Auditoría integrada de Información + Conocimiento: aplicación en un caso de estudio

Se presentan los resultados de la aplicación de una metodología integradora de auditoría de información y conocimiento, llevada a cabo en un Centro de Investigación del Ministerio de Ciencia, Tecnología y Medio Ambiente de la provincia de Holguín, Cuba, conformada por siete etapas con un enfoque híbrido dirigida a revisar la estrategia y la política de gestión de información y conocimiento, identificar e inventariar y mapear los recursos de I+C y sus flujos, y valorar los procesos asociados a su gestión. La alta dirección de este centro, sus especialistas e investigadores manifestaron la efectividad de la metodología aplicada cuyos resultados propiciaron reajustar la proyección estratégica en relación con la gestión de la I+C, rediseñar los flujos informativos de los procesos claves, disponer de un directorio de sus expertos por áreas y planificar el futuro aprendizaje y desarrollo profesional.

Towards a threat assessment framework for apps collusion

App collusion refers to two or more apps working together to achieve a malicious goal that they otherwise would not be able to achieve individually. The permissions based security model (PBSM) for Android does not address this threat, as it is rather limited to mitigating risks due to individual apps. This paper presents a technique for assessing the threat of collusion for apps, which is a first step towards quantifying collusion risk, and allows us to narrow down to candidate apps for collusion, which is critical given the high volume of Android apps available. We present our empirical analysis using a classified corpus of over 29000 Android apps provided by Intel Security.

LES of flame acceleration and DDT in hydrogen-air mixture using artificially thickened flame approach and detailed chemical kinetics

A large eddy simulation is performed to study the deflagration to detonation transition phenomenon in an obstructed channel containing premixed stoichiometric hydrogen–air mixture. Two-dimensional filtered reactive Navier–Stokes equations are solved utilizing the artificially thickened flame approach (ATF) for modeling sub-grid scale combustion. To include the effect of induction time, a 27-step detailed mechanism is utilized along with an in situ adaptive tabulation (ISAT) method to reduce the computational cost due to the detailed chemistry. The results show that in the slow flame propagation regime, the flame–vortex interaction and the resulting flame folding and wrinkling are the main mechanisms for the increase of the flame surface and consequently acceleration of the flame. Furthermore, at high speed, the major mechanisms responsible for flame propagation are repeated reflected shock–flame interactions and the resulting baroclinic vorticity. These interactions intensify the rate of heat release and maintain the turbulence and flame speed at high level. During the flame acceleration, it is seen that the turbulent flame enters the ‘thickened reaction zones’ regime. Therefore, it is necessary to utilize the chemistry based combustion model with detailed chemical kinetics to properly capture the salient features of the fast deflagration propagation.

Air Pollution and High Density Lipoprotein Structure and Function

Thesis (Ph.D.)--University of Washington, 2016-06

It’s Cheaper than Acquiring New Ones: Retaining Members to Your Service

The benefits of retention are well known: it’s cheaper and easier to retain existing customers than it is to try to acquire new ones. Secondly, retained customers play a vital role in the acquisition of new customers through word-of-mouth. Thirdly, retained customers have a higher monetary value, due to being more susceptible to cross-selling initiatives. So, retention is important. But, it’s difficult to achieve, especially for membership organisations. Numerous organisations work on a membership-based business model, such as health clubs, trade unions, charities and even professional associations. Often, recruiting and retaining members is fundamental to the success of these enterprises which rely on membership fees as their primary revenue stream.

