Psychosocial analysis of policy confidence through multifactorial statistics

Trust is defined as widespread belief or rooted value orientation in evaluative standards of technical and ethical competence, and in the future actions of a person (interpersonal trust) or an institution (institutional trust).From a psychosocial perspective, trust transcends positive or negative affectivity, alludes to the belief that the behavior of others can be predicted and implies a positive attitude and expectation regarding the behavior of the person or institution. This belief refers to the likelihood that individuals or institutions will take certain actions or refrain from inflicting harm, for the sake of personal or collective well-being. The objective of this study is to examine psychosocial factors related to the interaction between the police and the public that predict the perception of trust in police groups in Colombia

Big data marketing during the period 2012–2019: a bibliometric review

The present study identifies the most significant trends in production of high impact scientific papers related to the Big Data Marketing variable during the period between the years 2012 and 2019 through a revision of the Scopus database, which manages to highlight the relevance of 113 indexed papers. For this purpose, the following descriptive bibliometric indicators are implemented: production volume, type of document, number of citations, and country of application. In the studied time period, the evidence suggests an annual growth in the production volume of papers related to the variable, but with a significant drop in 2017. The knowledge areas that showcases more researches about the Big Data Marketing variable are computer science, mathematics, decision-making, and engineering domain

Effect on the demand and stock returns: cross-sectional of Big Data and time-series analysis

For reducing the degree of uncertainty caused by constant change in the environment, large, medium or small, private or public organizations must support their decisions in something more than experience or intuition; they must be supported by the development of accurate and reliable forecasts in order to meet the needs in the organization planning tasks. This case study presents a growing company dedicated to the storage of perishable products and incorporates time series forecasting techniques to estimate the volume of storage to foresee the requirements of additional facilities, personnel and materials needed for product mobility

Educational psychology in latin america: with linear hierarchical models

Research in clinical psychology, since its inception, has been aimed at analyzing, predicting and explaining the effect of treatments, by studying the change of patients in the course of them. To study the effects of therapy, research based on quantitative analysis models has historically used classical methods of parametric statistics, such as Pearson correlations, least squares regressions Student’s T-Tests and Variance Analysis (ANOVA). Hierarchical linear models (HLMs) represent a fundamental statistical strategy for research in psychotherapy, as they allow to overcome dependence on the observations usually presented in your data. The objective of this work is to present a guide to understanding, applying and reporting HLMs to study the effects of psychotherapy

The <i>Castalia</i> mission to Main Belt Comet 133P/Elst-Pizarro

We describe Castalia, a proposed mission to rendezvous with a Main Belt Comet (MBC), 133P/Elst-Pizarro. MBCs are a recently discovered population of apparently icy bodies within the main asteroid belt between Mars and Jupiter, which may represent the remnants of the population which supplied the early Earth with water. Castalia will perform the first exploration of this population by characterising 133P in detail, solving the puzzle of the MBC’s activity, and making the first in situ measurements of water in the asteroid belt. In many ways a successor to ESA’s highly successful Rosetta mission, Castalia will allow direct comparison between very different classes of comet, including measuring critical isotope ratios, plasma and dust properties. It will also feature the first radar system to visit a minor body, mapping the ice in the interior. Castalia was proposed, in slightly different versions, to the ESA M4 and M5 calls within the Cosmic Vision programme. We describe the science motivation for the mission, the measurements required to achieve the scientific goals, and the proposed instrument payload and spacecraft to achieve these

Basaltic Dyke with Specific Volcanogenic Structures and its geomorphic evolution: Unique Geoheritage of the Faroe Islands (North Atlantic Ocean)

Volcanic landforms resulting from Cenozoic volcanism represent the most peculiar features of global geodiversity and provide eminent narratives for geoeducation. Among them, however, relict volcanic forms and site-specific landforms in remote areas have received less attention. In this paper, we provide the first description of unique volcanogenic features (hereinafter referred to as pseudo-hieroglyphs) developed on a summit rock wall at the Sandfelli ridge near the village of Gjógv in the N Eysturoy Island (Faroe Islands). The geomorphic evolution of the ridge and rock wall during the Quaternary is described and detailed petrographic analyses of the volcanogenic features are provided. Based on observed petrographical features, we interpret the pseudo-hieroglyphs to probably represent unique examples of chaotic horizontal columnar jointing. Following the geomorphological and petrographic examination of the study site, we analyse current Faroese legislation aiming at nature conservation and use this case to discuss broader implications of geoheritage conservation and geotourism in distant regions.Vulkanická krajina vzniklá kenozoickým vulkanismem představuje nejpodivnější rysy globální geodiversity a poskytuje vynikající příběhy pro geovzdělávání. Méně pozornosti se však dostává reliktním vulkanickým formám a lokálním tvarům reliéfu v odlehlých oblastech. V tomto článku přinášíme první popis unikátních vulkanogenních tvarů (dále jen "pseudo-hieroglyfy"), které se vyvíjely na skalní stěně vrcholu na hřbetě Sandfelli u vesnice Gjógv v severní části ostrova Eysturoy (Faerské ostrovy). Je popsán geomorfologický vývoj hřbetu a skalní stěny během kvartéru a jsou uvedeny podrobné petrografické analýzy vulkanogenních vlastností. Na základě pozorovaných petrografických rysů interpretujeme pseudo-hieroglyfy, které pravděpodobně představují jedinečné příklady chaotické horizontální sloupcovité odlučnosti. Po geomorfologickém a petrografickém studiu zájmové lokality analyzujeme současnou faerskou legislativu zaměřenou na ochranu přírody a užíváme tohoto příkladu k diskusi širších důsledků zachování a geoturistiky geografického dědictví ve vzdálených regionech

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost