate satellite data of ocean color with the surface Chlorophyll-a concentration (Chl-a), a proxy of phytoplankton biomass. These mathematical tools work best when they are adapted to the unique bio-optical properties of a particular oceanic province. Ocean color algorithms should also consider that there are significant differences between datasets derived from different sensors. Common solutions are to provide different parameters for each sensor or use merged satellite data. In this paper, we use satellite data from the Copernicus merged product suite and in situ data from the southernmost part of the California Current System to test two widely used global algorithms, OCx and CI, and a regional algorithm, CalCOFI2. The OCx algorithm yielded the most favorable results. Consequently, we regionalized it and conducted further testing, leading to significant improvements, especially in eutrophic and oligotrophic waters. The database was then separated according to (a) dynamic boundaries in the area, (b) bio-optical properties, and (c) climatic conditions (El Niño/La Niña). Regional algorithms were obtained and tested for each partition. The Chl-a retrievals for each model were tested and compared. The best fit for the data was for the regional algorithms that considered the climatic conditions (El Niño/La Niña). These results will allow for the construction of consistent regionally adapted time series and, therefore, will demonstrate the importance of El Niño/La Niña events on the bio-optical properties of the area.

Rapid in-situ calibration of computational micro-spectrometer with few-shot meta-learning

Computational micro-spectrometers comprised of detector arrays and encoding structure arrays, such as on-chip Fabry-Perot (FP) cavity filters, have great potential in many in-situ applications owing to their compact size and snapshot imaging ability. Given manufacturing deviation and environmental influence are inevitable, easy and effective calibration for spectrometer is necessary, especially for in-situ applications. Currently calibration strategies based on iterative algorithms or neural networks require accurate measurements of pixel-level (spectral) encoding functions through monochromator or large amounts of standard samples. These procedures are time-consuming and expensive, thereby impeding in-situ applications. Meta-learning algorithms with few-shot learning ability can address this challenge by incorporating the prior knowledge in the simulated dataset. In this work, we propose a meta-learning algorithm free of measuring encoding function or large amounts of standard samples to calibrate a micro-spectrometer with manufacturing deviation effectively. Our micro-spectrometer comprises 16 types of FP filters covering a wavelength range of 550-720 nm. The center wavelength of each filter type deviates from the design up to 6 nm. After calibration with 15 different color data, the average reconstruction error on the test dataset decreased from 7.2 × 10 − 3 to 1.2 × 10 − 3, and further decreased to 9.4 × 10 − 4 when the calibration data increased to 24. The performance is comparable to algorithms trained with measured encoding function both in reconstruction error and generalization ability. We estimated that the cost of in-situ calibration through reflectance measurements of color chart decreased to one percent of the cost through monochromator measurements. By exploiting prior deviation information in simulation data with meta-learning, the efficiency and cost of calibration are significantly improved, thereby facilitating the large-scale production and in-situ application of micro-spectrometers.

Anomalous trends in global ocean carbon concentrations following the 2022 eruptions of Hunga Tonga-Hunga Ha’apai

We report on observed trend anomalies in climate-relevant global ocean biogeochemical properties, as derived from satellite ocean color measurements, that show a substantial decline in phytoplankton carbon concentrations following eruptions of the submarine volcano Hunga Tonga-Hunga Ha’apai in January 2022. The anomalies are seen in remotely-sensed ocean color data sets from multiple satellite missions, but not in situ observations, thus suggesting that the observed anomalies are a result of ocean color retrieval errors rather than indicators of a major shift in phytoplankton carbon concentrations. The enhanced concentration of aerosols in the stratosphere following the eruptions results in a violation of some fundamental assumptions in the processing algorithms used to obtain marine biogeochemical properties from satellite radiometric observations, and it is demonstrated through radiative transfer simulations that this is the likely cause of the anomalous trends. We note that any future stratospheric aerosol disturbances, either natural or geoengineered, may lead to similar artifacts in satellite ocean color and other remote-sensing measurements of the marine environment, thus confounding our ability to track the impact of such events on ocean ecosystems.

