Multiple Scales Research Articles

Scalable Macroscopic Engineering from Polymer-Based Nanoscale Building Blocks: Existing Challenges and Emerging Opportunities.

Natural materials exhibit exceptional properties due to their hierarchical structures spanning from the nano- to the macroscale. Replicating these intricate spatial arrangements in synthetic materials presents a significant challenge as it requires precise control of nanometric features within large-scale structures. Addressing this challenge depends on developing methods that integrate assembly techniques across multiple length scales to construct multiscale-structured synthetic materials in practical, bulk forms. Polymers and polymer-hybrid nanoparticles, with their tunable composition and structural versatility, are promising candidates for creating hierarchically organized materials. This review highlights advances in scalable techniques for nanoscale organization of polymer-based building blocks within macroscopic structures, including block copolymer self-assembly with additive manufacturing, polymer brush nanoparticles capable of self-assembling into larger, ordered structures, and direct-write colloidal assembly. These techniques offer promising pathways toward the scalable fabrication of materials with emergent properties suited for advanced applications such as bioelectronic interfaces, artificial muscles, and other biomaterials.

Characterization of Three-Mode Combination Internal Resonances in Electrostatically Actuated Flexible–Flexible Microbeams

Abstract Nonlinear intermodal coupling based on internal resonances in MEMS resonators has advanced significantly over the past two decades for various real-world applications. In this study, we demonstrate the existence of various three-mode combination internal resonances between the first five flexural modes of electrostatically actuated flexible–flexible beams and dynamic modal interaction between three modes via internal resonance. We first calculate the natural frequencies of the beam as a function of the stiffnesses of the transverse and rotational springs of the flexible supports, utilizing both analytical formulation and finite element analysis (FEA). Following this, we identify six combination internal resonances among the first five modes and use applied DC voltage to validate the exactness of one commensurable internal resonance condition (ω2=ω5−ω4). Subsequently, we studied a detailed forced vibration analysis corresponding to this resonance condition by solving the five-mode coupled governing equations through numerical time integration and the method of multiple scales. The results compellingly exhibit three-mode intermodal coupling among the second, fourth, and fifth modes as a function of excitation amplitude and frequency. Alongside this, intriguing nonlinear phenomena such as threshold behavior, saturation phenomena, and autoparametric instability are observed. Finally, this paper provides a systematic methodology for investigating three-mode combination internal resonances and related nonlinear dynamics, offering significant insights that could be used in observing phonon or mechanical lasing phenomena in MEMS resonators.

Dissonance and disobedience in Brazilian quilombola heritages

ABSTRACT This article discusses Brazilian quilombo heritage by focusing on three dimensions of the intersection between this heritage and emancipation: 1) the emancipatory power of heritage, 2) the possibilities that result from emancipating heritage from a consonant approach, and 3) the possibilities of emancipation from heritage. Quilombos are collective societies formed mainly by people of African descent who created independent communities outside the Brazilian slavery system. Using a case study based on ethnographic and archival research conducted at the Quilombo Campinho da Independência and the city of Paraty between 2015 and 2024, I begin by detailing the dominant heritage narratives in the town of Paraty and then examine how suppressed quilombo voices navigate these narratives in legal and metaphorical terms for emancipatory purposes. Ultimately, the article pushes for approaching heritage from multiple scales and angles.

Dual-attention transformer-based hybrid network for multi-modal medical image segmentation

Accurate medical image segmentation plays a vital role in clinical practice. Convolutional Neural Network and Transformer are mainstream architectures for this task. However, convolutional neural network lacks the ability of modeling global dependency while Transformer cannot extract local details. In this paper, we propose DATTNet, DualATTentionNetwork, an encoder-decoder deep learning model for medical image segmentation. DATTNet is exploited in hierarchical fashion with two novel components: (1) Dual Attention module is designed to model global dependency in spatial and channel dimensions. (2) Context Fusion Bridge is presented to remix the feature maps with multiple scales and construct their correlations. The experiments on ACDC, Synapse and Kvasir-SEG datasets are conducted to evaluate the performance of DATTNet. Our proposed model shows superior performance, effectiveness and robustness compared to SOTA methods, with mean Dice Similarity Coefficient scores of 92.2%, 84.5% and 89.1% on cardiac, abdominal organs and gastrointestinal poly segmentation tasks. The quantitative and qualitative results demonstrate that our proposed DATTNet attains favorable capability across different modalities (MRI, CT, and endoscopy) and can be generalized to various tasks. Therefore, it is envisaged as being potential for practicable clinical applications. The code has been released on https://github.com/MhZhang123/DATTNet/tree/main.

Leveraging Multi-Modality and Enhanced Temporal Networks for Robust Violence Detection

In this paper, we present a novel model that enhances performance by extending the dual-modality TEVAD model—originally leveraging visual and textual information—into a multi-modal framework that integrates visual, audio, and textual data. Additionally, we refine the multi-scale temporal network (MTN) to improve feature extraction across multiple temporal scales between video snippets. Using the XD-Violence dataset, which includes audio data for violence detection, we conduct experiments to evaluate various feature fusion methods. The proposed model achieves an average precision (AP) of 83.9%, surpassing the performance of single-modality approaches (visual: 73.9%, audio: 67.1%, textual: 29.9%) and dual-modality approaches (visual + audio: 78.8%, visual + textual: 78.5%). These findings demonstrate that the proposed model outperforms models based on the original MTN and reaffirm the efficacy of multi-modal approaches in enhancing violence detection compared to single- or dual-modality methods.

