Bioclimatic modelling in the holocene and in future warming scenarios in Arbutus unedo L.

Growing forests wildfires in Portugal are an increasing concern since forests in the Mediterranean region are vulnerable to recent global warming. Long-term negative effects are expected on the vegetation with the coming increasing drought. The strawberry tree (Arbutus unedo L.) displays potential to be a successfully business-like cultured in several regions of Portugal and southern Europe, as it is well adapted to climate and soils. In Portugal, this species has been used by local populations particularly for spirit production and for fruit consumption, although it has different possible commercial uses, from processed and fresh fruit production to ornamental, pharmaceutical and chemical applications. In addition, due to its pioneer status, it is valuable for land recovery and desertification avoidance, besides being fire resistant. The available strawberry tree’s data is presence-only. For modelling purposes, a set of placements within the landscape of interest (Portugal) was applied. The species, observed in 318 plots, together with a vector of environmental covariates (7 bioclimatic attributes, slope and altitude) and a defined background were used for modeling purposes. Maxent 3.4.1 was the used software, where the estimated quantity is the probability of the presence of the species, conditioned on the environment. Maxent uses the environmental covariate data from the occurrence records and the background sample, to estimate the ratio between the conditional density of the covariates at the presence sites and the marginal (i.e., unconditional) density of covariates across the study area and so, estimating the relative suitability of one place vs. another. Three different climate scenarios (control run; 2050 and 2070) were tested for two emission scenarios (RCP 4.5 and RCP 8.5, WorldClim), besides the past, 6,000 BP (Mid-Holocene). The reduction of habitat suitable for this species is very significant in the southern regions, even for the best warming scenario (RCP 4.5) in 2050. Central and Northern mountain regions are predicted refuge for this species. Forest policies and management should consider the impact of climate change on the usable areas for forestry, seeing a case-study species particularly adapted to the Mediterranean regions and wildfires, such as strawberry tree. The distribution of the species in the Middle Holocene agrees with previous genetic and fossils studies in the region, which supported two putative refuges for the species since the Last Glacial Maximum and a cryptic refugia in the East-Central mountain region.info:eu-repo/semantics/publishedVersio

Enhancing water quality prediction for fluctuating missing data scenarios: A dynamic Bayesian network-based processing system to monitor cyanobacteria proliferation

Tackling the impact of missing data in water management is crucial to ensure the reliability of scientific research that informs decision-making processes in public health. The goal of this study is to ascertain the root causes associated with cyanobacteria proliferation under major missing data scenarios. For this purpose, a dynamic missing data management methodology is proposed using Bayesian Machine Learning for accurate surface water quality prediction of a river from Limia basin (Spain). The methodology used entails a sequence of analytical steps, starting with data pre-processing, followed by the selection of a reliable dynamic Bayesian missing value prediction system, leading finally to a supervised analysis of the behavioral patterns exhibited by cyanobacteria. For that, a total of 2,118,844 data points were used, with 205,316 (9.69 %) missing values identified. The machine learning testing showed the iterative structural expectation maximization (SEM) as the best performing algorithm, above the dynamic imputation (DI) and entropy-based dynamic imputation methods (EBDI), enhancing in some cases the accuracy of imputations by approximately 50 % in R2, RMSE, NRMSE, and logarithmic loss values. These findings can impact how data on water quality is being processed and studied, thus, opening the door for more reliable water management strategies that better inform public health decisionsAgencia Estatal de Investigación | Ref. PID2020-116013RB-I00Fundação para a Ciência e a Tecnologia | Ref. UIDB/04683/2020Fundação para a Ciência e a Tecnologia | Ref. UIDP/04683/202

Unpacking occupational health data in the service sector: From Bayesian networking and spatial clustering to policy-making

