Resiliencia organizacional y capital psicológico

Desde la noción universal sobre la empresa como un sistema de interacción con un entorno determinado para alcanzar un objetivo, de manera planificada y en función de satisfacer las demandas de un mercado mediante la actividad económica, su viabilidad, sostenibilidad y crecimiento dependerán, por supuesto, de una serie de estrategias adecuadas no solo para tales fines, sino también para enfrentar diversidad de agentes endógenos y exógenos que puedan afectar el normal desempeño de su gestión. Estamos hablando de la importancia de la resiliencia organizacional y del Capital Psicológico. En un escenario tan impredecible como el de la economía mundial, donde la constante son los cambios en su comportamiento —unos propios de su dinámica e interdependencia, naturales de fenómenos como la globalización, y otros derivados de eventos disruptivos— hoy más que nunca es necesario implementar el modelo de la empresa resiliente, que es aquella entidad capaz de adaptarse y recuperarse frente a una perturbación. Al mismo tiempo, más allá de su tamaño, naturaleza u objeto social, es indispensable reconocer básicamente que toda organización está constituida por personas, lo cual implica la trascendencia que para su funcionamiento tiene el factor humano-dependiente, y por lo tanto se crea la necesidad de promover el Capital Psicológico y la resiliencia a nivel de las organizaciones a través de una cultura empresarial.

Riesgo de accidentes biológicos en estudiantes de la salud: revisión de la literatura en los últimos 14 años.

Introducción: Todos los trabajadores del área de la salud están en riesgo de padecer un accidente biológico. No obstante los estudiantes de estas aéreas, pueden presentar más riesgo porque apenas están en formación y no tienen la práctica o experiencia suficiente. Existen varios artículos que han estudiado la incidencia y prevalencia de accidentes biológicos en los trabajadores del área de la salud, Sin embargo, sobre esta problemática de la población estudiantil del área de la salud, se encuentra menos literatura. Por lo tanto con esta revisión sistemática se busca analizar y actualizar este tema. Métodos: Se realizó una revisión de la literatura científica de artículos publicados en los últimos 14 años, en relación con la prevalencia de accidentes biológicos en estudiantes de medicina, odontología, enfermería y residentes del área de la salud a nivel mundial. Se llevó a cabo la búsqueda en la base de datos de Pubmed, encontrando un total de 100 artículos, escritos en inglés, francés, español o portugués. Resultados: Las prevalencias encontradas sobre accidentes biológicos en estudiantes fueron las siguientes: en países europeos a nivel de enfermería los valores oscilan entre 10.2 % a 32%, en medicina fueron del 16%-58.8%, y en odontología del 21 %. En países asiáticos, se encontró que en enfermería el porcentaje varía de 49%-96 %, en medicina van del 35% -68%, y en odontología varia de 68.a 75.4%. En Norte América, en medicina las cifras fluctúan alrededor del 11-72.7 % y en odontología giran alrededor del 19.1%. Finalmente respecto a Suramérica la prevalencia fue de 31.2 a 46.7% en medicina, y del 40% en enfermería. Conclusiones: Por lo anterior se pudo concluir que, la prevalencia de accidentes biológicos en los estudiantes del área de la salud es elevada y varía según el continente en el que se encuentren.

Knowledge-based modelling and simulation of operational problems of microbiological origin in wastewater treatment plants

The activated sludge and anaerobic digestion processes have been modelled in widely accepted models. Nevertheless, these models still have limitations when describing operational problems of microbiological origin. The aim of this thesis is to develop a knowledge-based model to simulate risk of plant-wide operational problems of microbiological origin.For the risk model heuristic knowledge from experts and literature was implemented in a rule-based system. Using fuzzy logic, the system can infer a risk index for the main operational problems of microbiological origin (i.e. filamentous bulking, biological foaming, rising sludge and deflocculation). To show the results of the risk model, it was implemented in the Benchmark Simulation Models. This allowed to study the risk model's response in different scenarios and control strategies. The risk model has shown to be really useful providing a third criterion to evaluate control strategies apart from the economical and environmental criteria.

Comparison of subcellular partitioning, distribution, and internal speciation of Cu between Cu-tolerant and naive populations of Dendrodrilus rubidus Savigny

When considering contaminated site ecology and ecological risk assessment a key question is whether organisms that appear unaffected by accumulation of contaminants are tolerant or resistant to those contaminants. A population of Dendrodrilus rubidus Savigny earthworms from the Coniston Copper Mines, an area of former Cu mining, exhibit increased tolerance and accumulation of Cu relative to a nearby non-Cu exposed population. Distribution of total Cu between different body parts (posterior, anterior, body wall) of the two populations was determined after a 14 day exposure to 250 mg Cu kg(-1) in Cu-amended soil. Cu concentrations were greater in Coniston earthworms but relative proportions of Cu in different body parts were the same between populations. Cu speciation was determined using extended X-ray absorption fine structure spectroscopy (EXAFS). Cu was coordinated to 0 atoms in the exposure soil but to S atoms in the earthworms. There was no difference in this speciation between the different earthworm populations. In another experiment earthworms were exposed to a range of Cu concentrations (200-700 mg Cu kg(-1)). Subcellular partitioning of accumulated Cu was determined. Coniston earthworms accumulated more Cu but relative proportions of Cu in the different fractions (cytosol > granular > tissue fragments, cell membranes, and intact cells) were the same between populations. Results suggest that Coniston D. rubidus are able to survive in the Cu-rich Coniston Copper Mines soil through enlargement of the same Cu storage reservoirs that exist in a nearby non-Cu exposed population.

Plant uptake of non-ionic organic chemicals

Plant uptake of organic chemicals is an important process when considering the risks associated with land contamination, the role of vegetation in the global cycling of persistent organic pollutants, and the potential for industrial discharges to contaminate the food chain. There have been some significant advances in our understanding of the processes of plant uptake of organic chemicals in recent years; most notably there is now a better understanding of the air to plant transfer pathway, which may be significant for a number of industrial chemicals. This review identifies the key processes involved in the plant uptake of organic chemicals including those for which there is currently little information, e.g., plant lipid content and plant metabolism. One of the principal findings is that although a number of predictive models exist using established relationships, these require further validation if they are to be considered sufficiently robust for the purposes of contaminated land risk assessment or for prediction of the global cycling of persistent organic pollutants. Finally, a number of processes are identified which should be the focus of future research

Measuring and modelling mixture toxicity of imidacloprid and thiacloprid on Caenorhabditis elegans and Eisenia fetida

While the standard models of concentration addition and independent action predict overall toxicity of multicomponent mixtures reasonably, interactions may limit the predictive capability when a few compounds dominate a mixture. This study was conducted to test if statistically significant systematic deviations from concentration addition (i.e. synergism/antagonism, dose ratio- or dose level-dependency) occur when two taxonomically unrelated species, the earthworm Eisenia fetida and the nematode Caenorhabditis elegans were exposed to a full range of mixtures of the similar acting neonicotinoid pesticides imidacloprid and thiacloprid. The effect of the mixtures on C. elegans was described significantly better (p<0.01) by a dose level-dependent deviation from the concentration addition model than by the reference model alone, while the reference model description of the effects on E. fetida could not be significantly improved. These results highlight that deviations from concentration addition are possible even with similar acting compounds, but that the nature of such deviations are species dependent. For improving ecological risk assessment of simple mixtures, this implies that the concentration addition model may need to be used in a probabilistic context, rather than in its traditional deterministic manner. Crown Copyright (C) 2008 Published by Elsevier Inc. All rights reserved.

