Multiscale Approach Research Articles

Embedded multi-boson exchange: A step beyond quantum cluster theories

We introduce a diagrammatic multi-scale approach to the Hubbard model based on the interaction-irreducible (multi-boson) vertex of a small cluster embedded in a self-consistent medium. The vertex captures short-range correlations up to the length scale of the cluster, while long-range correlations are recovered from a set of diagrammatic equations for the Hedin three-leg vertex. By virtue of the crossing symmetry, the Fierz decoupling ambiguity of the Hubbard interaction is resolved exactly. Our benchmarks for the half-filled Hubbard model on the square lattice are in very good agreement with numerically exact diagrammatic Monte Carlo simulations. Published by the American Physical Society 2024

A multi-scale integrative approach to study the impact of a common pesticide, the dimethoate, on a mangrove fiddler crab Tubuca urvillei.

At land-sea interface, mangroves are likely to be exposed to pesticides due to agricultural run-offs. In Mayotte Island (Comoros archipelago, Mozambique Channel), dimethoate (DMT) is found in high concentrations in tomatoes, but no data confirm its presence in mangroves. We aimed at screening the presence of DMT in three mangroves of Mayotte at different levels (highest point above crops, village, upstream mangrove, downstream mangrove) and assessing the impact of DMT coupled with reduced salinity on mangrove crab physiology. To do so, we performed 24-h exposures at sublethal concentrations (10 and 100µg L-1) corresponding to 100 × and 1000 × the environmental standard (no data exist on environmental concentrations), in seawater (SW) and diluted SW (dSW). We exposed male fiddler crab Tubuca urvillei, one of the most common fiddler crabs living in mangrove areas regularly flooded and exposed to agricultural run-offs. Different physiological endpoints were considered: behaviour, acetylcholinesterase (AChE) activity, muscle energy metabolism, DNA oxidative damage and osmoregulatory capacity using hemolymph samples, posterior gills and claw muscle. We confirmed the presence of DMT in one mangrove and the effect of pesticide exposure at the different endpoints. Changes in behavioural and physiological parameters highlighted in this study could warn us of recent pesticide use upstream and help us understand past or future community-level changes in mangrove ecosystems affected by pesticide inputs.

Multi-scale finite element simulation of needle-punched quartz fiber reinforced composites

Abstract To investigate the effects of different needling parameters on the mechanical properties of quartz needled felts and their composites, this paper proposes a multi-scale modeling approach to simulate and calculate the mechanical properties of needled quartz fiber-reinforced composites. Firstly, based on the characteristics of needle-punching technology, the morphology of needle-punched composites was analyzed at a microscopic scale, and the quartz fiber structure was divided into three typical representative regions. Then, a periodic single-cell model of needled composites was established. Meanwhile, this article also analyzes the influence of parameters such as needling density, needle type, and needling depth on the mechanical properties of needled composites.

Nonspecific Yet Selective Interactions Contribute to Small Molecule Condensate Binding.

Biomolecular condensates are essential in various cellular processes, and their misregulation has been demonstrated to underlie disease. Small molecules that modulate condensate stability and material properties offer promising therapeutic approaches, but mechanistic insights into their interactions with condensates remain largely lacking. We employ a multiscale approach to enable long-time, equilibrated all-atom simulations of various condensate-ligand systems. Systematic characterization of the ligand binding poses reveals that condensates can form diverse and heterogeneous chemical environments with one or multiple chains to bind small molecules. Unlike traditional protein-ligand interactions, these chemical environments are dominated by nonspecific hydrophobic interactions. Nevertheless, the chemical environments feature unique amino acid compositions and physicochemical properties that favor certain small molecules over others, resulting in varied ligand partitioning coefficients within condensates. Notably, different condensates share similar sets of chemical environments but at different populations. This population shift drives ligand selectivity toward specific condensates. Our approach can enhance the interpretation of experimental screening data and may assist in the rational design of small molecules targeting specific condensates.

From Molecules to Devices: A Multiscale Approach to Evaluating Organic Photovoltaics.

