Estimating low toxic effects

Mining frequent patterns has been a topic of active research because it is computationally the most expensive step in association rule discovery. In this paper, we discuss the use of compact data structure design for improving the efficiency of frequent pattern mining. It is based on our work in developing efficient algorithms that outperform the best available frequent pattern algorithms on a number of typical data sets. We discuss improvements to the data structure design that has resulted in faster frequent pattern discovery. The performance of our algorithms is studied by comparing their running times on typical test data sets against the fastest Apriori, Eclat, FP-Growth and OpportuneProject algorithms. We discuss the performance results as well as the strengths and limitations of our algorithms.

The US Environmental Protection Agency (USEPA) is responsible for the development of water quality criteria, regulatory standards that protect aquatic organisms from harmful chemical exposure. Although these criteria are intended to be broadly protective of aquatic life, the data used to derive criteria do not necessarily reflect the actual diversity of natural communities nor are they available for most chemicals. In addition, although the USEPA's current procedures emphasize using toxicity data with a certain minimum amount of biological diversity, the quantitative impact of such diversity on criteria is unclear. In the present study we assessed the changes to acute toxicity data over time, determined the prevalence of significant taxonomic differences in sensitivity, and investigated the effect of biological diversity on criteria. We found major gaps in existing toxicity data that we hypothesize have contributed to the absence of acute criteria for the majority of chemical pollutants. Taxonomic patterns of sensitivity in these data are abundant, although the resolution of the patterns is relatively poor. In addition, we found that the amount of biological diversity in a toxicity data set and the data set's taxonomic composition does not quantitatively affect criteria in most cases. Because the USEPA has published acute criteria for fewer than 20% of priority pollutants and the persistence of major gaps in toxicity data over the last 37 years, we recommend that the USEPA consider revisions to their water quality criteria guidelines that will expedite the criteria development process and advance the responsible management of pollutants in the aquatic environment. Environ Toxicol Chem 2022;41:1333-1343. © 2022 SETAC.

This study demonstrates the removal of fly ash with Penicillium chrysogenum, a newly isolated species of fungus, and acute toxicity assessment with Daphnia magna. In the study, two different removal mechanisms were compared, both bio-removal and bio-sorption. Six different ash and three different biomass concentrations were used simultaneously. Although other fungal species in the literature failed at such a high concentration of fly ash, P. chrysogenum was able to tolerate it even at 10% concentration. The highest bio-removal yield was recorded as 100% at 0.5% fly ash concentration. Maximum bio-sorption yield was 95.27% after 24th hour. The evaluation results of fly ash bio-toxicity by D. magna showed that the no observed effect level (NOEL) was 0.2 mg/L and the low observed effect level (LOEL) was 0.5 mg/L. The element analysis, determined by ED-XRF, clarified that Ca, Si, Fe and S were the common elements in this ash. This is the first study in the literature where fly ash removal was carried out using P. chrysogenum for both bio-removal and bio-sorption and needs to be developed in the future.

Paraquat toxicity in a mouse embryonic stem cell model

Different crops, as well as the same crop at different growth stages, display distinct spectral and spatial characteristics in hyperspectral images (HSIs) due to variations in their chemical composition and structural features. However, the narrow bandwidth and closely spaced spectral channels of HSIs result in significant data redundancy, posing challenges to crop identification and classification. Therefore, the dimensionality reduction in HSIs is crucial. Band selection as a widely used method for reducing dimensionality has been extensively applied in research on crop identification and mapping. In this paper, a crop superpixel-based affinity propagation (CS-AP) band selection method is proposed for crop identification and mapping in agriculture using HSIs. The approach begins by gathering crop superpixels; then, a spectral band selection criterion is developed by analyzing the variations in the spectral and spatial characteristics of crop superpixels. Finally, crop identification bands are determined through an efficient clustering approach, AP. Two typical agricultural hyperspectral data sets, the Salinas Valley data set and the Indian Pines data set, are selected for validation, each containing 16 crop classes, respectively. The experimental results show that the proposed CS-AP method achieves a mapping accuracy of 92.4% for the Salinas Valley data set and 88.6% for the Indian Pines data set. When compared to using all bands, two unsupervised band selection techniques, and three semi-supervised band selection techniques, the proposed method outperforms others with an improvement of 3.1% and 4.3% for the Salinas Valley and Indian Pines data sets, respectively. Indicate that the proposed CS-AP method achieves superior mapping accuracy by selecting fewer bands with greater crop identification capability compared to the other band selection methods. This research’s significant results demonstrate the potential of this approach in precision agriculture, offering a more cost-effective and timely solution for large-scale crop mapping and monitoring in the future.

