Relevant Physical Features Research Articles

The regulation of tendon stem cell distribution, morphology, and gene expression by the modulus of microfibers

The mechanical properties of a stem cell culture substrate significantly impact cell adhesion, survival, migration, proliferation, and differentiation in vitro. A major challenge in engineering artificial stem cell substrate is to properly identify the relevant physical features of native stem cell niches, which are likely different for each stem cell type. The behavior of tendon stem cells has potentially significant implications for tendon repair. Here, microfiber scaffolds with various modulus of elasticity are fabricated by near-field electrospinning, and their regulating effects on the in vitro behavior of tendon stem cells (TSCs) are discussed in this study. The number of pseudopodia shows a biphasic relationship with the modulus of scaffold. The proliferation, polarization ratio and alignment degree along the fibers of the TSCs increase with the increase of fiber modulus. TSCs cultured on the scaffold with moderate modulus (1429 MPa) show the upregulation of tendon-specific genes (Col-I, Tnmd, SCX and TNCF). These microfiber scaffolds provide great opportunities to modulate TSCs behavior at the micrometer scales. In conclusion, this study provides an instructive mechanical microenvironment for TSCs behaviors and may lead to the development of desirable engineered artificial stem cell substrate for tendon healing.

π Learning: A Performance‐Informed Framework for Microstructural Electrode Design

AbstractDesigning high‐performance porous electrodes is the key to next‐generation electrochemical energy devices. Current machine‐learning‐based electrode design strategies are mainly orientated toward physical properties; however, the electrochemical performance is the ultimate design objective. Performance‐orientated electrode design is challenging because the current data driven approaches do not accurately extract high‐dimensional features in complex multiphase microstructures. Herein, this work reports a novel performance‐informed deep learning framework, termed π learning, which enables performance‐informed microstructure generation, toward overall performance prediction of candidate electrodes by adding most relevant physical features into the learning process. This is achieved by integrating physics‐informed generative adversarial neural networks (GANs) with convolutional neural networks (CNNs) and with advanced multi‐physics, multi‐scale modeling of 3D porous electrodes. This work demonstrates the advantages of π learning by employing two popular design philosophies: forward and inverse designs, for the design of solid oxide fuel cells electrodes. π learning thus has the potential to unlock performance‐driven learning in the design of next generation porous electrodes for advanced electrochemical energy devices such as fuel cells and batteries.

Hierarchical Model Reduction Driven by Machine Learning for Parametric Advection-Diffusion-Reaction Problems in the Presence of Noisy Data

We propose a new approach to generate a reliable reduced model for a parametric elliptic problem, in the presence of noisy data. The reference model reduction procedure is the directional HiPOD method, which combines Hierarchical Model reduction with a standard Proper Orthogonal Decomposition, according to an offline/online paradigm. In this paper we show that directional HiPOD looses in terms of accuracy when problem data are affected by noise. This is due to the interpolation driving the online phase, since it replicates, by definition, the noise trend. To overcome this limit, we replace interpolation with Machine Learning fitting models which better discriminate relevant physical features in the data from irrelevant unstructured noise. The numerical assessment, although preliminary, confirms the potentialities of the new approach.

Estimating Geophysical Parameters Using Petrophysical Algorithm to Enhance Hydrocarbon Recovery in Lokaka Field, Niger Delta, Nigeria

Adoption of Enhanced Oil Recovery (EOR) to boost the hydrocarbon saturation (Sh) of reservoirs has caught the interests of many researchers in Geosciences. Evidence from literature shows that both primary and secondary recovery methods have failed to account for about 60% hydrocarbon (HC) that is trapped in the reservoirs and getting to discover large productive new fields has become a herculean task. This study identified the fluid nature and boundaries of reservoirs using some relevant geophysical (petrophysical) parameters and reservoir rocks physical features such as shale volume (Vsh), permeability (K), water and hydrocarbon saturation (Sw& Sh). Petrophysical data were sourced from the data bank of the Department of Geology, Obafemi Awolowo University, Ile-Ife, Nigeria. Analysis of data was done using the PETREL 2010 and OpendTect 4.6.0 versions for quality checking, delineation of identified reservoirs, fluid contacts demarcation and fluid types’ determination. The interpreted data were thereafter loaded into Microsoft Excel environment in order to adopt suitable statistical relations for the estimation of Vsh, K, Sw and Sh. Exploration of about 59.4% HC with NaOH, 64.5% HC with KOH, 69.5% HC with NH4OH and 78.5% HC with LiOH were discovered after the (EOR) flooding process. Comparison of the Vsh, K, Sw and Sh values before EOR with the values after EOR further showed that the reservoirs produced more HC with EOR. This study concluded that more hydrocarbon saturation can be achieved from reservoirs when EOR is carried out.