Due to their efficient molecular design, nonfullerene acceptors (NFAs) have significantly advanced organic photovoltaics (OPVs). However, the lack of models to screen and evaluate candidate NFAs based on the resulting device performance has impeded the rapid development of high-performance molecules. This work introduces a computational framework utilizing a kinetic Monte Carlo (kMC) model to derive device parameters from molecular properties computed through first principles. By analyzing the quantum chemical properties of diverse dimeric conformers, we estimate the relative probabilities of microscopic processes such as charge separation, recombination, and transport along with charge transfer state formation in the active layer of OPVs. These probabilities set up a random walk of charge carriers in a grid with disordered molecular sites, allowing us to track their average behavior and calculate key device parameters. Our model consistently predicts measured device parameters, including the short-circuit current and open-circuit voltage, for OPVs with diverse NFAs with high accuracy. Additionally, we applied the model to evaluate donor-acceptor combinations of known compounds and newly designed NFA molecules, identifying high-performing pairs. This model offers a computationally efficient approach for designing novel NFA molecules and optimizing the OPV performance.

Structural, Solvent, and Temperature Effects on Protein Junction Conductance.

Cytochrome b562 is a small redox-active heme protein that has served as an important model system for understanding biological electron transfer processes. Here, we present a comprehensive theoretical study of electron transport mechanisms in protein-metal junctions incorporating cytochrome b562 using a multi-scale computational approach. Employing molecular dynamics (MD) simulations, we generated junction geometries for both vacuum-dried and solvated conditions, with the protein covalently bound to gold contacts in various configurations. Coherent tunneling, described by the Landauer-Buttiker formalism within the density functional theory (DFT) framework, is compared to the incoherent hopping charge transport mechanism captured by the semi-classical Marcus theory. The tunneling was identified as the dominant mechanism explaining the experimental data measured on the cytochrome b562 junctions, exhibiting exponential yet very shallow distance dependence. While the structural orientations and protein contacts with the electrodes influence the junction conductance significantly, the solvation effects are relatively small, affecting the electronic properties mostly via the adsorption arrangement. On the other hand, the considerable temperature dependence of the conductance was found strong only for hopping, while the tunneling current magnitudes remain practically unaffected and are a good indicator of the coherent mechanism in this case.

Geospatial Capacity Building for Flood Resilience in the Sahel: the SLAPIS project case study

Abstract. This study focuses on the development of a people-centred early warning system (EWS) against floods in the Sirba River basin between Niger and Burkina Faso. This densely populated area has witnessed an increase in extreme flooding events in recent years. Several flood forecasting systems in the Sahel exist, although there is no EWS that integrates the four components of people-centred EWSs, namely risk knowledge, monitoring and warning service, dissemination and communication, and response capacity. The proposed EWS, named SLAPIS, includes a risk knowledge component that involves defining four levels of vigilance. Its monitoring and alert component involves a user-friendly web application containing real-time data collected through automatic stations. The EWS communicate seamlessly with the national alert system. The response capacity is strengthened through the creation of a flood zone atlas. In this framework, the EWS integrates significant geoinformatics in preparation of local risk reduction plans and the awareness of local communities. In the SLAPIS case study, multi-temporal classifications were conducted using Sentinel-2 data and high-resolution images (approximately 10 cm) generated through Structure from Motion (SfM) techniques. Digital Terrain Model (DTM) creation for hydraulic model calibration employed a multiscale approach, incorporating GNSS survey data processed via Precise Point Positioning (PPP), HydroSHEDS (approximately 100m resolution), and commercial 10m resolution data. All information was calibrated, harmonised, and integrated into the EWS model, which is accessible via a web platform. Capacity building encompassed direct training and field implementation to streamline the primary EWS generation steps.