3D Ionospheric Effects on HF Propagation and Heating

The thesis uses a three-dimensional, first-principles model of the ionosphere in combination with High Frequency (HF) raytracing model to address key topics related to the physics of HF propagation and artificial ionospheric heating. In particular: 1. Explores the effect of the ubiquitous electron density gradients caused by Medium Scale Traveling Ionospheric Disturbances (MSTIDs) on high-angle of incidence HF radio wave propagation. Previous studies neglected the all-important presence of horizontal gradients in both the cross- and down-range directions, which refract the HF waves, significantly changing their path through the ionosphere. The physics-based ionosphere model SAMI3/ESF is used to generate a self-consistently evolving MSTID that allows for the examination of the spatio-temporal progression of the HF radio waves in the ionosphere. 2. Tests the potential and determines engineering requirements for ground- based high power HF heaters to trigger and control the evolution of Equatorial Spread F (ESF). Interference from ESF on radio wave propagation through the ionosphere remains a critical issue on HF systems reliability. Artificial HF heating has been shown to create plasma density cavities in the ionosphere similar to those that may trigger ESF bubbles. The work explores whether HF heating may trigger or control ESF bubbles. 3. Uses the combined ionosphere and HF raytracing models to create the first self-consistent HF Heating model. This model is utilized to simulate results from an Arecibo experiment and to provide understanding of the physical mechanism behind observed phenomena. The insights gained provide engineering guidance for new artificial heaters that are being built for use in low to middle latitude regions. In accomplishing the above topics: (i) I generated a model MSTID using the SAMI3/ESF code, and used a raytrace model to examine the effects of the MSTID gradients on radio wave propagation observables; (ii) I implemented a three- dimensional HF heating model in SAMI3/ESF and used the model to determine whether HF heating could artificially generate an ESF bubble; (iii) I created the first self-consistent model for artificial HF heating using the SAMI3/ESF ionosphere model and the MoJo raytrace model and ran a series of simulations that successfully modeled the results of early artificial heating experiments at Arecibo.

Impact of Thresholds and Load Patterns when Executing HPC Applications with Cloud Elasticity

Elasticity is one of the most known capabilities related to cloud computing, being largely deployed reactively using thresholds. In this way, maximum and minimum limits are used to drive resource allocation and deallocation actions, leading to the following problem statements: How can cloud users set the threshold values to enable elasticity in their cloud applications? And what is the impact of the application’s load pattern in the elasticity? This article tries to answer these questions for iterative high performance computing applications, showing the impact of both thresholds and load patterns on application performance and resource consumption. To accomplish this, we developed a reactive and PaaS-based elasticity model called AutoElastic and employed it over a private cloud to execute a numerical integration application. Here, we are presenting an analysis of best practices and possible optimizations regarding the elasticity and HPC pair. Considering the results, we observed that the maximum threshold influences the application time more than the minimum one. We concluded that threshold values close to 100% of CPU load are directly related to a weaker reactivity, postponing resource reconfiguration when its activation in advance could be pertinent for reducing the application runtime.

Separate compilation of structured documents

This paper draws a parallel between document preparation and the traditional processes of compilation and link editing for computer programs. A block-based document model is described which allows for separate compilation of various portions of a document. These portions are brought together and merged by a linker program, called dlink, whose pilot implementation is based on ditroff and on its underlying intermediate code. In the light of experiences with dlink the requirements for a universal object-module language for documents are discussed. These requirements often resemble the characteristics of the intermediate codes used by programming-language compilers but with interesting extra constraints which arise from the way documents are executed .

Evaluating, predicting and mapping belowground carbon stores in Kenyan mangroves.

Despite covering only approximately 138,000 km2, mangroves are globally important carbon sinks with carbon density values 3 to 4 times that of terrestrial forests. A key challenge in evaluating the carbon benefits from mangrove forest conservation is the lack of rigorous spatially resolved estimates of mangrove sediment carbon stocks; most mangrove carbon is stored belowground. Previous work has focused on detailed estimations of carbon stores over relatively small areas, which has obvious limitations in terms of generality and scope of application. Most studies have focused only on quantifying the top 1m of belowground carbon (BGC). Carbon stored at depths beyond 1m, and the effects of mangrove species, location and environmental context on these stores, is poorly studied. This study investigated these variables at two sites (Gazi and Vanga in the south of Kenya) and used the data to produce a country-specific BGC predictive model for Kenya and map BGC store estimates throughout Kenya at spatial scales relevant for climate change research, forest management and REDD+ (Reduced Emissions from Deforestation and Degradation). The results revealed that mangrove species was the most reliable predictor of BGC; Rhizophora muronata had the highest mean BGC with 1485.5t C ha-1. Applying the species-based predictive model to a base map of species distribution in Kenya for the year 2010 with a 2.5m2 resolution, produced an estimate of 69.41 Mt C (± 9.15 95% C.I.) for BGC in Kenyan mangroves. When applied to a 1992 mangrove distribution map, the BGC estimate was 75.65 Mt C (± 12.21 95% C.I.); an 8.3% loss in BGC stores between 1992 and 2010 in Kenya. The country level mangrove map provides a valuable tool for assessing carbon stocks and visualising the distribution of BGC. Estimates at the 2.5m2 resolution provide sufficient detail for highlighting and prioritising areas for mangrove conservation and restoration.