Comparison of optical spectra between asteroids Ryugu and Bennu: I. Cross calibration between Hayabusa2/ONC-T and OSIRIS-REx/MapCam

Asteroids (162173) Ryugu and (101955) Bennu observed by Hayabusa2 and Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) share many global properties, but high-spatial-resolution spectral observations by the telescopic Optical Navigation Camera (ONC-T) and MapCam detected subtle but significant differences (e.g., opposite space weathering trends), which may reflect differences in their origin and evolution. Comparing these differences on the same absolute scale is necessary for understanding their causes and obtaining implications for C-complex asteroids. However, ONC-T and MapCam have a large imager-to-imager systematic error of up to 15% caused by the difference in radiometric calibration targets. To resolve this problem, we cross calibrated albedo and color data between the two instruments using the Moon as the common calibration standard. The images of the Moon taken by ONC-T and MapCam were compared with those simulated using photometry models developed from lunar orbiter data. Our results show that the cross-calibrated reflectance of Ryugu and Bennu can be obtained by upscaling the pre-cross-calibrated reflectance of Bennu by 13.3 ± 1.6% at b band, 13.2 ± 1.5% at v band, 13.6 ± 1.7% at w band, and 14.8 ± 1.8% at x band, while those for Ryugu are kept the same. These factors compensate for the imager-to-imager bias caused by differences in targets used for radiometric calibration and solar irradiance models used for data reduction. Need for such large upscaling underscore the importance of using the cross-calibrated data for accurately comparing the Ryugu and Bennu data. The uncertainty in these factors show that the reflectance of Ryugu and Bennu can be compared with <2% accuracy after applying our results. By applying our cross calibration, the geometric albedo of Bennu became consistent with those observed by ground-based telescopes and the OSIRIS-REx Visible and InfraRed Spectrometer (OVIRS). Our result can be simply applied by multiplying a constant to the publicly available data and enables accurate comparison of the optical spectra of Ryugu and Bennu in future studies.

Abstract PO5-09-12: Unraveling the Intricacies of Racial Disparities in Breast Cancer in Brazil

Abstract Abstract Background: Over the past three decades, numerous countries have experienced a substantial decrease in breast cancer (BC) fatality, attributed to heightened consciousness, widened and earlier screening, and enhanced therapeutic interventions. However, these advancements have not eradicated the disparity in BC treatment across different racial and ethnic groups. In Brazil, the causes of these disparities are diverse and intricate, resembling health inequalities among ethnic communities observed in other countries. This study aimed to investigate the trends in BC incidence and mortality rates in Brazil, considering clinicopathological and sociodemographic variations, and placing particular emphasis on the severity of racial disparity concerning race/skin color. Patients and methods: Incidence trends for women with BC were analyzed using data from 13 Brazilian Population-Based Cancer Registries covering the period 2010-2015. Crude incidence ratios (CIRs) and the annual average percent change (AAPC) were calculated. Clinical and sociodemographic information from 348 Hospital-Based Cancer Registries spanning from 2000 to 2019 was also utilized. Meanwhile, mortality data for the years 2000-2020 were obtained from the National Mortality Information System. Racial and skin color data were collected through a self-declaration process, following the classification proposed by the Brazilian Institute of Geography and Statistics, which included categories such as white, black, and brown/mixed race. In the analysis, black and brown/mixed race categories were combined as black. Yellow and indigenous categories were excluded. Statistical significance was defined as a p-value less than 0.05. To determine clinically relevant differences, a threshold of more than 5% disparity in proportional values was used as an arbitrary criterion. Results: Between 2010 and 2015, a total of 70,896 newly diagnosed cases of BC were recorded. Mean CIRs were 83.9/100,000 for the overall population, 101.3/100,000 for white women, and 59.7/100,000 for black women. Neither the group of white women (AAPC -1.7; 95% CI: -7.9 to 4.9; p = 0.506) nor the group of black women (AAPC -0.4; 95% CI: -5.0 to 4.5; p = 0.831) exhibited any significant changes in the incidence rate. Black women predominantly originated from underdeveloped regions (56.4%, p &amp;lt; 0.001), were referred by the public healthcare system in Brazil (79.0%, p &amp;lt; 0.001), had ≤ 8 years of schooling (68.7%, p &amp;lt; 0.001), were unmarried (50.9%, p&amp;lt; 0.001), reported alcohol consumption (20.9%, p &amp;lt; 0.001), and were diagnosed with advanced stage disease (60.1%, p &amp;lt; 0.001). The escalation in mortality rates among black women exhibited a notable difference, amounting to 3.8 times the increase observed among white women (AAPC + 2.3; 95% CI: 2.1 to 2.5; p &amp;lt; 0.001) (AAPC + 0.6; 95% CI: 0.5 to 0.7; p &amp;lt; 0.001). Conclusion: Despite the lower incidence of BC in black women compared to their white counterparts, there is a concerning rise in BC-related mortality rates among black women. This group exhibits a significantly unfavorable sociodemographic and clinicopathological profile. These results highlight the pressing need to address the multifaceted factors contributing to these disparities, encompassing socioeconomic disparities and unequal healthcare access. Keywords: Breast cancer; racial disparities; incidence; mortality. Citation Format: Jesse Lopes da Silva, Andreia Cristina De Melo, Mariana Espirito Santo Rodrigues, Lucas Zanetti de Albuquerque, Luiz Claudio Santos Thuler. Unraveling the Intricacies of Racial Disparities in Breast Cancer in Brazil [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-09-12.