Visualizing the pulsar population using graph theory

ABSTRACT The $P\dot{P}$ diagram is a cornerstone of pulsar research. It is used in multiple ways for classifying the population, understanding evolutionary tracks, identifying issues in our theoretical reach, and more. However, we have been looking at the same plot for more than five decades. A fresh appraisal may be healthy. Is the $P \dot{P}$-diagram the most useful or complete way to visualize the pulsars we know? Here we pose a fresh look at the information we have on the pulsar population. First, we use principal component analysis over magnitudes depending on the intrinsic pulsar’s timing properties (proxies to relevant physical pulsar features), to analyse whether the information contained by the pulsar’s period and period derivative is enough to describe the variety of the pulsar population. Even when the variables of interest depend on P and $\dot{P}$, we show that $P \dot{P}$ are not principal components. Thus, any distance ranking or visualization based only on P and $\dot{P}$ is potentially misleading. Next, we define and compute a properly normalized distance to measure pulsar nearness, calculate the minimum spanning tree of the population, and discuss possible applications. The pulsar tree hosts information about pulsar similarities that go beyond P and $\dot{P}$, and are thus naturally difficult to read from the $P\dot{P}$-diagram. We use this work to introduce the pulsar tree website containing visualization tools and data to allow users to gather information in terms of MST and distance ranking.

Urban morphology parameters towards multi-risk scenarios for squares in the historical centers: Analyses and definition of square typologies and application to the Italian context

• Historical squares are prone to multi-risks depending on their physical features. • A methodology for defining physical features relevant for multi-risks is proposed. • Physical features of squares are quantitatively identified into parameters. • Identified parameters are tested on case studies for further detailing and testing. • Parameters describe squares typologies performance to multi-risks scenarios. The historical built environment (HBE) in urban areas is prone to disasters, which threaten both people and the historical built heritage itself. In such a scenario, risks depend on the combination between different possible (multi-) hazards (including climate change-related ones), the vulnerability and exposure of HBE users, and the physical (morphology-, typology- and construction-related) features of the HBE. In this context, squares are relevant components of the HBE from a meso‑scale perspective, which is based on the layout and morphology of open spaces, buildings blocks and their users. Squares host cultural heritage and attract users, both tourists and citizens. Moreover, squares are nodal points for the emergency path network and are crucial and significantly affected during the immediate aftermath of the disaster occurrence (e.g. by debris on the ground in the case of seismic hazard during the evacuation phase). Current approaches for risk assessment and mitigation entail the consideration of each specific square, but this approach is time-consuming, scattered between the different hazards and complex to apply to a multi-risk perspective. Therefore, this work provides a methodology to identify and classify the most relevant physical features of squares in the HBE, which are able to improve or worsen the performance of the HBEs to multi-risks from multi-hazard scenarios. The research is rooted in the existing literature and strengthened by experts’ judgement analyses. The proposed methodology synthesizes the considered relevant features of the squares in the HBE into quantitative parameters, which allow to verify the vulnerability to multi-risk of the squares. Such parameters are further organized into classes for the typological assessment of the multi-risk. To test and detail the parameters, the method is tested on a relevant case study, which is the Italian context. Indeed, such a case study is relevant not only for being subject to multi-risks (e.g., seismic, terrorist, heatwave and air pollution), but also because the vast majority of the urban areas are composed by HBEs and hosts cultural heritage sites. Moreover, in this context, squares have not only an environmental, but also a social and economic importance in the HBE and are thus particularly relevant. Then, in the relevant case study of Italy, further detailing and calibration of the defined classes of the relevant parameters, as well as their ranges, are tested on 133 squares, which cover the entire Italian territory. Results demonstrate that the identified classes and ranges of the parameters are suitable for describing historical squares by arranging them into typologies for multi-risks assessment. Although the parameters are here presented for the Italian context, they are of general value and could be tested in other contexts, by calibrating the ranges as illustrated for the specific considered sample. Thus, the outcomes of this work allow moving towards the classification of squares into built environment typologies according to the meso‑scale perspective, provide the bases for and promoting the application of expeditious approaches to multi-risk assessment in the HBE.