The health status of the service sector workforce is a significant unknown in the field of medical geography. While spatial epidemiology has made progress in predicting the relationship between human health and the environment, there are still important challenges that remain unsolved. The main issue lies in the inability to statistically determine and visually represent all spatial concepts, as there is a need to cover a wide range of service activities while also considering the impact of numerous traditional medical variables and emerging risk factors, such as those related to socioeconomic and bioclimatic factors. This study aims to address the needs of health professionals by defining, prioritizing, and visualizing multiple occupational health risk factors that contribute to the well-being of workers. To achieve this, a methodological approach based on the synergy of Bayesian machine learning and geostatistics is proposed. Extensive data from occupational health surveillance tests were collected in Spain, along with socioeconomic and bioclimatic covariates, to assess potential social and climate impacts on health. This integrated approach enabled the identification of relevant patterns related to risk factors. A three-step geostatistical modeling process, including variography, ordinary kriging, and G clustering, was used to generate national distribution maps for various factors such as annual mean temperature, annual rainfall, spine health, limb health, cholesterol, age, and sleep quality. These maps considered four target activities—administration, finances, education, and hospitality. Remarkably, bioclimatic variables were found to contribute approximately 9% to the overall health status of workersFundação para a Ciência e a Tecnologia | Ref. UIDB/00681/2020Fundação para a Ciência e a Tecnologia | Ref. UIDB/04683/2020Universidade de Vig

Differentiating between fatal and non-fatal mining accidents using artificial intelligence techniques

Using statistical methods for categorical data analysis, namely multiple correspondence analysis and Artificial Intelligence through Bayesian networks, we analysed a database of occupational mining accidents for Spain for the period 2004–2017 to identify the factors most associated with the occurrence of fatal and non-fatal accidents. The results obtained allow to shed light on the hidden patterns present in different work situations where accidents can have fatal consequences. In addition, this study exemplifies the application of statistical techniques suitable for Big Data and data-driven decision making in the mining sector.Xunta de Galicia | Ref. ED431C 2018/4