PnP-GA+: Plug-and-Play Domain Adaptation for Gaze Estimation Using Model Variants.

Appearance-based gaze estimation has garnered increasing attention in recent years. However, deep learning-based gaze estimation models still suffer from suboptimal performance when deployed in new domains, e.g., unseen environments or individuals. In our previous work, we took this challenge for the first time by introducing a plug-and-play method (PnP-GA) to adapt the gaze estimation model to new domains. The core concept of PnP-GA is to leverage the diversity brought by a group of model variants to enhance the adaptability to diverse environments. In this article, we propose the PnP-GA+ by extending our approach to explore the impact of assembling model variants using three additional perspectives: color space, data augmentation, and model structure. Moreover, we propose an intra-group attention module that dynamically optimizes pseudo-labeling during adaptation. Experimental results demonstrate that by directly plugging several existing gaze estimation networks into the PnP-GA+ framework, it outperforms state-of-the-art domain adaptation approaches on four standard gaze domain adaptation tasks on public datasets. Our method consistently enhances cross-domain performance, and its versatility is improved through various ways of assembling the model group.

Optimizing Vision Transformers for Histopathology: Pretraining and Normalization in Breast Cancer Classification.

This paper introduces a self-attention Vision Transformer model specifically developed for classifying breast cancer in histology images. We examine various training strategies and configurations, including pretraining, dimension resizing, data augmentation and color normalization strategies, patch overlap, and patch size configurations, in order to evaluate their impact on the effectiveness of the histology image classification. Additionally, we provide evidence for the increase in effectiveness gathered through geometric and color data augmentation techniques. We primarily utilize the BACH dataset to train and validate our methods and models, but we also test them on two additional datasets, BRACS and AIDPATH, to verify their generalization capabilities. Our model, developed from a transformer pretrained on ImageNet, achieves an accuracy rate of 0.91 on the BACH dataset, 0.74 on the BRACS dataset, and 0.92 on the AIDPATH dataset. Using a model based on the prostate small and prostate medium HistoEncoder models, we achieve accuracy rates of 0.89 and 0.86, respectively. Our results suggest that pretraining on large-scale general datasets like ImageNet is advantageous. We also show the potential benefits of using domain-specific pretraining datasets, such as extensive histopathological image collections as in HistoEncoder, though not yet with clear advantages.

