Linear and nonlinear viscoelastic and viscoplastic analysis of asphalt binders with warm mix asphalt additives

Warm-Mix Asphalt (WMA) is a widely used product, which proved a contribution to the reduction in asphalt mixing and compaction temperatures. This reduction leads to lower fuel consumption and smoke emission in asphalt plants. Most of the characterisation of binders used in WMA has focused in the past on measuring linear viscoelastic properties and associated Superpave parameters. Several studies have shown that the average stresses and strains of the asphalt mixture remain mostly within the linear viscoelastic response. However, localised strains in the binder phase of the mixture could reach values high enough to induce nonlinear viscoelastic and viscoplastic deformations. Therefore, this study focuses on an experimental and analytical evaluation of linear, nonlinear viscoelastic and viscoplastic responses of selected binders modified for use in WMA. The first part of the paper analyses the linear viscoelastic material properties and their ability to evaluate permanent deformation resistance. Then, the non-recoverable creep compliance parameter obtained from the Multiple Stress Creep Recovery (MSCR) test is analysed to assess the nonlinear response and permanent deformation of asphalt binders. The paper utilises a nonlinear plasto-viscoelastic (NPVE) approach to assess and quantify the nonlinear plasto-viscoelastic response of binders by separating the recoverable and irrecoverable strains measured in the MSCR test. Two WMA additives were included in this study by mixing them with polymer-modified and unmodified asphalt binders. Analysis of results showed that the NPVE approach captured a higher percentage of recovery than the NLVE approach. However, binder’s performance evaluation and ranking did not change by adopting the NPVE approach. The nonlinear viscoelastic parameters provided insight on the behaviour of asphalt binders mixed with WMA additives during loading cycles. Sasobit showed higher influence than Advera on binders in resisting permanent deformation by increasing the recoverable strain during the unloading phase.     

On Conducting Simultaneous Versus Sequential Engineering Activities in Risky R&D

We consider development projects in which the activities can be classified in two types: uncertainty resolving R&D efforts that accumulate in the form of knowledge to eventually give rise to a discovery; and routine technical investments which aim at improving the profitability of the project once the discovery has been made. The advantage of initiating routine preparatory activities prior to the achievement of a technological breakthrough in such projects, sometimes termed as 'concurrent engineering', is analyzed within a suitable dynamic optimization framework. The optimal double expenditure policy is compared with the conservative delayed investment policy, under which all the routine engineering activities are delayed until the risky R&D efforts culminate in a breakthrough. A criterion for the optimality of the latter policy is developed on the basis of the probability distribution of the discovery date. The application of the criterion is illustrated for a variety of specifications regarding the uncertainty associated with the R&D process. We find that simultaneous investment is the optimal policy for a large class of probability distributions. However, if the conditional probability of immediate discovery is always below some critical value, it is optimal to delay the preparatory activities until the discovery.     

Fault diagnosis using partial syndromes: a modified Hopfield neural network approach

This paper presents a modified Hopfield neural network (HNN) for solving the system-level fault diagnosis problem which aims at identifying the set of faulty nodes. This problem has been extensively studied in the last three decades. Nevertheless, identifying the set of all faulty nodes using only partial syndromes, i.e. when some of the testing or comparison outcomes are missing prior to initiating the diagnosis phase, remains an outstanding research issue. The new HNN-based diagnosis algorithm does not require any prior learning or knowledge about the system, nor about any faulty situation, hence providing a better generalisation performance. Results from a thorough simulation study demonstrate the effectiveness of the HNN-based fault diagnosis algorithm in terms of diagnosis correctness, diagnosis latency and diagnosis scalability, for randomly generated diagnosable systems of different sizes and under various fault scenarios. We have also conducted extensive simulations using partial syndromes. Simulations showed that the HNN-based diagnosis performed efficiently, i.e. diagnosis correctness was around 99% when at most half of the test or comparison outcomes are missing, making it a viable alternative to existing diagnosis algorithms.     

Osmotic Dehydration Kinetics of Pomegranate Seeds Using Date Juice as an Immersion Solution Base

Pomegranate seeds were osmodehydrated using date juice added with sucrose (final °Brix, 55) as immersion solution. The kinetics of osmotic dehydration showed that the most significant changes of mass transfer took place during the first 20 min of the process, regardless of date juice varieties. During this time, seed water loss and solid gain were estimated to be ∼39% and ∼6%, respectively. After 20 min of the process, the percentage of water loss and solid gain varied slightly and ranged on average close to ∼40% and ∼9%, respectively. During osmotic dehydration, there was a leaching of natural solutes from seeds into the solution, which is quantitatively not negligible, and might have an important impact on the sensorial and nutritional value of seeds and date juices. Both scanning electron microscopy and texture (compression) analysis revealed that osmotic dehydration process induced modifications of seed texture and cell structure. Sucrose was found to be the essential element which influences the texture of seed and the viscosity of date juice. Additionally, natural sugar present in date juice permits substituting 35% of the total quantity of sucrose added to the osmotic solution.     