Quantitative Analysis of the Evolution of Production–Living–Ecological Space in Traditional Villages: A Comparative Study of Rural Areas in Tibet

Since the introduction of the rural revitalization strategy by the 19th National Congress of the Communist Party of China in 2017, there have been significant transformations in the production–living–ecological space (PLES) within villages. Evaluating and enhancing villages’ PLES are crucial for fostering sustainable development. Therefore, this study utilized a multi-scale environmental assessment model and mathematical approach to conduct horizontal and vertical nested correlation analyses of indicators at different levels through a path analysis, a Spearman correlation analysis, a variance analysis, the entropy weight method, data simulation, and other methods to establish a “three-dimensional” comprehensive evaluation system for traditional village PLES. The findings indicate the following: (1) The ecological space of traditional villages in Tibet significantly impacts the overall environment of the PLES, with the components’ impact ranked as follows: ecological space > production space > living space. Furthermore, industry and tourism resources show a significant positive correlation with traditional villages’ PLES; (2) There are no significant differences in natural environmental factors, such as air relative humidity, temperature, humidity index, and wind efficiency index among traditional villages in neighboring cities in Tibet. However, they all possess profound ecological and cultural heritage; (3) There are notable disparities in living space between traditional villages in Lhasa and Nyingchi, indicating unbalanced development. It is evident that traditional village construction should not only focus on the development of certain PLES but also pay attention to the balanced development of the overall spatial environment. This study holds great significance for enhancing the living environment of traditional villages in Tibet and promoting sustainable development through protection efforts in these villages.

Interfaces do Método de Leitura da Paisagem e Sistemas de Informações Geográficas (SIG): contribuições do estudo das Unidades de Paisagem para o ensino-aprendizagem no Ateliê de Projeto Urbano

The interdisciplinary and multiscale methodological approach presented here seeks to understand the biogeophysical support in repeated interactions with society, highlighting the process of socio-spatial production. It outlines the structuring and adaptation of the Landscape Reading method for didactic workshops in Geographic Information Systems, as a result of the GIS research project applied to Landscape Planning developed in (omitted for blind reading). Structuring such method adapted to geotechnology tools and environmental modeling in a GIS environment proved to be of great value for application in Architecture and Urbanism undegraduate courses, which is undergoing an important process of teaching-learning design methodological adaptation, especially in the areas of landscape, urbanism, and urban and regional planning.

Experimental and multiscale analysis of volume fraction effects in tensile properties of recycled PLA-PDA/kenaf fiber 3D printing filament composites

Polylactic acid, a biodegradable thermoplastic derived from renewable sources, has notable environmental advantages and versatile properties. However, recycled PLA often experiences reduced mechanical strength and altered chemical composition due to the recycling process, limiting its suitability for 3D printing filament applications. Recognizing the inherent limitations of recycled PLA (rPLA), this research employs a unique method of coating rPLA with dopamine (DA) and reinforcing it with kenaf fibers (KF) at various weight fractions, introducing a novel multiscale modeling approach to address the challenges of interfacial bonding and tensile performance in recycled polymers. The methodology integrates experimental single filament tensile testing with multiscale finite element modeling to accurately predict the composite’s mechanical behavior across different scales. The findings reveal that a 5% kenaf fiber weight/volume fraction provides the optimal balance between tensile strength, stiffness, and toughness, achieving a significant 49.3% improvement over the unreinforced rPLA. However, increasing the kenaf fiber content beyond 5% leads to a decline in mechanical properties, attributed to higher fiber agglomeration and suboptimal fiber-matrix interactions. The novelty of this research lies in its combined use of PDA coating, natural kenaf fiber reinforcement, and advanced multiscale modeling to enhance and predict the performance of rPLA-PDA/kenaf composites, paving the way for sustainable, high-performance materials in 3D printing applications.

Neural Networks for Audio Classification: Multi-scale CNN-LSTM Approach to Animal Sound Recognition

Abstract. This research explores the application of neural networks, specifically CNN-LSTM models, for classifying sound signals from dogs, frogs, and cats, selected from the ESC-50 dataset. The sound data was preprocessed using Mel-frequency cepstral coefficients (MFCCs) and augmented through time stretching, pitch shifting, and noise addition to enhance model generalization in varied acoustic environments. We compared two deep learning models: a traditional CNN-LSTM and an improved version with multi-scale feature extraction, allowing for capturing both short-term and long-term sound patterns. Our findings show that the multi-scale CNN-LSTM architecture outperforms the traditional CNN-LSTM, achieving a test accuracy of 86.11% compared to 80.56%. These results highlight the effectiveness of multi-scale feature extraction for handling complex audio signals. This research offers valuable insights into bioacoustics and has broader applications in areas such as environmental sound monitoring, wildlife preservation, and animal behavior analysis.