Color Image Compression Using Wavelet Compression and Zero-MeanCoding

Thousands pictures require a very large amount of storage. So differenttechniques of digital image compression is used to reduce the storage requirementsfor these images. In this paper an adaptive compression method applied on colorimage. Firstly the color image is transformed to the RGB system, and then convertsthe color data to a luminance/chrominance color space (YIQ).The adaptivecompression method is applied on Y. The image may contain a uniform region andedge region; the uniform regions compress depending on zero-mean coding and theedge region compress depending on Daubechies wavelet transform, then returnedthe final image to RGB system. The proposed algorithms has many advantageswhich make it very efficient, these are, low bit rate, low computational complexity,fast processing and edge preservation with good reconstructed image quality.

Geographic analysis of the distribution of surface temperature, vegetation cover and its relationship with socioeconomic indicators – Cuiabá/MT

In Brazil and around the world, the problems resulting from urban planning, changes in land cover and the increase in urban infrastructure influence temperature change. The aim of this research is to understand the difference in temperature over the last 36 years (1985-2021) in the city of Cuiabá/MT, and its relationship with vegetation and socioeconomic and racial-ethnic factors. Annual images (36 images) from the Landsat 5 and 8 satellites between 1985 and 2021 were used, and Google Earth Engine was used to obtain this data for the urban area of the city of Cuiabá. Mapping of the spatial distribution of surface temperature, vegetation and changes in land use and cover was carried out using data from the MapBiomas project and socio-economic data from the Instituto Brasileiro de Geografia e Estatística (IBGE, Brazilian Institute of Geography and Statistics in English) 2010 census. The results indicated that over the last 36 years Cuiabá's surface temperature has increased by an average of 13°C, as part of the result of the growth of urban infrastructure and loss of vegetation cover, specifically in the peripheral areas. There is an inequality between the high-income population living in neighborhoods with milder temperatures and the low-income population living in neighborhoods with higher temperatures. In the temperature and skin color data, the white population generally lives in neighborhoods with milder temperatures, unlike the black population, which mostly suffers from high temperatures.

Quantitative analysis methods for evaluating colour on architectural heritage: survey of colour restoration and perception of the Wen Yuan Ge in the Forbidden City

Three methods can be used to evaluate the colour used on architectural heritage: manual evaluation, instrumental measurement, and process restoration. Optical instruments that can obtain relatively accurate chromaticity data are ideal. As architectural heritage sites age, their colour fluctuates. Researchers must screen colour data and analyse the features based on experience with colour perception. This study combines quantitative colour analysis and a scaled perception survey to analyse the Wen Yuan Ge in the Forbidden City and provide a new reference for researching and designing colour in architectural heritage.

Hand drawing-based daylight analysis using deep learning and augmented reality

The utilization of artificial intelligence (AI) in daylight analysis has recently experienced an increase. Its capabilities have been demonstrated through different studies. While using AI for daytime simulation can reduce time, it also presents significant challenges. Whether we utilize the conventional approach of simulating daylight or employ artificial intelligence, the process of going from hand drawing to obtaining the daylight analysis result involves intricate procedures associated with modeling, programming, and integrating AI models. This paper introduces an efficient approach to rapidly predict daylight performance directly from the user's hand-drawn sketches. The core idea is to leverage a deep learning model for predicting directly from hand drawing and augmented reality (AR) to seamlessly display results in an AR environment, overlaying them on the original drawings. This method not only increases user engagement and accessibility but also eliminates the dependency on traditional daylight simulation software and workflow. Artificial neural network (ANN) was exploited using data derived from 3D building models and Useful Daylight Illuminance simulations created by Rhino3D and Honeybee. The color code method, various imaging, and data processing techniques were implemented to ensure compatibility between the hand-drawn input and the simulation model. Unity3D is used as the development platform with the AR engine Vuforia. Notably, the ANN model yields promising results, with R2 ranging from 0.872 to 0.985. This is achieved by efficiently capturing and converting grid-based hand-drawn images into suitable input for the ANN. The obtained results demonstrate the feasibility and emphasize the concept's practicality.

