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Keywords = SAX-TD

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19 pages, 2972 KiB  
Article
Similarity Measurement and Classification of Temporal Data Based on Double Mean Representation
by Zhenwen He, Chi Zhang and Yunhui Cheng
Algorithms 2023, 16(7), 347; https://doi.org/10.3390/a16070347 - 19 Jul 2023
Viewed by 1590
Abstract
Time series data typically exhibit high dimensionality and complexity, necessitating the use of specific approximation methods to perform computations on the data. The currently employed compression methods suffer from varying degrees of feature loss, leading to potential distortions in similarity measurement results. Considering [...] Read more.
Time series data typically exhibit high dimensionality and complexity, necessitating the use of specific approximation methods to perform computations on the data. The currently employed compression methods suffer from varying degrees of feature loss, leading to potential distortions in similarity measurement results. Considering the aforementioned challenges and concerns, this paper proposes a double mean representation method, SAX-DM (Symbolic Aggregate Approximation Based on Double Mean Representation), for time series data, along with a similarity measurement approach based on SAX-DM. Addressing the trade-off between compression ratio and accuracy in the improved SAX representation, SAX-DM utilizes the segment mean and the segment trend distance to represent corresponding segments of time series data. This method reduces the dimensionality of the original sequences while preserving the original features and trend information of the time series data, resulting in a unified representation of time series segments. Experimental results demonstrate that, under the same compression ratio, SAX-DM combined with its similarity measurement method achieves higher expression accuracy, balanced compression rate, and accuracy, compared to SAX-TD and SAX-BD, in over 80% of the UCR Time Series dataset. This approach improves the efficiency and precision of similarity calculation. Full article
(This article belongs to the Special Issue Algorithms for Time Series Forecasting and Classification)
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Figure 1

Figure 1
<p>Symbolized approximate representation of time series data SAX.</p> Full article ">Figure 2
<p>Some examples of SAX-TD. (<b>a</b>–<b>f</b>) represent the trend distances calculated by the SAX-TD method for representing sequences of various common shapes.</p> Full article ">Figure 3
<p>Some cases of SAX-BD. (<b>a</b>–<b>d</b>) represent four sequences with the same size mean, where the left and right endpoints of (<b>a</b>,<b>b</b>) are the same, while the maximum and minimum values of (<b>c</b>,<b>d</b>) are the same, respectively.</p> Full article ">Figure 4
<p>Schematic diagram of SAX-DM trend distance. (<b>a</b>) represents the trend distance calculated by the SAX-DM method for sequences with an upward trend. (<b>b</b>) represents the trend distance calculated by the SAX-DM method for sequences with a downward trend.</p> Full article ">Figure 5
<p>Comparison results of SAX-DM and ED.</p> Full article ">Figure 6
<p>Comparison results of SAX-DM and SAX.</p> Full article ">Figure 7
<p>Comparison results of SAX-DM and SAX-TD.</p> Full article ">Figure 8
<p>Comparison results of SAX-DM and SAX-BD.</p> Full article ">Figure 9
<p>Comparison of classification error rates under different α and w conditions.</p> Full article ">Figure 9 Cont.
<p>Comparison of classification error rates under different α and w conditions.</p> Full article ">Figure 10
<p>SAX and its improved method accuracy and compression ratio.</p> Full article ">
36 pages, 3337 KiB  
Review
Comparative Analysis of Spectroscopic Studies of Tungsten and Carbon Deposits on Plasma-Facing Components in Thermonuclear Fusion Reactors
by Vladimir G. Stankevich, Nickolay Y. Svechnikov and Boris N. Kolbasov
Symmetry 2023, 15(3), 623; https://doi.org/10.3390/sym15030623 - 1 Mar 2023
Cited by 3 | Viewed by 2016
Abstract
Studies on the erosion products of tungsten plasma-facing components (films, surfaces, and dust) for thermonuclear fusion reactors by spectroscopic methods are considered and compared with those of carbon deposits. The latter includes: carbon–deuterium CDx (x ~ 0.5) smooth films deposited at [...] Read more.
