Why microalgal biofuels won't save the internal combustion machine

Proponents of microalgae biofuel technologies often claim that the world demand of liquid fuels, about 5 trillion liters per year, could be supplied by microalgae cultivated on only a few tens of millions of hectares. This perspective reviews this subject and points out that such projections are greatly exaggerated, because (1) the pro- ductivities achieved in large-scale commercial microalgae production systems, operated year-round, do not surpass those of irrigated tropical crops; (2) cultivating, harvesting and processing microalgae solely for the production of biofuels is simply too expensive using current or prospective technology; and (3) currently available (limited) data suggest that the energy balance of algal biofuels is very poor. Thus, microalgal biofuels are no panacea for depleting oil or global warming, and are unlikely to save the internal combustion machine.

Psychosocial analysis of the collective processes in the United States after September 11

This article studies alterations in the values, attitudes, and behaviors that emerged among U.S. citizens as a consequence of, and as a response to, the attacks of September 11, 2001. The study briefly examines the immediate reaction to the attack, before focusing on the collective reactions that characterized the behavior of the majority of the population between the events of 9/11 and the response to it in the form of intervention in Afghanistan. In studying this period an eight-phase sequential model (Botcharova, 2001) is used, where the initial phases center on the nation as the ingroup and the latter focus on the enemy who carried out the attack as the outgroup. The study is conducted from a psychosocial perspective and uses "social identity theory" (Tajfel & Turner, 1979, 1986) as the basic framework for interpreting and accounting for the collective reactions recorded. The main purpose of this paper is to show that the interpretation of these collective reactions is consistent with the postulates of social identity theory. The application of this theory provides a different and specific analysis of events. The study is based on data obtained from a variety of rigorous academic studies and opinion polls conducted in relation to the events of 9/11. In line with social identity theory, 9/11 had a marked impact on the importance attached by the majority of U.S. citizens to their identity as members of a nation. This in turn accentuated group differentiation and activated ingroup favoritism and outgroup discrimination (Tajfel & Turner, 1979, 1986). Ingroup favoritism strengthened group cohesion, feelings of solidarity, and identification with the most emblematic values of the U.S. nation, while outgroup discrimination induced U.S. citizens to conceive the enemy (al-Qaeda and its protectors) as the incarnation of evil, depersonalizing the group and venting their anger on it, and to give their backing to a military response, the eventual intervention in Afghanistan. Finally, and also in line with the postulates of social identity theory, as an alternative to the virtual bipolarization of the conflict (U.S. vs al-Qaeda), the activation of a higher level of identity in the ingroup is proposed, a group that includes the United States and the largest possible number of countries¿ including Islamic states¿in the search for a common, more legitimate and effective solution.

Lagrangian-Hamiltonian unified formalism for field theory

The RuskSkinner formalism was developed in order to give a geometrical unified formalism for describing mechanical systems. It incorporates all the characteristics of Lagrangian and Hamiltonian descriptions of these systems (including dynamical equations and solutions, constraints, Legendre map, evolution operators, equivalence, etc.). In this work we extend this unified framework to first-order classical field theories, and show how this description comprises the main features of the Lagrangian and Hamiltonian formalisms, both for the regular and singular cases. This formulation is a first step toward further applications in optimal control theory for partial differential equations. 2004 American Institute of Physics.

Psychosocial analysis of the collective processes in the United States after September 11

This article studies alterations in the values, attitudes, and behaviors that emerged among U.S. citizens as a consequence of, and as a response to, the attacks of September 11, 2001. The study briefly examines the immediate reaction to the attack, before focusing on the collective reactions that characterized the behavior of the majority of the population between the events of 9/11 and the response to it in the form of intervention in Afghanistan. In studying this period an eight-phase sequential model (Botcharova, 2001) is used, where the initial phases center on the nation as the ingroup and the latter focus on the enemy who carried out the attack as the outgroup. The study is conducted from a psychosocial perspective and uses "social identity theory" (Tajfel & Turner, 1979, 1986) as the basic framework for interpreting and accounting for the collective reactions recorded. The main purpose of this paper is to show that the interpretation of these collective reactions is consistent with the postulates of social identity theory. The application of this theory provides a different and specific analysis of events. The study is based on data obtained from a variety of rigorous academic studies and opinion polls conducted in relation to the events of 9/11. In line with social identity theory, 9/11 had a marked impact on the importance attached by the majority of U.S. citizens to their identity as members of a nation. This in turn accentuated group differentiation and activated ingroup favoritism and outgroup discrimination (Tajfel & Turner, 1979, 1986). Ingroup favoritism strengthened group cohesion, feelings of solidarity, and identification with the most emblematic values of the U.S. nation, while outgroup discrimination induced U.S. citizens to conceive the enemy (al-Qaeda and its protectors) as the incarnation of evil, depersonalizing the group and venting their anger on it, and to give their backing to a military response, the eventual intervention in Afghanistan. Finally, and also in line with the postulates of social identity theory, as an alternative to the virtual bipolarization of the conflict (U.S. vs al-Qaeda), the activation of a higher level of identity in the ingroup is proposed, a group that includes the United States and the largest possible number of countries¿ including Islamic states¿in the search for a common, more legitimate and effective solution.