Unveiling the mechanism of diachronic changes and regional characteristics differences in the built environment of traditional villages from the perspective of color data: evidence from Macheng City, China

Traditional villages hold a vital place in Chinese vernacular built heritage continuation with their colors being a significant constituent of the built environment. While digital technology has advanced our understanding of built environment, current research pays little attention on how color influences the environment, often overlooking temporal changes and regional differences, especially neglects color protection measures. This study establishes an diachronic image dataset using national traditional villages in Macheng City, Hubei Province. By using of K-Means and CIEDE2000 algorithms, we analyze color characteristics differences among 4 regions in Macheng City in 2022. And, we select five year’ s diachronic image data of Dongyuan Village (2017–2022) to elucidate the color changes mechanisms. The results show that: (1) Elements influenced by the government, construction techniques dissemination, and environmental factors exhibit regional similarity, while the color of wall differs significantly. Specifically, K-Means values for northern and eastern wall elements indicate light brown tones [RGB: (159, 151, 136), (163, 159, 147)], whereas southwestern and central wall elements exhibit distinct colors, including grayish brown [RGB: (132, 127, 119)] and light yellow [RGB: (190, 182, 161)], respectively. (2) Dongyuan Village underwent color corrections in accordance with government policies, resulting in a shift from blue [RGB: (138, 154, 213)] to yellow [RGB: (223, 206, 196)] for the walls and a gradual unification of road colors into light gray [RGB: (239, 238, 243)]. This initiative led to increased consistency with government guidelines, and the color data stabilized with minimal changes in the surrounding environment. This study enhances our comprehension of traditional village color and regional characteristics changes, contributing to effective measures for the preservation of these culturally significant built heritage.

Molecular imprinting-based triple-emission ratiometric fluorescence sensor with aggregation-induced emission effect for visual detection of doxycycline

The development of high-performance sensors for doxycycline (DOX) detection is necessary because its residue accumulation will cause serious harm to human health and the environment. Here, a novel tri-emission ratiometric fluorescence sensor was proposed by using “post-mixing” strategy of different emissions fluorescence molecularly imprinted polymers with salicylamide as dummy template (DMIPs). BSA was chosen as assistant functional monomer, and also acted as sensitizers for the aggregation-induced emission (AIE) effect of DOX. The blue-emitting carbon dots and the red-emitting CdTe quantum dots were separately introduced into DMIPs as the response signals. Upon DOX recognition within 2 min, blue and red fluorescence of the tri-emission DMIPs sensor were quenched while green fluorescence of DOX was enhanced, resulting in a wide range of color variations observed over bluish violet-rosered-light pink-orange-yellow-green with a detection limit of 0.061 μM. The sensor possessed highly selective recognition and was successfully applied to detect DOX in complicated real samples. Moreover, with the fluorescent color collection and data processing, the smartphone-assisted visual detection of the sensors showed satisfied sensitivity with low detection limit. This work provides great potential applications for rapid and visual detection of antibiotics in complex substrates.

Analysis of Chlorophyll-a Variability in the Eastern Indonesian Waters Using Sentinel-3 OLCI from 2020-2021

The Eastern Indonesian waters are significant in influencing the global climate system and oceanic connectivity. However, the Indonesian Through Flow (ITF) facilitates the movement of waters from the Pacific Ocean to the Indian Ocean. This flow vertically mixes water masses in the Eastern regions, leading to the concentration of phytoplankton. In addition, the distribution of phytoplankton, indicative of chlorophyll-a concentration, is influenced by upwelling and downwelling phenomena. Chlorophyll-a, responsible for capturing carbon and producing oxygen in marine ecosystems, is important in regulating climate change. Moreover, oceanographic conditions play a significant role in the dispersion of chlorophyll-a concentration. Therefore, this study adopted ocean colour remote sensing technology to assess chlorophyll-a distribution. Monthly ocean colour data was collected by the multi-temporal Sentinel-3 Ocean and Land Colour Instrument (OLCI). The analysis output included chlorophyll-a concentration associated with currents and the El Nino Southern Oscillation (ENSO). Data processing using the Case-2 Regional Coast Colour (C2RCC) processor resulted in an average chlorophyll-a concentration in the Eastern Indonesian waters ranging from 0.16 to 0.52. The results showed higher chlorophyll-a levels during the southeast monsoon (July to September) and lower levels during the northwest monsoon (January to March).

