pinecone/core-2020-05-10-deduplication huggingface dataset:
Environment and Global Climate Change

This module is intended to convey a broad understanding of the nature of climate change and its potential impacts. Students will come to understand the effects of radiation imbalance in the Arctic, fluctuations in albedo, and ecological consequences of decreasing albedo in the Arctic. Upon completion of the module, they will be able to explain: the consequences of decreasing stratospheric ozone, potential hazards of POP's entering Arctic food chains, and the possible impacts of environmental changes on traditional lifestyles in the Arctic. Educational levels: High school, Undergraduate lower division
The future of leadership

The good news with regard to this (or any) chapter on the future of leadership is that there is one.  There was a time when researchers called for a moratorium on new leadership theory and research (e.g., Miner, 1975) citing the uncertain future of the field. Then for a time there was a popular academic perspective that leadership did not really matter when it came to shaping organizational outcomes (Meindl &amp; Ehrlich, 1987; Meindl, Ehrlich, &amp; Dukerich, 1985; Pfeffer, 1977).  That perspective was laid to rest by "realists" in the field (Day &amp; Antonakis, 2012a) by means of empirical re-interpretation of the results used to support the position that leadership does not matter (Lieberson &amp; O'Connor, 1972; Salancik &amp; Pfeffer, 1977).  Specifically, Day and Lord (1988) showed that when proper methodological concerns were addressed (e.g., controlling for industry and company size effects; incorporating appropriate time lags) that the impact of top-level leadership was considerable - explaining as much as 45% of the variance in measures of organizational performance.  Despite some recent pessimistic sentiments about the "curiously unformed" state of leadership research and theory (Hackman &amp; Wageman, 2007), others have argued that the field has continued to evolve and is potentially on the threshold of some significant breakthroughs (Day &amp; Antonakis, 2012a).
Leadership scholars have been re-energized by new directions in the field and research efforts have revitalized areas previously abandoned for apparent lack of consistency in findings (e.g., leadership trait theory). Our accumulated knowledge now allows us to explain the nature of leadership including its biological bases and other antecedents, and consequences with some degree of confidence. There are other comprehensive sources that review the extensive theoretical and empirical foundation of leadership (Bass, 2008; Day &amp; Antonakis, 2012b) so that will not be the focus of the present chapter. Instead, we will take a future-oriented perspective in identifying particular areas within the leadership field that we believe offer promising perspectives on the future of leadership.
Nonetheless, it is worthwhile as background to first provide an overview of how we see the leadership field changing over the past decade or so. This short chronicle will set the stage for a keener understanding of where the future contributions are likely to emerge. Overall, across nine major schools of leadership - trait, behavioural, contingency, contextual, relational, sceptics, information processing, New Leadership, biological and evolutionary - researchers have seen a resurgence in interest in one area, a high level of activity in at least four other areas, inactivity in three areas, and one that was modestly active in the previous decade but we think holds strong promise for the future (Gardner, Lowe, Moss, Mahoney, &amp; Cogliser, 2010).  We will next provide brief overviews of these nine schools and their respective levels of research activity (see Figure 1)
Reduction of energy consumption and GHGs emission in investment casting process by application of a new casting method

In conventional investment casting process of aluminium alloys for the application of the aerospace industry, to assure the quality of the final casting, usually the foundry uses large runner system which results in low yield, high energy consumption and high GHGs emission. In one case of this investigation, the yield is between 5 ~ 10% which means that the current casting process wastes large amount of energy and generates lots of scraps. By applying a new casting method, the bulky runner system and the melting/holding time are significantly reduced, the quality of the casting is improved, and in the meantime the related energy consumption and GHGs emission are significantly decreased. In the investigation, the energy efficiencies and GHGs emission between the current and traditional processes are calculated and compared
A Python package for Bayesian estimation using Markov Chain Monte Carlo

Markov chain Monte Carlo (MCMC) estimation provides a solution to the complex integration problems that are faced in the Bayesian analysis of statistical problems. The implementation of MCMC algorithms is, however, code intensive and time consuming. We have developed a Python package, which is called PyMCMC, that aids in the construction of MCMC samplers and helps to substantially reduce the likelihood of coding error, as well as aid in the minimisation of repetitive code. PyMCMC contains classes for Gibbs, Metropolis Hastings, independent Metropolis Hastings, random walk Metropolis Hastings, orientational bias Monte Carlo and slice samplers as well as specific modules for common models such as a module for Bayesian regression analysis. PyMCMC is straightforward to optimise, taking advantage of the Python libraries Numpy and Scipy, as well as being readily extensible with C or Fortran
Simulation of Risk Processes

