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We introduce a convolutional framework (CF) for computational color constancy, building upon the established low-level image feature-based framework, which utilized simple image statistics for illuminant estimation. Our framework expands upon this through an end-to-end learnable neural architecture. This adaptation enables the learning and usage of advanced filters that are not restricted to Gaussian kernels operating on individual color channels, thus generalizing the capabilities of the original framework. Additionally, our general framework supports deeper convolutional architectures, thus increasing its computational power. It can also be efficiently applied to estimate multiple spatially varying illuminants within a single scene. Our experimental results on standard datasets demonstrate that the CF outperforms the best methods in the low-level framework, improving the illuminant estimation accuracy by up to 34% for single illuminant estimation and 30% for multiple illuminants estimation. Additionally, our framework exhibits superior performance even when the number of training images is reduced. Finally, we document the inference speedup of our implementation reaching up to 30 × , making the CF especially suitable for applications where efficiency is critical. Source code and trained models available at: https://github.com/MarcoBauzz/convolutional-color-constancy.

The authentication process involves all the supply chain stakeholders, and it is also adopted to verify food quality and safety. Food authentication tools are an essential part of traceability systems as they provide information on the credibility of origin, species/variety identity, geographical provenance, production entity. Moreover, these systems are useful to evaluate the effect of transformation processes, conservation strategies and the reliability of packaging and distribution flows on food quality and safety. In this manuscript, we identified the innovative characteristics of food authentication systems to respond to market challenges, such as the simplification, the high sensitivity, and the non-destructive ability during authentication procedures. We also discussed the potential of the current identification systems based on molecular markers (chemical, biochemical, genetic) and the effectiveness of new technologies with reference to the miniaturized systems offered by nanotechnologies, and computer vision systems linked to artificial intelligence processes. This overview emphasizes the importance of convergent technologies in food authentication, to support molecular markers with the technological innovation offered by emerging technologies derived from biotechnologies and informatics. The potential of these strategies was evaluated on real examples of high-value food products. Technological innovation can therefore strengthen the system of molecular markers to meet the current market needs; however, food production processes are in profound evolution. The food 3D-printing and the introduction of new raw materials open new challenges for food authentication and this will require both an update of the current regulatory framework, as well as the development and adoption of new analytical systems., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

We introduce a method that enhances RGB color constancy accuracy by combining neural network and k -means clustering techniques. Our approach stands out from previous works because we combine multispectral and color information together to estimate illuminants. Furthermore, we investigate the combination of the illuminant estimation in the RGB color and in the spectral domains, as a strategy to provide a refined estimation in the RGB color domain. Our investigation can be divided into three main points: (1) identify the spatial resolution for sampling the input image in terms of RGB color and spectral information that brings the highest performance; (2) determine whether it is more effective to predict the illuminant in the spectral or in the RGB color domain, and finally, (3) assuming that the illuminant is in fact predicted in the spectral domain, investigate if it is better to have a loss function defined in the RGB color or spectral domain. Experimental results are carried out on NUS: a standard dataset of multispectral radiance images with an annotated spectral global illuminant. Among the several considered options, the best results are obtained with a model trained to predict the illuminant in the spectral domain using an RGB color loss function. In terms of comparison with the state of the art, this solution improves the recovery angular error metric by 66% compared to the best tested spectral method, and by 41% compared to the best tested RGB method.

Multispectral imaging is a technique that captures data across several bands of the light spectrum, and it can be useful in many computer vision fields, including color constancy. We propose a method that exploits multispectral imaging for illuminant estimation, and then applies illuminant correction in the raw RGB domain to achieve computational color constancy. Our proposed method is composed of two steps: first, a selected number of existing camera-independent algorithms for illuminant estimation, originally designed for RGB data, are applied in generalized form to work with multispectral data. We demonstrate that the sole multispectral extension of such algorithms is not sufficient to achieve color constancy, and thus we introduce a second step, in which we re-elaborate the multispectral estimations before conversion into raw RGB with the use of the camera response function. Our results on the NUS dataset show that an improvement of 60% in the color constancy performance, measured in terms of reproduction angular error, can be obtained according to our method when compared to the traditional raw RGB pipeline.