An Improved Data Interpolating Empirical Orthogonal Function Method for Data Reconstruction: A Case Study of the Chlorophyll-a Concentration in the Bohai Sea, China

Chlorophyll-a (chl-a) serves as a key indicator in water quality and harmful algal blooms (HABs) research. While satellite ocean color data have greatly advanced chl-a research and HABs monitoring, missing data caused by cloud cover and other factors limit the spatiotemporal continuity and the utility of remote sensing data products. The Data Interpolating Empirical Orthogonal Function (DINEOF) method, widely used to reconstruct missing values in remote sensing datasets, is open to improvement in terms of computational accuracy and efficiency. We propose an improved method called Concentration-Stratified DINEOF (CS-DINEOF), which uses a coordinate–value correlative data division strategy to stratify the study area into several subregions based on annual average chl-a concentration. The proposed method clusters data points with similar spatiotemporal patterns, allowing for more targeted and effective reconstruction in each sub-dataset. The feasibility and advantage of the proposed method are tested and evaluated in the experiments of chl-a data reconstruction in the water of the Bohai Sea. Compared with the ordinary DINEOF method, the CS-DINEOF method improves the reconstruction accuracy, with an average Root Mean Square Error (RMSE) reduction of 0.0281 mg/m3, and saves computational time by 228.9%. Furthermore, the gap-free images generated from CS-DINEOF are able to illustrate small variations and details of the chl-a distribution in local areas. We can conclude that the proposed CS-DINEOF method is superior in providing significant insights for water quality and HABs studies in the Bohai Sea region.

Stereo Vision for Plant Detection in Dense Scenes.

Automated precision weed control requires visual methods to discriminate between crops and weeds. State-of-the-art plant detection methods fail to reliably detect weeds, especially in dense and occluded scenes. In the past, using hand-crafted detection models, both color (RGB) and depth (D) data were used for plant detection in dense scenes. Remarkably, the combination of color and depth data is not widely used in current deep learning-based vision systems in agriculture. Therefore, we collected an RGB-D dataset using a stereo vision camera. The dataset contains sugar beet crops in multiple growth stages with a varying weed densities. This dataset was made publicly available and was used to evaluate two novel plant detection models, the D-model, using the depth data as the input, and the CD-model, using both the color and depth data as inputs. For ease of use, for existing 2D deep learning architectures, the depth data were transformed into a 2D image using color encoding. As a reference model, the C-model, which uses only color data as the input, was included. The limited availability of suitable training data for depth images demands the use of data augmentation and transfer learning. Using our three detection models, we studied the effectiveness of data augmentation and transfer learning for depth data transformed to 2D images. It was found that geometric data augmentation and transfer learning were equally effective for both the reference model and the novel models using the depth data. This demonstrates that combining color-encoded depth data with geometric data augmentation and transfer learning can improve the RGB-D detection model. However, when testing our detection models on the use case of volunteer potato detection in sugar beet farming, it was found that the addition of depth data did not improve plant detection at high vegetation densities.

Harmonizing culture and consumer psychology: optimizing color schemes for children’s product design inspired by traditional ornaments

Color is one of the vital components of product design and the choice of color combination is a critical factor that affects consumer reaction and purchase decisions. However, the impact of traditional colors on color extraction and product design has seldom been studied. The purpose of this study is to discover suitable colors from traditional patterns to create color schemes that will be used in the design of children’s products. Colors were extracted from five major traditional ornaments obtained from Turkmenistan’s national carpets, each representing one of the main Turkmen tribes, using tools like Photoshop, Adobe Color, and EasyRGB, with the objective of investigating psychological-emotional attachment, reaction, and attitudes towards the colors obtained. This initial extraction provided 124 color data points. Initially, extracted colors were refined and used to develop novel color schemes by two independent survey studies. The first survey, based on a sample of 104 parents of preschool children, identified colors that have a positive emotional effect on consumer preferences and provided the basis to develop color schemes. As a result, 25 colors were identified for use in developing 14 color schemes. The second survey, based on a sample of 48 parents of preschool children, identified which color schemes have an attraction for consumers. The survey indicated that 11 out of these 14 schemes were highly favored by the respondents. Based on these results, this study proposed new color schemes for children’s products, extracted from traditional patterns of Turkmenistan and aligned with the psychological perception of the consumers.

