What drives ICT clustering in European Cities?

ICT clusters have attracted much attention because of their rapid growth and their value for other economic activities. Using a nested multi-level model, we examine how conditions at the country level and at the city level affect ICT clustering activity in 227 cities across 22 European countries. We test for the influence of three country regulations (starting a business, registering property, enforcing contracts) and two city conditions (proximity to university, network density) on ICT clustering. We consider heterogeneity within the sector and study two types of ICT activities: ICT product firms and ICT content firms. Our results indicate that country conditions and city conditions each have idiosyncratic implications for ICT clustering, and further, that these can vary by activities in ICT products or ICT content manufacturing.

On L2 Clustering and Resetting the Null Subject Parameter in L2 Spanish: Implications and Observations.

In this article, along with others, we take the position that the Null-Subject Parameter (NSP) (Chomsky 1981; Rizzi 1982) cluster of properties is narrower in scope than some originally contended. We test for the resetting of the NSP by English L2 learners of Spanish at the intermediate level, including poverty-of-the stimulus knowledge of the Overt Pronoun Constraint (Montalbetti 1984). Our participants are tested before and after five months' residency in Spain in an effort to see if increased amounts of native exposure are particularly beneficial for parameter resetting. Although we demonstrate NSP resetting for some of the L2 learners, our data essentially demonstrate that even with the advent of time/exposure to native input, there is no immediate gainful effect for NSP resetting.

Syk and Src family kinases regulate C-type lectin receptor 2 (CLEC-2)-mediated clustering of podoplanin and platelet adhesion to lymphatic endothelial cells

The interaction of C-type lectin receptor 2 (CLEC-2) on platelets with Podoplanin on lymphatic endothelial cells initiates platelet signaling events that are necessary for prevention of blood-lymph mixing during development. In the present study, we show that CLEC-2 signaling via Src family and Syk tyrosine kinases promotes platelet adhesion to primary mouse lymphatic endothelial cells at low shear. Using supported lipid bilayers containing mobile Podoplanin, we further show that activation of Src and Syk in platelets promotes clustering of CLEC-2 and Podoplanin. Clusters of CLEC-2-bound Podoplanin migrate rapidly to the center of the platelet to form a single structure. Fluorescence lifetime imaging demonstrates that molecules within these clusters are within 10 nm of one another and that the clusters are disrupted by inhibition of Src and Syk family kinases. CLEC-2 clusters are also seen in platelets adhered to immobilized Podoplanin using direct stochastic optical reconstruction microscopy. These findings provide mechanistic insight by which CLEC-2 signaling promotes adhesion to Podoplanin and regulation of Podoplanin signaling, thereby contributing to lymphatic vasculature development.

Using proxies to calculate the carbon impact of investment into electricity network assets

Replacement and upgrading of assets in the electricity network requires financial investment for the distribution and transmission utilities. The replacement and upgrading of network assets also represents an emissions impact due to the carbon embodied in the materials used to manufacture network assets. This paper uses investment and asset data for the GB system for 2015-2023 to assess the suitability of using a proxy with peak demand data and network investment data to calculate the carbon impacts of network investments. The proxies are calculated on a regional basis and applied to calculate the embodied carbon associated with current network assets by DNO region. The proxies are also applied to peak demand data across the 2015-2023 period to estimate the expected levels of embodied carbon that will be associated with network investment during this period. The suitability of these proxies in different contexts are then discussed, along with initial scenario analysis to calculate the impact of avoiding or deferring network investments through distributed generation projects. The proxies were found to be effective in estimating the total embodied carbon of electricity system investment in order to compare investment strategies in different regions of the GB network.

Customer retention management in the recession

The purpose of this article is to explore customer retention strategies and tactics implemented by firms in recession. Our investigations show just how big a challenge many organizations face in their ability to manage customer retention effectively. While leading organizations have embedded real-time customer life cycle management, developed accurate early warning systems, price elasticity models and ‘deal calculators’, the organizations we spoke to have only gone as far as calculating the value at risk and building simple predictive models.

Achieving customer satisfaction through integrated products and services: an exploratory study

Complex products such as manufacturing equipment have always needed maintenance and repair services. Increasingly, leading manufacturers are integrating products and services to generate increased revenues and achieve customer satisfaction. Designing integrated products and services requires a different approach to new product development and a clear understanding of how customers perceive the value they obtain from actual usage of products and services—so-called value-in-use. However, there is a lack of research on integrated products and services and how they impact customer satisfaction. An exploratory study was undertaken to understand customers’ views on integrated products and services and the value-in-use derived from such offerings. As value-in-use and its impacts are complicated concepts, a technique from psychology—Repertory Grid Technique—was used to gather data in 33 interviews. The interviews allowed a deep understanding of customer views on integrated products and services to be obtained, and a systematic analysis identified the key attributes of value-in-use. In order to probe further, the data were then analyzed using Honey’s procedure, which identified the impact of the attributes of value-in-use on customer satisfaction. Two key attributes—relational dynamic and access—were found to have the most influence on customer satisfaction. This paper contributes to the innovation field by identifying customer needs for integrated products and services and how these impact customer satisfaction. These are key points and need to be fully considered by managers during new product and service development. Similarly, the paper identifies a number of important areas for further research.