Learning finite element convergence with the Multi-fidelity Graph Neural Network

Machine learning techniques have emerged as potential alternatives to traditional physics-based modeling and partial differential equation solvers. Among these machine learning techniques, Graph Neural Networks (GNNs) simulate physics via graph models; GNNs embed relevant physical features into graph data structures, perform message passing within the graphs, and produce new attributes based on the system’s relationships. Like many machine learning frameworks, GNNs are limited by excessive data generation costs and limited generalizability outside of a narrow training domain. To address these limitations, we introduce the Multi-Fidelity Graph Neural Network (MFGNN), a supervised machine learning framework that uses low-fidelity projections to inform high-fidelity modeling across arbitrary subdomains represented by subgraphs. We implement the MFGNN for two-dimensional elastostatic problems with finite element training data. The MFGNN is trained to produce accurate analysis given low-fidelity evaluations and emulate the convergence behavior of traditional finite element analysis (FEA). Through subdomain abstraction, we also extend the MFGNN as a general model for new boundary conditions and material domains outside of the training domain.

Learning physical properties of anomalous random walks using graph neural networks

Single particle tracking allows probing how biomolecules interact physically with their natural environments. A fundamental challenge when analysing recorded single particle trajectories is the inverse problem of inferring the physical model or class of models of the underlying random walks. Reliable inference is made difficult by the inherent stochastic nature of single particle motion, by experimental noise, and by the short duration of most experimental trajectories. Model identification is further complicated by the fact that main physical properties of random walk models are only defined asymptotically, and are thus degenerate for short trajectories. Here, we introduce a new, fast approach to inferring random walk properties based on graph neural networks (GNNs). Our approach consists in associating a vector of features with each observed position, and a sparse graph structure with each observed trajectory. By performing simulation-based supervised learning on this construct [], we show that we can reliably learn models of random walks and their anomalous exponents. The method can naturally be applied to trajectories of any length. We show its efficiency in analysing various anomalous random walks of biological relevance that were proposed in the AnDi challenge []. We explore how information is encoded in the GNN, and we show that it learns relevant physical features of the random walks. We furthermore evaluate its ability to generalize to types of trajectories not seen during training, and we show that the GNN retains high accuracy even with few parameters. We finally discuss the possibility to leverage these networks to analyse experimental data.

Rotating black holes in Eddington-inspired Born-Infeld gravity: an exact solution

We find an exact, rotating charged black hole solution within Eddington-inspired Born-Infeld gravity. To this end we employ a recently developed correspondence or mapping between modified gravity models built as scalars out of contractions of the metric with the Ricci tensor, and formulated in metric-affine spaces (Ricci-Based Gravity theories) and General Relativity. This way, starting from the Kerr-Newman solution, we show that this mapping bring us the axisymmetric solutions of Eddington-inspired Born-Infeld gravity coupled to a certain model of non-linear electrodynamics. We discuss the most relevant physical features of the solutions obtained this way, both in the spherically symmetric limit and in the fully rotating regime. Moreover, we further elaborate on the potential impact of this important technical progress for bringing closer the predictions of modified gravity with the astrophysical observations of compact objects and gravitational wave astronomy.

Hyper-reduced order models for parametrized unsteady Navier-Stokes equations on domains with variable shape

In this work, we set up a new, general, and computationally efficient way to tackle parametrized fluid flows modeled through unsteady Navier-Stokes equations defined on domains with variable shape, when relying on the reduced basis method. We easily describe a domain by flexible boundary parametrizations, and generate domain (and mesh) deformations by means of a solid extension, obtained by solving a harmonic extension or a linear elasticity problem. The proposed procedure is built over a two-stage reduction: (i) first, we construct a reduced basis approximation for the mesh motion problem, irrespective of the fluid flow problem we focus on; (ii) then, we generate a reduced basis approximation of the unsteady Navier-Stokes problem, relying on finite element snapshots evaluated over a set of reduced deformed configuration, and approximating both velocity and pressure fields simultaneously. To deal with unavoidable nonaffine parametric dependencies arising in both the mesh motion and the state problem, we apply a matrix version of the discrete empirical interpolation method, allowing treating a wide range of geometrical deformations in an efficient and purely algebraic way. The same strategy is used to perform hyper-reduction of nonlinear terms. To assess the numerical performances of the proposed technique, we address the solution of parametrized fluid flows where the parameters describe both the shape of the domain and relevant physical features. Complex flow patterns such as the ones appearing in a patient-specific carotid bifurcation are accurately approximated, as well as derived quantities of potential clinical interest.