This paper is intended as a guide to simulation of risk processes. A typical model for insurance risk, the so-called collective risk model, treats the aggregate loss as having a compound distribution with two main components: one characterizing the arrival of claims and another describing the severity (or size) of loss resulting from the occurrence of a claim. The collective risk model is often used in health insurance and in general insurance, whenever the main risk components are the number of insurance claims and the amount of the claims. It can also be used for modeling other non-insurance product risks, such as credit and operational risk. In this paper we present efficient simulation algorithms for several classes of claim arrival processes.
Phylogenetic Tools in Astrophysics

Multivariate clustering in astrophysics is a recent development justified by
the bigger and bigger surveys of the sky. The phylogenetic approach is probably
the most unexpected technique that has appeared for the unsupervised
classification of galaxies, stellar populations or globular clusters. On one
side, this is a somewhat natural way of classifying astrophysical entities
which are all evolving objects. On the other side, several conceptual and
practical difficulties arize, such as the hierarchical representation of the
astrophysical diversity, the continuous nature of the parameters, and the
adequation of the result to the usual practice for the physical interpretation.
Most of these have now been solved through the studies of limited samples of
stellar clusters and galaxies. Up to now, only the Maximum Parsimony
(cladistics) has been used since it is the simplest and most general
phylogenetic technique. Probabilistic and network approaches are obvious
extensions that should be explored in the future
Approximate Bayesian Computation

Bayesian statistics provides a principled framework for performing statistical inference for an unknown parameter of a stochastic model assumed to be responsible for generating some observed data.  However, standard Bayesian algorithms to sample from the posterior require that the likelihood function, the probability density of the data given the parameter represented as a function of the parameter for fixed observed data, is computationally tractable.  However, there are an increasing number of models across Science and Technology where the likelihood function is difficult or impossible to compute.  When simulation from the model is comparatively cheaper, a class of likelihood-free methods called approximate Bayesian computation (ABC) can be used.  However, ABC introduces an approximation to the posterior.  This paper gives an introduction to ABC, describes the approximation behaviour of ABC and provides advice on the successful implementation of ABC. Some current challenges facing ABC methods are also discussed
Project Management in the Oil & Gas Industry - A Bayesian Approach

A reliable ‘Estimate at Completion’ from the early stage of project execution is essential in order to enable efficient and proactive project management. The non-repetitive and uncertain nature of projects and the involvement of multiple stakeholders require the use and integration of multiple informative sources in order to provide accurate forecasts. Moreover, in the Oil&Gas industry projects are characterized by a high level of complexity and financial impact. \ud
The paper aims at multiple objectives: introducing the need for the identification and utilization of all the available knowledge in order to improve the forecasting process; developing a Bayesian approach in order to integrate the diverse knowledge sources; exploring the integration of data records and experts’ judgment related to the ongoing project; exploring the integration of data records related to projects completed in the past and to the ongoing project and finally developing a Bayesian model capable of using three different knowledge sources: data records and experts’ judgments related to the ongoing project and data records related to similar projects completed in the past.\ud
The model has been tested in a set of large and complex projects in the Oil&Gas industry, in order to forecast the final duration and the final cost. The results show a higher forecasting accuracy of the Bayesian model compared to the traditional Earned Value Management (EVM) methodology
Bounds for Markov Decision Processes

We consider the problem of producing lower bounds on the optimal cost-to-go function of a Markov decision problem. We present two approaches to this problem: one based on the methodology of approximate linear programming (ALP) and another based on the so-called martingale duality approach. We show that these two approaches are intimately connected. Exploring this connection leads us to the problem of finding "optimal" martingale penalties within the martingale duality approach which we dub the pathwise optimization (PO) problem. We show interesting cases where the PO problem admits a tractable solution and establish that these solutions produce tighter approximations than the ALP approach. © 2013 The Institute of Electrical and Electronics Engineers, Inc
Time--The Emotional Asymmetry

A person is future-biased when she would rather, other things being equal, that bad
things be in the past than in the future, and that good things be the future than in
the past.

Most of us are, at least to some degree, future-biased. Consider:

Your Past or Future Pain
You wake up with an aching jaw. What is going on? You remember that you were
scheduled to have your wisdom teeth removed, painfully but safely, under a weak local
anesthetic, on Thursday. But has that happened yet? In your groggy condition you are not
sure. It could be Friday morning. The ache in your jaw would then be an after-effect of
the operation. And it could be Thursday morning. The ache in your jaw would then be the
distress of your impacted teeth
Membrane Bioreactors for Waste gas Treatment

Producción CientíficaIn the chapter on “Membrane Bioreactors for Waste Gas Treatment” an overview of recent progress in this field is given. In contrast with waste water treatment, membrane systems for waste gas treatment are still in the exploratory stage, although advantages are obvious: in membrane systems the air and aqueous phase are separated, which allows better performance for hydrophobic compounds. The bottleneck, however, is the scaling up of the system. In this chapter the basics of membrane permeation; reactor configuration; results of lab and pilot scale membrane waste gas treatment studies; microbiological aspects and modeling are discussed and future perspectives are presented
Human genetics and clinical aspects of neurodevelopmental disorders

