The study of immersion chloride extraction of iron artifacts

The chloride extraction rules of iron artifacts were studied by immersion methods. Different chloride extraction results between the alkaline solution and a washing solution were obtained. The microstructure and the anti-corrosion performance of the samples before and after treatment, were, respectively studied by the scanning electron microscope (SEM) and the potentiodynamic scanning method. The results indicated that Cl- removed according to the diffusion law. The microstructure of the samples transformed after treatment. The rusts became more compact, and the porosity also increased. The chloride extraction effect in the washing solution was better than that in the NaOH solution.

A study of electrical chloride extraction of iron artifacts

The chloride extraction rule of iron artifacts was studied by electrical methods. The effect of the current and potential value on the desalination result of simulated iron artifacts was studied through the galvanostatic and potentiostatic experiments the ingredients of the rust before and after treatments were also analyzed by the X-ray diffraction (XRD). It has been found that the optimal current density was between -0.50 and -0.75 mA/cm(2) and the optimal potential was between -1.175 and -1.200 V. The phase of the samples rusts transformed after treatment, as well as the anti-corrosion performance improved.

POCS-based reconstruction of multiplexed sensitivity encoded MRI (POCSMUSE): A general algorithm for reducing motion-related artifacts.

PURPOSE: A projection onto convex sets reconstruction of multiplexed sensitivity encoded MRI (POCSMUSE) is developed to reduce motion-related artifacts, including respiration artifacts in abdominal imaging and aliasing artifacts in interleaved diffusion-weighted imaging. THEORY: Images with reduced artifacts are reconstructed with an iterative projection onto convex sets (POCS) procedure that uses the coil sensitivity profile as a constraint. This method can be applied to data obtained with different pulse sequences and k-space trajectories. In addition, various constraints can be incorporated to stabilize the reconstruction of ill-conditioned matrices. METHODS: The POCSMUSE technique was applied to abdominal fast spin-echo imaging data, and its effectiveness in respiratory-triggered scans was evaluated. The POCSMUSE method was also applied to reduce aliasing artifacts due to shot-to-shot phase variations in interleaved diffusion-weighted imaging data corresponding to different k-space trajectories and matrix condition numbers. RESULTS: Experimental results show that the POCSMUSE technique can effectively reduce motion-related artifacts in data obtained with different pulse sequences, k-space trajectories and contrasts. CONCLUSION: POCSMUSE is a general post-processing algorithm for reduction of motion-related artifacts. It is compatible with different pulse sequences, and can also be used to further reduce residual artifacts in data produced by existing motion artifact reduction methods.

A Cross-modal Approach for Karaoke Artifacts Correction

Karaoke singing is a popular form of entertainment in several parts of the world. Since this genre of performance attracts amateurs, the singing often has artifacts related to scale, tempo, and synchrony. We have developed an approach to correct these artifacts using cross-modal multimedia streams information. We first perform adaptive sampling on the user's rendition and then use the original singer's rendition as well as the video caption highlighting information in order to correct the pitch, tempo and the loudness. A method of analogies has been employed to perform this correction. The basic idea is to manipulate the user's rendition in a manner to make it as similar as possible to the original singing. A pre-processing step of noise removal due to feedback and huffing also helps improve the quality of the user's audio. The results are described in the paper which shows the effectiveness of this multimedia approach.

Adaptability in organisms and artifacts: A multi level perspective on adaptive processes

Abstract Adaptability to changing circumstances is a key feature of living creatures. Understanding such adaptive processes is central to developing successful autonomous artifacts. In this paper two perspectives are brought to bear on the issue of adaptability. The first is a short term perspective which looks at adaptability in terms of the interactions between the agent and the environment. The second perspective involves a hierarchical evolutionary model which seeks to identify higher-order forms of adaptability based on the concept of adaptive meta-constructs. Task orientated and agent-centered models of adaptive processes in artifacts are considered from these two perspectives. The former isrepresented by the fitness function approach found in evolutionary learning, and the latter in terms of the concepts of empowerment and homeokinesis found in models derived from the self-organizing systems approach. A meta-construct approach to adaptability based on the identification of higher level meta-metrics is also outlined. 2009 Published by Elsevier B.V.