Objectives: The objective of this study is to describe the United States and allied military medical response during the withdrawal from Afghanistan., Background: The military withdrawal from Afghanistan concluded with severe hostilities resulting in numerous civilian and military casualties. The clinical care provided by coalition forces capitalized on decades of lessons learned and enabled unprecedented accomplishments., Methods: In this retrospective, observational analysis, casualty numbers, and operative information was collected and reported from military medical assets in Kabul, Afghanistan. The continuum of medical care and the trauma system, from the point of injury back to the United States was captured and described., Results: Prior to a large suicide bombing resulting in a mass casualty event, the international medical teams managed distinct 45 trauma incidents involving nearly 200 combat and non-combat civilian and military patients over the preceding 3 months. Military medical personnel treated 63 casualties from the Kabul airport suicide attack and performed 15 trauma operations. US air transport teams evacuated 37 patients within 15 hours of the attack., Conclusion: Lessons learned from the last 20 years of combat casualty care were successfully implemented during the culmination of the Afghanistan conflict. Ultimately, the effort, teamwork, and system adaptability exemplify not only the attitudes and character of service members who provide modern combat casualty care but also the paramount importance of the battlefield learning health care system. A continued posture to maintain military surgical preparedness in unique environments remain crucial as the US military prepares for the future.Retrospective observational analysis., Level of Evidence: Therapeutic/Care Management; Level V., (Copyright © 2023 Written work prepared by employees of the Federal Government as part of their official duties is, under the U.S. Copyright Act, a “work of the United States Government” for which copyright protection under Title 17 of the United States Code is not available. As such, copyright does not extend to the contributions of employees of the Federal Government.)

Full-reference image quality measures are a fundamental tool to approximate the human visual system in various applications for digital data management: from retrieval to compression to detection of unauthorized uses. Inspired by both the effectiveness and the simplicity of hand-crafted Structural Similarity Index Measure (SSIM), in this work, we present a framework for the formulation of SSIM-like image quality measures through genetic programming. We explore different terminal sets, defined from the building blocks of structural similarity at different levels of abstraction, and we propose a two-stage genetic optimization that exploits hoist mutation to constrain the complexity of the solutions. Our optimized measures are selected through a cross-dataset validation procedure, which results in superior performance against different versions of structural similarity, measured as correlation with human mean opinion scores. We also demonstrate how, by tuning on specific datasets, it is possible to obtain solutions that are competitive with (or even outperform) more complex image quality measures.

This paper examines the recent growth of experiential learning (EL) and the university-community (or so-called town-gown, TG) connections created as a result of this expansion. The research is framed by critical scholarship on the nature and role of the university and the place of liberal education specifically, as well as policy drivers aimed at social and economic impacts from EL. Two subthemes are also examined: first, the role of the arts, humanities and social sciences disciplines in EL expansion and, second, the extent to which TG connections focus on the university's local host community as opposed to more distant and even international arrangements. Mixed research methods including public document analysis and key informant interviews are used to document and interpret EL developments across nine varied universities in Ontario, Canada. The results underline broad sector commitment to EL that in turn creates new and different TG connections for the university. Rapid expansion has brought a variety of challenges identified both by universities and community EL partners. The paper concludes with discussion of policy implications and consideration of the future of EL in light of the 'digital pivot' of the COVID-19 pandemic., (© The Author(s), under exclusive licence to Springer Nature B.V. 2022.)

The angle-retaining color space (ARC) and the corresponding chromaticity diagram encode information following a cylindrical color model. Their main property is that angular distances in RGB are mapped into Euclidean distances in the ARC chromatic components, making the color space suitable for data representation in the domain of color constancy. In this paper, we present an in-depth analysis of various properties of ARC: we document the variations in the numerical precisions of two alternative formulations of the ARC-to-RGB transformation and characterize how various perturbations in RGB impact the ARC representation. This was done empirically for the ARC diagram in a direct comparison against other commonly used chromaticity diagrams, and analytically for the ARC space with respect to its three components. We conclude by describing the color space in terms of perceptual uniformity, suggesting the need for new perceptual color metrics.

Computational color constancy algorithms are commonly evaluated only through angular error analysis on annotated datasets of static images. The widespread use of videos in consumer devices motivated us to define a richer methodology for color constancy evaluation. To this extent, temporal and spatial stability are defined here to determine the degree of sensitivity of color constancy algorithms to variations in the scene that do not depend on the illuminant source, such as moving subjects or a moving camera. Our evaluation methodology is applied to compare several color constancy algorithms on stable sequences belonging to the Gray Ball and Burst Color Constancy video datasets. The stable sequences, identified using a general-purpose procedure, are made available for public download to encourage future research. Our investigation proves the importance of evaluating color constancy algorithms according to multiple metrics, instead of angular error alone. For example, the popular fully convolutional color constancy with confidence-weighted pooling algorithm is consistently the best performing solution for error evaluation, but it is often surpassed in terms of stability by the traditional gray edge algorithm, and by the more recent sensor-independent illumination estimation algorithm.