Studies on the erosion products of tungsten plasma-facing components (films, surfaces, and dust) for thermonuclear fusion reactors by spectroscopic methods are considered and compared with those of carbon deposits. The latter includes: carbon–deuterium CDx (x ~ 0.5) smooth films deposited at the vacuum chamber during the erosion of the graphite limiters in the T-10 tokamak and mixed CHx-Me films (Me = W, Fe, etc.) formed by irradiating a tungsten target with an intense H-plasma flux in a QSPA-T plasma accelerator. It is shown that the formerly developed technique for studying CDx films with 15 methods, including spectroscopic methods, such as XPS, TDS, EPR, Raman, and FT-IR, is universal and can be supplemented by a number of new methods for tungsten materials, including in situ analysis of the MAPP type using XPS, SEM, TEM, and probe methods, and nuclear reaction method. In addition, the analysis of the fractality of the CDx films using SAXS + WAXS is compared with the analysis of the fractal structures formed on tungsten and carbon surfaces under the action of high-intensity plasma fluxes. A comparative analysis of spectroscopic studies on carbon and tungsten deposits makes it possible to identify the problems of the safe operation of thermonuclear fusion reactors. Full article
(This article belongs to the Special Issue Symmetry in Physics of Plasma Technologies II)
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Figure 1

Figure 1
<p>XPS survey spectrum of the CH<span class="html-italic"><sub>x</sub></span>–Me film on a Si(100) substrate, with the highlighted color of the tungsten peaks W4<span class="html-italic">f</span>, W5<span class="html-italic">s</span>, W4<span class="html-italic">d</span>, W4<span class="html-italic">p</span>, and W4<span class="html-italic">s</span> (weak). The C1s and O1s peaks are cut off by intensity.</p> Full article ">Figure 2
<p>(<b>a</b>) XPS spectrum of CH<span class="html-italic"><sub>x</sub></span>-Me film in the region of the W4<span class="html-italic">f</span> doublet; (<b>b</b>) W4<span class="html-italic">f</span> doublet fitting by 2 Gaussians.</p> Full article ">Figure 3
<p>IR reflectance spectrum of CD<sub>x</sub> flake No. 2, 30 µm thick, with D/C = 0.57 and H/C = 0.23, at room temperature, with the indication of the main vibrational modes and their band positions.</p> Full article ">Figure 4
<p>(<b>a</b>) Typical Raman spectra of CD<sub>x</sub> films T3 and T5, normalized to the maximum intensity. The arrow is approximately 1060 cm<sup>−1</sup>; (<b>b</b>) Raman spectrum of the T3 film normalized to the maximum intensity and its decomposition into 4 Gaussians (dotted lines at 1176, 1371, 1468, and 1571 cm<sup>−1</sup>), see <a href="#symmetry-15-00623-t002" class="html-table">Table 2</a>; (<b>c</b>) Raman spectrum of the CD<span class="html-italic"><sub>x</sub></span> T5 film normalized to the maximum intensity (solid line) and its decomposition into 4 Gaussians (dotted lines, at 1229, 1378, 1477, and 1574 cm<sup>−1</sup>).</p> Full article ">Figure 5
<p>The SAXS spectrum of a gold CD<span class="html-italic"><sub>x</sub></span> flake at the scattering vector range <span class="html-italic">q</span> = 0.04–2.6 nm<sup>−1</sup> in a double log scale. The Guinier region extends from <span class="html-italic">q</span><sub>0</sub> up to ln<span class="html-italic">I</span> ~ <span class="html-italic">R</span><sub>g</sub><sup>2</sup><span class="html-italic">q</span><sup>2</sup>.</p> Full article ">Figure 6
<p>The experimental SAXS curve modeled by the unified scattering function in a shorter range of <span class="html-italic">q</span> = 0.04–0.5 nm<sup>−1</sup> [<a href="#B70-symmetry-15-00623" class="html-bibr">70</a>]. The model starts to deviate from the experimental curve at q = q*.</p> Full article ">Figure 7
<p>Experimental WAXS curve at <span class="html-italic">q</span> = 3–100 nm<sup>−1</sup> (with 3 Gaussians peak fittings) and a neutron diffraction curve of the same CD<span class="html-italic"><sub>x</sub></span> flake.</p> Full article ">Figure 8
<p>Simulation of WAXS patterns with 3 wide peaks for two simple patterns “C13” and “9 × C13” with corresponding curves calculated using the Debye equation, and displaying the minimum fractal aggregate “9 × C13”. The bottom curve is experimental.</p> Full article ">
23 pages, 4431 KiB  
Article
Hexadecimal Aggregate Approximation Representation and Classification of Time Series Data
by Zhenwen He, Chunfeng Zhang, Xiaogang Ma and Gang Liu
Algorithms 2021, 14(12), 353; https://doi.org/10.3390/a14120353 - 2 Dec 2021
Cited by 3 | Viewed by 3082
Abstract
Time series data are widely found in finance, health, environmental, social, mobile and other fields. A large amount of time series data has been produced due to the general use of smartphones, various sensors, RFID and other internet devices. How a time series [...] Read more.
Time series data are widely found in finance, health, environmental, social, mobile and other fields. A large amount of time series data has been produced due to the general use of smartphones, various sensors, RFID and other internet devices. How a time series is represented is key to the efficient and effective storage and management of time series data, as well as being very important to time series classification. Two new time series representation methods, Hexadecimal Aggregate approXimation (HAX) and Point Aggregate approXimation (PAX), are proposed in this paper. The two methods represent each segment of a time series as a transformable interval object (TIO). Then, each TIO is mapped to a spatial point located on a two-dimensional plane. Finally, the HAX maps each point to a hexadecimal digit so that a time series is converted into a hex string. The experimental results show that HAX has higher classification accuracy than Symbolic Aggregate approXimation (SAX) but a lower one than some SAX variants (SAX-TD, SAX-BD). The HAX has the same space cost as SAX but is lower than these variants. The PAX has higher classification accuracy than HAX and is extremely close to the Euclidean distance (ED) measurement; however, the space cost of PAX is generally much lower than the space cost of ED. HAX and PAX are general representation methods that can also support geoscience time series clustering, indexing and query except for classification. Full article
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Figure 1

Figure 1
<p>The relationship between PAA and SAX ([<a href="#B5-algorithms-14-00353" class="html-bibr">5</a>]).</p> Full article ">Figure 2
<p>Summary of time series 1 and 2. (<b>a</b>) Summary of time series 1 (<b>b</b>) Summary of time series 2.</p> Full article ">Figure 2 Cont.
<p>Summary of time series 1 and 2. (<b>a</b>) Summary of time series 1 (<b>b</b>) Summary of time series 2.</p> Full article ">Figure 3
<p>The transformations distance between <span class="html-italic">AB</span> and <span class="html-italic">CD</span>. (<b>a</b>) translation transformation; (<b>b</b>) rotation transformation; (<b>c</b>) scale transformation; (<b>d</b>) <span class="html-italic">AB</span> = <span class="html-italic">CD</span>.</p> Full article ">Figure 4
<p>Change the order of the transformation distance calculation between <span class="html-italic">AB</span> and <span class="html-italic">CD</span>. (<b>a</b>) rotation transformation; (<b>b</b>) translation transformation; (<b>c</b>) scale transformation; (<b>d</b>) <span class="html-italic">AB</span> = <span class="html-italic">CD</span>.</p> Full article ">Figure 5
<p>Transformable interval objects <span class="html-italic">AB</span> and <span class="html-italic">CD</span>.</p> Full article ">Figure 6
<p>TIO points on the TIO plane.</p> Full article ">Figure 7
<p>A 4 × 4 HAX grid plane.</p> Full article ">Figure 8
<p>Accuracy comparison plot for <a href="#algorithms-14-00353-t004" class="html-table">Table 4</a>.</p> Full article ">Figure 9
<p>Accuracy comparison between HAX and SAX (71 points in the upper triangle and 43 points in the lower triangle).</p> Full article ">Figure 10
<p>Accuracy comparison between PAX and SAX (111 points in the upper triangle and 3 points in the lower triangle).</p> Full article ">Figure 11
<p>Accuracy comparison among HAX, ED and SAX.</p> Full article ">Figure 12
<p>Accuracy comparison among PAX, ED and SAX.</p> Full article ">Figure 13
<p>Accuracy comparison between PAX and ED (48 points in the upper triangle and 63 points in the lower triangle).</p> Full article ">Figure 14
<p>Accuracy comparison between PAX and SAX-BD (50 points in the upper triangle and 54 points in the lower triangle).</p> Full article ">
20 pages, 3310 KiB  
Article
A Boundary Distance-Based Symbolic Aggregate Approximation Method for Time Series Data
by Zhenwen He, Shirong Long, Xiaogang Ma and Hong Zhao
Algorithms 2020, 13(11), 284; https://doi.org/10.3390/a13110284 - 9 Nov 2020
Cited by 9 | Viewed by 6022
Abstract
A large amount of time series data is being generated every day in a wide range of sensor application domains. The symbolic aggregate approximation (SAX) is a well-known time series representation method, which has a lower bound to Euclidean distance and may discretize [...] Read more.
A large amount of time series data is being generated every day in a wide range of sensor application domains. The symbolic aggregate approximation (SAX) is a well-known time series representation method, which has a lower bound to Euclidean distance and may discretize continuous time series. SAX has been widely used for applications in various domains, such as mobile data management, financial investment, and shape discovery. However, the SAX representation has a limitation: Symbols are mapped from the average values of segments, but SAX does not consider the boundary distance in the segments. Different segments with similar average values may be mapped to the same symbols, and the SAX distance between them is 0. In this paper, we propose a novel representation named SAX-BD (boundary distance) by integrating the SAX distance with a weighted boundary distance. The experimental results show that SAX-BD significantly outperforms the SAX representation, ESAX representation, and SAX-TD representation. Full article
(This article belongs to the Special Issue Algorithms and Applications of Time Series Analysis)
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Figure 1

Figure 1
<p>Financial time series <b>A</b> and <b>B</b> have the same SAX symbolic representation ‘decfdb’ in the same condition where the length of time series is 30, the number of segments is 6 and the size of symbols is 6. However, they are different time series.</p> Full article ">Figure 2
<p>Several typical segments with the same average value but different trends [<a href="#B26-algorithms-13-00284" class="html-bibr">26</a>]. Segment <b>a</b> and <b>d</b>, <b>b</b> and <b>e</b>, <b>c</b> and <b>f</b> are in opposite directions while all in same mean value.</p> Full article ">Figure 3
<p>Several typical segments with the same average value and same trends but different boundary distance. Segment <b>b</b> and <b>c</b>, <b>e</b> and <b>f</b> with the same SAX representation and trend distance while they are different segments.</p> Full article ">Figure 4
<p>Several typical segments with the same average value but boundary distance. Segment <b>a</b> and <b>d</b>, <b>b</b> and <b>e</b>, <b>c</b> and <b>f</b> are in opposite directions while all in same mean value. The trend distance is replaced by boundary distance.</p> Full article ">Figure 5
<p>Time series represented as ‘adfeeffcaefffdaabc’ by ESAX [<a href="#B25-algorithms-13-00284" class="html-bibr">25</a>]. Where the length of time series is 30, the number of segments is 6 and the size of symbols is 6. The capital letters A–H represent the maximum and minimum values in every segment.</p> Full article ">Figure 6
<p>The SAX-BD algorithm is compared with other algorithms for accuracy. (<b>a</b>–<b>d</b>) represents a comparison between SAX-BD with Euclidean, SAX, ESAX, SAX-TD. The more dots above the red slash, the better performs of SAX-BD.</p> Full article ">Figure 7
<p>The classification error rates of SAX, ESAX, SAX-TD and SAX-BD with different parameters <span class="html-italic">w</span> and <span class="html-italic">α</span>. For (<b>a</b>), on Gun-Point, w varies while <span class="html-italic">α</span> is fixed at 3, for (<b>b</b>), on Gun-Point, varies while w is fixed at 4. For (<b>c</b>), on Yoga, w varies while <span class="html-italic">α</span> is fixed at 10, for (<b>d</b>), on Yoga, varies while w is fixed at 128.</p> Full article ">Figure 8
<p>Dimensionality reduction ratio of the four methods.</p> Full article ">Figure 8 Cont.
<p>Dimensionality reduction ratio of the four methods.</p> Full article ">Figure 9
<p>The running time of different methods with different values of <span class="html-italic">α</span>.</p> Full article ">
18 pages, 3783 KiB  
Article
Local Order and Dynamics of Nanoconstrained Ethylene-Butylene Chain Segments in SEBS
by Michele Mauri, George Floudas and Roberto Simonutti
Polymers 2018, 10(6), 655; https://doi.org/10.3390/polym10060655 - 11 Jun 2018
Cited by 11 | Viewed by 4725
Abstract
Subtle alterations in the mid-block of polystyrene-b-poly (ethylene-co-butylene)-b-polystyrene (SEBS) have a significant impact on the mechanical properties of the resulting microphase separated materials. In samples with high butylene content, the ethylene-co-butylene (EB) phase behaves as a rubber, [...] Read more.
Subtle alterations in the mid-block of polystyrene-b-poly (ethylene-co-butylene)-b-polystyrene (SEBS) have a significant impact on the mechanical properties of the resulting microphase separated materials. In samples with high butylene content, the ethylene-co-butylene (EB) phase behaves as a rubber, as seen by differential scanning calorimetry (DSC), time domain (TD) and Magic Angle Spinning (MAS) NMR, X-ray scattering at small (SAXS), and wide (WAXS) angles. In samples where the butylene content is lower—but still sufficient to prevent crystallization in bulk EB—the DSC thermogram presents a broad endothermic transition upon heating from 221 to 300 K. TD NMR, supported by WAXS and dielectric spectroscopy measurements, probed the dynamic phenomena of EB during this transition. The results suggest the existence of a rotator phase for the EB block below room temperature, as a result of nanoconfinement. Full article
(This article belongs to the Special Issue NMR in Polymer Science)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Overview of the Differential Scanning Calorimetry (DSC) traces of all samples during heating from 200 to 375 K at 10 K/min.</p> Full article ">Figure 2
<p>Heating and Cooling DSC traces of LB1, a sample with low butyl content, at 10 K/min.</p> Full article ">Figure 3
<p>(<b>Left</b>) X-ray scattering at small (SAXS) intensity profiles for HB1 shown for different temperatures on heating and subsequent cooling. Annealing causes reversible and irreversible changes on the nano-domain morphology. (<b>Right</b>) The same data are now shown as intensity contour plots. The dependence of the first and higher order reflections following annealing at 473 K indicates shrinkage upon heating. Arrows indicate peaks with ratio 1:3<sup>1/2</sup>:4<sup>1/2</sup> relative to the first peak corresponding to 293 K.</p> Full article ">Figure 4
<p>SAXS intensity profiles measured at 303 K following annealing at 473 K. The thin arrows represent the first- and higher order reflections from the nanodomain morphology. The thick arrow in HB2 gives the intra-particle scattering peak.</p> Full article ">Figure 5
<p>(<b>a</b>) CP-MAS and (<b>b</b>) HPDEC <sup>13</sup>C NMR spectra of all SEBS samples at 298 K.</p> Full article ">Figure 6
<p>Wide-Angle X-ray Diffraction (WAXD) intensity profiles for LB2 shown for selected temperatures: 223 K (blue), 283 K (magenta), and 353 K (red). 2D images are also shown at two temperatures (lower): 303 K and (upper): 223 K. In both representations, there is a noticeable narrowing of the reflection at ~14.3 nm<sup>−1</sup>.</p> Full article ">Figure 7
<p>Dielectric permittivity (top), derivative of the dielectric permittivity with temperature (middle) and dielectric loss (bottom) of LB1 (red line) and HB1 (block line) obtained during the second heating run with a rate of 2 K/min at a frequency of 1.33 × 10<sup>5</sup> Hz. The derivative of LB1 has been shifted vertically for clarity. The arrows in the derivative representation provide the temperature of the segmental relaxations at the chosen frequency.</p> Full article ">Figure 8
<p>Temperature dependent measurement of the rigid fraction of LB1 and HB1 SEBS. Dashed lines represent the amount of rigid PS in the plateau region.</p> Full article ">Figure 9
<p><span class="html-italic">T</span><sub>2</sub> as a function of temperature for reported SEBS samples, measured by the Hahn-Echo.</p> Full article ">Figure 10
<p>(<b>a</b>) nDQ data for sample LB1 and HB1 acquired at 353 K, with data fitting represented by dashed and solid lines, respectively. (<b>b</b>) Corresponding distributions of residual dipolar couplings.</p> Full article ">Figure 11
<p>Simulated DSC curves for LB1 and HB1 central blocks. The <span class="html-italic">y</span> axis represents the incremental fraction of melting PE.</p> Full article ">Scheme 1
<p>conceptual description of the polystyrene-<span class="html-italic">block</span>-poly(ethylene-<span class="html-italic">co</span>-but-1-ene)-<span class="html-italic">block</span>-polystyrene triblock (SEBS) copolymers, with the LB on top and the HB on the bottom. The different amount of side chains in the mid block is highlighted.</p> Full article ">
490 KiB  
Article
Alert-QSAR. Implications for Electrophilic Theory of Chemical Carcinogenesis
by Mihai V. Putz, Cosmin Ionaşcu, Ana-Maria Putz and Vasile Ostafe
Int. J. Mol. Sci. 2011, 12(8), 5098-5134; https://doi.org/10.3390/ijms12085098 - 11 Aug 2011
Cited by 39 | Viewed by 10313
Abstract
Given the modeling and predictive abilities of quantitative structure activity relationships (QSARs) for genotoxic carcinogens or mutagens that directly affect DNA, the present research investigates structural alert (SA) intermediate-predicted correlations ASA of electrophilic molecular structures with observed carcinogenic potencies in rats (observed [...] Read more.
Given the modeling and predictive abilities of quantitative structure activity relationships (QSARs) for genotoxic carcinogens or mutagens that directly affect DNA, the present research investigates structural alert (SA) intermediate-predicted correlations ASA of electrophilic molecular structures with observed carcinogenic potencies in rats (observed activity, A = Log[1/TD50], i.e., ASA=f(X1SA,X2SA,...)). The present method includes calculation of the recently developed residual correlation of the structural alert models, i.e., ARASA=f(A-ASA,X1SA,X2SA,...) . We propose a specific electrophilic ligand-receptor mechanism that combines electronegativity with chemical hardness-associated frontier principles, equality of ligand-reagent electronegativities and ligand maximum chemical hardness for highly diverse toxic molecules against specific receptors in rats. The observed carcinogenic activity is influenced by the induced SA-mutagenic intermediate effect, alongside Hansch indices such as hydrophobicity (LogP), polarizability (POL) and total energy (Etot), which account for molecular membrane diffusion, ionic deformation, and stericity, respectively. A possible QSAR mechanistic interpretation of mutagenicity as the first step in genotoxic carcinogenesis development is discussed using the structural alert chemoinformation and in full accordance with the Organization for Economic Co-operation and Development QSAR guidance principles. Full article
(This article belongs to the Special Issue Recent Advances in QSAR/QSPR Theory)
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Graphical abstract

Graphical abstract
Full article ">
<p>The <span class="html-italic">alert-QSAR method</span> uses structural alerts to assemble a molecular fragment QSAR model that has predictive power similar to that of full molecular modeling.</p> Full article ">
<p>Graphical representation of the working activities for molecules in <a href="#t1-ijms-12-05098" class="html-table">Tables 1</a> and <a href="#t2-ijms-12-05098" class="html-table">2</a>, classified under the “Gaussian” and “quasi-Gaussian” series for the training and testing QSARs, respectively.</p> Full article ">
<p>The electrophilic docking structure-reactivity algorithm correlating electronegativity and chemical hardness with chemical carcinogenesis. The algorithm starts with electronegativity docking (equalization) between the ligand and the receptor (the middle dashed line). Next, intra-molecular (in connection with specific structural alerts) maximization of the HOMO-LUMO gap (<span class="html-italic">i.e</span>., of chemical hardness) is accomplished by exo-electrophilic transfer of an electron from ligand to receptor.</p> Full article ">
<p>Illustration on a ligand-receptor cyclic interaction coordinate of the molecular mechanism of genotoxic carcinogenesis as given by the residual-alert-QSAR correlation-path hierarchy of <a href="#FD13a" class="html-disp-formula">Equations (13a)</a>–<a href="#FD13c" class="html-disp-formula">(13c)</a> then summarized in <a href="#FD17" class="html-disp-formula">Equation (17)</a>. The mechanism is superimposed over an immunohistochemical analysis of paraffin-embedded sections of rat intestinal cancer using the Caspase-2 antibody [<a href="#b51-ijms-12-05098" class="html-bibr">51</a>]. In these evolving molecular graphs (the SA region is circumvented), steric movement is represented by mirroring, electronegativity docking by changing SA colors, diffusion by translation arrows; polarizability by vibration arrows, and electrophilic docking (the final stage including the maximum hardness principle) by positive charging.</p> Full article ">
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