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A statistical functional, such as the mean or the median, is called elicitable if there is a scoring function or loss function such that the correct forecast of the functional is the unique minimizer of the expected score. Such scoring functions are called strictly consistent for the functional. The elicitability of a functional opens the possibility to compare competing forecasts and to rank them in terms of their realized scores. In this paper, we explore the notion of elicitability for multi-dimensional functionals and give both necessary and sufficient conditions for strictly consistent scoring functions. We cover the case of functionals with elicitable components, but we also show that one-dimensional functionals that are not elicitable can be a component of a higher order elicitable functional. In the case of the variance, this is a known result. However, an important result of this paper is that spectral risk measures with a spectral measure with finite support are jointly elicitable if one adds the \"correct\" quantiles. A direct consequence of applied interest is that the pair (Value at Risk, Expected Shortfall) is jointly elicitable under mild conditions that are usually fulfilled in risk management applications.

We consider estimation of the parameters of a Gaussian Stochastic Process (GaSP), in the context of emulation (approximation) of computer models for which the outcomes are real-valued scalars. The main focus is on estimation of the GaSP parameters through various generalized maximum likelihood methods, mostly involving finding posterior modes; this is because full Bayesian analysis in computer model emulation is typically prohibitively expensive. The posterior modes that are studied arise from objective priors, such as the reference prior. These priors have been studied in the literature for the situation of an isotropic covariance function or under the assumption of separability in the design of inputs for model runs used in the GaSP construction. In this paper, we consider more general designs (e.g., a Latin Hypercube Design) with a class of commonly used anisotropic correlation functions, which can be written as a product of isotropic correlation functions, each having an unknown range parameter and a fixed roughness parameter. We discuss properties of the objective priors and marginal likelihoods for the parameters of the GaSP and establish the posterior propriety of the GaSP parameters, but our main focus is to demonstrate that certain parameterizations result in more robust estimation of the GaSP parameters than others, and that some parameterizations that are in common use should clearly be avoided. These results are applicable to many frequently used covariance functions, for example, power exponential, Matérn, rational quadratic and spherical covariance. We also generalize the results to the GaSP model with a nugget parameter. Both theoretical and numerical evidence is presented concerning the performance of the studied procedures.

Coriandrum sativum L. seeds are traditionally used to treat diabetes and its complications (inflammation and formation of reactive oxygen species) around the world. The present study investigates the antidiabetic, anti-inflammatory, and antioxidant effects of the polyphenol fraction of Coriandrum sativum seeds (PCS). Diabetic mice were orally administered with PCS (25 and 50 mg/kg b.w.) for 28 days. Oral glucose tolerance (OGTT) was also evaluated along with the anti-inflammatory effect, assessed by measuring paw edema development induced with carrageenan in Wistar rat and the antioxidant activity assessed using two tests (β-carotene discoloration and DPPH). Treatment of diabetic mice with PCS for four weeks managed their high fasting blood glucose levels, improved their overall health, also revealed an excellent antihyperlipidemic activity. The OGTT result showed a potent antihyperglycemic activity, and following the anti-inflammatory and antioxidant effects, the PCS exhibited a perfect activity. LC-MS/MS result revealed the presence of 9 polyphenols. This modest work indicates that the PCS have an important antidiabetic, antihyperglycemic, antihyperlipidemic, anti-inflammatory, and antioxidant effect that can be well established treatment of diabetes and its complications.

In recent years, the phytoconstituents of foods in the Mediterranean diet (MD) have been the subject of several studies for their beneficial effects on human health. The traditional MD is described as a diet heavy in vegetable oils, fruits, nuts, and fish. The most studied element of MD is undoubtedly olive oil due precisely to its beneficial properties that make it an object of interest. Several studies have attributed these protective effects to hydroxytyrosol (HT), the main polyphenol contained in olive oil and leaves. HT has been shown to be able to modulate the oxidative and inflammatory process in numerous chronic disorders, including intestinal and gastrointestinal pathologies. To date, there is no paper that summarizes the role of HT in these disorders. This review provides an overview of the anti-inflammatory and antioxidant proprieties of HT against intestinal and gastrointestinal diseases.