The <sup>18</sup>O ecohydrology of a grassland ecosystem – predictions and observations

Abstract. The oxygen isotope composition (δ18O) of leaf water (δ18Oleaf) is an important determinant of environmental and physiological information found in biological archives, but the system-scale understanding of the propagation of the δ18O of rain through soil and xylem water to δ18Oleaf has not been verified for grassland. Here we report a unique and comprehensive dataset of fortnightly δ18O observations in soil, stem and leaf waters made over seven growing seasons in a temperate, drought-prone, mixed-species grassland. Using the ecohydrology part of a physically based, 18O-enabled soil–plant–atmosphere transfer model (MuSICA), we evaluated our ability to predict the dynamics of δ18O in soil water, the depth of water uptake, and the effects of soil and atmospheric moisture on 18O enrichment of leaf water (Δ18Oleaf) in this ecosystem. The model accurately predicted the δ18O dynamics of the different ecosystem water pools, suggesting that the model generated realistic predictions of the vertical distribution of soil water and root water uptake dynamics. Observations and model predictions indicated that water uptake occurred predominantly from shallow (<20 cm) soil depths throughout dry and wet periods in all years, presumably due (at least in part) to the effects of high grazing pressure on root system turnover and placement. Δ18Oleaf responded to both soil and atmospheric moisture contents and was best described in terms of constant proportions of unenriched and evaporatively enriched water (two-pool model). The good agreement between model predictions and observations is remarkable as model parameters describing the relevant physical features or functional relationships of soil and vegetation were held constant with one single value for the entire mixed-species ecosystem.

Rotating microchannel flow velocity measurements using the stationary micro-PIV technique with application to lab-on-a-CD devices

The measurement of microfluidic flows is an essential instrument to understand the governing physical mechanisms at small scales. This fact has motivated the adaptation of well-established “macroscale” experimental technics to deal with the specificities of microfluidic flows; a prominent example is the micro particle image velocimetry (micro-PIV) technique. In a different manner, the progress experienced by experimental techniques to measure flows in rotating frames has been more limited, with most studies concerned with macroscale turbomachinery applications. It turns out that the scale reduction in this field establishes a new and important flow class, known as centrigually-driven microfluidics, with application to lab-on-a-CD devices. However, the experimental characterization of rotating microflows has been, so far, limited to bulk flow measurements and/or visualization practices. For that reason, in this work, we propose extending the stationary micro-PIV technique to undertake quantitative, whole-field, velocity measurements inside rotating microchannel flow platforms. For this task, actual lab-on-a-CD prototypes are used. This work develops in two parts. First, we describe the most relevant changes in the micro-PIV equipment viewing the introduction of the test section rotation, namely: (i) hardware changes related to the micro-PIV/CD synchronization and (ii) software changes aiming at the preservation of the velocity measurement accuracy, through the removal of the circumferential velocity component. While this last step follows a well-known methodology, called image de-rotation, we propose tackling it in a new and automated fashion by means of the image registration method, whose implementation and advantages are explained in detail here. The second part of this work evaluates the capabilities of the modified micro-PIV technique by critically assessing the results of preliminary tests undertaken in dynamical regimes where rotation is dominant. Here, we present for the first time velocity profile measurements of centrifugally-driven microchannel flows, which display marked structural differences from classical stationary pressure-driven flows. The quality of these experimental profiles is further examined through comparisons with computational fluid dynamics simulations, based on the lattice Boltzmann method. Overall, this study indicates the effectiveness of the proposed micro-PIV system, which is able to accurately capture the most relevant physical features of rotating microfluidic flows over regions sufficiently far away from the walls. On the other hand, inside the boundary layers, the present micro-PIV measurements remain difficult to execute; the reasons for this limitation are discussed and clearly identified in the present preliminary studies, which pave the way for future studies in the field.

