Dysprosium doped copper oxide (Cu1-xDyxO) nanoparticles enabled bifunctional electrode for overall water splitting

The production of hydrogen, a favourable alternative to an unsustainable fossil fuel remains as a significant hurdle with the pertaining challenge in the design of proficient, highly productive and sustainable electrocatalyst for both oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). Herein, the dysprosium (Dy) doped copper oxide (Cu_1-xDy_xO) nanoparticles were synthesized via solution combustion technique and utilized as a non-noble metal based bi-functional electrocatalyst for overall water splitting. Due to the improved surface to volume ratio and conductivity, the optimized Cu_1-xDy_xO (x = 0.01, 0.02) electrocatalysts exhibited impressive HER and OER performance respectively in 1 M KOH delivering a current density of 10 mAcm^?2 at a potential of ?0.18 V vs RHE for HER and 1.53 V vs RHE for OER. Moreover, the Dy doped CuO electrocatalyst used as a bi-functional catalyst for overall water splitting achieved a potential of 1.56 V at a current density 10 mAcm^?2 and relatively high current density of 66 mAcm^?2 at a peak potential of 2 V. A long term stability of 24 h was achieved for a cell voltage of 2.2 V at a constant current density of 30 mAcm^?2 with only 10% of the initial current loss. This showcases the accumulative opportunity of dysprosium as a dopant in CuO nanoparticles for fabricating a highly effective and low-cost bi-functional electrocatalyst for overall water splitting.     

Reoptimization framework and policy analysis for maritime inventory routing under uncertainty

We study a maritime inventory routing problem, in which shipments between production and consumption nodes are carried out by a fleet of vessels. The vessels have specific capacities and can be chartered under different agreements. The inventory levels of all consumption nodes and some production nodes should be maintained within specified bounds; for the remaining production nodes, orders should be picked up within pre-defined time windows. We propose a discrete-time mixed-integer programming model. In the face of new information and uncertainty, this optimization model has to be re-solved, as the horizon is rolled forward. We discuss how to account for different sources of uncertainty. We present a rolling-horizon reoptimization framework that allows us to study different policies that impact the quality of the implemented solution, so we can identify the optimal set of policies.     

Scalability and performance of an agent‐based network management middleware

Rapid growth of computer network sizes and uses necessitate analysis of network application middleware in terms of its scalability as well as performance. In this paper we analyze a distributed network management middleware based on agents that can be dispatched to locations where they can execute close to the managed nodes. The described middleware operates between the network protocol layer and the application layer and uses standard TCP protocol and SNMP probes to interface the network. By aggregating requests from many users into a single agent, our system allows multiple managers to probe problem areas with minimal management traffic overhead. We discuss and quantify the benefits of the described middleware by implementing real‐time network managers using our system. The main result of this paper is a comparison of scalability and efficiency of our agent‐based management middleware and traditional SNMP‐based data collection. To this end, we measured traffic in both real and simulated networks. In the latter case, we designed, used and described here a method of separating simulated application flow into separate subflows to simplify design of simulations. Copyright © 2004 John Wiley & Sons, Ltd.     

The role of perspective in the accessibility of goals during reading.

An implicit assumption of several causal reasoning models is that readers adopt the goals of a narrative's protagonist during text comprehension. In apparent violation of this assumption, readers participating in Experiment 1 of the present study drew inferences relevant to a protagonist's goal even when that goal was already satisfied from the perspective of the protagonist. In Experiments 2 and 3, participants were explicitly asked to view the text situation from the point of view of the protagonist. In this case, the goals of the reader and the protagonist should be the same. In these experiments, participants focused on the goals of the protagonist only when those goals had not been satisfied from the perspective of the protagonist. These results are discussed in terms of reader- and character-based perspectives and in terms of text characteristics that cue perspective taking. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Study of a BGO calorimeter using electron and hadron beams from 1 to 50 GeV

A calorimeter of 25 bismuth germanate (BGO) crystals equipped with silicon photodiode readout has been tested at the CERN SPS in the energy range 1–50 GeV. The response for electrons has been shown to be linear in this energy range and the rms resolution obtained ( ) is approximately 1%, for E > 4 GeV. The electron/pion separation was found to be better than 1:500 in the energy range 1–20 GeV. Data on lateral and longitudinal shower development were compared with the results of a Monte Carlo simulation using the SLAC-EGS program and found to be in good agreement.     

Transparent monolithic zirconia gels: effects of acetylacetone content on gelation

Transparent yttria-stabilized zirconia monolithic gels were synthesized under controlled conditions for hydrolysis and chemical polymerization. The influence of acetylacetone on the previous chemical reactions was explored, and a plausible explanation was proposed concerning the outstanding role of this additive during the gelation process. Small-angle X-ray scattering was used to estimate the size of the polymeric species and to investigate the influence of acetylacetone on the structure of the gels prepared.     

Drying Kinetics of a Porous Spherical Particle and the Inversion Temperature

A model is presented for drying of a single porous particle with superheated steam and humid air. Experimental data for spherical porous ceramic particle reported in the literature were used for the validation of the model. An inversion temperature at which the evaporation rates within superheated steam and humid air are equal was predicted. The effect of thermophysical properties of the particle (permeability 10^-14 - 10^-17 m², diameter 3 × 10^-3 - 10 × 10^-3 m) and operating variables (gas mass flux 0.26 - 0.78 kg m^-2 s^-1, drying agent temperature 120-200°C) is tested. The inversion temperature is shown to be affected by the thermophysical properties of the porous particle and of the drying agent.     

Learning Automata from Ordered Examples

Connectionist learning models have had considerable empirical success, but it is hard to characterize exactly what they learn. The learning of finite-state languages (FSL) from example strings is a domain which has been extensively studied and might provide an opportunity to help understand connectionist learning. A major problem is that traditional FSL learning assumes the storage of all examples and thus violates connectionist principles. This paper presents a provably correct algorithm for inferring any minimum-state deterministic finite-state automata (FSA) from a complete ordered sample using limited total storage and without storing example strings. The algorithm is an iterative strategy that uses at each stage a current encoding of the data considered so far, and one single sample string. One of the crucial advantages of our algorithm is that the total amount of space used in the course of learning for encoding any finite prefix of the sample is polynomial in the size of the inferred minimum state deterministic FSA. The algorithm is also relatively efficient in time and has been implemented. More importantly, there is a connectionist version of the algorithm that preserves these properties. The connectionist version requires much more structure than the usual models and has been implemented using the Rochester Connectionist Simulator. We also show that no machine with finite working storage can iteratively identify the FSL from arbitrary presentations.