'Marfim pattern’ of wood species in the Southern Brazilian timber market: colorimetry and visible spectra

ABSTRACT Evaluating wood with similar characteristics is a challenging task. Taking into consideration the need for an efficient and rapid technique, the use of visible spectrophotometry to discriminate wood species is proposed. This study aimed to differentiate nine wood species with a similar yellowish color using visible spectrophotometry, associated with two classification models, to verify predictions. Color data were obtained from different anatomical sections of the wood, along with reflectance spectra in the range of 360–740 nm. Two machine learning approaches were applied: artificial neural network (ANN) and k-nearest neighbors (k-NN). In general, the mean spectra of the samples shared similar information, as they are materials of lignocellulosic origin. However, in all tests, the models built using the ANN algorithm demonstrated an accuracy in discriminating species exceeding 97%. Notably, when applying ANN to the spectra obtained solely from the longitudinal surface, superior results were obtained. Discriminative patterns can be obtained by visible spectra independent of the anatomical section. The integration of visible spectra and ANN proved to be a suitable approach to recognize “marfim pattern” wood.

The phenology of the spring phytoplankton bloom in the North Atlantic does not trend with temperature

Climate change is anticipated to alter the phenology of phytoplankton blooms in the ocean, making their recent dynamics of interest to inform models of future ocean states. We characterized temperature change in the North Atlantic using metrics that track the patterns of sea surface water temperature (SST) defined by quantiles. To complement these thermal indicators, we estimated a thermal phenology index in the form of the date of the spring transition, taken as the date that temperature achieved the long-term mean at a specific location. We then used ocean color data (1998–2022) and characterized spring bloom phenology using change point methods to derive bloom initiation, duration, magnitude, and intensity. The North Atlantic has warmed over recent decades, averaging a rate of increase of 0.27°C decade−1, yet throughout most of the basin, spring transition timing has remained constant, with the exception of small areas with either delayed or advanced transitions. There were no clear trends in bloom start or duration in the North Atlantic, indicating that spring bloom phenology was independent of climate-driven temperature change. Bloom magnitude and intensity trended downward in some North Atlantic continental shelf seas, indicating that increased temperatures may have had negative effects on overall bloom productivity. However, exclusive of the areas where the bloom parameters were trending, there was a decrease in magnitude and intensity with warmer winter temperatures, suggesting that the inter-annual variability of these parameters may be affected by thermal conditions at the onset of the bloom. While temperature has increased in the North Atlantic, vernal light availability has remained unchanged, which may explain why spring bloom phenology has remained resistant to climate change. Consequently, it seems plausible that future climate change may have limited effects on spring bloom phenology, but could have substantial effects on overall phytoplankton production.

Practical RGB-to-XYZ Color Transformation Matrix Estimation under Different Lighting Conditions for Graffiti Documentation.

Color data are often required for cultural heritage documentation. These data are typically acquired via standard digital cameras since they facilitate a quick and cost-effective way to extract RGB values from photos. However, cameras' absolute sensor responses are device-dependent and thus not colorimetric. One way to still achieve relatively accurate color data is via camera characterization, a procedure which computes a bespoke RGB-to-XYZ matrix to transform camera-dependent RGB values into the device-independent CIE XYZ color space. This article applies and assesses camera characterization techniques in heritage documentation, particularly graffiti photographed in the academic project INDIGO. To this end, this paper presents COOLPI (COlor Operations Library for Processing Images), a novel Python-based toolbox for colorimetric and spectral work, including white-point-preserving camera characterization from photos captured under diverse, real-world lighting conditions. The results highlight the colorimetric accuracy achievable through COOLPI's color-processing pipelines, affirming their suitability for heritage documentation.