This paper examines how ambient noise, an important environmental variable, can affect creativity. Results from five experiments demonstrate that a moderate (70 dB) versus low (50 dB) level of ambient noise enhances performance on creative tasks and increases the buying likelihood of innovative products. A high level of noise (85 dB), on the other hand, hurts creativity. Process measures reveal that a moderate (vs. low) level of noise increases processing difficulty, inducing a higher construal level and thus promoting abstract processing, which subsequently leads to higher creativity. A high level of noise, however, reduces the extent of information processing and thus impairs creativity.

The objective was to review the effects of the drying and storage conditions of corn on the physical–chemical quality in the processing of starch and flour, in the production of animal feed, and in the industrialization of ethanol. Initially, the review presented an overview of the post-harvest stages of corn grains, highlighting drying and storage. The main drying and storage methods used for corn grains were presented. Among the drying conditions, the air temperature was the main factor that affected the properties of starch, flour, feed, and ethanol produced from corn. It was verified that the corn grains submitted to drying at temperatures below 60 °C obtained better results in the industry. In storage, in addition to the storage time, factors such as temperature and moisture content of the grains affected the physical–chemical quality of the processed products. In this stage, the moisture content below 14% and the storage temperature below 25 °C conserved the physical–chemical quality of the grains and obtained better processing results. Further studies are needed to assess the effects of the drying and storage conditions of corn on the properties of flour, starch, animal feed, and, mainly, ethanol production.

This paper presents a paragraph of my thesis whose guiding thread is the theme of language in Heidegger, and which advances two basic claims: 1) Being and Time is an unfinished book and it is thus in the understanding of the planetary achievement of “nihilism” – i.e., of “technique” – that this work from 1927 assumes its whole meaning; and 2) that said, Heidegger’s work, taken as a whole, is a cohesive work that aims at overcoming “nihilism” understood originarily as “forgetfulness of being”. This overcoming is therefore achieved in two stages: 1) the understanding of the phenomenon of “being” arising from the transcendental understanding of the “world”; and 2) the overcoming of that transcendental understanding of the “world” in the full understanding of the phenomenon of “being” as “history,” a process in which the dialogue with poetry will prove to be decisive. This paper emphasizes one aspect of that evolution of Heidegger’s thought “in dialogue with Heidegger,” showing how the understanding of “Ereignis” allows us to conciliate the understanding of the concept of “Entschlossenheit,” presented in Being and Time, with the concept of “Gelassenheit,” that is central in the second stage of Heidegger’s work.

Existing research reports inconsistent findings with regard to the effect of color on cognitive task performances. Some research suggests that blue or green leads to better performances than red; other studies record the opposite. Current work reconciles this discrepancy. We demonstrate that red (versus blue) color induces primarily an avoidance (versus approach) motivation (study 1, n = 69) and that red enhances performance on a detail-oriented task, whereas blue enhances performance on a creative task (studies 2 and 3, n = 208 and 118). Further, we replicate these results in the domains of product design (study 4, n = 42) and persuasive message evaluation (study 5, n = 161) and show that these effects occur outside of individuals' consciousness (study 6, n = 68). We also provide process evidence suggesting that the activation of alternative motivations mediates the effect of color on cognitive task performances.

Carbon dioxide and methane emissions are major greenhouse gases contributing to global warming, thus calling for rapid techniques of sequestration. For instance, dry reforming of methane transforms CO2 and CH4 into syngas, a mixture of H2 and CO, yet the reaction catalyst becomes inactivated by carbon formation and metal sintering. Here, we review catalytic systems used for dry reforming of methane. Improved catalysts of high catalytic performance and stability are obtained by selecting the active metal, supporting materials, promoters and preparation techniques. We found a strong correlation between the support morphology, physicochemical properties and catalytic performances. In particular, fibrous structures show optimal metal–support interaction, distribution, particle size, basicity, storage of oxygen space, surface area and porosity, resulting in high performance of anti-coking and anti-sintering.

