Interfacial electron density profile in Nb/Si bilayer films: an X-ray reflectivity study

This article describes a systematic study of the nature of interfaces involved in a Nb layer deposited on Si (Nb-on-Si) and Si layer deposited on Nb (Si-on-Nb) bilayer films by using a UHV electron beam evaporation technique, having individual layer thickness of 35 and 100 Å each. By using Grazing angle X-ray reflectivity and adopting a proper modelling technique the electron density profile (EDP) as a function of depth has been determined in the samples. EDP determined in as-deposited 35 Å Nb and 35 Å Si bilayer films show that the width of Si-on-Nb and Nb-on-Si interfaces are 20 Å and 40 Å, respectively. The difference observed in the width of two interfaces is attributed to the different growth morphology of 35 Å Nb and 35 Å Si single-layer films as revealed by atomic force microscopy (AFM) investigations. EDP determined from measured XRR data for 100 Å Nb and 100 Å Si deposited bilayer film shows that the width of Si-on-Nb interface is 10 Å. This observed width is smaller than the similar interface in the case of samples having an individual layer thickness of 35 Å. The corresponding interface width of Nb-on-Si is found to be 45 Å and marginally more than the similar interface in the case of the 35 Å Nb/35 Å Si bilayer samples. AFM studies carried out on 100 Å Nb and Si layers deposited separately on float glass substrate indicate similar gross as well as subtle morphological features and cannot be attributed to the observed asymmetry in this case. The observed asymmetry in EDP of two interfaces in this case is due to the enhanced diffusion of Si into the formed metal layer relative to the diffusion into the already deposited metal layer.     

Estimation of composite damage model parameters using spectral finite element and neural network

A multi-layer perceptron (MLP) network using error back propagation algorithm is employed in this paper to estimate the damage parameters from broad-band spectral data as diagnostic signal. Various existing models of damage in laminated composite and the resulting stiffness degradation are discussed from comparative view-point. Degradation of ply properties can be considered to be one of the damage model parameters while monitoring transverse matrix cracks in cross-ply, splitting in longitudinal ply, and evolution of consecutive stages of damage, such as delaminations and fiber fracture. The stiffness degradation factor, the location and size of the damaged zone in laminated composite beam are considered as damage model parameters in the present paper. Fourier spectral data, which is typical to most of the diagnostic wave measurements, are used as input to the neural network. Since, training the neural network in such case involves many data sets and all of these data are difficult to generate using experiments, a spectral finite element model (SFEM) with embedded degraded zone in laminated composite beam is developed. Numerical simulation using this element is carried out, which shows the nature of temporal signal that are likely to be measured. Analytical studies on the performance of the neural network are presented based on numerically simulated data. Effect of measurement noise on the network performance is also reported.     

Structural Health Monitoring by Piezo–Impedance Transducers. II: Applications

This paper, the second in a two-part series, presents a new methodology for structural identification and nondestructive evaluation by piezo–impedance transducers. The theoretical development and experimental validation of the underlying lead–zirconium–titanate (PZT)–structure interaction model was presented in the first part. In our newly proposed method, the damage in evaluated on the basis of the equivalent system parameters “identified” by the surface-bonded piezo–impedance transducer. As proof of concept, the proposed method is applied to perform structural identification and damage diagnosis on a representative lab-sized aerospace structural component. It is then extended to identify and monitor a prototype reinforced concrete bridge during a destructive load test. The proposed method was found to be able to successfully identify as well as evaluate damages in both the structures.     

MARKOV DECISION PROCESSES WITH UNCERTAIN TRANSITION RATES: SENSITIVITY AND MAX HYPHEN MIN CONTROL

Solution techniques for Markov decision problems rely on exact knowledge of the transition rates, which may be difficult or impossible to obtain. In this paper, we consider Markov decision problems with uncertain transition rates represented as compact sets. We first consider the problem of sensitivity analysis where the aim is to quantify the range of uncertainty of the average per‐unit‐time reward given the range of uncertainty of the transition rates. We then develop solution techniques for the problem of obtaining the max‐min optimal policy, which maximizes the worst‐case average per‐unit‐time reward. In each of these problems, we distinguish between systems that can have their transition rates chosen independently and those where the transition rates depend on each other. Our solution techniques are applicable to Markov decision processes with fixed but unknown transition rates and to those with time‐varying transition rates.     

A note on productivity gains in flexible robotic cells

Flexible robotic cells combine the capabilities of robotic flow shops with those of flexible manufacturing systems. In an m-machine flexible cell, each part visits each machine in the same order. However, the m operations can be performed in any order, and each machine can be configured to perform any operation. We derive the maximum percentage increase in throughput that can be achieved by changing the assignment of operations to machines and then keeping that assignment constant throughout a lot's processing. We find that no increase can be gained in two-machine cells, and that the gain in three- and four-machine cells each is at most 14 %.     

