AbstractTwo cases of an overlying inversion imposed on a stable boundary layer are investigated, extending the work of Hancock and Hayden (Boundary-Layer Meteorol 168:29–57, 2018; 175:93–112, 2020). Vertical profiles of Reynolds stresses and heat flux show closely horizontally homogeneous behaviour over a streamwise fetch of more than eight boundary-layer heights. However, profiles of mean temperature and velocity show closely horizontally homogeneous behaviour only in the top two-thirds of the boundary layer. In the lower one-third the temperature decreases with fetch, directly as a consequence of heat transfer to the surface. A weaker effect is seen in the mean velocity profiles, curiously, such that the gradient Richardson number is invariant with fetch, while various other quantities are not. Stability leads to a ‘blocking’ of vertical influence. Inferred aerodynamic and thermal roughness lengths increase with fetch, while the former is constant in the neutral case, as expected. Favourable validation comparisons are made against two sets of local-scaling systems over the full depth of the boundary layer. Close concurrence is seen for all stable cases for z/L < 0.2, where z and L are the vertical height and local Obukhov length, respectively, and over most of the layer for some quantities.

Context. The analytical procedures used in the audit are currently based on data mining techniques. The work solves the problem of increasing the efficiency and effectiveness of analytical audit procedures by clustering based on spectral decomposition. The object of the research is the process of auditing the compliance of payment and supply sequences for raw materials. Objective. The aim of the work is to increase the effectiveness and efficiency of the audit due to the method of spectral clustering of sequences of payment and supply of raw materials while automating procedures for checking their compliance. Method. The vectors of features are generated for the objects of the sequences of payment and supply of raw materials, which are then used in the proposed method. The created method improves the traditional spectral clustering method by automatically determining the number of clusters based on the explained and sample variance rule; automatic determination of the scale parameter based on local scaling (the rule of K-nearest neighbors is used); resistance to noise and random outliers by replacing the k-means method with a modified PAM method, i.e. replacing centroid clustering with medoid clustering. As in the traditional approach, the data can be sparse, and the clusters can have different shapes and sizes. The characteristics of evaluating the quality of spectral clustering are selected. Results. The proposed spectral clustering method was implemented in the MATLAB package. The results obtained made it possible to study the dependence of the parameter values on the quality of clustering. Conclusions. The experiments carried out have confirmed the efficiency of the proposed method and allow us to recommend it for practical use in solving audit problems. Prospects for further research may lie in the creation of intelligent parallel and distributed computer systems for general and special purposes, which use the proposed method for segmentation, machine learning and pattern recognition tasks.

The characterization of the spatial distribution of soil pore structures is essential to obtain different parameters that will be useful in developing predictive models for a range of physical, chemical, and biological processes in soils. Over the last decade, major technological advances in X-ray computed tomography (CT) have allowed for the investigation and reconstruction of natural porous soils at very fine scales. Delimiting the pore structure (pore space) from the CT soil images applying image segmentation methods is crucial when attempting to extract complex pore space geometry information.

Different segmentation methods can result in different spatial distributions of pores influencing the parameters used in the models [1]. A new combined global & local segmentation (2D) method called “Combining Singularity-CA method” was successfully applied [2]. This method combines a local scaling method (Singularity-CA method) with a global one (Maximum Entropy method). The Singularity-CA method, based on fractal concepts, creates singularity maps, and the CA (Concentration Area) method is used to define local thresholds that can be applied to binarize CT images [3]. Comparing Singularity-CA method with classical methods, such as Otsu and Maximum Entropy, we observed that more pores can be detected mainly due to its ability to amplify anomalous concentrations. However, some small pores were detected incorrectly. Combining Singularity-CA (2D) method gives better pore detection performance than the Singularity-CA and the Maximum Entropy method applied individually to the images.

The Combining Singularity-CV (3D) method is presented in this work. It combines the Singularity – CV (Concentration Volume) method [4] and a global one to improve 3D pore space detection.

References:

[1] Zhang, Y.J. (2001). A review of recent evaluation methods for image segmentation: International symposium on signal processing and its applications. Kuala Lumpur, Malaysia, 13–16, pp. 148–151.

[2] Martín-Sotoca, J.J., Saa-Requejo, A., Grau, J.B., Paz-González, A., and Tarquis, A.M. (2018). Combining global and local scaling methods to detect soil pore space. J. of Geo. Exploration, vol. 189, June 2018, pp 72-84.

[3] Martín-Sotoca, J.J., Saa-Requejo, A., Grau, J.B. and Tarquis, A.M. (2017). New segmentation method based on fractal properties using singularity maps. Geoderma, vol. 287, February 2017, pp 40-53. http://dx.doi.org/10.1016/j.geoderma.2016.09.005.

[4] Martín-Sotoca, J.J., Saa-Requejo, A., Grau, J.B. and Tarquis, A.M. (2018). Local 3D segmentation of soil pore space based on fractal properties using singularity maps. Geoderma, vol. 311, February 2018, pp 175-188. http://dx.doi.org/10.1016/j.geoderma.2016.11.029.

Acknowledgements:

The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain and the funding from the Comunidad de Madrid (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330.