In macromolecular X-ray crystallography, typical data sets have substantial multiplicity. This can be used to calculate the consistency of repeated measurements and thereby assess data quality. Recently, the properties of a correlation coefficient, CC1/2, that can be used for this purpose were characterized and it was shown that CC1/2 has superior properties compared with `merging' R values. A derived quantity, CC*, links data and model quality. Using experimental data sets, the behaviour of CC1/2 and the more conventional indicators were compared in two situations of practical importance: merging data sets from different crystals and selectively rejecting weak observations or (merged) unique reflections from a data set. In these situations controlled `paired-refinement' tests show that even though discarding the weaker data leads to improvements in the merging R values, the refined models based on these data are of lower quality. These results show the folly of such data-filtering practices aimed at improving the merging R values. Interestingly, in all of these tests CC1/2 is the one data-quality indicator for which the behaviour accurately reflects which of the alternative data-handling strategies results in the best-quality refined model. Its properties in the presence of systematic error are documented and discussed.

An unsupervised learning method is proposed for variable selection and its performance assessed using three typical QSAR data sets. The aims of this procedure are to generate a subset of descriptors from any given data set in which the resultant variables are relevant, redundancy is eliminated, and multicollinearity is reduced. Continuum regression, an algorithm encompassing ordinary least squares regression, regression on principal components, and partial least squares regression, was used to construct models from the selected variables. The variable selection routine is shown to produce simple, robust, and easily interpreted models for the chosen data sets.

We describe a method for assessing data set complexity based on the estimation of the underlining probability distribution and Hellinger distance. Contrary to some popular measures it is not focused on the shape of decision boundary in a classification task but on the amount of available data with respect to attribute structure. Complexity is expressed in terms of graphical plot, which we call complexity curve. We use it to propose a new variant of learning curve plot called generalisation curve. Generalisation curve is a standard learning curve with x-axis rescaled according to the data set complexity curve. It is a classifier performance measure, which shows how well the information present in the data is utilised. We perform theoretical and experimental examination of properties of the introduced complexity measure and show its relation to the variance component of classification error. We compare it with popular data complexity measures on 81 diverse data sets and show that it can contribute to explaining the performance of specific classifiers on these sets. Then we apply our methodology to a panel of benchmarks of standard machine learning algorithms on typical data sets, demonstrating how it can be used in practice to gain insights into data characteristics and classifier behaviour. Moreover, we show that complexity curve is an effective tool for reducing the size of the training set (data pruning), allowing to significantly speed up the learning process without reducing classification accuracy. Associated code is available to download at: https://github.com/zubekj/complexity_curve (open source Python implementation).

Multifractal nature of network induced time delay in networked control systems

This paper built a optimization model and proposed an improved firefly optimization algorithm called CFA, which is based on firefly Cooperative.The main idea of CFA is to extend the single population FA to the interacting multi-swarms by cooperative Models.In this work, firstly, CFA algorithm is used for optimizing six widely-used benchmark functions and the comparative results produced by, firefly optimization algorithm(FA) are studied.Secondly, CFA algorithm used in data mining, clustering analysis on several typical data sets.The performance of typical data clustering results showed that the biological heuristic algorithm based on clustering analysis algorithm with the existing success of FA compared to faster convergence, and the clustering of higher quality.

