28 papers · ranked by Valyu relevance
Wacław Kuś, Waldemar Mucha, Iyasu Tafese Jiregna, Beomjoo Yang
Structures made of heterogeneous materials, such as composites, often require a multiscale approach when their behavior is simulated using the finite element method. By solving the boundary value problem of the macroscale model, for previously homogenized material properties, the resulting stress maps can be obtained.…
Cemal Cagatay Bilgin, Shayoni Ray, Banu Baydil, William P. Daley + 3 more
Pattern formation in developing tissues involves dynamic spatio-temporal changes in cellular organization and subsequent evolution of functional adult structures. Branching morphogenesis is a developmental mechanism by which patterns are generated in many developing organs, which is controlled by underlying molecular…
William J. Bosl, Tobias Loddenkemper, Solveig Vieluf
Background Multiscale entropy (MSE) has become increasingly common as a quantitative tool for analysis of physiological signals. The MSE computation involves first decomposing a signal into multiple sub-signal ‘scales’ using a coarse-graining algorithm. Methods The coarse-graining algorithm averages adjacent values in…
Chao Ma, Yikai Hou, Li Xiang, Yinggang Sun + 3 more
'Jiaxing Qu'] Long-term time-series forecasting is essential for planning and decision-making in economics, energy, and transportation, where long foresight is required. To obtain such long foresight, models must be both efficient and effective in processing long sequence. Recent advancements have enhanced the…
Yafei Wang, Zhiqiang Tian, Songyan Hu
In the present study, a new multiscale method is proposed for the statistical analysis of spatial distribution of massive corrosion pits, based on the image recognition of high resolution and large field-of-view (montage) optical images. Pitting corrosion for high strength pipeline steel exposed to sodium chloride…
Tarek Eseholi, François-Xavier Coudoux, Patrick Corlay, Rahmad Sadli + 1 more
'Maxence Bigerelle'] In this paper, we evaluate the effect of scale analysis as well as the filtering process on the performances of an original compressed-domain classifier in the field of material surface topographies classification. Each surface profile is multiscale analyzed by using a Gaussian Filter analyzing…
Syed Zaki Hassan Kazmi, Nazneen Habib, Rabia Riaz, Sanam Shahla Rizvi + 3 more
'Sanam Shahla Rizvi' 'Syed Ali Abbas' 'Tae-Sun Chung' 'Elena G. Tolkacheva'] Acceleration change index (ACI) is a fast and easy to understand heart rate variability (HRV) analysis approach used for assessing cardiac autonomic control of the nervous systems. The cardiac autonomic control of the nervous system is an…
Rajshekhar Gannavarpu, Dario Ambrosini
The paper introduces a multi-scale processing method for quantitative study and visualization of convective heat transfer using diffractive optical element based background-oriented schlieren technique. The method relies on robust estimation of phase encoded in the fringe pattern using windowed Fourier transform and…
Momo Ando, Sou Nobukawa, Mitsuru Kikuchi, Tetsuya Takahashi
Alzheimer's disease (AD) is the most common form of dementia and is a progressive neurodegenerative disease that primarily develops in old age. In recent years, it has been reported that early diagnosis of AD and early intervention significantly delays disease progression. Hence, early diagnosis and intervention are…
Hamid Karimi-Rouzbahani
Distinct neural processes are often encoded across distinct time scales of neural activations. However, it has remained unclear if this multiscale coding strategy is also implemented for separate features of the same process. One difficulty is that the conventional methods of time scale analysis provide imperfect…
Pasquale Arpaia, Maria Cacciapuoti, Andrea Cataldo, Sabatina Criscuolo + 5 more
'Sabatina Criscuolo' 'Egidio De Benedetto' 'Antonio Masciullo' 'Marisa Pesola' 'Raissa Schiavoni' 'Alessandro Bevilacqua'] This study investigates the effectiveness of amplitude transformation in enhancing the performance and robustness of Multiscale Fuzzy Entropy for Alzheimer’s disease detection using…
Amir Omidvarnia, Andrew Zalesky, Dimitri Van De Ville, Graeme D. Jackson + 1 more
In 2014, McDonough and Nashiro [1] derived multiscale entropy –a marker of signal complexity– from resting state functional MRI data (rsfMRI), and found that functional brain networks displayed unique multiscale entropy fingerprints. This is a finding with potential impact as an imaging-based marker of normal brain…
Philipp Meschenmoser, Juri Buchmüller, Daniel Seebacher, Martin Wikelski + 1 more
MultiSegVA has been developed in close collaboration with movement ecologists and fulfills our audience's requirements (Section 2). We show MultiSegVA's usefulness and applicability by two extensive realworld use cases that were discussed with our domain experts. The use cases originate from live sessions where the…
Authors not listed
Modeling multiscale patterns is crucial for long-term time series forecasting (TSF). However, redundancy and noise in time series, together with semantic gaps between non-adjacent scales, make the efficient alignment and integration of multi-scale temporal dependencies challenging. To address this, we propose SEMixer…