Distribution forecasts P over future quantities or events are routinely made in hydrology but usually traded for a (likelihood‐weighted) mean or median prediction to accommodate error measures or scoring functions such as the mean absolute error or mean squared error. Case in point is the so‐called KG efficiency (KGE) of Gupta et al. (2009, https://doi.org/10.1016/j.jhydrol.2009.08.003) and improvements thereof (Lamontagne et al., 2020, https://doi.org/10.1029/2020wr027101), which have rapidly gained popularity among hydrologists as alternative scoring functions to the commonly used Nash and Sutcliffe (1970, https://doi.org/10.1016/0022‐1694(70)90255‐6) efficiency, but are equally exclusive in how they quantify model performance using only single‐valued output of the quantities of interest. This point‐valued mapping necessarily implies a loss of information about model performance. This paper advocates the use of probabilistic watershed model training, evaluation and diagnostics. Distribution evaluation opens a mature literature on scoring rules whose strong statistical underpinning provides, as we will demonstrate, the theory, context and guidelines necessary for the development of robust information‐theoretically principled metrics for watershed signatures. These so‐called hydrograph functionals are scalar‐valued mappings of major behavioral watershed functions embodied in a strictly proper scoring rule. We discuss past developments that led to the current state‐of‐the‐art of distribution evaluation in hydrology and review scoring rules for dichotomous and categorical events, quantiles (intervals) and density forecasts. We are particularly concerned with elicitable functionals and scoring rule propriety, discuss the decomposition of scoring rules into a sharpness, reliability and entropy term and present diagnostically appealing strictly proper divergence scores of hydrograph functionals for flood frequency analysis, flow duration and recession curves. The usefulness and power of distribution‐based model evaluation and diagnostics by means of scoring rules is demonstrated on simple illustrative problems and discharge distributions simulated with watershed models using random sampling and Bayesian model averaging. The presented theory (a) enables a more complete evaluation of distribution forecasts, (b) offers a statistically principled means for watershed model training, evaluation, diagnostics and selection using hydrograph functionals and/or extreme events and (c) provides a universal framework for metric development of watershed signatures, promoting metric standardization and reproducibility. Plain Language Summary The past decades have witnessed an unbridled growth in goodness‐of‐fit metrics of hydrologic models. These metrics may satisfy the needs of hydrologists but lack conforming theory and principles. This state of affairs (a) elicits improper model training and evaluation, (b) provokes and supports misguided inferences, (c) impedes statistically‐principled uncertainty quantification, metric standardization and development of universal model benchmarks and (d) obfuscates determination of whether the model has finished learning. What is more, most hydrologic model evaluation metrics in use today are rather exclusive in how they quantify model performance using only single‐valued simulated output of the quantities of interest. Predictive distributions derived from (quasi)‐Bayesian methods or ensembles are usually traded for a (likelihood‐weighted) mean or median prediction to accommodate error measures (scoring functions) such as the mean absolute error. This implies a large loss of information. This paper develops a distribution‐based approach to hydrologic model evaluation and diagnostics. Distribution evaluation opens the necessary theory and guidelines for development of robust information‐theoretically principled metrics of watershed signatures. These so‐called hydrograph functionals are scalar‐valued mappings of major behavioral watershed functions embodied in a strictly proper scoring rule. The hydrograph functionals offer a statistically principled means for hydrologic model evaluation, diagnostics and selection. Key Points Scoring rules of hydrograph functionals provide an information‐theoretically principled means for watershed model training, evaluation, and diagnostics We present strictly proper (divergence) scores for flood frequency analysis, flow duration, and recession curves Propriety and elicitability offer useful working paradigms for metric development of hydrograph functionals