Attentional modulation of neural responses to illusory shapes: Evidence from steady-state and evoked visual potentials

Our perception of illusory shapes, such as the classic Kanizsa triangle, is thought to reflect the visual system’s capacity to fill in missing information associated with fragmented or partially occluded objects. Previous work has suggested that such ‘modal’ filling-in arises at relatively early stages of visual processing, prior to the allocation of focused attention, but few studies have examined whether attention influences neural responses to illusory shapes. Here we asked whether spatial attention affects neural activity evoked by illusory shapes by having participants focus on stimuli presented in one visual field (left or right), while ignoring those on the other side. In two separate experiments, we used electroencephalography (EEG) to measure steady-state visual evoked potentials (SSVEPs; Experiment 1) and event-related potentials (ERPs; Experiment 2), elicited by a Kanizsa square in one hemifield and a competing control stimulus, matched for all relevant physical features, in the other. In Experiment 1, the inducers used to construct the Kanizsa and control stimuli flickered at unique frequencies (10 and 13.33 Hz) to elicit SSVEPs. In Experiment 2, segments were removed briefly from static circular placeholders within the left and right visual hemifields to induce ERPs. In both experiments, participants were instructed to attend covertly to the stimulus on the left or right side to detect brief contrast changes of the inducers. SSVEP analyses revealed that illusory shapes that were ignored yielded a significantly larger neural response than those elicited by non-illusory control stimuli. By contrast, when the stimuli were attended, neural responses to the illusory and control stimuli were statistically indistinguishable. Consistent with these steady-state findings, results of the ERP analyses revealed that evoked responses to illusory shapes emerged earlier relative to control stimuli when they were ignored than when they were attended. Taken together the findings suggest that neural activity associated with modal completion is reliably modulated by spatial attention. Specifically, we show that neural responses to actively ignored illusory shapes are elicited earlier and are sustained at a higher amplitude than responses to control stimuli. This advantage for ignored shapes might reflect adaptive signalling of the presence of an object available for future goal-directed behaviour.

A 360-degrees Holographic True 3D Display Unit Using a Fresnel Phase Plate

Holographic true 3D displays attempt to duplicate the original light field with all its relevant physical features. To this end, spatial light modulators (SLM) are commonly used as display devices. Currently available SLMs do not satisfy the technical specifications to achieve a consumer quality true 3D display; one of the important drawbacks is the rather large pixel size, and this results in a low spatial resolution of the fringe pattern written on the SLM which in turn severely restricts the diffraction/viewing angle. A design that uses low spatial resolution, but still achieves a 360-degrees viewing angle, using a parabolic mirror was already reported. Here in this design, the parabolic mirror is replaced by a Fresnel phase plate which is mounted as a covering layer on top of the SLM.

Coacervation-Mediated Combinatorial Synthesis of Biomatrices for Stem Cell Culture and Directed Differentiation.

Combinatorial screening represents a promising strategy to discover biomaterials for tailored cell culture applications. Although libraries incorporating different biochemical cues have been investigated, few simultaneously recapitulate relevant biochemical, physical, and dynamic features of the extracellular matrix (ECM). Here, a noncovalent system based on liquid-liquid phase separation (coacervation) and gelation mediated by glycosaminoglycan (GAG)-peptide interactions is reported. Multiple biomaterial libraries are generated using combinations of sulfated glycosaminoglycans and poly(ethylene glycol)-conjugated peptides. Screening these biomaterials reveals preferred biomatrices for the attachment of six cell types, including primary mesenchymal stromal cells (MSCs) and primary neural precursor cells (NPCs). Incorporation of GAGs sustains the expansion of all tested cell types comparable to standard cell culture surfaces, while osteogenic differentiation of MSC and neuronal differentiation of NPC are promoted on chondroitin and heparan biomatrices, respectively. The presented noncovalent system provides a powerful tool for developing tissue-specific ECM mimics.

Using geospatial techniques to develop an emergency referral transport system for suspected sepsis patients in Bangladesh.