Análisis de datos y toma de decisiones en el sector minero

La minería es un sector de alto riesgo, ya que no sólo es difícil conocer el volumen extraíble de un metal o mineral, sino que también es difícil calcular el capital de inversión y humano requerido o el precio del producto antes de iniciar su extracción. Sin embargo, en los últimos tiempos se ha visto una creciente posibilidad de mejorar la eficiencia de este sector mediante el análisis de datos y la aplicación de técnicas matemáticas de simulación que permiten obtener explotaciones mineras más competitivas y respetuosas ambientalmente (Tarshizi et al., 2015). De esta manera, el objetivo del presente trabajo de investigación se centra en la aplicación de modelos matemáticos de optimización que permitan evaluar el factor aleatorio de las distintas variables involucradas en la actividad minera con el fin de dar solución a las elevadas exigencias técnicas y económicas actuales. Para llevar a cabo este trabajo, se parte de la necesidad de disponer de simulaciones numéricas aplicadas a minería cada vez más precisas que permitan optimizar la eficacia y la seguridad de los movimientos de tierras, la ejecución de voladuras o las técnicas de mantenimiento. De este modo, se tratará de optimizar aquellas situaciones en las que existen múltiples factores intervinientes difíciles de estimar y cuantificar. Un caso de elevado interés se corresponde con la optimización de las técnicas de mantenimiento en maquinaria pesada empleada en explotación de minas y obra civil. Los principales constructores de maquinaria ofrecen con sus productos protocolos de mantenimiento que a menudo distan de la realidad de obra. Para afrontar esta tesitura, se aplican distintos modelos probabilísticos en base a al criterio técnico y a la experiencia del ingeniero con el fin de analizar cómo varían las decisiones en función de la estrategia tomada. Con el fin de conocer qué método se ajusta más a la realidad técnica, se implementan diferentes modelos de simulación matemática, tratando de obtener la bondad de cada estimador y evaluando qué repercusión económica y técnica supone para el contexto de obra minera. Ante este objetivo, se considera esencial analizar los historiales de trabajo, los datos de campo y toda la información disponible en empresas del sector sobre estas situaciones particulares de incertidumbre para poder optimizar de manera adecuada las ecuaciones que permiten resolver este tipo de problemas en minería. Para ello se recurrirá a la aplicación de la teoría de matemática de la información (Shannon, 1948) a través de la cual será posible analizar cuáles son las variables más relevantes en la consecución de ciertos objetivos, lo cual permitirá modificar las ecuaciones de la simulación, adaptándose de manera específica al caso particular de la simulación minera bajo estudio. Finalmente, una vez obtenidos los resultados de la simulación tras la optimización de los parámetros en las ecuaciones se desea analizar la actitud hacia el riesgo en la consecución de los objetivos. Esto garantizará la viabilidad necesaria de cualquier proyecto, algo que ya se está comenzando a aplicar en temas ambientales en minas de carbón (Yu et al., 2016). Para ello, se analizarán actitudes conservadoras, neutrales y proclives al riesgo dentro de los modelos matemáticos optimizados lo cual permitirá alcanzar resultados de especial interés acerca de los efectos que suponen las decisiones bajo riesgo, un campo ampliamente estudiado en el ámbito económico y dominado por la teoría prospectiva (Kahneman et al, 1979) según la cual existe una atracción por el riesgo en aquellas decisiones en las que existen pérdidas seguras de antemano. De esta manera, con este proyecto de investigación se trata de aplicar y demostrar de manera exitosa el empleo de las últimas técnicas en análisis de datos y simulación numérica en la mejora de la eficiencia sector minero, tratando de evaluar el riesgo empresarial que supone la decisión de implementar dichas soluciones en la práctica real.A minería é un sector de alto risco, xa que non so é difícil coñecer o volumen extraíble dun metal ou mineral, senón que tamén é difícil calcular o capital de investimento requerido ou o precio do produto antes de iniciar a súa extracción. Emporiso, nos últimos tempos tivo lugar unha crecente posibilidade de mellorar a eficiencia de este sector mediante a aplicación das novidosas técnicas de análise de datos e simulación numérica que permiten obter explotacións mineiras máis competitivas e respetuosas ambientalmente (Tarshizi et al., 2015). De este xeito, o obxectivo do presente traballo de investigación céntrase na aplicación de modelos matemáticos de optimización que permitan evaluar o factor aleatorio das distintas vaiables involucradas na actividade mineira co fin de dar solución aos elevados requerimentos técnicos e económicos actuais. Para levar isto a cabo, pártese da necesidade de dispor de simulacións numéricas aplicadas a minería cada vez máis precisas que permitan optimizar a eficacia e a seguridade dos movementos de terras, a execución de voaduras ou as tarefas de mantemento. Así, tratarase de optimizar aquelas situación nas que existen múltiples factores intervintes difícles de estimar e cuantificar. Un caso de elevado interese é o relativo á optimización das técnicas de mantemento en maquinaria pesada empleada na explotación de minas e obra civil. Os principais construtores de maquinaria ofrecen cos seus productos protocolos de mantemento que a miúdo nada teñen que ver coa realidade e complexidade dos traballos desenvoltos na obra. Para combater esta situación, aplícanse modelos probabilísticos axustados aos criterios dos expertos a fin de analizar como varían as decisións dos técnicos en función da estratexia tomada. Para coñecer o método máis fiable, execútanse diferentes modelos de simulación matemática, identificando a bonbade dos estimadores e analizando que repercusión económica e técnica supón a súa adopción para o contexto da obra mineira. Para cumplir estas tarefas, revisaranse os historiales de traballo, datos de campo e toda a información disponible en empresas do sector sobre estas situacións particulares de incertidume para poder optimizar de maneira axeitada as ecuacións que permiten resolver este tipo de problemas en minería. Ademais, empregarase a teoría matemática da información (Shannon, 1948) a través da cal é posible analizar cales son as variables máis relevantes na consecución de certos obxectivos, o cal permitirá adaptar as ecuacións de simulación ao caso particular baixo estudio. Finalmente, una vez obtidos os resultados das simulacións tras a optimizacións das ecuacións tratarase de analizar a actitude fronte o risco na consecución dos obxectivos. Isto permitirá garantir a viabilidade necesaria de calqueira proxecto, algo que xa se está comezando a aplicar en temas ambientais en minas de carbón (Yu et al., 2016). Atitudes conservadoras, neutrales e proclives ao risco analizaranse dentro dos modelos matemáticos optimizados o cal permitirá acadar resultados de especial interese sobre os efectos que suponen as decisión baixo risco, un campo amplamente estudiado en economía e dominado pola teoría prospetiva (Kahneman et al, 1979), segundo a cal existe una atracción polo risco en aquelas decisións nas que existen pérdidas seguras de antemán. En resumo, este proxecto de investigación tratará de aplicar e demostrar o éxito do empleo das últimas técnicas en análise de datos e simulación numérica na mellora da eficiencia do sector mineiro, tratando de evaluar o risco empresarial que supón a decisión de implementar ditas solucións na práctica real.Mining is a high-risk sector because it is difficult to know either the volume of mineral which is possible to extract or the investment or the human capital required prior to the commencement of the work. However, in the recent years a new possibility to improve the efficiency of this sector has appeared. The research and development of data analysis and the application of mathematical simulation techniques allow to obtain mining operations more competitive and environmentally friendly (Tarshizi et al., 2015). For these reasons, the goal of this research work is based on the application of data analysis and mathematical methods of optimization that shed light into the randomness present in the main factors responsible for success of the manifold mining operations. To do this work, it is important to bear in mind the huge necessity of having advanced mathematical simulations that accurately reflect the performance and the safety of earthmovings, blasting operations or maintenance practices. Thus, the different situations in which several risk factors are involved will be analyzed and optimized in order to obtain illuminating results for the technical stuff. A great example of high relevance is the optimization of the maintenance practices in the heavy machinery employed in mines and civil works. The principal machinery constructors offer with its products a wide range of protocols for the maintenance execution. However, this protocols are far from the reality of field operations. To tackle this problem a set of probabilistic models are applied based on the engineer’s criteria and experience. These probabilistic models are evaluated by means of different mathematical methods of simulation trying to offer an answer about which model has the highest goodness-of-fit statistics and how its implementation may influence the technical and economical context. In this scenario, it is considered fundamental to analyze work histories, field data and any other available information from companies related to the mining sector. The revision of this information is expected to shed light about the uncertainty associated to the different factors required for the optimization of the models. Moreover, it is intended to make use of the information theory studies (Shannon, 1948) in order to evaluate and identify those risk factors that have a greater influence to the attainment of the objectives. Finally, once the simulation results are achieved after the optimization of the different models representing different mining scenarios it is intended to analyze the attitude towards risk. This approach will guarantee more consistency to the projects under this frame, something that has been newly introduced to coal mines due to environmental issues (Yu et al., 2016). Averse or conservative, neutral and risk-loving attitudes will be evaluated in the mathematical and probabilistic models trying to attach results that offer a new insight especially into decision making under risk in the mining sector. This is a field amply studied in economics and dominated by the prospect theory (Kahneman et al, 1979) according to which there exists an attraction for risk in those situations where loses are certain beforehand. To sum up, this research work tries to successfully employ data analysis and mathematical methods of simulation in order to improve the efficiency of the mining sector, offering a new insight about the benefits and the business risk regarding the implementation of these solutions in real mining scenarios