This chapter traverses contemporary understandings of the genetic architecture of human disease, and explores the clinical implications of the current state of knowledge. Many different classes of genetic mutations have been implicated as being involved in predisposition to certain diseases, and researchers are continually uncovering other means by which genetics plays an important role in human disease, such as with somatic genetic mosaicism. Putative “de novo” mutations can represent cases of parental mosaicism (including in the germline), which could be revealed by careful genotyping of parental tissues other than peripheral blood lymphocytes. There is an increasingly rich literature regarding rare mutations with seemingly large phenotypic effects. Privacy concerns have added to the difficulties of implementing genomics-guided medicine. With the advent of exome and whole genome sequencing (WGS), one needs to focus again on families over several generations, so as to attempt to minimize genetic differences, locus heterogeneity and environmental influences
Milk minor constituents, enzymes, hormones, growth factors, and organic acids

Milk and derived products contain essential nutrients, such as proteins, lactose,
minerals, vitamins, and enzymes. Additionally, despite of their low concentrations in
milk, many other minor constituents present important physiological and/or
technological roles (e.g. hormones, growth factors). Dairy industries face many
challenges regarding milk processing. Also, the full knowledge on these constituents’
physiological roles and effects on health is still lacking. Technological advances,
innovative research approaches using metabolic engineering, systems and synthetic
biology, and novel production methods will allow the production of higher amounts of
such constituents, to produce them in such a way that recovery is easier and to
produce differentiated compounds, thus revolutionizing the fields of personalized
nutrition and functional foods. This chapter focuses on the description of the minor milk
constituents, their applications and main technological challenges. Future perspectives
and concerns related to these constituents are discusse
Latest developments in 3D analysis of geomaterials by Morpho+

At the Centre for X-ray Tomography of the Ghent University (Belgium) (www.ugct.ugent.be) besides hardware development for high-resolution X-ray CT scanners, a lot of progress is being made in the field of 3D analysis of the scanned samples. Morpho+ is a flexible 3D analysis software which provides the necessary petrophysical parameters of the scanned samples in 3D. Although Morpho+ was originally designed to provide any kind of 3D parameter, it contains some specific features especially designed for the analysis of geomaterial properties like porosity, partial porosity, pore-size distribution, grain size, grain orientation and surface determination. Additionally, the results of the 3D analysis can be visualized which enables to understand and interpret the analysis results in a straightforward way. The complementarities between high-quality X-ray CT images and flexible 3D software are opening up new gateways in the study of geomaterials
Multiscale daylight modeling for urban environments

Daylight in buildings is the natural illumination experienced by the occupants of any man-\ud
made construction with openings to the outside. The quantity and quality of daylight\ud
in buildings is continually varying due to the natural changes in sun and sky conditions\ud
from one moment to the next. In urban settings, the dynamics of daylight illumination are\ud
ampli ed by the vertical extent of buildings and the density of the built form. Traditional\ud
schema for evaluating daylight in the built environment consider only one or perhaps a\ud
few `snapshot' conditions, e.g. a single overcast sky, or a sequence of shadow patterns for\ud
selected hours. This chapter describes the application of a technique called climate-based\ud
daylight modelling (CBDM) across various urban scales { from o ce spaces to large-\ud
scale city models. Climate-based daylight modelling is the prediction of various radiant\ud
or luminous quantities (e.g. irradiance, illuminance, radiance and luminance) using sun\ud
and sky conditions that are derived from standard meteorological datasets. Climate-\ud
based modelling delivers predictions of absolute quantities (e.g. illuminance) that are\ud
dependent both on the locale (i.e. geographically-speci c climate data is used) and the\ud
building orientation (i.e. the illumination e ect of the sun and non-overcast sky conditions\ud
are included), in addition to the building's composition and con guration. The examples\ud
described in this chapter include theoretical studies and two `live' projects
Adhesive wear characteristics of natural fiber-reinforced composites

In this chapter, glass fiber and palm oil fiber were selected as synthetic and natural reinforcements, respectively, for polyester composites. A hand lay-up technique was adopted in order to fabricate a chopped strand, mat glass fiber-reinforced polyester composite (FRPC). Meanwhile, a closed compressed mold was used to fabricate palm oil fiber-reinforced polyester (PORP) composites. Two PORP composites were developed based on untreated or treated (with 6% NaOH) palm oil fibers. These have been denoted UT-PORP and T-PORP composites, respectively. In addition, a neat polyester material was prepared to investigate the effect of each fiber on its tribo-performance.
The macroscopic effects of microscopic heterogeneity

