London: pocket map and guide.

Guía de bolsillo para descubrir Londres y sus mejores museos, monumentos, restaurantes, bares y tiendas, horarios, y datos sobre el transporte. Repleta de palabras, fotos, mapas para ayudar al visitante en la localización de más de doscientos lugares de interés.

Madrid : pocket map and guide.

Guía de bolsillo para descubrir Madrid y sus mejores museos, monumentos, restaurantes, bares y tiendas, horarios, y datos sobre el transporte. Repleta de palabras, fotos, mapas para ayudar al visitante en la localización de más de doscientos lugares de interés.

Theoretical investigation of infrared spectra and pocket dynamics of photodissociated carbonmonoxy myoglobin

Molecular dynamics simulations of the photodissociated state of carbonmonoxy myoglobin (MbCO) are presented using a fluctuating charge model for CO. A new three-point charge model is fitted to high-level ab initio calculations of the dipole and quadrupole moment functions taken from the literature. The infrared spectrum of the CO molecule in the heme pocket is calculated using the dipole moment time autocorrelation function and shows good agreement with experiment. In particular, the new model reproduces the experimentally observed splitting of the CO absorption spectrum. The splitting of 3–7 cm−1 (compared to the experimental value of 10 cm−1) can be directly attributed to the two possible orientations of CO within the docking site at the edge of the distal heme pocket (the B states), as previously suggested on the basis of experimental femtosecond time-resolved infrared studies. Further information on the time evolution of the position and orientation of the CO molecule is obtained and analyzed. The calculated difference in the free energy between the two possible orientations (Fe···CO and Fe···OC) is 0.3 kcal mol−1 and agrees well with the experimentally estimated value of 0.29 kcal mol−1. A comparison of the new fluctuating charge model with an established fixed charge model reveals some differences that may be critical for the correct prediction of the infrared spectrum and energy barriers. The photodissociation of CO from the myoglobin mutant L29F using the new model shows rapid escape of CO from the distal heme pocket, in good agreement with recent experimental data. The effect of the protein environment on the multipole moments of the CO ligand is investigated and taken into account in a refined model. Molecular dynamics simulations with this refined model are in agreement with the calculations based on the gas-phase model. However, it is demonstrated that even small changes in the electrostatics of CO alter the details of the dynamics.

Pocket data mining: towards collaborative data mining in mobile computing environments

Pocket Data Mining (PDM) is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld devices. Wireless communication among these devices using Bluetooth and WiFi technologies has opened the door wide for collaborative mining among the mobile devices within the same range that are running data mining techniques targeting the same application. This paper proposes a new architecture that we have prototyped for realizing the significant applications in this area. We have proposed using mobile software agents in this application for several reasons. Most importantly the autonomic intelligent behaviour of the agent technology has been the driving force for using it in this application. Other efficiency reasons are discussed in details in this paper. Experimental results showing the feasibility of the proposed architecture are presented and discussed.

Distributed hoeffding trees for pocket data mining

Collaborative mining of distributed data streams in a mobile computing environment is referred to as Pocket Data Mining PDM. Hoeffding trees techniques have been experimentally and analytically validated for data stream classification. In this paper, we have proposed, developed and evaluated the adoption of distributed Hoeffding trees for classifying streaming data in PDM applications. We have identified a realistic scenario in which different users equipped with smart mobile devices run a local Hoeffding tree classifier on a subset of the attributes. Thus, we have investigated the mining of vertically partitioned datasets with possible overlap of attributes, which is the more likely case. Our experimental results have validated the efficiency of our proposed model achieving promising accuracy for real deployment.

Distributed classification for pocket data mining

Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining PDM. Large amounts of available data streams to which smart phones can subscribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system. This emerging area of study has shown to be feasible in an earlier study using technological enablers of mobile software agents and stream mining techniques [1]. A typical PDM process would start by having mobile agents roam the network to discover relevant data streams and resources. Then other (mobile) agents encapsulating stream mining techniques visit the relevant nodes in the network in order to build evolving data mining models. Finally, a third type of mobile agents roam the network consulting the mining agents for a final collaborative decision, when required by one or more users. In this paper, we propose the use of distributed Hoeffding trees and Naive Bayes classifers in the PDM framework over vertically partitioned data streams. Mobile policing, health monitoring and stock market analysis are among the possible applications of PDM. An extensive experimental study is reported showing the effectiveness of the collaborative data mining with the two classifers.

Homogeneous and heterogeneous distributed classification for pocket data mining

Pocket Data Mining (PDM) describes the full process of analysing data streams in mobile ad hoc distributed environments. Advances in mobile devices like smart phones and tablet computers have made it possible for a wide range of applications to run in such an environment. In this paper, we propose the adoption of data stream classification techniques for PDM. Evident by a thorough experimental study, it has been proved that running heterogeneous/different, or homogeneous/similar data stream classification techniques over vertically partitioned data (data partitioned according to the feature space) results in comparable performance to batch and centralised learning techniques.

Pocket data mining - big data on small devices

Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed.

Totally implantable venous catheters: insertion via internal jugular vein with pocket implantation in the arm is an alternative for diseased thoracic walls

Purpose: Insertion of totally implantable catheters via deep vessels that drain into the superior vena cava results in a lower incidence of venous thrombosis and infection as compared to catheters inserted into femoral and arm veins. Superior vena cava obstruction and inadequacy of the thoracic wall are conditions that prevent reservoir implantation in the chest wall. In this article, we describe a technical innovation that enables the pocket to be fixed in the arm while still allowing access to be achieved via the internal jugular vein. Method: The procedure reported maintains the use of the internal jugular vein for access even when the patient's chest is not suited for reservoir implantation, which is localized in the arm. Results: The procedure was successful and no complications occurred. The position of the catheter tip did not alter with arm movement. Conclusion: The implantation of a port reservoir in the arm following venous access via the internal jugular vein is both safe and convenient.

A pocket beach that reaches equilibrium after becoming disconnected from a large system of beaches. Playa Barca (Fuerteventura)

[EN] Playa Barca is a 370 m long beach located within the system of the Leeward beaches on the Jandía peninsula, Fuerteventura. This system of beaches represents one of the major sources of economic income to the island, both because of its natural landscape that attract a specific type of tourism, and because of its particular climate conditions that make these beaches ideal for practicing wind-water sports. Nevertheless, in the past decades, this area has suffered from a significant and worrying coastline retreat. In order to look for an explanation to this retreat, five topographic surveys were carried out in October 1999, February 2001, February 2002, February 2003 and February 2013 to track the beach behavior in the last 15 years. A total station Topcon GTS-303D was used for this purpose. Surveys were carried out during low spring tides, so that the outer limit was the furthest possible depending on wave conditions. The inner limit covered part of the dunes in the backshore. From these topographic data both coastline changes and the sedimentary balance have been obtained.