We address the task of classifying car images at multiple levels of detail, ranging from the top-level car type, down to the specific car make, model, and year. We analyze existing datasets for car classification, and identify the CompCars as an excellent starting point for our task. We show that convolutional neural networks achieve an accuracy above 90% on the finest-level classification task. This high performance, however, is scarcely representative of real-world situations, as it is evaluated on a biased training/test split. In this work, we revisit the CompCars dataset by first defining a new training/test split, which better represents real-world scenarios by setting a more realistic baseline at 61% accuracy on the new test set. We also propagate the existing (but limited) type-level annotation to the entire dataset, and we finally provide a car-tight bounding box for each image, automatically defined through an ad hoc car detector. To evaluate this revisited dataset, we design and implement three different approaches to car classification, two of which exploit the hierarchical nature of car annotations. Our experiments show that higher-level classification in terms of car type positively impacts classification at a finer grain, now reaching 70% accuracy. The achieved performance constitutes a baseline benchmark for future research, and our enriched set of annotations is made available for public download.

Color constancy algorithms are typically evaluated with a statistical analysis of the recovery angular error and the reproduction angular error between the estimated and ground truth illuminants. Such analysis provides information about only the magnitude of the errors, and not about their chromatic properties. We propose an Angle-Retaining Chromaticity diagram (ARC) for the visual analysis of the estimated illuminants and the corresponding errors. We provide both quantitative and qualitative proof of the superiority of ARC in preserving angular distances compared to other chromaticity diagrams, making it possible to quantify the reproduction and recovery errors in terms of Euclidean distances on a plane. We present two case studies for the application of the ARC diagram in the visualization of the ground truth illuminants of color constancy datasets, and the visual analysis of error distributions of color constancy algorithms.

The province of Ontario has shown great commitment towards the development of renewable energy and, specifically, wind power. Fuelled by the Green Energy Act (GEA) of 2009, the Province has emerged as Canada's leader in wind energy development (WED). Nonetheless, Ontario's WED trajectory is characterized by social conflicts, particularly around environmental health. Utilizing the Social Amplification of Risk Framework, this paper presents an eight-year longitudinal media content analysis conducted to understand the role Ontario's media may be playing in both reflecting and shaping public perceptions of wind turbine health risks. We find that before and after the GEA, instances of health risk amplification were far greater than attenuations in both quantity and quality. Discourses that amplified turbine health risks often simultaneously highlighted injustices in the WED process, especially after the GEA. Based on these findings, we suggest that Ontario's media may be amplifying perceptions of wind turbine health risks within the public domain. We conclude with policy recommendations around public engagement for more just WED.

The objective of this study was to review environmental equity research on outdoor air pollution and, specifically, methods and tools used in research, published in English, with the aim of recommending the best methods and analytic tools. English language publications from 2000 to 2012 were identified in Google Scholar, Ovid MEDLINE, and PubMed. Research methodologies and results were reviewed and potential deficiencies and knowledge gaps identified. The publications show that exposure to outdoor air pollution differs by social factors, but findings are inconsistent in Canada. In terms of study designs, most were small and ecological and therefore prone to the ecological fallacy. Newer tools such as geographic information systems, modeling, and biomarkers offer improved precision in exposure measurement. Higher-quality research using large, individual-based samples and more precise analytic tools are needed to provide better evidence for policy-making to reduce environmental inequities.

Accreditation criteria by the Council on Education for Public Health (CEPH) state that prior to graduation, Masters of Public Health (MPH) students must demonstrate the application of knowledge and skills through a practice experience, commonly called the "Practicum." The purpose of this research was to review those MPH Practicum requirements. Practicum guidelines from US-based schools of public health that were accredited as of October 2011 were reviewed. Data on each Practicum's level of coordination, timing, and credit and contact hours as well as information about written agreements, preceptors, and how the Practicum was graded were collected. Seventy-four Practicums in 46 accredited schools of public health were reviewed. The majority (85 %) of accredited schools controlled the Practicum at the school-level. Among the Practicums reviewed, most did not require completion of any credit hours or the MPH core courses (57 and 74 %, respectively) prior to starting the Practicum; 82 % required written agreements; 60 % had stated criteria for the approval of preceptors; and 76 % required students to submit a product for grading at the conclusion of the Practicum. The results of this research demonstrate that the majority of accredited schools of public health designed Practicum requirements that reflect some of the criteria established by CEPH; however, issues related to timing, credit and contact hours, and preceptor qualifications vary considerably. We propose that a national dialogue begin among public health faculty and administrators to address these and other findings to standardize the Practicum experience for MPH students.

Land use regression (LUR) models have been widely used to characterize the spatial distribution of urban air pollution and estimate exposure in epidemiologic studies. However, spatial patterns of air pollution vary greatly between cities due to local source type and distribution. London, Ontario, Canada, is a medium-sized city with relatively few and isolated industrial point sources, which allowed the study to focus on the contribution of different transportation sectors to urban air pollution. This study used LUR models to estimate the spatial distribution of nitrogen dioxide (NO2) and to identify local sources influencing NO2 concentrations in London, ON. Passive air sampling was conducted at 50 locations throughout London over a 2-week period in May-June 2010. NO2 concentrations at the monitored locations ranged from 2.8 to 8.9 ppb, with a median of 5.2 ppb. Industrial land use, dwelling density, distance to highway, traffic density, and length of railways were significant predictors of NO2 concentrations in the final LUR model, which explained 78% of NO2 variability in London. Traffic and dwelling density explained most of the variation in NO2 concentrations, which is consistent with LUR models developed in other Canadian cities. We also observed the importance of local characteristics. Specifically, 17% of the variation was explained by distance to highways, which included the impacts of heavily traveled corridors transecting the southern periphery of the city. Two large railway yards and railway lines throughout central areas of the city explained 9% of NO2 variability. These results confirm the importance of traditional LUR variables and highlight the importance of including a broader array of local sources in LUR modeling. Finally, future analyses will use the model developed in this study to investigate the association between ambient air pollution and cardiovascular disease outcomes, including plaque burden, cholesterol, and hypertension.

Land use regression (LUR) is a method for predicting the spatial distribution of traffic-related air pollution. To facilitate risk and exposure assessment, and the design of future monitoring networks and sampling campaigns, we sought to determine the extent to which LUR can be used to predict spatial patterns in air pollution in the absence of dedicated measurements. We evaluate the transferability of one LUR model to two other geographically comparable areas with similar climates and pollution types. The source model, developed in 2003 to estimate ambient nitrogen dioxide (NO(2)) concentrations in Vancouver (BC, Canada) was applied to Victoria (BC, Canada) and Seattle (WA, USA). Model estimates were compared with measurements made with Ogawa passive samplers in both cities. As part of this study, 42 locations were sampled in Victoria for a 2-week period in June 2006. Data obtained for Seattle were collected for a different project at 26 locations in March 2005. We used simple linear regression to evaluate the fit of the source model under three scenarios: (1) using the same variables and coefficients as the source model; (2) using the same variables as the source model, but calculating new coefficients for local calibration; and (3) developing site-specific equations with new variables and coefficients. In Scenario 1, we found that the source model had a better fit in Victoria (R(2)=0.51) than in Seattle (R(2)=0.33). Scenario 2 produced improved R(2)-values in both cities (Victoria=0.58, Seattle=0.65), with further improvement achieved under Scenario 3 (Victoria=0.61, Seattle=0.72). Although it is possible to transfer LUR models between geographically similar cities, success may depend on the between-city consistency of the input data. Modest field sampling campaigns for location-specific model calibration can help to produce transfer models that are equally as predictive as their sources.

The advent of spatial analysis and geographic information systems (GIS) has led to studies of chronic exposure and health effects based on the rationale that intra-urban variations in ambient air pollution concentrations are as great as inter-urban differences. Such studies typically rely on local spatial covariates (e.g., traffic, land use type) derived from circular areas (buffers) to predict concentrations/exposures at receptor sites, as a means of averaging the annual net effect of meteorological influences (i.e., wind speed, wind direction and insolation). This is the approach taken in the now popular land use regression (LUR) method. However spatial studies of chronic exposures and temporal studies of acute exposures have not been adequately integrated. This paper presents an innovative LUR method implemented in a GIS environment that reflects both temporal and spatial variability and considers the role of meteorology. The new source area LUR integrates wind speed, wind direction and cloud cover/insolation to estimate hourly nitric oxide (NO) and nitrogen dioxide (NO(2)) concentrations from land use types (i.e., road network, commercial land use) and these concentrations are then used as covariates to regress against NO and NO(2) measurements at various receptor sites across the Vancouver region and compared directly with estimates from a regular LUR. The results show that, when variability in seasonal concentration measurements is present, the source area LUR or SA-LUR model is a better option for concentration estimation.