Selective Protonation of Catalytic Dyad for γ-Secretase-Mediated Hydrolysis Revealed by Multiscale Simulations.

γ-Secretase plays a crucial role in producing disease-related amyloid-β proteins by cleaving the amyloid precursor protein (APP). The enzyme employs its catalytic dyad containing two aspartates (Asp257 and Asp385) to hydrolyze the substrate by a general acid-base catalytic mechanism, necessitating monoprotonation of the two aspartates for efficient hydrolysis. However, the precise protonation states of the aspartates remain uncertain. In this study, we employed a multiscale computational approach to investigate the dependence of the catalytic efficiency of γ-secretase on the protonation states of its catalytic dyad. Over 200 ms unbiased atomistic simulations of the substrate-enzyme complex reveal diverse orientations of the scissile bond of the bound substrate and accessible structural ensembles of the catalytic dyad with Asp257-Asp385 distances fluctuating between 4 and 10 Å. With a quantum mechanics/molecular mechanics (QM/MM) approach accelerated by enhanced sampling techniques, we find that the first step of the hydrolysis reaction, i.e., the formation of a gem-diol intermediate, experiences a higher reaction barrier by ∼2 kcal/mol when Asp385 is protonated. Furthermore, we find that Arg269 of the enzyme is most likely responsible for this preference of the protonation state: its basic side chain is spatially close to that of Asp257 and specifically stabilizes the transition state electrostatically when Asp257 is protonated. Collectively, our study suggests that Asp257 is likely the favored protonation site for APP cleavage by γ-secretase.

Unraveling global species richness patterns of the Lysmatidae family: a multi-scale and multi-hypothesis ecological approach

While diversity gradients are well-explored in macroecology, factors shaping species richness at broad scales remain debated. We investigated the species richness of the decapod family Lysmatidae across 4 spatial scales: realm, province, ecoregion, and local (2° × 2° grid cells). We tested 4 ecological hypotheses: physiological stress (PSH), resource availability (RAH), habitat heterogeneity (HHH), and anthropogenic impact (AIH). Occurrence data (52 Lysmatidae species) and environmental variables (salinity, temperature, primary productivity, bathymetry, coral richness, anthropogenic impact index) were obtained from online databases and literature. Fifteen regression models, incorporating spatial filters, were tested to assess the hypotheses. The highest Lysmatidae species richness occurred in the Tropical Atlantic and Central Indo-Pacific realms. Richness varied with scale, with the highest values in the transition between the tropical and subtropical zones. Bathymetry was associated with Lysmatidae richness across all scales, especially at local and ecoregional scales, while coral richness was related to province and realm scales. HHH explained Lysmatidae richness patterns at the realm scale. Variables related to PSH and AIH were associated with richness at the ecoregion and province scales. Our study emphasized the importance of scale in biodiversity research, influencing richness patterns in Lysmatidae, and pointed to bathymetry, coral richness, and temperature range as the main drivers of richness. As this study showed a relationship between Lysmatidae richness, coral richness, and temperature at 3 spatial scales, this family may be susceptible to the effects of climate change, such as tropicalization of subtropical zones and defaunation of tropical ecosystems, including coral reefs.

Odorant Binding Proteins Facilitate the Gas-Phase Uptake of Odorants Through the Nasal Mucus.

Mammalian odorant binding proteins (OBPs) have long been suggested to transport hydrophobic odorant molecules through the aqueous environment of the nasal mucus. While the function of OBPs as odorant transporters is supported by their hydrophobic beta-barrel structure, no rationale has been provided on why and how these proteins facilitate the uptake of odorants from the gas phase. Here, a multi-scale computational approach validated through available high-resolution spectroscopy experiments reveals that the conformational space explored by carvone inside the binding cavity of porcine OBP (pOBP) is much closer to the gas than the aqueous phase, and that pOBP effectively manages to transport odorants by lowering the free energy barrier of odorant uptake. Understanding such perireceptor events is crucial to fully unravel the molecular processes underlying the olfactory senseand move towards the development of protein-based biomimetic sensor units that can serve as artificial noses.

