Coping with production time variability via dynamic lead-time quotation

In this paper, we propose two dynamic lead-time quotation policies in an M/GI/1 type make-to-stock queueing system serving lead-time sensitive customers with a single type of product. Incorporating non-exponential service times in an exact method for make-to-stock queues is usually deemed difficult. Our analysis of the proposed policies is exact and requires the numerical inversion of the Laplace transform of the sojourn time of an order to be placed. The first policy assures that the long-run probability of delivering the product within the quoted lead-time is the same for all backlogged customers. The second policy is a refinement of the first which improves the profitability if customers are oversensitive to even short delays in delivery. Numerical results show that both policies perform close to the optimal policy that was characterized only for exponential service times. The new insight gained is that the worsening impact of the production time variability, which is felt significantly in systems accepting all customers by quoting zero lead times, decreases when dynamic lead-time quotation policies are employed.     

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.