Properties of a risk measure derived from ruin theory

This paper studies a risk measure inherited from ruin theory and investigates some of its properties. Specifically, we consider a value-at-risk (VaR)-type risk measure defined as the smallest initial capital needed to ensure that the ultimate ruin probability is less than a given level. This VaR-type risk measure turns out to be equivalent to the VaR of the maximal deficit of the ruin process in infinite time. A related Tail-VaR-type risk measure is also discussed.

A new aggregation method for strategic decision making and its application in assignment theory

A new aggregation method for decision making is presented by using induced aggregation operators and the index of maximum and minimum level. Its main advantage is that it can assess complex reordering processes in the aggregation that represent complex attitudinal characters of the decision maker such as psychological or personal factors. A wide range of properties and particular cases of this new approach are studied. A further generalization by using hybrid averages and immediate weights is also presented. The key issue in this approach against the previous model is that we can use the weighted average and the ordered weighted average in the same formulation. Thus, we are able to consider the subjective attitude and the degree of optimism of the decision maker in the decision process. The paper ends with an application in a decision making problem based on the use of the assignment theory.

The Uncertain generalized probabilistic weighted average and its application in the theory of expertons

A new model for dealing with decision making under risk by considering subjective and objective information in the same formulation is here presented. The uncertain probabilistic weighted average (UPWA) is also presented. Its main advantage is that it unifies the probability and the weighted average in the same formulation and considering the degree of importance that each case has in the analysis. Moreover, it is able to deal with uncertain environments represented in the form of interval numbers. We study some of its main properties and particular cases. The applicability of the UPWA is also studied and it is seen that it is very broad because all the previous studies that use the probability or the weighted average can be revised with this new approach. Focus is placed on a multi-person decision making problem regarding the selection of strategies by using the theory of expertons.

The social disembedding of business theory and practice. A (neo-)institutional analysis of the homo economicus and corporate social responsibility, and the inherent responsibility of business scholars

Aim Structure of the Thesis In the first article, I focus on the context in which the Homo Economicus was constructed - i.e., the conception of economic actors as fully rational, informed, egocentric, and profit-maximizing. I argue that the Homo Economicus theory was developed in a specific societal context with specific (partly tacit) values and norms. These norms have implicitly influenced the behavior of economic actors and have framed the interpretation of the Homo Economicus. Different factors however have weakened this implicit influence of the broader societal values and norms on economic actors. The result is an unbridled interpretation and application of the values and norms of the Homo Economicus in the business environment, and perhaps also in the broader society. In the second article, I show that the morality of many economic actors relies on isomorphism, i.e., the attempt to fit into the group by adopting the moral norms surrounding them. In consequence, if the norms prevailing in a specific group or context (such as a specific region or a specific industry) change, it can be expected that actors with an 'isomorphism morality' will also adapt their ethical thinking and their behavior -for the 'better' or for the 'worse'. The article further describes the process through which corporations could emancipate from the ethical norms prevailing in the broader society, and therefore develop an institution with specific norms and values. These norms mainly rely on mainstream business theories praising the economic actor's self-interest and neglecting moral reasoning. Moreover, because of isomorphism morality, many economic actors have changed their perception of ethics, and have abandoned the values prevailing in the broader society in order to adopt those of the economic theory. Finally, isomorphism morality also implies that these economic actors will change their morality again if the institutional context changes. The third article highlights the role and responsibility of business scholars in promoting a systematic reflection and self-critique of the business system and develops alternative models to fill the moral void of the business institution and its inherent legitimacy crisis. Indeed, the current business institution relies on assumptions such as scientific neutrality and specialization, which seem at least partly challenged by two factors. First, self-fulfilling prophecy provides scholars with an important (even if sometimes undesired) normative influence over practical life. Second, the increasing complexity of today's (socio-political) world and interactions between the different elements constituting our society question the strong specialization of science. For instance, economic theories are not unrelated to psychology or sociology, and economic actors influence socio-political structures and processes, e.g., through lobbying (Dobbs, 2006; Rondinelli, 2002), or through marketing which changes not only the way we consume, but more generally tries to instill a specific lifestyle (Cova, 2004; M. K. Hogg & Michell, 1996; McCracken, 1988; Muniz & O'Guinn, 2001). In consequence, business scholars are key actors in shaping both tomorrow's economic world and its broader context. A greater awareness of this influence might be a first step toward an increased feeling of civic responsibility and accountability for the models and theories developed or taught in business schools.