QSLE-v1.0: A New Software Package for the Calculation of Coupled Quantum-Classical Dynamics in Condensed Phases Based on a Stochastic Approach.

Recently, a stochastic version of the quantum-classical Liouville equation has been proposed [Campeggio, J.; Cortivo, R.; Zerbetto, M. J. Chem. Phys. 2023, 158, 244104], to compute the coupled quantum-classical dynamics of molecules in condensed phases. The approach, called quantum-stochastic Liouville equation (QSLE), is based on coupling the time evolution of electronic states to a stochastic description of the relevant (classical) nuclear coordinates. Natural internal coordinates are used, i.e., bond lengths, bond angles, and dihedral angles. The approach is tailored to directly propagate the populations of the electronic states over time, based on a classical Fokker-Planck equation for the nuclear degrees of freedom coupled to a master equation for the jumps among the electronic states. The QSLE is a multiscale approach requiring many ingredients to be assembled in order to carry out the numerical solution. To make the approach accessible, a software package that handles the main (and most cumbersome) details of the numerical workflow has been implemented into the software package QSLE-v1.0, which is introduced in the present paper. Here, it is considered the case of one torsional nuclear coordinate and two nonadiabatic electronic potential energy surfaces. This is a very basic model for interpreting photoisomerization or charge transfer phenomena, but despite its simplicity, it can be applied even in complex systems where the relevant quantum/classical parts affecting the phenomena under study are highly localized. A sand-box model system for describing photoisomerization processes is reported to demonstrate the usage of the software package. QSLE-v1.0 is open source and distributed under the GPLv2.0 license.

A Dual-Stream Fusion Network for Human Energy Expenditure Estimation with Wearable Sensor

With the increasing awareness of health, using wearable sensors to monitor individual activities and accurately estimate energy expenditure has become a current research focus. However, existing research encounters challenges including low estimation accuracy, a deficiency of frequency domain features, and difficulty in integrating time domain and frequency domain features. To address these issues, we propose an innovative framework called the Dual-Stream Fusion Network (DSFN). This framework combines the Time Domain Encoding (TDE) module, the Frequency Domain Hierarchical-Split Encoding (FDHSE) module, and a Two-Stage Feature Fusion (TSF) module. Specifically, the temporal stream of the framework employs the TDE module to capture deep temporal features that reflect the complex dynamic variations in time-series data. The frequency domain stream introduces the FDHSE module, which extracts frequency domain features using a multi-level, multi-scale approach, ensuring a comprehensive and diverse representation of frequency information. Through this dual-stream architecture, our model effectively learns both time and frequency domain features, addressing the limitations of frequency domain features observed in prior studies. Additionally, we propose the TSF module to fully integrate time and frequency domain features, effectively overcoming the challenge of fusing these two types of features. We conducted experiments on two public datasets, namely the GOTOV dataset (elderly people) and the JSI dataset (young people). Experimental results demonstrate that our method achieves excellent performance across different age groups. Compared to the baseline models, the proposed DSFN significantly improves the accuracy of human energy expenditure estimation.

A Multi-Scale Approach to Early Fire Detection in Smart Homes

In recent years, advancements in smart home technologies have underscored the need for the development of early fire and smoke detection systems to enhance safety and security. Traditional fire detection methods relying on thermal or smoke sensors exhibit limitations in terms of response time and environmental adaptability. To address these issues, this paper introduces the multi-scale information transformer–DETR (MITI-DETR) model, which incorporates multi-scale feature extraction and transformer-based attention mechanisms, tailored specifically for fire detection in smart homes. MITI-DETR achieves a precision of 99.00%, a recall of 99.50%, and a mean average precision (mAP) of 99.00% on a custom dataset designed to reflect diverse lighting and spatial conditions in smart homes. Extensive experiments demonstrate that MITI-DETR outperforms state-of-the-art models in terms of these metrics, especially under challenging environmental conditions. This work provides a robust solution for early fire detection in smart homes, combining high accuracy with real-time deployment feasibility.