Semisupervised classification (SSC) consists of using both labeled and unlabeled data to classify unseen instances. Due to the large number of unlabeled data typically available, SSC algorithms must be able to handle large-scale data sets. Recently, various ensemble algorithms have been introduced with improved generalization performance when compared to single classifiers. However, existing ensemble methods are not able to handle typical large-scale data sets. We propose efficient cluster-based boosting (ECB), a multiclass SSC algorithm with cluster-based regularization that avoids generating decision boundaries in high-density regions. A semisupervised selection procedure reduces time and space complexities by selecting only the most informative unlabeled instances for the training of each base learner. We provide evidences to demonstrate that ECB is able to achieve good performance with small amounts of selected data and a relatively small number of base learners. Our experiments confirmed that ECB scales to large data sets while delivering comparable generalization to state-of-the-art methods.

Currently, people who document and publish data do so in printed journals, displaying their results in graphical or tabular form. This is an effective way to publicize the existence of validated observations or model results and to present some selected characteristics of the data. However, it is not reasonable to use this medium to exchange the information content of typical geophysical data sets for others to study. In many cases a calibrated, documented data set itself is the important contribution of a scientific investigation.

Passive optical remote sensing of river bathymetry involves establishing a relation between depth and reflectance that can be applied throughout an image to produce a depth map. Building upon the Optimal Band Ratio Analysis (OBRA) framework, we introduce sampling strategies for constructing calibration data sets that lead to strong relationships between an image-derived quantity and depth across a range of depths. Progressively excluding observations that exceed a series of cutoff depths from the calibration process improved the accuracy of depth estimates and allowed the maximum detectable depth (dmax) to be inferred directly from an image. Depth retrieval in two distinct rivers also was enhanced by a stratified version of OBRA that partitions field measurements into a series of depth bins to avoid biases associated with under-representation of shallow areas in typical field data sets. In the shallower, clearer of the two rivers, including the deepest field observations in the calibration data set did not compromise depth retrieval accuracy, suggesting that dmax was not exceeded and the reach could be mapped without gaps. Conversely, in the deeper and more turbid stream, progressive truncation of input depths yielded a plausible estimate of dmax consistent with theoretical calculations based on field measurements of light attenuation by the water column. This result implied that the entire channel, including pools, could not be mapped remotely. However, truncation improved the accuracy of depth estimates in areas shallower than dmax, which comprise the majority of the channel and are of primary interest for many habitat-oriented applications.

Unsupervised and simultaneous training of multiple object detectors from unlabeled surveillance video

As one of the most important techniques in data mining, clustering has always been highly concerned. Most clustering algorithms have encountered challenges, such as the difficulty of cluster centers selection, the artificial determination of the number of clusters K, low accuracy of clustering, and uneven clustering efficiency of different data sets. Considering the difficulty of cluster centers chosen, a new algorithm of fast selecting the initial cluster centers is proposed in this paper. Generally, cluster centers are those data points with higher density, smaller radius threshold and far away from each other, this method uses MNN (M nearest neighbors), density and distance to determine the initial cluster centers. First, the neighborhood radius r of each point is measured by MNN based on distance, and the average value of all r is marked as r̅; second, the densities ρ of each point in the region within r̅ are calculated; and then, factor f is defined to describe the probability that points become cluster centers, based on which, the initial cluster centers are determined by the candidates with bigger f . In the end, the method proposed in this paper is tested by using 12 groups of typical benchmark data sets and applied in the stellar spectral data of LAMOST survey. The experiment results compared with the other six algorithms indicate that the initial cluster centers obtained by this method are of higher quality than that of the six algorithms. Meanwhile, the initial cluster centers of spectral data are of good agreement with the actual stellar classifications.