Electricity derivatives pricing with forward-looking information

In order to increase overall transparency on key operational information, power transmission system operators publish an increasing amount of fundamental data, including forecasts of electricity demand and available capacity. We employ a fundamental model for electricity prices which lends itself well to integrating such forecasts, while retaining ease of implementation and tractability to allow for analytic derivatives pricing formulae. In an extensive futures pricing study, the pricing performance of our model is shown to further improve based on the inclusion of electricity demand and capacity forecasts, thus confirming the general importance of forward-looking information for electricity derivatives pricing. However, we also find that the usefulness of integrating forecast data into the pricing approach is primarily limited to those periods during which electricity prices are highly sensitive to demand or available capacity, whereas the impact is less visible when fuel prices are the primary underlying driver to prices instead.

Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks

With the fast development of wireless communications, ZigBee and semiconductor devices, home automation networks have recently become very popular. Since typical consumer products deployed in home automation networks are often powered by tiny and limited batteries, one of the most challenging research issues is concerning energy reduction and the balancing of energy consumption across the network in order to prolong the home network lifetime for consumer devices. The introduction of clustering and sink mobility techniques into home automation networks have been shown to be an efficient way to improve the network performance and have received significant research attention. Taking inspiration from nature, this paper proposes an Ant Colony Optimization (ACO) based clustering algorithm specifically with mobile sink support for home automation networks. In this work, the network is divided into several clusters and cluster heads are selected within each cluster. Then, a mobile sink communicates with each cluster head to collect data directly through short range communications. The ACO algorithm has been utilized in this work in order to find the optimal mobility trajectory for the mobile sink. Extensive simulation results from this research show that the proposed algorithm significantly improves home network performance when using mobile sinks in terms of energy consumption and network lifetime as compared to other routing algorithms currently deployed for home automation networks.

Applications of electrified dust and dust devil electrodynamics to Martian atmospheric electricity

Atmospheric transport and suspension of dust frequently brings electrification, which may be substantial. Electric fields of 10 kVm-1 to 100 kVm-1 have been observed at the surface beneath suspended dust in the terrestrial atmosphere, and some electrification has been observed to persist in dust at levels to 5 km, as well as in volcanic plumes. The interaction between individual particles which causes the electrification is incompletely understood, and multiple processes are thought to be acting. A variation in particle charge with particle size, and the effect of gravitational separation explains to, some extent, the charge structures observed in terrestrial dust storms. More extensive flow-based modelling demonstrates that bulk electric fields in excess of 10 kV m-1 can be obtained rapidly (in less than 10 s) from rotating dust systems (dust devils) and that terrestrial breakdown fields can be obtained. Modelled profiles of electrical conductivity in the Martian atmosphere suggest the possibility of dust electrification, and dust devils have been suggested as a mechanism of charge separation able to maintain current flow between one region of the atmosphere and another, through a global circuit. Fundamental new understanding of Martian atmospheric electricity will result from the ExoMars mission, which carries the DREAMS (Dust characterization, Risk Assessment, and Environment Analyser on the Martian Surface)-MicroARES (Atmospheric Radiation and Electricity Sensor) instrumentation to Mars in 2016 for the first in situ measurements.

Tensor LRR and sparse coding-based subspace clustering

Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data, and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established state-of-the-art methods.

Heterogeneous tensor decomposition for clustering via manifold optimization

Tensor clustering is an important tool that exploits intrinsically rich structures in real-world multiarray or Tensor datasets. Often in dealing with those datasets, standard practice is to use subspace clustering that is based on vectorizing multiarray data. However, vectorization of tensorial data does not exploit complete structure information. In this paper, we propose a subspace clustering algorithm without adopting any vectorization process. Our approach is based on a novel heterogeneous Tucker decomposition model taking into account cluster membership information. We propose a new clustering algorithm that alternates between different modes of the proposed heterogeneous tensor model. All but the last mode have closed-form updates. Updating the last mode reduces to optimizing over the multinomial manifold for which we investigate second order Riemannian geometry and propose a trust-region algorithm. Numerical experiments show that our proposed algorithm compete effectively with state-of-the-art clustering algorithms that are based on tensor factorization.

Simultaneous activities in the household and residential electricity demand in Spain

Recent research and policy studies on the low-carbon future highlight the importance of flexible electricity demand. This might be problematic particularly for residential electricity demand, which is related to simultaneous consumers’ practices in the household. This paper analyses issues of simultaneity in residential electricity demand in Spain. It makes use of the 2011 Spanish Time Use Survey data with comparisons from the previous Spanish Time Use Survey and the Harmonised European Time Use Surveys. Findings show that media activities are associated the highest levels of continuity and simultaneity, particularly in the early and late parts of the evening during weekdays.