Meta-cognitive RBF Network and its Projection Based Learning algorithm for classification problems

‘Meta-cognitive Radial Basis Function Network’ (McRBFN) and its ‘Projection Based Learning’ (PBL) algorithm for classification problems in sequential framework is proposed in this paper and is referred to as PBL-McRBFN. McRBFN is inspired by human meta-cognitive learning principles. McRBFN has two components, namely the cognitive component and the meta-cognitive component. The cognitive component is a single hidden layer radial basis function network with evolving architecture. In the cognitive component, the PBL algorithm computes the optimal output weights with least computational effort by finding analytical minima of the nonlinear energy function. The meta-cognitive component controls the learning process in the cognitive component by choosing the best learning strategy for the current sample and adapts the learning strategies by implementing self-regulation. In addition, sample overlapping conditions are considered for proper initialization of new hidden neurons, thus minimizes the misclassification. The interaction of cognitive component and meta-cognitive component address the what-to-learn, when-to-learn and how-to-learn human learning principles efficiently. The performance of the PBL-McRBFN is evaluated using a set of benchmark classification problems from UCI machine learning repository and two practical problems, viz., the acoustic emission signal classification and the mammogram for cancer classification. The statistical performance evaluation on these problems has proven the superior performance of PBL-McRBFN classifier over results reported in the literature.     

Investigating the multi-causal and complex nature of the accident causal influence of construction project features

Construction project features (CPFs) are organisational, physical and operational attributes that characterise construction projects. Although previous studies have examined the accident causal influence of CPFs, the multi-causal attribute of this causal phenomenon still remain elusive and thus requires further investigation. Aiming to shed light on this facet of the accident causal phenomenon of CPFs, this study examines relevant literature and crystallises the attained insight of the multi-causal attribute by a graphical model which is subsequently operationalised by a derived mathematical risk expression that offers a systematic approach for evaluating the potential of CPFs to cause harm and consequently their health and safety (H&S) risk implications. The graphical model and the risk expression put forth by the study thus advance current understanding of the accident causal phenomenon of CPFs and they present an opportunity for project participants to manage the H&S risk associated with CPFs from the early stages of project procurement.     

Low temperature densification by lithium co-doping and its effect on ionic conductivity of samarium doped ceria electrolyte

Low temperature densification and improving the ionic conductivity of doped ceria electrolyte is important for the realization of efficient intermediate temperature solid oxide fuel cell system. Herein, we report the effect of lithium co-doping (1, 3, 5 and 7?mol%) in 20?mol% samarium doped ceria on the low temperature sinterability and conductivity. The synthesized nanoparticles by citrate-nitrate combustion method showed a decrease in lattice parameter and increase in oxygen vacancy with lithium content after calcination due to the substitution of Li⁺ into CeO₂ lattice. Upon sintering at 900?°C, the density improved and reached a maximum value of 98.6% for 5% Li which exhibited a dense microstructure than at 7% Li. 5%Li co-doping exhibited the best conductivity of 3.65?×?10^?04–1.81?×?10^?3 S?cm^?1 in the operative temperature range of IT-SOFC (550–700?°C).Our results demonstrate the significance of lithium as co-dopant for efficient low temperature sintering as well as improving the electrolyte conductivity.     

CVD synthesis of graphene nanoplates on MgO support

Synthesis of graphene directly on MgO has been carried out and the structural properties of the obtained material have been investigated. Few-layered graphene was produced by simple thermal decomposition of methane over MgO powder at 950 °C in a CVD reactor. The samples were purified by 10 N HNO₃ treatment, and studied by TEM, Raman spectroscopy, EDAX and SEM. TEM clearly indicated the formation of graphene. EDAX showed that the purified sample contained only carbon and no traces of MgO. The characteristic Raman features of graphene were also seen as D-band at 1316 cm^?1, G-band at 1602 cm^?1, and a small 2D-band at 2700 cm^?1 in the Raman spectra. The strong D-band suggests that the graphene possess large number of boundary defects. The small 2D-band indicates the formation of few-layered graphene.     

Stability Analysis of a Human Arm Interacting with a Force Augmenting Device

This paper presents a stability analysis of the interaction between a human and a linear moving Force Augmenting Device (FAD). The analysis employs a mathematical model of the human arm, the FAD and their interaction. As a depart from past works, this article presents a stability analysis considering time-delays in the human model. A key ingredient in the analysis is the use of the Rekasius substitution for replacing the time-delay terms. It is proved that the human machine interaction is stable when the human model has no delays. When delays are considered in the human model, the analysis provides an upper bound for the time-delays preserving a stable interaction. Numerical simulations allow to assess the human-FAD interaction. An experiment is performed with a laboratory prototype, where a human operator lifts a load. It is observed that the human machine interaction is stable and the human operator is able to move the load to a desired position by experiencing very little effort.