Jiyong Moon, Junseok Lee, Yun‐Ju Lee, Seongsik Park
Recently, vision Transformers (ViTs) have been actively applied to fine-grained visual recognition (FGVR). ViT can effectively model the interdependencies between patchdivided object regions through an inherent self-attention mechanism. In addition, patch selection is used with ViT to remove redundant patch information…
John W. Coulston, Nicola Zaccarelli, Kurt H. Riitters, Frank Koch + 1 more
'Giovanni Zurlini'] Multiple-scale and broad-scale assessments often require rescaling the original data to a consistent grain size for analysis. Rescaling categorical raster data by spatial aggregation is common in large area ecological assessments. However, distortion and loss of information are associated with…
Iman Deznabi, Madalina Fiterau
The analysis of multivariate time series data is challenging due to the various frequencies of signal changes that can occur over both short and long terms. Furthermore, standard deep learning models are often unsuitable for such datasets, as signals are typically sampled at different rates. To address these issues, we…
Vasile V. Moca, Adriana Nagy-Dăbâcan, Harald Bârzan, Raul C. Mureşan
Time-frequency analysis is ubiquitous in many fields of science. Due to the Heisenberg-Gabor uncertainty principle, a single measurement cannot estimate precisely the localization of a finite oscillation in both time and frequency. Classical spectral estimators, like the short-time Fourier transform (STFT) or the…
Andreas Trier Poulsen, Andreas Pedroni, Nicolas Langer, Lars Kai Hansen
EEG microstate analysis offers a sparse characterisation of the spatio-temporal features of large-scale brain network activity. However, despite the concept of microstates is straight-forward and offers various quantifications of the EEG signal with a relatively clear neurophysiological interpretation, a few important…
Reto Gerber, Jake Griner, Silvia Guglietta, Carsten Krieg + 1 more
Spatial omics is transforming our ability to interrogate local tissue microenvironments by enabling spatially resolved measurement of biomolecules such as transcripts, proteins, and metabolites. However, capturing the full biological complexity of tissues often requires combining multiple modalities, which introduces…
Julien Moehlin, Bastien Mollet, Bruno Maria Colombo, Marco Antonio Mendoza-Parra
Developments on spatial transcriptomics (ST) are providing means to interrogate organ/tissue architecture from the angle of the gene programs defining their molecular complexity. However, computational methods to analyze ST data under-exploits the spatial signature retrieved within the maps. Inspired by contextual…
Authors not listed
The Single-probe is a multifunctional device that can be coupled to mass spectrometry (MS) for molecular analysis of microscale samples, such as single cells, tissue slices, and multicellular spheroids, under ambient conditions. In Single-probe single cell MS (SCMS) studies, this technique leverages direct sampling and…
Kevin Mildau, Christoph Büschl, Jürgen Zanghellini, Justin J.J. van der Hooft
Computational metabolomics workflows have revolutionized the untargeted metabolomics field. However, the organization and prioritization of metabolite features remains a laborious process. Organizing metabolomics data is often done through mass fragmentation-based spectral similarity grouping, resulting in feature sets…
Ethan Yang, Jeong Hee Kim, Caitlin M. Tressler, Xinyi Elaine Shen + 4 more
Multimodal tissue imaging techniques that integrate two complementary modalities are powerful discovery tools for unraveling biological processes and identifying biomarkers of disease. Combining Raman spectroscopic imaging (RSI) and matrix-assisted laser-desorption/ionization (MALDI) mass spectrometry imaging (MSI) to…
Authors not listed
A thorough understanding of drug pharmacokinetics and target interaction within complex biological environments is critical for successful drug discovery. Quantitative matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI; qMSI) presents a promising avenue for such investigations. While…
Authors not listed
Amines play a significant role in everyday life, and their detection remains a crucial focus in research and development. Although conducting polymer-based gas sensors have been widely reported for amine detection using DC resistivity measurements, they often lack selectivity to distinguish between intragroup…
Authors not listed
The analysis of macromolecules - such as proteins - pose a significant bottleneck in analytical science. We have developed surface treatment techniques utilising direct and radiofrequency current argon plasmas, able to quickly digest condensed-phase macromolecules while maintaining spatial resolution of analytes. We…
Authors not listed
Mass spectrometric analysis of inorganic materials is widely used. However, no major advances were made in this area compared to the significant progress in the analysis of biological materials. This work introduces a novel open-source R workflow that efficiently processes and models isotopic distributions in LDI-TOF…