Over the past decade, advances in super-resolution microscopy and
particle-based modeling have driven an intense interest in investigating
spatial heterogeneity at the level of single molecules in cells. Remarkably, it
is becoming clear that spatiotemporal correlations between just a few molecules
can have profound effects on the signaling behavior of the entire cell. While
such correlations are often explicitly imposed by molecular structures such as
rafts, clusters, or scaffolds, they also arise intrinsically, due strictly to
the small numbers of molecules involved, the finite speed of diffusion, and the
effects of macromolecular crowding. In this chapter we review examples of both
explicitly imposed and intrinsic correlations, focusing on the mechanisms by
which microscopic heterogeneity is amplified to macroscopic effect.Comment: 20 pages, 5 figures. To appear in Advances in Chemical Physic
Energy stability analysis for a hybrid fluid-kinetic plasma model

In plasma physics, a hybrid fluid-kinetic model is composed of a
magnetohydrodynamics (MHD) part that describes a bulk fluid component and a
Vlasov kinetic theory part that describes an energetic plasma component. While
most hybrid models in the plasma literature are non-Hamiltonian, this paper
investigates a recent Hamiltonian variant in its two-dimensional configuration.
The corresponding Hamiltonian structure is described along with its Casimir
invariants. Then, the energy-Casimir method is used to derive explicit
sufficient stability conditions, which imply a stable spectrum and suggest
nonlinear stability
Ambient Intelligence: Science or Fad?

International audienceWeiser's vision has been used to justify and define new research within an extremely diverse collection of fields. It is increasingly evident that such research cannot be carried out in isolation. Research in this area is fundamentally multi-disciplinary, requiring the assimilation of problems and concepts from a variety of specializations. From our perspective, in its current state, ambient intelligence is only the latest stage in the evolution of informatics as a scientific discipline. In this chapter, we will provide an overview of the field in its current state
Orofacial cutaneous function in speech motor control and learning

International audienceSomatosensory signals from facial skin can provide a rich source of sensory input. However, it is unknown yet how cutaneous input works on speech motor control and learning. This chapter introduces a kinesthetic role of orofacial cutaneous afferents in speech processing. We argue for specificity of the orofacial somatosensory system from anatomical and physiological perspectives. The contribution of cutaneous afferents to speech production is evident in neurophysiological and psychophysical findings. Somatosensory modulation associated with facial skin deformation induces a reflex for articulatory motion adjustment in speech production and also an adaptive motion change in speech motor learning. In addition, cutaneous mechanoreceptors are narrowly tuned at the skin lateral to the oral angle. An intriguing function of somatosensory inputs associated with facial skin deformation is to interact with the processing of speech perception. Taken together, orofacial cutaneous afferents play an important role in both speech production and perception
3D fully coupled multiphase modelling of Ekofisk reservoir

During reservoir production, the depletion usually induces a compaction of the reservoir layers. To enhance oil recovery, waterflooding operations are generally performed but, in the North Sea chalk reservoir, they have also induced an additional compaction. This latter phenomenon has a positive impact (improve the ‘Compaction drive’ oil recovery) but endangers the offshore platforms and causes huge extra costs. The coupling effects between mechanical behaviour and fluids flow are thus a key issue in petroleum engineering. Pasachalk program I & II have proposed a constitutive model (referred as Pasachalk model), where the suction is the key parameter to explain the water weakening effect. This model has been used for the modelling of waterflooding experiment on chalk plug and bi-dimensional reservoir. However, as the reservoir modelling is actually a 3D model, the purpose of this paper is to present 3D fully coupled modelling of a simplified Ekofisk reservoir during production and waterflooding phases. In order to exhibit the influence of the constitutive mechanical model, different studies are performed: increasing the complexity of the mechanical model (from an elastic model up to the Pasachalk one with suction effect). The numerical results show the influence of the mechanical law on the oil production and exhibit that only an elasto-plastic law with suction effects succeeds to reproduce compactions during both production and waterflooding.Peer reviewe
Association, inhibition, and action

This chapter reviews some of the basic properties of conditioned inhibition as studied in animals, and considers the extent to which these phenomena also apply to humans. The response-interference account of negative priming is akin to the interference account of conditioned inhibition that assumes US-US interference. The chapter focuses then switches to top-down cognitive and motor inhibition and an evaluation of the extent to which it can be associatively mediated. The idea that responses or motor actions can be inhibited in a top-down fashion receives the strongest support from paradigms such as the go/no-go paradigm and the stop-signal paradigm. The chapter reviews the evidence for this phenomenon and again seeks to establish some of its basic characteristics. It ends by taking an overtly computational perspective on both sets of phenomena as one can look for similarities and differences between them