Enhancing perceptual, attentional, and working memory demands through variable practice schedules: insights from high-density EEG multi-scale analyses.

Contextual interference (CI) enhances learning by practicing motor tasks in a random order rather than a blocked order. One hypothesis suggests that the benefits arise from enhanced early perceptual/attentional processes, while another posits that better learning is due to highly activated mnemonic processes. We used high-density electroencephalography in a multi-scale analysis approach, including topographic analyses, source estimations, and functional connectivity, to examine the intertwined dynamics of attentional and mnemonic processes within short time windows. We recorded scalp activity from 35 participants as they performed an aiming task at three different distances, under both random and blocked conditions using a crossover design. Our results showed that topographies associated with processes related to perception/attention (N1, P3a) and working memory (P3b) were more pronounced in the random condition. Source estimation analyses supported these findings, revealing greater involvement of the perceptual ventral pathway, anterior cingulate and parietal cortices, along with increased functional connectivity in ventral alpha and frontoparietal theta band networks during random practice. Our results suggest that CI is driven, in the random compared to the blocked condition, by enhanced specific processes such as perceptual, attentional, and working memory processes, as well as large-scale functional networks sustaining more general attentional and executive processes.

A multi-scale assessment of desertification severity in the Eastern Mediterranean region based on albedo-MSAVI feature space.

Resampling the same satellite image to conduct a multi-scale assessment of desertification can be accompanied by distortion of terrestrial objects and spectral information, which can lead to uncertainty in the generated information. To address this, this study assesses desertification severity in an area of arid and semi-arid climate in the Eastern Mediterranean (Jordan)that is characterised by cloudless scenesusingmulti-sensor data of the same scene at the same time. To this end, Sentinel-2 at 10 m and 60 m, Landsat-8 at 30 m and MODIS at 250 m and 500 m were collected to extract albedo and modified soil adjusted vegetation index (MSAVI), and subsequently to construct albedo-MSAVI feature space. Using the negative correlation between albedo and MSAVI, desertification degree index (DDI) was generated. The resulting multi-scale DDI maps bear a relative resemblance in terms of spatial distribution, patterns, and proportions. The DDI maps indicate that extremely serious and serious desertification are widespread, accounting for 50% of the study area, primarily in the eastern portions. However, finer DDI maps (10 m, 30 m and 60 m) are essential for detecting small-scale desertification characteristics due to their ability to capture local spatial variabilities, while coarser ones (250 m and 500 m) are better suited for capturing broad-scale desertification patterns driven by climatic factors, in which MODIS data exhibit a relatively higher positive correlation with seasonal average precipitation. Although finer DDI maps show higher accuracy compared to coarser ones, the accuracy of DDI maps of MODIS has shown an increase within a homogeneous landscape. Accordingly, synchronised multi-scale assessment of desertification severity is not only influenced by the spatial resolution but also by the landscape heterogeneity and the type of satellite sensor utilised. The multi-scale approach applied in this study can provide insights on scale-dependent desertificationthat help in devising overarching mitigation strategies.

An affine formulation of eigenstrain-based homogenization method and its application to polycrystal plasticity

Abstract Reduced order models (ROMs) are typically incorporated into concurrent multiscale approaches to allow for efficient nonlinear multiscale simulations and to alleviate high cost of direct nonlinear computational homogenization schemes. ROMs based on the ideas of transformation field analysis are among the most popular in the literature since they only require linear elastic simulations for model construction and typically have low number of degrees of freedom. However, these models have been shown to deliver overly stiff response in simulating wide range of materials. The present study focuses on mitigating this problem in the context of eigenstrain-based homogenization method (EHM) using instantaneous moduli information for polycrystal elastoviscoplasticity. For this purpose, a new EHM model is developed with the intention of using affine moduli for recomputation of the instantaneous localization tensors. The accuracy of the method is compared to the original EHM and direct crystal plasticity finite element simulations for several synthetic polycrystal microstructures, loading conditions and varying phase contrast. We show that the affine model delivers consistently softer response compared to the original EHM model. In particular, the affine model delivers notably more accurate response in the presence of high phase contrast. The affine EHM is able to capture local load redistribution through recomputation of the localization tensors.