Evaluating the risk of non-point source pollution from biosolids: integrated modelling of nutrient losses at field and catchment scales

A semi-distributed model, INCA, has been developed to determine the fate and distribution of nutrients in terrestrial and aquatic systems. The model simulates nitrogen and phosphorus processes in soils, groundwaters and river systems and can be applied in a semi-distributed manner at a range of scales. In this study, the model has been applied at field to sub-catchment to whole catchment scale to evaluate the behaviour of biosolid-derived losses of P in agricultural systems. It is shown that process-based models such as INCA, applied at a wide range of scales, reproduce field and catchment behaviour satisfactorily. The INCA model can also be used to generate generic information for risk assessment. By adjusting three key variables: biosolid application rates, the hydrological connectivity of the catchment and the initial P-status of the soils within the model, a matrix of P loss rates can be generated to evaluate the behaviour of the model and, hence, of the catchment system. The results, which indicate the sensitivity of the catchment to flow paths, to application rates and to initial soil conditions, have been incorporated into a Nutrient Export Risk Matrix (NERM).

Hybridization between Brassica napus and B. rapa on a national scale in the United Kingdom

Measures blocking hybridization would prevent or reduce biotic or environmental change caused by gene flow from genetically modified (GM) crops to wild relatives. The efficacy of any such measure depends on hybrid numbers within the legislative region over the life-span of the GM cultivar. We present a national assessment of hybridization between rapeseed (Brassica napus) and B. rapa from a combination of sources, including population surveys, remote sensing, pollen dispersal profiles, herbarium data, local Floras, and other floristic databases. Across the United Kingdom, we estimate that 32,000 hybrids form annually in waterside B. rapa populations, whereas the less abundant weedy populations contain 17,000 hybrids. These findings set targets for strategies to eliminate hybridization and represent the first step toward quantitative risk assessment on a national scale.

Plants growing on contaminated and brownfield sites appropriate for use in Organisation for Economic Co-operation and Development terrestrial plant growth test

The Organisation for Economic Co-operation and Development (OECD) Terrestrial plant test is often used for the ecological risk assessment of contaminated land. However, its origins in plant protection product testing mean that the species recommended in the OECD guidelines are unlikely to occur on contaminated land. Six alternative species were tested on contaminated soils from a former Zn smelter and a metal fragmentizer with elevated concentrations of Cd, Cu, Pb, and Zn. The response of the alternative species was compared to two species recommended by the OECD; Lolium perenne (perennial ryegrass) and Trifolium pratense (red clover). Urtica dioica (stinging nettle) and Poa annua (annual meadow-grass) had low emergence rates in the control soil so may be considered unsuitable. Festuca rubra (chewings fescue), Holcus lanatus (Yorkshire fog), Senecio vulgaris (common groundsel), and Verbascum thapsus (great mullein) offer good alternatives to the OECD species. In particular, H. lanatus and S. vulgaris were more sensitive to the soils with moderate concentrations of Cd, Cu, Pb, and Zn than the OECD species.

The extrapolation problem and how population modeling can help

We argue that population modeling can add value to ecological risk assessment by reducing uncertainty when extrapolating from ecotoxicological observations to relevant ecological effects. We review other methods of extrapolation, ranging from application factors to species sensitivity distributions to suborganismal (biomarker and "-omics'') responses to quantitative structure activity relationships and model ecosystems, drawing attention to the limitations of each. We suggest a simple classification of population models and critically examine each model in an extrapolation context. We conclude that population models have the potential for adding value to ecological risk assessment by incorporating better understanding of the links between individual responses and population size and structure and by incorporating greater levels of ecological complexity. A number of issues, however, need to be addressed before such models are likely to become more widely used. In a science context, these involve challenges in parameterization, questions about appropriate levels of complexity, issues concerning how specific or general the models need to be, and the extent to which interactions through competition and trophic relationships can be easily incorporated.

Population-level assessment of risks of pesticides to birds and mammals in the UK

It is generally acknowledged that population-level assessments provide,I better measure of response to toxicants than assessments of individual-level effects. population-level assessments generally require the use of models to integrate potentially complex data about the effects of toxicants on life-history traits, and to provide a relevant measure of ecological impact. Building on excellent earlier reviews we here briefly outline the modelling options in population-level risk assessment. Modelling is used to calculate population endpoints from available data, which is often about Individual life histories, the ways that individuals interact with each other, the environment and other species, and the ways individuals are affected by pesticides. As population endpoints, we recommend the use of population abundance, population growth rate, and the chance of population persistence. We recommend two types of model: simple life-history models distinguishing two life-history stages, juveniles and adults; and spatially-explicit individual-based landscape models. Life-history models are very quick to set up and run, and they provide a great deal or insight. At the other extreme, individual-based landscape models provide the greatest verisimilitude, albeit at the cost of greatly increased complexity. We conclude with a discussion of the cations of the severe problems of parameterising models.