Hourly precipitation extremes can intensify with higher temperatures at higher rates than theoretically expected from thermodynamic increases explained by the Clausius-Clapeyron (CC) relationship (~6.5%/K), but local scaling with surface air temperature is highly variable. Here, we use daily dewpoint temperature, a direct proxy of absolute humidity, as the scaling variable instead of surface air temperature. Using a global dataset of over 7000 hourly precipitation gauges, we estimate the at-gauge local scaling across six macro-regions; this ranges from CC to 2xCC for more than 60% of gauges. We find positive scaling in subtropical and tropical regions in contrast to previous work. Moreover, regional scaling rates show surprisingly universal behaviour at around CC, with higher scaling rates in Europe. Our results show a much greater consistency of scaling across the globe than previous work, usually at or above the CC rate, suggesting the relevance of dewpoint temperature scaling to understand future changes.   

Ultra diffuse galaxies (UDGs) are a type of large low surface brightness (LSB) galaxies with particularly large effective radii (reff >  1.5 kpc) that are now routinely studied in the Local (z <  0.1) Universe. While they are found to be abundant in clusters, groups, and in the field, their formation mechanisms remain elusive and comprise an active topic of debate. New insights may be found by studying their counterparts at higher redshifts (z >  1.0), even though cosmological surface brightness dimming makes them particularly difficult to detect and study in this channel. In this work, we use the deepest Hubble Space Telescope (HST) imaging stacks of z >  1 clusters, namely, SPT-CL J2106−5844 and MOO J1014+0038. These two clusters, at z = 1.13 and z = 1.23, respectively, were monitored as part of the HST See-Change programme. In making a comparison with the Hubble Extreme Deep Field as the reference field, we find statistical over-densities of large LSB galaxies in both clusters. Based on stellar-population modelling and assuming no size evolution, we find that the faintest sources we can detect are about as bright as expected for the progenitors of the brightest local UDGs. We find that the LSBs we detect in SPT-CL J2106−5844 and MOO J1014−5844 already have old stellar populations that place them on the red sequence. In correcting for incompleteness and based on an extrapolation of local scaling relations, we estimate that distant UDGs are relatively under-abundant, as compared to local UDGs, by a factor ∼3. A plausible explanation for the implied increase over time would be the significant growth of these galaxies over the last ∼8 Gyr, as also suggested by hydrodynamical simulations.

Studies of the correlations of environmental factors with vegetation growth using remotely sensed measurements are necessarily made against a background of biophysical and anthropogenic factors, such as local fertility, microclimate, and the effects of human land use, in addition to the factors of interest. This is an inevitable outcome of a natural (unplanned) design where the effects of the factors of interest are confounded with other, often unknown factors, possibly rendering the results inaccurate or poorly-constrained. The problems associated with a natural design would be reduced if sites could be identified in which uncontrolled variables had no impact. However, rarely are such sites known a priori. Here, a component of the net primary production (NPP) local scaling (LNS) method was used to estimate the potential NPP in the absence of confounding factors. Subsequent analyses of the effects of the selected environmental variables were carried out using the potential NPP. The method was tested in relation to NPP along the transitional ecotone from desert to semiarid conditions in the northern Negev, Israel. The effects of four environmental factors were tested: precipitation, topography, land cover, and interannual variability. While precipitation is generally the only environmental variable that is considered in drylands, the other factors were found to be significant. The results provided unambiguous evidence of the value of the method.

(1) Robotic walkers have gradually been developed over the last decade, and their use has caused changes in gait. However, detailed gait analyses during robotic walker-assisted walking have not been performed. In this study, we aim to identify the changes in determinism of gait dynamics owing to the intervention of a robotic walker. (2) Eleven healthy subjects participated in walking experiments under normal walking, rollator-assisted walking, and robotic walker-assisted walking conditions. We analyzed the measured trunk acceleration to derive the gait parameters, local scaling exponent (LSE, from correlation sum), and percentage of determinism (%DET, from recurrence plot). (3) The walking speed during rollator-assisted walking was significantly lower than that during robotic walker-assisted walking. Changes in the shape of the LSE along the anterior–posterior direction revealed the influence of the robotic walker at an individual level. The changes in %DET along the anterior–posterior direction were also significantly different between normal walking and robotic walker-assisted walking. (4) The rollator decreased the walking speed in comparison to normal walking. The changed LSE and reduced %DET imply reduced deterministic patterns and disturbance to the gait dynamics. The robotic walker only affects the gait dynamics in the anterior–posterior direction. Furthermore, the burden on the subjects was reduced during robotic walker-assisted walking.

Advancement on computer and sensing technologies has generated exponential growth in the data available for the development of systems that support decision-making in fields such as health, entertainment, manufacturing, among others. This fact has made that the fusion of data from multiple and heterogeneous sources became one of the most promising research fields in machine learning. However, in real-world applications, to reduce the number of sources while maintaining optimal system performance is an important task due to the availability of data and implementation costs related to processing, implementation, and development times. In this work, a novel method for the objective selection of relevant information sources in a multimodality system is proposed. This approach takes advantage of the ability of multiple kernel learning (MKL) and the support vector machines (SVM) classifier to perform an optimal fusion of data by assigning weights according to their discriminative value in the classification task; when a kernel is designed for representing each data source, these weights can be used as a measure of their relevance. Moreover, three algorithms for tuning the Gaussian kernel bandwidth in the classifier prediction stage are introduced to reduce the computational cost of searching for an optimal solution; these algorithms are an adaptation of a common technique in unsupervised learning named local scaling. Two real application tasks were used to evaluate the proposed method: the selection of electrodes for a classification task in Brain–Computer Interface (BCI) systems and the selection of relevant Magnetic Resonance Imaging (MRI) sequences for detection of breast cancer. The obtained results show that the proposed method allows the selection of a small number of information sources.