Smart techniques for flying-probe testing

In the production of printed circuit boards, in-circuit tests verify whether the electric and electronic components of the board have been correctly soldered. When the test is performed using flying-probes, several probes are simultaneously moved on the board to reach and touch multiple test points. Taking into consideration the layout of the board, the characteristics of the tester, and several other physical constraints, not all movements of the probes are mutually compatible nor they can always be performed through simple straight lines. As the cost of the test is mainly related to its length, and patching the path of one probe may create new incompatibilities with the trajectory of the other probes, one should carefully trade off the time required to find the trajectories with the time required by the probes to follow them. In this paper, we model the movements of our flying probes as a multiple and collaborative planning problem. We describe an approach for detecting invalid movements and we design a strategy to correct them with the addition of new intermediate points in the trajectory. We report the entire high-level procedure and we explore the optimizations performed in the more expensive and complex steps. We also present parallel implementations of our algorithms, either relying on multi-core CPU devices or many-cores GPU platforms, when these units may be useful to achieve greater speedups. Experimental results show the effectiveness of the proposed solution in terms of elapsed computation times

Ag85A DNA Vaccine Delivery by Nanoparticles: Influence of the Formulation Characteristics on Immune Responses.

The influence of DNA vaccine formulations on immune responses in combination with adjuvants was investigated with the aim to increase cell-mediated immunity against plasmid DNA (pDNA) encoding Mycobacterium tuberculosis antigen 85A. Different ratios of pDNA with cationic trimethyl chitosan (TMC) nanoparticles were characterized for their morphology and physicochemical characteristics (size, zeta potential, loading efficiency and pDNA release profile) applied in vitro for cellular uptake studies and in vivo, to determine the dose-dependent effects of pDNA on immune responses. A selected pDNA/TMC nanoparticle formulation was optimized by the incorporation of muramyl dipeptide (MDP) as an immunostimulatory agent. Cellular uptake investigations in vitro showed saturation to a maximum level upon the increase in the pDNA/TMC nanoparticle ratio, correlating with increasing Th1-related antibody responses up to a definite pDNA dose applied. Moreover, TMC nanoparticles induced clear polarization towards a Th1 response, indicated by IgG2c/IgG1 ratios above unity and enhanced numbers of antigen-specific IFN-γ producing T-cells in the spleen. Remarkably, the incorporation of MDP in TMC nanoparticles provoked a significant additional increase in T-cell-mediated responses induced by pDNA. In conclusion, pDNA-loaded TMC nanoparticles are capable of provoking strong Th1-type cellular and humoral immune responses, with the potential to be further optimized by the incorporation of MDP

Partitioning Interpolant-Based Verificationfor effective Unbounded Model Checking

Interpolant-based model checking has been shown to be effective on large verification instances, as it efficiently combines automated abstraction and reachability fixed-point checks. On the other hand, methods based on variable quantification have proved their ability to remove free inputs, thus projecting the search space over state variables. In this paper we propose an integrated approach which combines the abstraction power of interpolation with techniques that rely on AIG and/or BDD representations of states, directly supporting variable quantification and fixed-point checks. The underlying idea of this combination is to adopt AIG- or BDD-based quantifications to limit and restrict the search space and the complexity of the interpolant-based approach. The exploited strategies, most of which are individually well-known, are integrated with a new flavor, specifically designed to improve their effectiveness on difficult verification instances. Experimental results, specifically oriented to hard-to-solve verification problems, show the robustness of our approach