AI approaches to environmental impact assessments (EIAs) in the mining and metals sector using AutoML and Bayesian modeling

Mining engineers and environmental experts around the world still identify and evaluate environmental risks associated with mining activities using field-based, basic qualitative methods The main objective is to introduce an innovative AI-based approach for the construction of environmental impact assessment (EIA) indexes that statistically reflects and takes into account the relationships between the different environmental factors, finding relevant patterns in the data and minimizing the influence of human bias. For that, an AutoML process developed with Bayesian networks is applied to the construction of an interactive EIA index tool capable of assessing dynamically the potential environmental impacts of a slate mine in Galicia (Spain) surrounded by the Natura 2000 Network. The results obtained show the moderate environmental impact of the whole exploitation; however, the strong need to protect the environmental factors related to surface and subsurface runoff, species or soil degradation was identified, for which the information theory results point to a weight between 6 and 12 times greater than not influential variables

Modeling Bitcoin plus Ethereum as an open system of systems of public blockchains to improve their resilience against intentional risk

In this article, we model the two most market-capitalised public, open and permissionless blockchain implementations, Bitcoin (BTC) and Ethereum (ETH), as a System of Systems (SoS) of public blockchains. We study the concepts of blockchain, BTC, ETH, complex networks, SoS Engineering and intentional risk. We analyse BTC and ETH from an open SoS perspective through the main properties that seminal System of Systems Engineering (SoSE) references propose. This article demonstrates that these public blockchain implementations create networks that grow in complexity and connect with each other. We propose a methodology based on a complexity management lever such as SoSE to better understand public blockchains such as BTC and ETH and manage their evolution. Our ultimate objective is to improve the resilience of public blockchains against intentional risk: a key requirement for their mass adoption. We conclude with specific measures, based on this novel systems engineering approach, to effectively improve the resilience against intentional risk of the open SoS of public blockchains, composed of a non-inflationary money system, “sound money”, such as BTC, and of a world financial computer system, “a financial conduit”, such as ETH. The goal of this paper is to formulate a SoS that transfers digital value and aspires to position itself as a distributed alternative to the fiat currency-based financial system

Bioclimatic modeling in the Last Glacial Maximum, Mid-Holocene and facing future climatic changes in the strawberry tree (Arbutus unedo L.)