Las carencias de la prensa tradicional, un lastre para el periodismo emprendedor

Introducción: La prensa tradicional ha heredado a la digital cuatro retos; cada uno de ellos es objeto de análisis en esta investigación: atraer nuevos lectores con narrativas informativas multimedia, generar estrategias de proximidad con sus usuarios, crear nuevos modelos de negocio y proyectarse como espacio profesional que refleje la feminización de la carrera de periodismo. Metodología: Este artículo presenta los resultados de un análisis de contenido aplicado a 281 medios creados por el periodismo emprendedor. Resultados y conclusiones: Los resultados de este estudio constatan que el periodismo emprendedor no ha innovado en la presentación de la información (utiliza como principal reclamo la imagen fija). Adoptan el mismo modelo de negocio basado en la publicidad. Carecen de canales para establecer feedbacks con los usuarios. Por último, igual que el medio en papel, no es un espacio para el empoderamiento femenino.

Predicting Plant Performance Under Simultaneously Changing Environmental Conditions-The Interplay Between Temperature, Light, and Internode Growth

Plant performance is significantly influenced by prevailing light and temperature conditions during plant growth and development. For plants exposed to natural fluctuations in abiotic environmental conditions it is however laborious and cumbersome to experimentally assign any contribution of individual environmental factors to plant responses. This study aimed at analyzing the interplay between light, temperature and internode growth based on model approaches. We extended the light-sensitive virtual plant model L-Cucumber by implementing a common Arrhenius function for appearance rates, growth rates, and growth durations. For two greenhouse experiments, the temperature-sensitive model approach resulted in a precise prediction of cucumber mean internode lengths and number of internodes, as well as in accurately predicted patterns of individual internode lengths along the main stem. In addition, a system's analysis revealed that environmental data averaged over the experimental period were not necessarily related to internode performance. Finally, the need for a species-specific parameterization of the temperature response function and related aspects in modeling temperature effects on plant development and growth is discussed.

Gap junctions and emergent rhythms

Gap junction coupling is ubiquitous in the brain, particularly between the dendritic trees of inhibitory interneurons. Such direct non-synaptic interaction allows for direct electrical communication between cells. Unlike spike-time driven synaptic neural network models, which are event based, any model with gap junctions must necessarily involve a single neuron model that can represent the shape of an action potential. Indeed, not only do neurons communicating via gaps feel super-threshold spikes, but they also experience, and respond to, sub-threshold voltage signals. In this chapter we show that the so-called absolute integrate-and-fire model is ideally suited to such studies. At the single neuron level voltage traces for the model may be obtained in closed form, and are shown to mimic those of fast-spiking inhibitory neurons. Interestingly in the presence of a slow spike adaptation current the model is shown to support periodic bursting oscillations. For both tonic and bursting modes the phase response curve can be calculated in closed form. At the network level we focus on global gap junction coupling and show how to analyze the asynchronous firing state in large networks. Importantly, we are able to determine the emergence of non-trivial network rhythms due to strong coupling instabilities. To illustrate the use of our theoretical techniques (particularly the phase-density formalism used to determine stability) we focus on a spike adaptation induced transition from asynchronous tonic activity to synchronous bursting in a gap-junction coupled network.

A Framework for Energy-Efficiency in Smart Home Environments

Part 7: Cyber-Physical Systems

Modelling and simulating retail management practices: a first approach

Multi-agent systems offer a new and exciting way of understanding the world of work. We apply agent-based modeling and simulation to investigate a set of problems in a retail context. Specifically, we are working to understand the relationship between people management practices on the shop-floor and retail performance. Despite the fact we are working within a relatively novel and complex domain, it is clear that using an agent-based approach offers great potential for improving organizational capabilities in the future. Our multi-disciplinary research team has worked closely with one of the UK’s top ten retailers to collect data and build an understanding of shop-floor operations and the key actors in a department (customers, staff, and managers). Based on this case study we have built and tested our first version of a retail branch agent-based simulation model where we have focused on how we can simulate the effects of people management practices on customer satisfaction and sales. In our experiments we have looked at employee development and cashier empowerment as two examples of shop floor management practices. In this paper we describe the underlying conceptual ideas and the features of our simulation model. We present a selection of experiments we have conducted in order to validate our simulation model and to show its potential for answering “what-if” questions in a retail context. We also introduce a novel performance measure which we have created to quantify customers’ satisfaction with service, based on their individual shopping experiences.

Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN

Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets.