Influence Of Different Algorithms Research Articles

Quantitative two/three-dimensional spatial characterization and fluid transport prediction of macro/micropores in Gaomiaozi bentonite

The sealing performance of a bentonite barrier is highly dependent on its seepage characteristics, which are directly related to the characteristics of its pore structure. Based on scanning electron microscopy (SEM) and focused ion beam-SEM (FIB-SEM), the pore structure of bentonite was characterized at different scales. First, a reasonable gray threshold was determined through back analysis, and the image was binarized based on the threshold. In addition, binary images were used to analyze bentonite's pore structure (porosity and pore size distribution). Furthermore, the effects of different algorithms on the pore structure characterization were evaluated. Then, permeability calculations were performed based on the previous pore structure characteristics and a modified permeability prediction model. For permeability prediction based on the three-dimensional model, the effect of pore tortuosity was also considered. Finally, the accuracy of numerical calculations was verified by conducting macroscopic gas and alcohol permeability experiments. This approach provides a better understanding of the microscale mechanism of gas transport in bentonite and the importance of pore structures at different scales in determining its seepage characteristics. • The influence of image magnification on the quantitative characterization of the pore structure and the prediction of permeability is discussed. • The influence of different algorithms on the characterization of pore size distribution is discussed. • An improved permeability prediction model that can consider the effect of different pore sizes on seepage characteristics is proposed.

Using the full-text content of academic articles to identify and evaluate algorithm entities in the domain of natural language processing.

In the era of big data, the advancement, improvement, and application of algorithms in academic research have played an important role in promoting the development of different disciplines. Academic papers in various disciplines, especially computer science, contain a large number of algorithms. Identifying the algorithms from the full-text content of papers can determine popular or classical algorithms in a specific field and help scholars gain a comprehensive understanding of the algorithms and even the field. To this end, this article takes the field of natural language processing (NLP) as an example and identifies algorithms from academic papers in the field. A dictionary of algorithms is constructed by manually annotating the contents of papers, and sentences containing algorithms in the dictionary are extracted through dictionary-based matching. The number of articles mentioning an algorithm is used as an indicator to analyze the influence of that algorithm. Our results reveal the algorithm with the highest influence in NLP papers and show that classification algorithms represent the largest proportion among the high-impact algorithms. In addition, the evolution of the influence of algorithms reflects the changes in research tasks and topics in the field, and the changes in the influence of different algorithms show different trends. As a preliminary exploration, this paper conducts an analysis of the impact of algorithms mentioned in the academic text, and the results can be used as training data for the automatic extraction of large-scale algorithms in the future. The methodology in this paper is domain-independent and can be applied to other domains.

Comparison of Software Tools for Liquid Chromatography-High-Resolution Mass Spectrometry Data Processing in Nontarget Screening of Environmental Samples.

The field of high-resolution mass spectrometry has undergone a rapid progress in the last years due to instrumental improvements leading to a higher sensitivity and selectivity of instruments. A variety of qualitative screening approaches, summarized as nontarget screening, have been introduced and have successfully extended the environmental monitoring of organic micropollutants. Several automated data processing workflows have been developed to handle the immense amount of data that are recorded in short time frames by these methods. Most data processing workflows include similar steps, but underlying algorithms and implementation of different processing steps vary. In this study the consistency of data processing with different software tools was investigated. For this purpose, the same raw data files were processed with the software packages MZmine2, enviMass, Compound Discoverer, and XCMS online and resulting feature lists were compared. Results show a low coherence between different processing tools, as overlap of features between all four programs was around 10%, and for each software between 40% and 55% of features did not match with any other program. The implementation of replicate and blank filter was identified as one of the sources of observed divergences. However, there is a need for a better understanding and user instructions on the influence of different algorithms and settings on feature extraction and following filtering steps. In future studies it would be of interest to investigate how final data interpretation is influenced by different processing software. With this work we want to encourage more awareness on data processing as a crucial step in the workflow of nontarget screening.

An SSVEP-BCI in Augmented Reality.

Steady-State Visual Evoked Potentials (SSVEP) based Brain-Computer Interface (BCI) has achieved very high information transmission rate (ITR), but its portability and fundamental interactions with the surrounding environment were limited. The combination of Augmented Reality (AR) and BCI is expected to solve these problems. In this paper, we combined AR with the SSVEP-BCI to build a more portable and natural BCI system in Microsoft HoloLens. We designed the AR-BCI system and studied the influence of different algorithms on the system performance. The analysis of SSVEP signals collected in AR environment shows that the extended filter bank canonical correlation analysis was better than task-related component analysis. The average recognition accuracy and ITR obtained by using Electroencephalography (EEG) data of 1s, 1.5s, and 2s length were 87.7%,95.4%, 97.6% and 64.6 bit/min, 62.9 bit/min, 55.6 bit/min, respectively. Compared with the existing AR-BCI studies, the ITR has been greatly improved in this study.

Proper configuration of metallic energy dissipation system in shear-type building structures subject to seismic excitation

Passive control using metallic energy dissipation system has been proved to be a reliable means of seismic upgrading for building structures. Furthermore, configuration of the metallic energy dissipation system within a structure may have significant influence on the overall seismic performance. This paper presents a systematic approach for optimal configuration of the metallic energy dissipation system in shear-type building structures subject to seismic excitation consistent with the design code, where the mechanical parameters of the metallic energy dissipation component in each story level are taken as the optimization variables. Two objective functions as well as two optimization levels are proposed. An improved real-coded genetic algorithm is employed as the optimizer. The framework of the optimal configuration design process is given and demonstrated in an application example. Based on the results of the application example, the influence of different algorithms, optimization levels and objective functions are further analyzed. Moreover, a simplified fine level optimization approach is proposed based on the parameter sensitivity, which can yield satisfying results with less time.

The effect of the way of setting boundary conditions on the results of modeling a flow at the flameholder outlet

The influence of different algorithms of generating inlet turbulent fluctuations on the results of calculating the flow parameters downstream the flameholder is discussed in the paper. Large eddy simulation is used on a turbulence model with a subgrid Smagorinsky-Lilli model. Propane-air mixture with a temperature of 300 K in a square-section channel is investigated. A bluff body with a base of an equilateral triangle with the side length equal to 25 mm is placed inside the channel. The mass-averaged inlet velocity is 10 m/s. Several ways of setting inlet boundary conditions are considered. Two cases without turbulence at the inlet (uniform velocity distribution across the inlet section and pipe velocity profile) and two cases with artificially modeled turbulence (vortex method and spectral turbulence synthesizer) are analyzed. The values of flow intensity upstream the stabilizer, the distribution of flow velocity and kinetic energy in longitudinal and cross sections downstream the stabilizer have been obtained. The graphs of the velocity fluctuation power density spectrum are also presented.