A critical discussion on rebar electrical continuity and usual interpretation thresholds in the field of half-cell potential measurements in steel reinforced concrete

Corrosion of steel in reinforced concrete structures is a recurrent problem affecting civil engineering structures and costing the world billions of dollars per year. This physical phenomenon mainly results from chloride ingress or concrete carbonation. Corrosion can be diagnosed through a nondestructive method such as half-cell potential measurements. The present paper studies this method on a reinforced concrete wall containing eighteen unconnected steel bars and subjected to chloride-induced macrocell corrosion. Three corrosion systems with different configurations of connections between the steel bars are generated, involving three different anode-to-cathode surface ratios. Then, half-cell potential variations are observed versus macrocell corrosion current. The results lead to a critical discussion regarding the physical relevance of the usual potential threshold method to detect corroding rebars in reinforced concrete structures. In addition, the experiments demonstrate that electrical continuity between reinforcing steel bars is not necessary to get meaningful information about the macrocell corrosion system. At last, the paper show that the electric field (potential gradient) relative to a macrocell corrosion system may be measured by connecting the measurement system (reference electrode?+?voltmeter) to any electrochemical system in electrolytic contact with the concrete.     

Formation of Cu Nanodots on Diamond Surface to Improve Heat Transfer in Cu/D Composites

Diamond‐dispersed copper matrix (Cu/D) composite materials with different interfacial configurations are fabricated through powder metallurgy and their thermal performances are evaluated. An innovative solution to chemically bond copper (Cu) to diamond (D) has been investigated and compared to the traditional Cu/D bonding process involving carbide‐forming additives such as boron (B) or chromium (Cr). The proposed solution consists of coating diamond reinforcements with Cu particles through a gas–solid nucleation and growth process. The Cu particle‐coating acts as a chemical bonding agent at the Cu–D interface during hot pressing, leading to cohesive and thermally conductive Cu/D composites with no carbide‐forming additives. Investigation of the microstructure of the Cu/D materials through scanning electron microscopy, transmission electron microscopy, and atomic force microscopy analyses is coupled with thermal performance evaluations through thermal diffusivity, dilatometry, and thermal cycling. Cu/D composites fabricated with 40 vol% of Cu‐coated diamonds exhibit a thermal conductivity of 475 W m^?1 K^?1 and a thermal expansion coefficient of 12 × 10^?6 °C^?1. These promising thermal performances are superior to that of B‐carbide‐bonded Cu/D composites and similar to that of Cr‐carbide‐bonded Cu/D composites fabricated in this study. Moreover, the Cu/D composites fabricated with Cu‐coated diamonds exhibit higher thermal cycling resistance than carbide‐bonded materials, which are affected by the brittleness of the carbide interphase upon repeated heating and cooling cycles. The as‐developed materials can be applicable as heat spreaders for thermal management of power electronic packages. The copper‐carbon chemical bonding solution proposed in this article may also be found interesting to other areas of electronic packaging, such as brazing solders, direct bonded copper substrates, and polymer coatings.

     

Tunable electrochemistry of gold-silver alloy nanoshells

Nano Research - The widespread and increasing interest in enhancing biosensing technologies by increasing their sensitivities and lowering their costs has led to the exploration and application of...     

Interprocedural Array Region Analyses

Many program optimizations require exact knowledge of the sets of array elements that are referenced in or that flow between statements or procedures. Some examples are array privatization, generation of communications in distributed memory machines, or compile-time optimization of cache behavior in hierarchical memory machines. Exact array region analysis is introduced in this article. These regions exactly represent the effects of statements and procedures upon array variables. To represent the flow of these data, we also introduce two new types of array region analyses: in and out regions. The intraprocedural propagation is presented, as well as a general linear framework for interprocedural analyses, which handles array reshapes. The intra- and inter-procedural propagation of array regions is implemented in pips, the interprocedural parallelizer of fortran programs developed at École des mines de Paris.     