Parallel Multithread Analysis of Extremely Large Simulation Traces

With the explosion in the size of off-the-shelf integrated circuits and the advent of novel techniques related to failure modes, commercial Automatic Test Pattern Generator and fault simulation engines are often insufficient to measure the coverage of particular metrics. Consequently, a general working framework consists of storing simulation traces during the analysis phase and collecting test statistics from post-processing. Unfortunately, typical simulation traces can be hundreds of gigabytes long, and their analysis can require several days, even on large and powerful computational servers. In this paper, we propose a set of strategies to mitigate the evaluation time and the memory needed to analyze huge dump files stored in the standard Value Change Dump format. We concentrate on burn-in-related metrics that current commercial fault simulators and Automatic Test Pattern Generators cannot evaluate. We show how to divide the analysis process into several concurrent pipeline stages. We revise the logic process of each stage and all principal intermediate data structures, to adopt smart parallelization with very low contention and extremely low overhead. We exploit several low-level optimizations from modern programming techniques to reduce computation time and balance the different pipeline phases. We analyze simulation traces up to almost 250 GBytes computing different testing metrics. Overall, we can keep under control the memory usage, and we show time improvements of over two orders of magnitude compared to previously adopted state-of-the-art tools

Weak convergence of finite element approximations of linear stochastic evolution equations with additive noise II. Fully discrete schemes

We present an abstract framework for analyzing the weak error of fully discrete approximation schemes for linear evolution equations driven by additive Gaussian noise. First, an abstract representation formula is derived for sufficiently smooth test functions. The formula is then applied to the wave equation, where the spatial approximation is done via the standard continuous finite element method and the time discretization via an I-stable rational approximation to the exponential function. It is found that the rate of weak convergence is twice that of strong convergence. Furthermore, in contrast to the parabolic case, higher order schemes in time, such as the Crank-Nicolson scheme, are worthwhile to use if the solution is not very regular. Finally we apply the theory to parabolic equations and detail a weak error estimate for the linearized Cahn-Hilliard-Cook equation as well as comment on the stochastic heat equation

Does the Red Queen reign in the kingdom of digital organisms?

In competition experiments between two RNA viruses of equal or almost equal fitness, often both strains gain in fitness before one eventually excludes the other. This observation has been linked to the Red Queen effect, which describes a situation in which organisms have to constantly adapt just to keep their status quo. I carried out experiments with digital organisms (self-replicating computer programs) in order to clarify how the competing strains' location in fitness space influences the Red-Queen effect. I found that gains in fitness during competition were prevalent for organisms that were taken from the base of a fitness peak, but absent or rare for organisms that were taken from the top of a peak or from a considerable distance away from the nearest peak. In the latter two cases, either neutral drift and loss of the fittest mutants or the waiting time to the first beneficial mutation were more important factors. Moreover, I found that the Red-Queen dynamic in general led to faster exclusion than the other two mechanisms.Comment: 10 pages, 5 eps figure

Erratum: Author Correction: System immunology-based identification of blood transcriptional modules correlating to antibody responses in sheep.

[This corrects the article DOI: 10.1038/s41541-018-0078-0.]

Salvage carbon dioxide transoral laser microsurgery for laryngeal cancer after (chemo)radiotherapy: a European Laryngological Society consensus statement

Purpose: To provide expert opinion and consensus on salvage carbon dioxide transoral laser microsurgery (CO2 TOLMS) for recurrent laryngeal squamous cell carcinoma (LSCC) after (chemo)radiotherapy [(C)RT]. Methods: Expert members of the European Laryngological Society (ELS) Cancer and Dysplasia Committee were selected to create a dedicated panel on salvage CO2 TOLMS for LSCC. A series of statements regarding the critical aspects of decision-making were drafted, circulated, and modified or excluded in accordance with the Delphi process. Results: The expert panel reached full consensus on 19 statements through a total of three sequential evaluation rounds. These statements were focused on different aspects of salvage CO2 TOLMS, with particular attention on preoperative diagnostic work-up, treatment indications, postoperative management, complications, functional outcomes, and follow-up. Conclusion: Management of recurrent LSCC after (C)RT is challenging and is based on the need to find a balance between oncologic and functional outcomes. Salvage CO2 TOLMS is a minimally invasive approach that can be applied to selected patients with strict and careful indications. Herein, a series of statements based on an ELS expert consensus aimed at guiding the main aspects of CO2 TOLMS for LSCC in the salvage setting is presented