Entropic risk analysis by a high level decision support system (e-FDSS) for construction SMEs

The method of entropy has been useful in evaluating inconsistency on human judgments. This paper illustrates an entropy-based decision support system called e-FDSS to the solution of multicriterion risk and decision analysis in projects of construction small and medium enterprises (SMEs). It is optimized and solved by fuzzy logic, entropy, and genetic algorithms. A case study demonstrated the use of entropy in e-FDSS on analyzing multiple risk criteria in the predevelopment stage of SME projects. Survey data studying the degree of impact of selected project risk criteria on different projects were input into the system in order to evaluate the preidentified project risks in an impartial environment. Without taking into account the amount of uncertainty embedded in the evaluation process; the results showed that all decision vectors are indeed full of bias and the deviations of decisions are finally quantified providing a more objective decision and risk assessment profile to the stakeholders of projects in order to search and screen the most profitable projects.

A value chain approach to animal diseases risk management: technical foundations and practical framework for field application

Classical risk assessment approaches for animal diseases are influenced by the probability of release, exposure and consequences of a hazard affecting a livestock population. Once a pathogen enters into domestic livestock, potential risks of exposure and infection both to animals and people extend through a chain of economic activities related to producing, buying and selling of animals and products. Therefore, in order to understand economic drivers of animal diseases in different ecosystems and to come up with effective and efficient measures to manage disease risks from a country or region, the entire value chain and related markets for animal and product needs to be analysed to come out with practical and cost effective risk management options agreed by actors and players on those value chains. Value chain analysis enriches disease risk assessment providing a framework for interdisciplinary collaboration, which seems to be in increasing demand for problems concerning infectious livestock diseases. The best way to achieve this is to ensure that veterinary epidemiologists and social scientists work together throughout the process at all levels.

Migration, environmental change, and the ‘challenges of governance’

This paper provides a framework for the theme issue by exploring links between environmental change and human migration. We review evidence that demonstrates that millions of people have moved or are likely to move towards and not away from environmental risk and hazard by moving from rural areas to rapidly growing urban areas. Moreover, some people may choose not to move or be unable to move. Environmental change may further erode household resources in such a way that migration becomes less and not more likely, even in the context of quite significant environmental change posing serious threats to the sustainability of livelihoods. This creates the possibility that populations will be trapped in areas that expose them to serious risk. We argue that the links between environmental change, migration, and governance are of significant importance, and directly influence the modes and efficacy of migration governance at different levels.

Quantifying uncertainty in remotely sensed land cover maps

Remotely sensed land cover maps are increasingly used as inputs into environmental simulation models whose outputs inform decisions and policy-making. Risks associated with these decisions are dependent on model output uncertainty, which is in turn affected by the uncertainty of land cover inputs. This article presents a method of quantifying the uncertainty that results from potential mis-classification in remotely sensed land cover maps. In addition to quantifying uncertainty in the classification of individual pixels in the map, we also address the important case where land cover maps have been upscaled to a coarser grid to suit the users’ needs and are reported as proportions of land cover type. The approach is Bayesian and incorporates several layers of modelling but is straightforward to implement. First, we incorporate data in the confusion matrix derived from an independent field survey, and discuss the appropriate way to model such data. Second, we account for spatial correlation in the true land cover map, using the remotely sensed map as a prior. Third, spatial correlation in the mis-classification characteristics is induced by modelling their variance. The result is that we are able to simulate posterior means and variances for individual sites and the entire map using a simple Monte Carlo algorithm. The method is applied to the Land Cover Map 2000 for the region of England and Wales, a map used as an input into a current dynamic carbon flux model.