Increasing forest wildfires in Portugal remain a growing concern since forests in the Mediterranean region are vulnerable to recent global warming and reduction of precipitation. Therefore, a long-term negative effect is expected on the vegetation, with increasing drought and areas burnt by fires. The strawberry tree (Arbutus unedo L.) is particularly used in Portugal to produce a spirit by processing its fruits and is the main income for forestry owners. Other applications are possible due to the fruit and leaves’ anti-oxidant properties and bioactive compounds production, with a potential for clinical and food uses. It is a sclerophyllous plant, dry-adapted and fire resistant, enduring the Mediterranean climate, and recently considered as a possibility for afforestation, to intensify forest discontinuity where pines and eucalypts monoculture dominate the region. To improve our knowledge about the species’ spatial distribution we used 318 plots (the centroid of a 1 km2 square grid) measuring the species presence and nine environmental attributes. The seven bioclimatic variables most impacting on the species distribution and two topographic features, slope and altitude, were used. The past, current and future climate data were obtained through WorldClim. Finally, the vulnerability of the strawberry tree to the effects of global climate change was examined in the face of two emission scenarios (RCP 4.5 and 8.5), to predict distribution changes in the years 2050 and 2070, using a species distribution models (MaxEnt). The reduction of suitable habitat for this species is significant in the southern regions, considering the future scenarios of global warming. Central and northern mountainous regions are putative predicted refuges for this species. Forest policy and management should reflect the impact of climate change on the usable areas for forestry, particularly considering species adapted to the Mediterranean regions and wildfires, such as the strawberry tree. The distribution of the species in the Last Glacial Maximum (LGM) and Mid-Holocene (MH) agrees with previous genetic and paleontological studies in the region, which support putative refuges for the species. Two in the southern and coastal-central regions, since the LGM, and one in the east-central mountainous region, considered as cryptic refugia.Fundação para a Ciência e a Tecnologia | Ref. UID/AGR/00239/2013Fundação para a Ciência e a Tecnologia | Ref. SFRH/BSAB/113895/2015Fundação para a Ciência e a Tecnologia | Ref. UID/AMB/00681/2013Fundação para a Ciência e a Tecnologia | Ref. SFRH/BSAB/127907/2016Fundação para a Ciência e a Tecnologia | Ref. UID/AGR/00239/2013Fundação para a Ciência e a Tecnologia | Ref. UID/AGR/04129/2013Fundação para a Ciência e a Tecnologia | Ref. UID/AMB/00681/2013Fundação para a Ciência e a Tecnologia | Ref. UID/AMB/00681/201

A comparative analysis of health surveillance strategies for administrative video display terminal employees

Abstract Background The objective of this study was to develop a strategy to optimize medical health surveillance protocols for administrative employees using video display terminals (VDTs). A total of 2453 medical examinations were analysed for VDT users in various sectors. From these data, using Bayesian statistics we inferred which factors were most relevant to medical diagnosis of the main disorders affecting VDT users. This information was used to build an influence diagram to evaluate the time and monetary costs associated with each diagnostic test and define an optimal protocol strategy based on occupational risks. Results Musculoskeletal and ophthalmological diseases were identified as the most frequent disorders among VDT users. The Bayesian network inferred age, sleep quality, activity level, smoking and the consumption of alcohol as risk factors. The blood count was the most costly test (5.23 USD/employee) and the second most costly test in time terms (4 min/employee), yet is a diagnostic test that has little influence on the medical decision regarding an employee’s capacity to perform their job. Conclusions Current occupational health surveillance protocols for VDT users may lead to expenditure that is 54% greater than necessary. For many employees and employers, failure to perform a wide range of medical tests for occupational health surveillance purposes is subjectively perceived as a threat to health. Awareness needs to be raised of the appropriate role of different health areas, so as to optimize diagnostic efficiency on the basis of greater flexibility