The purpose of this paper is to demonstrate how to develop an air pollution monitoring network to characterize small-area spatial contrasts in ambient air pollution concentrations. Using residential woodburning emissions as our case study, this paper reports on the first three stages of a four-stage protocol to measure, estimate, and validate ambient residential woodsmoke emissions in Vancouver, British Columbia. The first step is to develop an initial winter nighttime woodsmoke emissions surface using inverse-distance weighting of emissions information from consumer woodburning surveys and property assessment data. Second, fireplace density and a compound topographic index based on hydrological flow regimes are used to enhance the emissions surface. Third, the spatial variation of the surface is used in a location-allocation algorithm to design a network of samplers for the woodsmoke tracer compound levoglucosan and fine particulate matter. Measurements at these network sites are then used in the fourth stage of the protocol (not presented here): a mobile sampling campaign aimed at developing a high-resolution surface of woodsmoke concentrations for exposure assignment in health effects studies. Overall the results show that relatively simple data inputs and spatial analysis can be effective in capturing the spatial variability of ambient air pollution emissions and concentrations.

In many urban areas, residential wood burning is a significant wintertime source of PM2.5. In this study, we used a combination of fixed and mobile monitoring along with a novel spatial buffering procedure to estimate the spatial patterns of woodsmoke. Two-week average PM2.5 and levoglucosan (a marker for wood smoke) concentrations were concurrently measured at upto seven sites in the study region. In addition, pre-selected routes spanning the major population areas in and around Vancouver, B.C. were traversed during 19 cold, clear winter evenings from November, 2004 to March, 2005 by a vehicle equipped with GPS receiver and a nephelometer. Fifteen-second-average values of light scattering coefficient (bsp) were adjusted for variations between evenings and then combined into a single, highly resolved map of nighttime winter bsp levels. A relatively simple but robust (R(2) = 0.64) land use regression model was developed using selected spatial covariates to predict these temporally adjusted bsp values. The bsp values predicted by this model were also correlated with the measured average levoglucosan concentrations at our fixed site locations (R(2) = 0.66). This model, the first application of land use regression for woodsmoke, enabled the identification and prediction of previously unrecognized high woodsmoke regions within an urban airshed.

The environmental justice literature faces a number of conceptual and methodological shortcomings. The purpose of this paper is to probe ways in which these shortcomings can be remedied via recent developments in related literatures: population health and air pollution epidemiology. More sophisticated treatment of social structure, particularly if based on Pierre Bourdieu's relational approach to forms of capital, can be combined with the methodological rigour and established biological pathways of air pollution epidemiology. The aim is to reformulate environmental justice research in order to make further meaningful contributions to the wider movement concerned with issues of social justice and equity in health research.

In a review of the multilevel modelling literature (MLM) we find that data on individuals and their social environment contexts (neighbourhoods, municipalities) are often drawn from different years/time periods. This temporal mismatch has scarcely attracted any attention though it can significantly influence modelling results and interpretation. We demonstrate the influence of temporal mismatch first by outlining the degree of neighbourhood mobility in large metropolitan areas in Britain, Canada and the United States and second with a brief MLM example. We conclude that researchers ought to provide more study context when such mismatch is unavoidable.

Scientists and policymakers have shown growing interest in the health effects of chronic air pollution exposure. In this study, we use geostatistical techniques in combination with small-area data to address a central research question: "Does chronic exposure to particulate air pollution significantly associate with mortality when the effects of other social, demographic, and lifestyle confounders are taken into account?" Our analysis relies on age-standardized mortality ratios for census tracts (CTs) of Hamilton (average population of 3419 persons), social and demographic data from the 1991 Census of Canada, smoking variables extracted from secondary surveys, and total suspended particulate (TSP) data from 23 monitoring stations operated by the Ministry of the Environment. Air pollution data are interpolated with a geostatistical procedure known as "kriging". This method translates fixed-site pollution monitoring observations into a continuous surface, which was overlaid onto the population-weighted centroids of the CTs. Our results show substantively large and statistically significant health effects for women and men. Evaluated over the inter-quartile range of the data, we found the relative risk of premature mortality for TSP exposure to be 1.19 (95% CI: 1.13-1.26) for women and 1.30 (95% CI: 1.24-1.37) for men. We also tested associations with cardio-respiratory and cancer mortality. We found positive, significant associations between particulate exposure and these causes of death in most models. Inclusion of socioeconomic, demographic, and lifestyle reduced but did not eliminate the health effects of exposure to particulate air pollution. Overall our results suggest that intra-urban variations in particulate air pollution significantly associate with premature, all-cause, cardio-respiratory, and cancer mortality in small areas of Hamilton.