Filtering artificial texts with statistical machine learning techniques

Fake content is flourishing on the Internet, ranging from basic random word salads to web scraping. Most of this fake content is generated for the purpose of nourishing fake web sites aimed at biasing search engine indexes: at the scale of a search engine, using automatically generated texts render such sites harder to detect than using copies of existing pages. In this paper, we present three methods aimed at distinguishing natural texts from artificially generated ones: the first method uses basic lexicometric features, the second one uses standard language models and the third one is based on a relative entropy measure which captures short range dependencies between words. Our experiments show that lexicometric features and language models are efficient to detect most generated texts, but fail to detect texts that are generated with high order Markov models. By comparison our relative entropy scoring algorithm, especially when trained on a large corpus, allows us to detect these “hard” text generators with a high degree of accuracy.     

A probabilistic reputation model based on transaction ratings

This work introduces a probabilistic model allowing to compute reputation scores as close as possible to their intrinsic value, according to the model. It is based on the following, natural, consumer-provider interaction model. Consumers are assumed to order items from providers, who each has some intrinsic, latent, “quality of service” score. In the basic model, the providers supply the items with a quality following a normal law, centered on their intrinsic “quality of service”. The consumers, after the reception and the inspection of the item, rate it according to a linear function of its quality - a standard regression model. This regression model accounts for the bias of the consumer in providing ratings as well as his reactivity towards changes in item quality. Moreover, the constancy of the provider in supplying an equal quality level when delivering the items is estimated by the standard deviation of his normal law of item quality generation. Symmetrically, the consistency of the consumer in providing similar ratings for a given quality is quantified by the standard deviation of his normal law of ratings generation. Two extensions of this basic model are considered as well: a model accounting for truncation of the ratings and a Bayesian model assuming a prior distribution on the parameters. Expectation-maximization algorithms, allowing to estimate the parameters based on the ratings, are developed for all the models. The experiments suggest that these models are able to extract useful information from the ratings, are robust towards adverse behaviors such as cheating, and are competitive in comparison with standard methods. Even if the suggested models do not show considerable improvements over other competing models (such as Brockhoff and Skovgaard’s model [12]), they, however, also permit to estimate interesting features over the raters - such as their reactivity, bias, consistency, reliability, or expectation.     

The musculoskeletal system complaints of office workers at a vehicle production factory

This study aimed to determine the prevalence of and factors contributing to musculoskeletal system complaints (MSC) in office workers of a vehicle production factory and absenteeism due to these complaints. A total of 86.2% (n = 333) of the workers completed the survey. Of the workers, 73 (21.9%) were women, and 260 (78.1%) were men, and the mean age was 36.9 ± 8.75 years. The majority of the personnel had more than one symptom with a prevalence of 80.8%. The most frequently observed discomfort was tension and pain in the back muscles (58.6%) and in the neck muscles (58.3%), followed by low back pain (41%) and shoulder pain (30.3%). Pain in the shoulders, neck, and the back and pain and numbness in the fingers were statistically significantly more prevalent in employees who spent more than 5 hours per day performing desk work. Furthermore, all of the complaints, excluding pain in the elbows and knees, were statistically significantly more common in the women. When each complaint and the effects of contributing risk factors were analyzed by logistic regression analysis, pain in the shoulders, neck, and back; pain that spread to the arms; and pain in the fingers were correlated with longer work hours and the female gender (p < 0.05). The number of employees who had used sick leave within the past year due to MSCs was 11.7%. In conclusion, duration of work hours, frequency of breaks, and gender were determined as the most important risk factors in computer users regarding MSCs. © 2011 Wiley Periodicals, Inc.     

Preserving spatial information and overcoming variations in appearance for face recognition

Local feature-based approaches mainly aim to achieve robustness to variations in facial images by assuming that only some parts of the facial images may be affected. However, such approaches may lose spatial information. In this study, a compromise feature extraction scheme is studied which extracts local features while preserving spatial information. The proposed scheme exploits an ensemble of classifiers where each member is constructed using randomly selected design parameters including the size, number and location of sub-images for local feature extraction. Experiments conducted on FERET and ORL databases have shown that proposed scheme surpasses the local feature-based reference systems which focus on either local information or preserving spatial information.