Reliable Communication Networks for Smart Grid Transmission Systems

In this paper, we discuss the communications reliability requirements posed by the smart power grid with a focus on communications in support of wide area situational awareness. Implementation of wide area situational awareness relies on both transmission substation networks and wide area optical networks. We study the reliability of a sample communications network of the California Power Grid and find that its reliability falls short of proposed requirements. To overcome this issue, we consider the problem of designing the substation network and the wide area network to meet the reliability requirements while minimizing the network cost. For the wide area network design problem, we propose two alternate design approaches, namely: (1) following the power lines and (2) a mesh based design interconnecting the nodes. For the first approach we develop two greedy iterative heuristics and a heuristic integer linear programming (H-ILP) model using minimum cut-sets for network reliability optimization. The greedy iterative algorithms outperform the H-ILP approach in terms of cost, but require a larger amount of computing resources. Both proposed models are in fact complementary and thus provide a framework to optimize the reliability of smart grid communications networks restricted to following the power lines. In the second approach a greenfield mesh network method is proposed based on starting with a minimum spanning tree which is then augmented through a greedy heuristic into a mesh. Comparative numerical results show that the reliable mesh design has advantages in terms of the number of links and total link distance needed.     

Looking Back to Move Forward: Measuring the Impact of Existing Digital Resources Relevant to Irish Archaeology

The proliferation of digital resources is an important manifestation of data sharing and knowledge sharing practice. There is an increasing recognition of the importance of understanding the actual impact of these resources on research and research practice. Impact assessment methodologies are emerging from this nascent scholarship and offer a means of demonstrating performance and utility. This study took a multi-strand approach, inspired by the Toolkit for the Impact of Digitised Scholarly Resources (TIDSR), to evaluate seven digital resources relating to Irish archaeology. The results indicated significant factors that can influence the impact of a digital resource, positively or negatively.     

Virtual life: self and identity redefined in the new media age

In this article, the author discusses her virtual reality installation The Parallel Dimension and goes on to explore the conflicts of identity experienced when a person is confronted with VR as a total immersion. She suggests that the various questions arising through this experience may lead us to a more ambivalent concept of the self.     

Fuzzy clustering based ET image fusion

Electrical tomography (ET) is a technique to visually reconstruct inhomogeneous medium distributions by injecting currents or voltages at the boundary of the medium and measuring the resulted changes in the investigated fields. The ET techniques have been widely used in industrial practices owing to the low cost, rapid response time, non-existent radiation exposure, and non-intrusive characteristics comparing to other tomographic modalities. However, the spatial resolution of ET images using single modality or single-driven patterns (adjacent pattern vs. opposite pattern for imaging reconstruction) is low, which may limit its applications. In this research, the application of fuzzy clustering based fusion techniques for ET imaging is studied. Both multi-modality imaging and multi-driven patterns are of interest. Specifically, two modality images are fused: Electrical Capacitance Tomography (ECT), which performs well for imaging material of large permittivity difference, and Electrical Resistance Tomography (ERT), which is suited for imaging materials having large conductivity differences. The research also explores the fusion of adjacent and opposite patterns for either ECT or ERT modalities. Experiments show that the proposed method can construct high quality ET images by discovering the strong complementary natures of the modalities and/or driven patterns.     

Travel time models for deep-lane unit-load autonomous vehicle storage and retrieval system (AVS/RS)

Autonomous vehicle storage and retrieval systems use vehicles that move horizontally along rails within the storage racks, while vertical movements are provided by lifts. The solution proposed in this paper addresses a particular system configuration that works with multiple deep storage lanes that are widely used in the food and beverage industry, characterised by large volumes of products of limited variety. The generic deep lane is single item, i.e. one stock keeping unit, and single batch, i.e. one production lot, thereby affecting the performance of the system in terms of storage capacity utilisation and throughput. Determining the number and depth of the lanes is crucial to aid the design and control of such a storage system. The aim of this paper was to support the design of AVS/RSs though a set of original analytic models for the determination of the travelled distance and time for single-command and dual-command cycles given alternative layout configurations. The models are validated by simulation and exemplified with a real-warehousing case study. The paper presents useful guidelines for the configuration of the system layout including the determination of the optimal shape ratio and the length of the lanes.     

GraftFast Surface Engineering to Improve MOF Nanoparticles Furtiveness

Controlling the outer surface of nanometric metal–organic frameworks (nanoMOFs) and further understanding the in vivo effect of the coated material are crucial for the convenient biomedical applications of MOFs. However, in most studies, the surface modification protocol is often associated with significant toxicity and/or lack of selectivity. As an alternative, how the highly selective and general grafting GraftFast method leads, through a green and simple process, to the successful attachment of multifunctional biopolymers (polyethylene glycol (PEG) and hyaluronic acid) on the external surface of nanoMOFs is reported. In particular, effectively PEGylated iron trimesate MIL‐100(Fe) nanoparticles (NPs) exhibit suitable grafting stability and superior chemical and colloidal stability in different biofluids, while conserving full porosity and allowing the adsorption of bioactive molecules (cosmetic and antitumor agents). Furthermore, the nature of the MOF–PEG interaction is deeply investigated using high‐resolution soft X‐ray spectroscopy. Finally, a cell penetration study using the radio‐labeled antitumor agent gemcitabine monophosphate (³H‐GMP)‐loaded MIL‐100(Fe)@PEG NPs shows reduced macrophage phagocytosis, confirming a significant in vitro PEG furtiveness.     

Application of the OBD method for optimization of neural state variable estimators of the two-mass drive system

This paper presents neural estimators of the mechanical state variables of the electrical drive system with elastic joints. The non-measurable state variables, as the torsional torque and the load machine speed are estimated using multilayer feed-forward neural networks. The main stages of the design methodology of these neural estimators are presented. The optimal brain damage method is implemented for the structure optimization of each neural network. Then signals estimated by neural estimators are tested in the electrical drive control structure with additional feedbacks from the estimated shaft torque and the difference between the motor and the load speeds. The simulation results show good accuracy of both presented neural estimators for the wide range of changes of the reference speed and the load torque. The simulation results are then verified by laboratory experiments.     

A socioeconomic model for managing water resources in Alicante,southeast Spain

The study analyzes variables that influence the water management model based on a simultaneous relationship between competing demands in the Marina Baja of Alicante, using a methodology that directly links together urban and irrigation demands, making them relevant to each other from a socioeconomic point of view. The paper identifies the relevant variables of this model and the agent behaviour in a closed and joint system. It also aims to demonstrate that by considering water demands for different yet simultaneous uses and regarding clean and reclaimed waters as integrated resources, the system could remain in equilibrium without external water supplies.     

A collaborative platform for cognitive decision making in the Knowledge Society

Two key aspects of the Knowledge Society are the interconnection between the actors involved in the decision making processes and the importance of the human factor, particularly the citizen’s continuous learning and education. This paper presents a new module devoted to knowledge extraction and diffusion that has been incorporated into a previously developed decision making tool concerning the Internet and related with the multicriteria selection of a discrete number of alternatives (PRIOR-Web). Quantitative and qualitative procedures using data and text mining methods have been employed in the extraction of knowledge. Graphical visualisation tools have been incorporated in the diffusion stage of the methodological approach suggested when dealing with decision making in the Knowledge Society. The resulting collaborative platform is being used as the methodological support for the cognitive democracy known as e-cognocracy.