BackgroundA geographic information system (GIS)-based transport network within an emergency referral system can be the key to reducing health system delays and increasing the chances of survival, especially during an emergency. We employed a GIS to design an emergency transport system for the rapid transfer of pregnant or early post-partum women, newborns, and children under 5 years of age with suspected sepsis under the Interrupting Pathways to Sepsis Initiative (IPSI) project.MethodsA GIS database was developed by mapping the villages, roads, and relevant physical features of the study area. A travel-time algorithm was developed to incorporate the time taken by different modes of local transport to reach the health complexes. These were used in a network analysis to identify the shortest routes to the hospitals from the villages, which were categorized into green, yellow, and red zones based on their proximity to the nearest hospitals to provide transport facilities. An emergency call-in centre established for the project managed the transport system, and its data was used to assess the uptake of this transport system amongst distant communities.ResultsFifteen pre-existing and two new routes were identified as the shortest routes to the health complexes. The call-in centre personnel used this route information to direct both patients and transport drivers to the nearest transport hubs or pick-up points. Adherence with referral advice was high in areas where the IPSI transport operated. Over the study period, the utilisation of the project’s transport doubled and referral compliance from distant zones similarly increased.ConclusionsThe GIS system created for this study facilitated rapid referral of patients in emergency from distant zones, using locally available transport and resources. The methodology described in this study to develop and implement an emergency transport system can be applied in similar, rural, low-income country settings.

Using Urban Design Qualities for Building a New Composite Walkability Index for Cairo Streets

This paper aims to build a Composite Walkability Index (CWI) for Cairo Streets. It is based on the field manual of the study of Ewing and Clemente (2013) to determine the most relevant Urban Design Qualities (UDQs) and physical features of walkability. The CWI is constructed in two stages; the Benefit-of-the-Doubt (BOD) weighting scheme is applied to each one. The reason for applying this mathematical programming method is to endogenously determine the relative importance of all UDQ and physical features with respect to walkability. Therefore, to investigate the credibility of the BOD technique, it has been applied to a sample of 46 different paths from Cairo streets. The resulted CWI values are discriminated such that no two streets have the same score. This in turn helps decision makers to assess these streets by relying on the distinguishable ranks of CWI and UDQs.

Elastic and dynamic properties of membrane phase-field models

.Phase-field models have been extensively used to study interfacial phenomena, from solidification to vesicle dynamics. In this article, we analyze a phase-field model that captures the relevant physical features that characterize biological membranes. We show that the Helfrich theory of elasticity of membranes can be applied to phase-field models, allowing to derive the expressions of the stress tensor, lateral stress profile and elastic moduli. We discuss the relevance and interpretations of these magnitudes from a phase-field perspective. Taking the sharp-interface limit we show that the membrane macroscopic equilibrium equation can be derived from the equilibrium condition of the phase-field interface. We also study two dynamic models that describe the behaviour of a membrane. From the study of the relaxational behaviour of the membrane we characterize the relevant dynamics of each model, and discuss their applications.Graphical abstract

Internal friction between fluid particles of MHD tangent hyperbolic fluid with heat generation: Using coefficients improved by Cash and Karp

The present work examines the internal resistance between fluid particles of tangent hyperbolic fluid flow due to a non-linear stretching sheet with heat generation. Using similarity transformations, the governing system of partial differential equations is transformed into a coupled non-linear ordinary differential system with variable coefficients. Unlike the current analytical works on the flow problems in the literature, the main concern here is to numerically work out and find the solution by using Runge-Kutta-Fehlberg coefficients improved by Cash and Karp (Naseer et al., Alexandria Eng. J. 53, 747 (2014)). To determine the relevant physical features of numerous mechanisms acting on the deliberated problem, it is sufficient to have the velocity profile and temperature field and also the drag force and heat transfer rate all as given in the current paper.

Selecting Multiple Evaluator’s Perception-Oriented Relevant Physical Features of Consumer Goods by Using Fuzzy Data Sensitivity and OWA Operators

AbstractThe assessment of goods quality using experts is costly task in addition to their often unavailability. In this paper, we present a new method for ranking physical features of consumer goods according to their relevancy to multiple evaluators’ perception at different levels and selecting the most important ones for quality characterization. The main contribution of the paper is combining of fuzzy method and ordered weighted averaging (OWA) operators to achieve our aim. The proposed selection method, considered as a Multi-Evaluators and Multi-Criteria Decision Making (ME-MCDM) technique, has been developed using fuzzy sensitivity (FS) criterion for ranking and OWA operator to aggregate the aforementioned ranking lists. Finally, by introducing a smart percolation technique we get automatically the most relevant physical features for a given sensory descriptor. The suggested approach is applied to a selection problem of textile physical features.