Interactions Between Reinforcement Corrosion and Chloride Ion Diffusion in Mortar

This study explored the diffusion of the chloride ions influenced by the reinforcement corrosion in the mortar. It is believed that, during the corroding process, a small current is generated at the surface of the reinforcement. Such current is supposed to influence the diffusion of the chloride ions, but the relationship between both was not well studied in the literature. In this study, the corroded reinforcements were prepared by applied currents. Reinforced mortar specimens with w/c of 0.6 were then prepared and cured by either salt or fresh water. Results showed that the chloride ion distribution was likely associated with the reinforcement corrosion. During the early hydration, the chloride ions were attracted by the reinforcement corrosion in the specimens prepared with fresh water and cured in salt water. The concentration of the chloride ions near the surface of the reinforcement was increased with the increases of the charging time during the preparation for the corroded reinforcement. On the contrary, the chloride ions were likely bound in those specimens prepared with salt water and cured by saturated lime water. The concentration of the chloride ions near the surface of the reinforcement was higher than those near the outer surface. However, such influencing effects were not clear in the long term, possibly due to the hydration. The results of this study show that the reinforcement corrosion have influences on the diffusion of the chloride ions and such effect should be considered during the refinement of the traditional chloride ion diffusion models

Phylo-mLogo: an interactive and hierarchical multiple-logo visualization tool for alignment of many sequences

BACKGROUND: When aligning several hundreds or thousands of sequences, such as epidemic virus sequences or homologous/orthologous sequences of some big gene families, to reconstruct the epidemiological history or their phylogenies, how to analyze and visualize the alignment results of many sequences has become a new challenge for computational biologists. Although there are several tools available for visualization of very long sequence alignments, few of them are applicable to the alignments of many sequences. RESULTS: A multiple-logo alignment visualization tool, called Phylo-mLogo, is presented in this paper. Phylo-mLogo calculates the variabilities and homogeneities of alignment sequences by base frequencies or entropies. Different from the traditional representations of sequence logos, Phylo-mLogo not only displays the global logo patterns of the whole alignment of multiple sequences, but also demonstrates their local homologous logos for each clade hierarchically. In addition, Phylo-mLogo also allows the user to focus only on the analysis of some important, structurally or functionally constrained sites in the alignment selected by the user or by built-in automatic calculation. CONCLUSION: With Phylo-mLogo, the user can symbolically and hierarchically visualize hundreds of aligned sequences simultaneously and easily check the changes of their amino acid sites when analyzing many homologous/orthologous or influenza virus sequences. More information of Phylo-mLogo can be found at URL

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training

Masking Improves Contrastive Self-Supervised Learning for ConvNets, and Saliency Tells You Where

While image data starts to enjoy the simple-but-effective self-supervised learning scheme built upon masking and self-reconstruction objective thanks to the introduction of tokenization procedure and vision transformer backbone, convolutional neural networks as another important and widely-adopted architecture for image data, though having contrastive-learning techniques to drive the self-supervised learning, still face the difficulty of leveraging such straightforward and general masking operation to benefit their learning process significantly. In this work, we aim to alleviate the burden of including masking operation into the contrastive-learning framework for convolutional neural networks as an extra augmentation method. In addition to the additive but unwanted edges (between masked and unmasked regions) as well as other adverse effects caused by the masking operations for ConvNets, which have been discussed by prior works, we particularly identify the potential problem where for one view in a contrastive sample-pair the randomly-sampled masking regions could be overly concentrated on important/salient objects thus resulting in misleading contrastiveness to the other view. To this end, we propose to explicitly take the saliency constraint into consideration in which the masked regions are more evenly distributed among the foreground and background for realizing the masking-based augmentation. Moreover, we introduce hard negative samples by masking larger regions of salient patches in an input image. Extensive experiments conducted on various datasets, contrastive learning mechanisms, and downstream tasks well verify the efficacy as well as the superior performance of our proposed method with respect to several state-of-the-art baselines

Relationship between maximal incremental and high-intensity interval exercise performance in elite athletes

