High-performance cVEP-BCI under minimal calibration

The ultimate goal of brain-computer interfaces (BCIs) based on visual modulation paradigms is to achieve high-speed performance without the burden of extensive calibration. Code-modulated visual evoked potential-based BCIs (cVEP-BCIs) modulated by broadband white noise (WN) offer various advantages, including increased communication speed, expanded encoding target capabilities, and enhanced coding flexibility. However, the complexity of the spatial-temporal patterns under broadband stimuli necessitates extensive calibration for effective target identification in cVEP-BCIs. Consequently, the information transfer rate (ITR) of cVEP-BCI under limited calibration usually stays around 100 bits per minute (bpm), significantly lagging behind state-of-the-art steady-state visual evoked potential-based BCIs (SSVEP-BCIs), which achieve rates above 200 bpm. To enhance the performance of cVEP-BCIs with minimal calibration, we devised an efficient calibration stage involving a brief single-target flickering, lasting less than a minute, to extract generalizable spatial-temporal patterns. Leveraging the calibration data, we developed two complementary methods to construct cVEP temporal patterns: the linear modeling method based on the stimulus sequence and the transfer learning techniques using cross-subject data. As a result, we achieved the highest ITR of 250 bpm under a minute of calibration, which has been shown to be comparable to the state-of-the-art SSVEP paradigms. In summary, our work significantly improved the cVEP performance under few-shot learning, which is expected to expand the practicality and usability of cVEP-BCIs.Comment: 35 pages, 5 figure

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A Combinatorial Solution to Point Symbol Recognition

Recent work has shown that recognizing point symbols is an essential task in the field of map digitization. For the identification of symbols, it is generally necessary to compare the symbols with a specific criterion and find the most similar one with each known symbol one by one. Most of the works can only identify a single symbol, a small number of works are to deal with multiple symbols simultaneously with a low recognition accuracy. Given the two deficiencies, this paper proposes a deep transfer learning architecture, where the task is to learn a symbol classifier with AlexNet. For the insufficient dataset, we develop a method for transfer learning that uses a MNIST dataset to pretrain the model, which makes up for the problem of small training dataset and enhances the generalization of the model. Before the recognition process, preprocessing the point symbols in the map to coarse screening out the areas suspected of point symbols. We show a significant improvement over using point symbol images to keep a high performance in being able to deal with many more categories of symbols simultaneously

Contamination and remediation of contaminated firing ranges—an overview

Land and groundwater resources are fundamental pillars of sustainable human development. The negligent abandonment of ammunition and its fragments during range activities can result in severe contamination of range sites, thereby posing a significant risk to both the ecological environment and human health. Nevertheless, numerous uncertainties persist regarding the comprehension of range contaminated sites. In this study, the literature on the range of contaminated sites decommissioned after 2000 was systematically examined to consolidate basic information related to these sites, such as contaminant types, contamination status, and remediation measures. Considerable attention is devoted to investigating the advancement of diverse techniques, such as phytoremediation, chemical leaching, and solidification/stabilization, to remediate polluted areas within decommissioned firing ranges. Among the various types of remediation means, physical remediation and chemical remediation have higher remediation efficiency, but generally have higher costs and are prone to secondary pollution. Bioremediation is low cost and environmentally friendly, but has a long restoration cycle. The choice of remediation method should be based on actual needs. Additionally, this study puts forth prospective avenues for future research. Ultimately, this endeavor aims to attract the interest of scholars toward the remediation of contaminated sites within firing ranges, thereby making a valuable contribution to both human wellbeing and sustainable progress

Waste rice noodle-based CQDs/ZnO composite nanorod array on steel wire mesh: Preparation and photocatalytic capability

In order to develop cost-effective photocatalytic devices, a nanorod array-structured carbon quantum dot/zinc oxide (CQDs/ZnO) composite was synthesized on a steel wire mesh substrate using waste rice noodle (WRN) as the raw material. The incorporation of CQDs derived from WRN played a crucial role in controlling the morphology of the ZnO-based array. By optimizing the loading of CQDs, the CQDs/ZnO composite achieved a regular hexagonal prism nanorod array distribution and exhibited highly efficient photocatalytic degradation of various organic pollutants. For instance, in the case of methylene blue, the CQDs/ZnO composite demonstrated a remarkable degradation rate of 99.4% within 90 min, with a high degradation rate constant of 0.033 min−1. Moreover, the composite material could be recycled and reused for five photocatalytic cycles without a significant decrease in its degradation performance, surpassing the performance of the pure ZnO array. Furthermore, the resulting CQDs/ZnO composite array exhibited effective photocatalytic degradation for other organic dyes such as malachite green, methyl violet, basic fuchsin, and rhodamine B. Additionally, this composite material was successfully applied to real water purification, achieving a high mineralization efficiency of 48% and a low leaching rate of Zn of 1.2% in a river water sample after a 90-minute photocatalytic process. The introduction of CQDs derived from WRN into the ZnO array led to efficient electron-hole pair separation, enabling more photogenerated electrons to reduce O2 and more photogenerated holes to oxidize H2O. This resulted in enhanced radical generation and improved photocatalytic degradation of organic pollutants, showcasing the superior performance of the CQDs/ZnO composite array

The topic entropy.

<p>(a) Comparison between PageRank, LeaderRank and TD-Rank; (b) Comparison between TD-Rank and TwitterRank on top 10 users.</p

The statistics of Weibo data.

<p>(a) the retweet source distribution, (b) the number of retweets distributed over log(number of users).</p

The spammer effect on ranking results.

<p>(a) PageRank, (b) LeaderRank (c) TwitterRank, (d) TD-Rank.</p

Comparison of the predictions’ MSEs.

<p>Comparison of the predictions’ MSEs.</p

Dataset Statistics.

<p>Dataset Statistics.</p