Prediction of the rheology parameters of ripened semi-hard cheeses using fluorescence spectra in the UV and visible ranges recorded at a young stage

Storage modulus (G′), loss modulus (G″), strain, tan (δ) and complex viscosity (η*) of 20 semi-hard cheeses were measured by dynamic oscillatory analysis after 2, 30 and 60 days of ripening. On the same cheeses and at the same ages, tryptophan and riboflavin fluorescence spectra were recorded. The aim was to predict the rheology parameters of ripened cheeses from spectra recorded on these cheeses at a young stage. Using partial least square, tryptophan fluorescence spectra recorded at 20 °C on 2-days-old cheeses predicted G′, G″, strain, tan (δ) and η* measured at 80 °C on the 60-days-old cheeses with correlation coefficients (R) of 0.98, 0.97, 0.98, 0.98 and 0.97, respectively. Riboflavin fluorescence spectra gave slightly lower correlation coefficients of 0.88, 0.88, 0.92, 0.87 and 0.88, respectively. Dependent only on visible light, the riboflavin fluorescence spectra potentially provide viable and economic prediction of the rheology of ripe cheese.     

Utilisation of attenuated total reflectance MIR and front-face fluorescence spectroscopies for the identification of Saint-Nectaire cheeses varying by manufacturing conditions

Twelve samples of cheeses (three types of Saint-Nectaire PDO cheeses and Savaron cheeses) differing by manufacturing and ripening conditions, from 12 different producers, were characterised by attenuated total reflectance mid-infrared (MIR) and front-face fluorescence spectroscopies, dynamic testing rheology and physico-chemical analysis. Fluorescence spectra (tryptophan residues, vitamin A and riboflavin) and MIR (3,000–2,800 (fat region), 1,700–1,500 (protein region) and 1,500–900 cm⁻¹ (fingerprint region)) spectra were recorded on cheese samples. The potential of the data tables was investigated for discriminating the four different groups of cheeses. The results of factorial discriminant analysis (FDA) performed on the fluorescence and mid-infrared spectra showed a good discrimination of the four cheese groups. Considering cross-validation results, the best classifications (100%) were achieved from mid-infrared and fluorescence spectra, while only 91.7 and 72.2% of correct classification were obtained by applying FDA to rheology and physico-chemical data, respectively. Spectroscopic techniques could provide useful fingerprints and allow the identification of investigated cheeses according to manufacturing conditions. Simple and rapid spectroscopic methods offer a promising approach to the authentication of cheeses.     

A decision model for operating two alternate energy generating systems

A solar energy generating system is to be supplemented by two conventional generating systems. A method for investigating the effect of various operating policies of the combined system on the productivity and availability is developed. Using a Markov-Chain approach for computing the long-run expectations of the objectives it was demonstrated that the use of a stand-by mode for one of the conventional systems was optimal. Numerical examples are presented to demonstrate the methodology both for special and more general cases.     

An evolutionary algorithm for abductive reasoning

Abductive reasoning (or abduction) is the process of inferring hypotheses from observed data using a certain ‘knowledge’ encoded in the form of inference rules (or causal relations). Many important kinds of intellectual tasks, including medical diagnosis, fault diagnosis, scientific discovery, legal reasoning, and natural language understanding have been characterised as abduction. Unfortunately, abduction is 𝒩𝒫-hard. Genetic algorithms and biologically motivated computational paradigms inspired by the natural evolution turned out to be efficient in solving many hard problems while other existing approaches failed to solve in general. In this article, we present a genetic algorithm called HAKIM, for solving abduction problems. We encode an explanation in a chromosome-like structure, where every gene models a possible single hypothesis. Thereafter, we develop a fitness function that characterises the overall ‘quality’ of a chromosome representing an explanation; and then use standard genetic operators to compute a set of hypotheses that best explains the observed data. Simulation results on large-scale medical problems reveal the good performance of our model HAKIM.     

Speech recognition in living rooms: Integrated speech enhancement and recognition system based on spatial,spectral and temporal modeling of sounds

Research on noise robust speech recognition has mainly focused on dealing with relatively stationary noise that may differ from the noise conditions in most living environments. In this paper, we introduce a recognition system that can recognize speech in the presence of multiple rapidly time-varying noise sources as found in a typical family living room. To deal with such severe noise conditions, our recognition system exploits all available information about speech and noise; that is spatial (directional), spectral and temporal information. This is realized with a model-based speech enhancement pre-processor, which consists of two complementary elements, a multi-channel speech–noise separation method that exploits spatial and spectral information, followed by a single channel enhancement algorithm that uses the long-term temporal characteristics of speech obtained from clean speech examples. Moreover, to compensate for any mismatch that may remain between the enhanced speech and the acoustic model, our system employs an adaptation technique that combines conventional maximum likelihood linear regression with the dynamic adaptive compensation of the variance of the Gaussians of the acoustic model. Our proposed system approaches human performance levels by greatly improving the audible quality of speech and substantially improving the keyword recognition accuracy.     

Fluorescence spectroscopy coupled with factorial discriminant analysis technique to identify sheep milk from different feeding systems

Rapid measurements of milk properties and discrimination of milk origin are necessary techniques for quality control of milk products. The present study was undertaken to evaluate the potential of using front face fluorescence spectroscopy (FFFS) and synchronous fluorescence spectroscopy (SFS) for monitoring the quality of forty-five ewe’s milk samples originating from different feeding systems. Physico-chemical analyses and fluorescence spectra were conducted on samples during lactation periods (the first 11 weeks). The principal component analysis (PCA) separately applied to the physico-chemical and fluorescence spectral data showed only small discrimination between milk samples based on lactation periods and diet compositions. Similar results were obtained by separately applying factorial discriminant analysis (FDA) on each technique. In a second step, concatenation technique were applied to FFF spectra acquired after excitation set at 250, 290, 380 nm and emission set at 410 nm. Results obtained showed a good discrimination among milk samples with regard to feeding systems given to the ewes throughout the lactation periods. In addition, a better discrimination was observed with FFFS than with SFS.