Effect of the percentage carbon equivalent on the nodule characteristics,density and modulus of elasticity of ductile cast iron

Successful melting and Mg treatment were conducted using a vortex unit which produced spheroidal graphite (SG) Fe ingots having a percentage carbon equivalent (%CE) ranging from 3.782 to 5.240. Microstructure examination revealed graphite nodules embedded in a white matrix (unetched condition). The variation of %CE proved to have a pronounced effect on the nodule characteristics. The nodule count showed a maximum value at %CE of 4.613. For metal-mould ingots it reached a value of 1584 nodules mm^–2, while for sand-mould ingots it reached a value of 970 nodules mm^–2. Nodule size was found to be inversely proportional to the nodule count for both sand- and metal-mould ingots. The nodularity of all ingots was, in general, higher than 95%. The density of SG Fe was found to decrease gradually (at %CE=3.782) from 7242 to 6969 kg m^–3. The modulus of elasticity (E) showed a boat-like curve having minimum values of 189 and 192 GPa for the sand and metal moulds, respectively.     

Constraints on CD4 recovery postchemotherapy in adults: thymic insufficiency and apoptotic decline of expanded peripheral CD4 cells

To examine the mechanisms of CD4 reconstitution in an adult population, lymphocyte repopulation was assessed following dose-intense chemotherapy in 25 breast cancer patients, ages 33 to 69 years. Chemotherapy resulted in a greater than 60% reduction in total CD4 T cells and, in particular, a greater than 90% loss of the CD45RA+ CD4 cells. CD4 recovery was protracted, achieving less than 50% of pretreatment levels after 12 to 14 months. Two facets of the CD4 recovery were notable. First, generation of CD45RA+ CD4 cells played only a minor role in the first year, suggesting that thymic production was not the main route of CD4 regeneration. Indeed, recovery of CD45RA+ CD4 cell levels remained limited in half of the patients even after 2 years. Second, expansion of the mature peripheral CD4 cells (CD45RO+) remaining after chemotherapy was the main source of early CD4 repopulation, peaking at 3 to 6 months postchemotherapy. This expansion was limited in duration, however, and was followed by a secondary decline, such that the total CD45RO+ CD4 levels at 9 to 12 months were lower than at 6 months. When stimulated by mitogens, an increased susceptibility to apoptosis was observed in postchemotherapy CD4 cells as compared with those from normal donors. The elevated expression of markers such as HLA-DR during chemotherapy and for several months postchemotherapy is consistent with the presence of an activated T-cell population. CD4 apoptotic frequency correlated with the frequency of HLA-DR expression on T cells. Thus, CD4 recovery is constrained in adults by a limited thymic regenerative capacity and by an increased susceptibility to apoptosis within the expanding peripheral CD4 population.     

Shape estimation of distorted flexible structures

The present work discusses the optimal placement of sensors in truss structures in order to obtain best possible information regarding the distortions of the structure. The estimation goal is to reconstruct the deformed shape of the structure, at the controlled degrees of freedom, from the sensor readings. A basic assumption is that the structure is subjected to a parametric disturbance field. We distinguish between disturbances which cause uniform or arbitrary distortions of the structure, and disturbances which cause structured distortions. Uniform distortions can be construed as white noise, that is, distortions which have no characteristics. Structured distortions are chromatic, they have some characteristics which can be helpful in estimating the shape. Although the disturbance in either case is random it is assumed that its magnitude is confined to a hyper sphere. The estimator is based on the least squares method, hence the estimated shape is the one with least RMS displacement for the given sensor readings. To evaluate the performance of each set of sensors a measure is derived based on the concept of the worst case distortion. The measure is the largest possible error between the estimated and the actual displacements, at the CDOF. For small number of sensors all possible arrangements can be generated and compared. Larger trusses with a moderate number of sensors generate prohibitively large number of possible configurations, hence heuristic search techniques are employed. The theory has been applied to 2D and 3D flexible trusses. Results show that for reasonable shape estimation a relatively large number of sensors is needed. It is also shown that when using sensors which measure mainly the distortions of the controlled degrees of freedom, significant improvements in the shape estimation can be obtained.     

Energy inefficiency diagnosis in industrial process through one-class machine learning techniques

In the era of Industry 4.0, the ease of access to precise measurements in real-time and the existence of machine-learning (ML) techniques will play a vital role in building practical tools to isolate inefficiencies in energy-intensive processes. This paper aims at developing an abnormal event diagnosis (AED) tool based on ML techniques for monitoring the operation of industrial processes. This tool makes it easier for operators to accomplish their tasks and to make quick and accurate decisions to ensure highly efficient processes. One of the most popular ML techniques for AED is the multivariate statistical control (MSC) method; it only requires the dataset of the normal operating conditions (NOC) to detect and identify the variables that contribute to abnormal events (AEs). Despite the popularity of MSC, it is challenging to select the appropriate method for detecting and isolating all possible abnormalities a complex industrial process can experience. To address this limitation and improve efficiency, we have developed a generic methodology that integrates different ML techniques into a unified multiagent based approach, the selected ML techniques are supposed to be built using only the normal operating condition. For the sake of demonstration, we chose a combination of two ML methods: principal component analysis and k-nearest neighbors (k-NN). The k-NN was integrated into the proposed multiagent to take into account the nonlinearity and multimodality that frequently occur in industrial processes. In addition, we modified a k-NN method proposed in the literature to reduce computation time during real-time detection and isolation. Finally, the proposed methodology was successfully validated to monitor the energy efficiency of a reboiler located in a thermomechanical pulp mill.