This descriptive study aimed to explore the physiological factors that determine tolerance to exertion during high-intensity interval effort. Forty-seven young women (15–28 years old) were enrolled: 23 athletes from Taiwan national or national reserve teams and 24 moderately active females. Each participant underwent a maximal incremental INC (modified Bruce protocol) cardiopulmonary exercise test on the first day and high-intensity interval testing (HIIT) on the second day, both performed on a treadmill. The HIIT protocol involved alternation between 1-min effort at 120% of the maximal speed, at the same slope reached at the end of the INC, and 1-min rest until volitional exhaustion. Gas exchange, heart rate (HR), and muscle oxygenation at the right vastus lateralis, measured by near-infrared spectroscopy, were continuously recorded. The number of repetitions completed (Rlim) by each participant was considered the HIIT tolerance index. The results showed a large difference in the Rlim (range, 2.6–12.0 repetitions) among the participants. Stepwise linear regression revealed that the variance in the Rlim within the cohort was related to the recovery rates of oxygen consumption (), HR at the second minute after INC, and muscle tissue saturation index at exhaustion (R = 0.644). In addition, age was linearly correlated with Rlim (adjusted R = −0.518, p \u3c 0.0001). In conclusion, the recovery rates for and HR after the incremental test, and muscle saturation index at exhaustion, were the major physiological factors related to HIIT performance. These findings provide insights into the role of the recovery phase after maximal INC exercise testing. Future research investigating a combination of INC and HIIT testing to determine training-induced performance improvement is warranted

Atomically-thin metallic Si and Ge allotropes with high Fermi velocities

Silicon and germanium are the well-known materials used to manufacture electronic devices for the integrated circuits but they themselves are not considered as promising options for interconnecting the devices due to their semiconducting nature. We have discovered that both Si and Ge atoms can form unexpected metallic monolayer structures which are more stable than the extensively studied semimetallic silicene and germanene, respectively. More importantly, the newly discovered two-dimensional allotropes of Si and Ge have Fermi velocities superior to the Dirac fermions in graphene, indicating that the metal wires needed in the silicon-based integrated circuits can be made of Si atom itself without incompatibility, allowing for all-silicon-based integrated circuits.Comment: 10 pages, 3 figures, 1 tabl

SinicView: A visualization environment for comparisons of multiple nucleotide sequence alignment tools

BACKGROUND: Deluged by the rate and complexity of completed genomic sequences, the need to align longer sequences becomes more urgent, and many more tools have thus been developed. In the initial stage of genomic sequence analysis, a biologist is usually faced with the questions of how to choose the best tool to align sequences of interest and how to analyze and visualize the alignment results, and then with the question of whether poorly aligned regions produced by the tool are indeed not homologous or are just results due to inappropriate alignment tools or scoring systems used. Although several systematic evaluations of multiple sequence alignment (MSA) programs have been proposed, they may not provide a standard-bearer for most biologists because those poorly aligned regions in these evaluations are never discussed. Thus, a tool that allows cross comparison of the alignment results obtained by different tools simultaneously could help a biologist evaluate their correctness and accuracy. RESULTS: In this paper, we present a versatile alignment visualization system, called SinicView, (for Sequence-aligning INnovative and Interactive Comparison VIEWer), which allows the user to efficiently compare and evaluate assorted nucleotide alignment results obtained by different tools. SinicView calculates similarity of the alignment outputs under a fixed window using the sum-of-pairs method and provides scoring profiles of each set of aligned sequences. The user can visually compare alignment results either in graphic scoring profiles or in plain text format of the aligned nucleotides along with the annotations information. We illustrate the capabilities of our visualization system by comparing alignment results obtained by MLAGAN, MAVID, and MULTIZ, respectively. CONCLUSION: With SinicView, users can use their own data sequences to compare various alignment tools or scoring systems and select the most suitable one to perform alignment in the initial stage of sequence analysis

Interplay between the magnetic and electric degrees-of-freedom in multiferroic Co3TeO6

Neutron diffraction, magnetic susceptibility, specific heat, and dielectric constant measurements of single crystal Co3TeO6 have been measured to study the interplay between the ferroelectricity and magnetic order. Long range incommensurate magnetic order develops below TM1=26 K, which is followed by three additional zero-field phase transitions at TM2=19.5 K, TM3=18 K, and TM4=16 K where the incommensurate order changes and commensurate order develops. In magnetic fields up to 14 T we find that the magnetic intensities and incommensurate wave vector are dramatically altered as ferroelectricity develops, with a fifth abrupt transition around 10 T. The overall behavior characterizes Co3TeO6 as a type-II multiferroic.Comment: Phys. Rev. B (in press