Recent advances in remote sensing image processing

Remote sensing image processing is nowadays a mature research area. The techniques developed in the field allow many real-life applications with great societal value. For instance, urban monitoring, fire detection or flood prediction can have a great impact on economical and environmental issues. To attain such objectives, the remote sensing community has turned into a multidisciplinary field of science that embraces physics, signal theory, computer science, electronics, and communications. From a machine learning and signal/image processing point of view, all the applications are tackled under specific formalisms, such as classification and clustering, regression and function approximation, image coding, restoration and enhancement, source unmixing, data fusion or feature selection and extraction. This paper serves as a survey of methods and applications, and reviews the last methodological advances in remote sensing image processing.

Graph theory reveals dysconnected hubs in 22q11DS and altered nodal efficiency in patients with hallucinations.

Schizophrenia is postulated to be the prototypical dysconnection disorder, in which hallucinations are the core symptom. Due to high heterogeneity in methodology across studies and the clinical phenotype, it remains unclear whether the structural brain dysconnection is global or focal and if clinical symptoms result from this dysconnection. In the present work, we attempt to clarify this issue by studying a population considered as a homogeneous genetic sub-type of schizophrenia, namely the 22q11.2 deletion syndrome (22q11.2DS). Cerebral MRIs were acquired for 46 patients and 48 age and gender matched controls (aged 6-26, respectively mean age = 15.20 ± 4.53 and 15.28 ± 4.35 years old). Using the Connectome mapper pipeline (connectomics.org) that combines structural and diffusion MRI, we created a whole brain network for each individual. Graph theory was used to quantify the global and local properties of the brain network organization for each participant. A global degree loss of 6% was found in patients' networks along with an increased Characteristic Path Length. After identifying and comparing hubs, a significant loss of degree in patients' hubs was found in 58% of the hubs. Based on Allen's brain network model for hallucinations, we explored the association between local efficiency and symptom severity. Negative correlations were found in the Broca's area (p < 0.004), the Wernicke area (p < 0.023) and a positive correlation was found in the dorsolateral prefrontal cortex (DLPFC) (p < 0.014). In line with the dysconnection findings in schizophrenia, our results provide preliminary evidence for a targeted alteration in the brain network hubs' organization in individuals with a genetic risk for schizophrenia. The study of specific disorganization in language, speech and thought regulation networks sharing similar network properties may help to understand their role in the hallucination mechanism.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

Diagnóstico de las dificultades de lectura y escritura y de la dislexia basado en la Teoría PASS de lainteligencia utilizando la batería DN-CAS. Origen cognitivo de la dislexia

Las dificultades de lectura y escritura se pueden detectar desde el momento en que los niños y niñas inician el aprendizaje de la lectoescritura en la etapa de Educación Infantil. En algunos casos estos alumnos reciben un apoyo escolar que en muchas ocasiones no conlleva las mejoras esperadas, siendo las técnicas y metodologías de refuerzo aplicadas ineficaces. El problema, desde nuestro punto de vista, empieza con el diagnóstico que se realiza a estos jóvenes, que determina las directrices de la intervención idónea en cada caso. La Teoría PASS de la inteligencia nos permite conocer qué procesos están implicados cuando el niño lee o escribe, y parte de la premisa de que si conocemos el perfil cognitivo de un alumno que presenta dificultades podremos entender como estas se originan. Para conocer este perfil cognitivo (los cuatro procesos cognitivos que describe esta teoría: Planificación, Atención, Simultaneo y Secuencial) utilizamos la batería DN-CAS (Das & Naglieri: Cognitive Assessment System). El perfil obtenido al aplicar el DN-CAS nos permitirá conocer el origen de las dificultades de lectura y escritura, saber cuando está justificada una dislexia, descartar problemas emocionales o la presencia de los mismos y diseñar la intervención más adecuada en cada situación