     

Analysis of information exchange activities to actualize and validate situation awareness during shift changeovers in nuclear power plants

Shift work situations occur in almost all safety‐critical organizations, and the investigations of some catastrophes like Chernobyl, Exxon Valdez, and the Gol/Legacy mid‐air collision indicated that shift work information exchange played an important role during the evolution of the situation before the accidents. Inadequate communications during shift changeovers challenged operators' work in the moments that preceded these accidents, because they got inadequate information about the current situation. Our research focuses on the information exchange activities (verbal, written, and nonverbal) of nuclear power plant control operators during shift changeovers. Our aim is to investigate how verbal exchanges and other representations enable operator crews to share information regarding the events that occurred in the previous shift to achieve adequate situation awareness. Our findings indicated the importance and richness of the information exchange during the shift changeover process to update and validate individual and collective situation awareness, showing that information adequately shared enables the ad hoc configurations of regulation loops and a safer use of simplified strategies that can be understood and be validated by other operators, reducing the occurrence of cognitive overloads and contributing to the construction of a common cognitive ground that enhances system resilience. © 2010 Wiley Periodicals, Inc.     

Surface Modification of Titania Nanoparticles Using Ultrathin Ceramic Films

Surface modification of titania nanoparticles was achieved by coating them with ultrathin alumina films using atomic layer deposition. The coating process was performed in a fluidized bed reactor at low pressure and under mechanical vibration. Films were deposited using self-limiting, sequential surface reactions of trimethylaluminum and water. The composition of alumina-coated particles was verified using infrared spectroscopy. The deposited films had an average growth rate of 0.2 nm/coating cycle and were highly uniform and conformal as demonstrated by transmission electron microscopy and X-ray spectroscopy. Deposited alumina films were amorphous as verified through X-ray diffraction and high-resolution electron microscopy. The coating process did not promote particle sintering as validated via particle size and surface area analysis.     

Cryptanalysis of an embedded systems’ image encryption

Multimedia Tools and Applications - Recently, a new image encryption-scheme for embedded systems based on continuous third-order hyperbolic sine chaotic system, has been proposed. The...     

Use of acoustic emission technique for the early detection of aluminum alloys exfoliation corrosion

Exfoliation corrosion of aluminum alloys is a form of localized corrosion which affects many industries, specially aeronautics. The study of this corrosion mode using only electrochemical techniques is not fully efficient for the detection and control on line of this phenomenon. Therefore, we developed a non-destructive testing technique based on the acoustic emission recordings in order to follow-up this form of corrosion on aluminum alloys. Indeed, recent works have shown the interest of the acoustic emission for the detection, the monitoring and the localization of pitting corrosion on aluminum alloys. This pitting corrosion phenomenon is currently well understood and the experimental methodology acquired during that study is transposed to the study of exfoliation corrosion of aluminum alloys.The present study is conducted on two aluminum alloys: (Al 2024 T3, and Al 7449 T6 and T7). Samples are immersed 4 days in the modified ASTM STP 1134 saline solution. Observations of the structures after tests show that the exfoliation corrosion sensitivity of alloy 7449 T6 is more important than for alloy 7449 T7 which exhibits only the presence of small and non-occluded pits. Very severe exfoliation corrosion was also observed on Al 2024 T3, but after a longer immersion time or in a more acid solution.The recording of the acoustic emission activity shows evident links between this activity and the exfoliation corrosion rate. The analysis of the signal's characteristics reveals a population corresponding to the release of hydrogen bubbles. A few more energetic signals have also been observed. Their source can be either, the cracking resulting from the separation of sheets of metal, or the development and evolution of hydrogen bubbles formed inside blisters during exfoliation corrosion.     

Siamese multi-layer perceptrons for dimensionality reduction and face identification

This paper presents a framework using siamese Multi-layer Perceptrons (MLP) for supervised dimensionality reduction and face identification. Compared with the classical MLP that trains on fully labeled data, the siamese MLP learns on side information only, i.e., how similar of data examples are to each other. In this study, we compare it with the classical MLP on the problem of face identification. Experimental results on the Extended Yale B database demonstrate that the siamese MLP training with side information achieves comparable classification performance with the classical MLP training on fully labeled data. Besides, while the classical MLP fixes the dimension of the output space, the siamese MLP allows flexible output dimension, hence we also apply the siamese MLP for visualization of the dimensionality reduction to the 2-d and 3-d spaces.