Towards high-quality simultaneous EEG-fMRI at 7T: Detection and reduction of EEG artifacts due to head motion.

The enhanced functional sensitivity offered by ultra-high field imaging may significantly benefit simultaneous EEG-fMRI studies, but the concurrent increases in artifact contamination can strongly compromise EEG data quality. In the present study, we focus on EEG artifacts created by head motion in the static B0 field. A novel approach for motion artifact detection is proposed, based on a simple modification of a commercial EEG cap, in which four electrodes are non-permanently adapted to record only magnetic induction effects. Simultaneous EEG-fMRI data were acquired with this setup, at 7T, from healthy volunteers undergoing a reversing-checkerboard visual stimulation paradigm. Data analysis assisted by the motion sensors revealed that, after gradient artifact correction, EEG signal variance was largely dominated by pulse artifacts (81-93%), but contributions from spontaneous motion (4-13%) were still comparable to or even larger than those of actual neuronal activity (3-9%). Multiple approaches were tested to determine the most effective procedure for denoising EEG data incorporating motion sensor information. Optimal results were obtained by applying an initial pulse artifact correction step (AAS-based), followed by motion artifact correction (based on the motion sensors) and ICA denoising. On average, motion artifact correction (after AAS) yielded a 61% reduction in signal power and a 62% increase in VEP trial-by-trial consistency. Combined with ICA, these improvements rose to a 74% power reduction and an 86% increase in trial consistency. Overall, the improvements achieved were well appreciable at single-subject and single-trial levels, and set an encouraging quality mark for simultaneous EEG-fMRI at ultra-high field.

Phylogenetic mixture models can reduce node-density artifacts

We investigate the performance of phylogenetic mixture models in reducing a well-known and pervasive artifact of phylogenetic inference known as the node-density effect, comparing them to partitioned analyses of the same data. The node-density effect refers to the tendency for the amount of evolutionary change in longer branches of phylogenies to be underestimated compared to that in regions of the tree where there are more nodes and thus branches are typically shorter. Mixture models allow more than one model of sequence evolution to describe the sites in an alignment without prior knowledge of the evolutionary processes that characterize the data or how they correspond to different sites. If multiple evolutionary patterns are common in sequence evolution, mixture models may be capable of reducing node-density effects by characterizing the evolutionary processes more accurately. In gene-sequence alignments simulated to have heterogeneous patterns of evolution, we find that mixture models can reduce node-density effects to negligible levels or remove them altogether, performing as well as partitioned analyses based on the known simulated patterns. The mixture models achieve this without knowledge of the patterns that generated the data and even in some cases without specifying the full or true model of sequence evolution known to underlie the data. The latter result is especially important in real applications, as the true model of evolution is seldom known. We find the same patterns of results for two real data sets with evidence of complex patterns of sequence evolution: mixture models substantially reduced node-density effects and returned better likelihoods compared to partitioning models specifically fitted to these data. We suggest that the presence of more than one pattern of evolution in the data is a common source of error in phylogenetic inference and that mixture models can often detect these patterns even without prior knowledge of their presence in the data. Routine use of mixture models alongside other approaches to phylogenetic inference may often reveal hidden or unexpected patterns of sequence evolution and can improve phylogenetic inference.

Visual representations as 'artifacts of knowing'

How does the manipulation of visual representations play a role in the practices of generating, evolving and exchanging knowledge? The role of visual representation in mediating knowledge work is explored in a study of design work of an architectural practice, Edward Cullinan Architects. The intensity of interactions with visual representations in the everyday activities on design projects is immediately striking. Through a discussion of observed design episodes, two ways are articulated in which visual representations act as 'artefacts of knowing'. As communication media they are symbolic representations, rich in meaning, through which ideas are articulated, developed and exchanged. Furthermore, as tangible artefacts they constitute material entities with which to interact and thereby develop knowledge. The communicative and interactive properties of visual representations constitute them as central elements of knowledge work. The paper explores emblematic knowledge practices supported by visual representation and concludes by pinpointing avenues for further research.