Peridotite Weathering and Ni Redistribution in New Caledonian Laterite Profiles: Influence of Climate, Hydrology, and Structure

The peridotite massifs of New Caledonia are characterised by complex hydrodynamics influenced by intense inherited fracturing, uplift, and erosion. Following the formation of the erosion surfaces and alteration processes, these processes drive chemical redistribution during weathering; particularly lateritisation and saprolitisation. Magnesium, silica, and trace elements such as nickel and cobalt—released as the dissolution front advances—are redistributed through the system. New observations and interpretations reveal how lateritic paleo-land surfaces evolved, and their temporal relationship with alteration processes since the Oligocene. Considering the geometry of discontinuity networks ranging from micro-fractures to faults, the transfers occur in dual-permeability environments. Olivine dissolution rates are heterogeneously due to differential solution renewal caused by erosion and valley deepening. Differential mass transfer occurs between mobile regions of highly transmissive faults, while immobile areas correspond to the rock matrix and the secondary fracture network. The progression of alteration fronts controls the formation of boulders and the distribution of nickel across multiple scales. In the saprolite, nickel reprecipitates mostly in talc-like phases, as well as minor nontronite and goethite with partial diffusion in inherited serpentine. The current nickel distribution results from a complex interplay of climatic, hydrological and structural factors integrated into a model across different scales and times.

A posteriori error estimation and adaptivity for temporal multiscale problems

AbstractIn science and engineering, problems over multiple scales in time often arise. Two examples are material damage in oscillating structures or plaque growth in pulsating blood vessels. Here the long term effects are of interest but they depend on the coupled fast‐changing physical processes which must be taken into account. One method to efficiently simulate these problems is to split them into an averaged slow scale process and a localized fast scale process. We apply the dual weighted residual method on a generalized formulation for this type of problem to obtain an error estimator which can be used for adaptive refinement. Special care must be taken to separate the slow scale and fast scale influences on the estimator.

Energy and Water Interventions That Contribute to the Climate-Proofing of Buildings on Multiple Scales: A Literature Review

In the framework of planning and designing resilient housing under a changing climate, the present study constitutes a comprehensive literature review exploring climate-proofing solutions for the built environment concerning energy supply and water availability. This study delved into a multitude of sources that included scientific papers and reports and European Union guidelines and tools. The identified solutions covered building, urban, and territorial scales. The hazards of interest included heatwaves, heavy precipitation, droughts, earthquakes, wildfires, and storms. Several types of solutions were found (e.g., nature-based, education/capacity-building, engineering/built environment, etc.) with different times of application and timescales of action (e.g., defensive measures, short-term solutions, long-term adaptive, etc.). The maturity of the identified solutions was assessed based on the Technology Readiness Level (TRL) and Societal Readiness Level (SRL). Moreover, each solution’s contribution to climate mitigation was investigated. The solutions were assessed in terms of self-sustainability and other key criteria, namely, effectiveness, contribution to resilience maturity and climate change mitigation, adaptive nature, financing access, risk reduction, and social cohesion. In total, 85 energy and water solutions were determined from the desk review analysis and 67 (30 for the energy sector and 37 for the water sector) solutions were finally retained and proposed.

The Unique Behavior of Vertical Velocity in Developing Deep Convection

AbstractWhen daytime tropical convection develops away from mesoscale disturbances, it typically transitions gradually from dry to shallow to deep convection on hourly timescales. The transition is commonly associated with the formation of larger horizontal boundary‐layer structures and an increasing level of cloud organization aloft. This study demonstrates that a spectral analysis of the resolved high‐resolution sub‐cloud flow features allows for a robust identification of dominant length scales and the quantification of their growth rates during the transition. Furthermore, it is shown that temperature, moisture, and horizontal winds develop multiple length scales with the largest ones growing up to several kilometers in magnitude. However, the vertical velocity behaves in a distinct manner developing significantly smaller values, comparable over land and ocean. This indicates stronger inherent limits of vertical velocity to self‐organize by size.

TACIT EVASION OR A MISPLACED POLICY GAZE: A POLITICAL ECOLOGY OF THE DEFORESTATION AND CHARCOAL CONUNDRUM IN MALAWI

ABSTRACT Although banned in many countries, charcoal production remains a key driver of deforestation in Africa, yet a livelihood source for many, with evidence showing the practice has risen to unprecedented levels. While rural poverty is often framed as the driver, the rise in charcoal consumption among a burgeoning urban population suggests a multiplicity of drivers. Using in-depth interviews with diverse stakeholders in Malawi, this paper examined the underlying drivers of the charcoal conundrum. Although poverty is indeed a driver, our findings suggest the charcoal conundrum is rooted in complex sociopolitical dynamics and class relations at multiple scales. Charcoal production, a highly stigmatized economic activity at the rural scale, is the key source of fuel for a burgeoning urban population lacking affordable alternative energy sources. Our findings further reveal what we theorize as “hierarchical corruption,” featuring an elite class of “big charcoal dealers” who pay bribes to freely transact charcoal while rural people continue to be disproportionately targeted with draconian forest laws. These dynamics suggest a tacit evasion of the real drivers of deforestation and a misplaced policy gaze in forest governance. Contemporary forest policy must look “beyond forest” to address both production and consumption side drivers of deforestation, particularly poverty, and the lack of clean energy alternatives.

Solvent-adaptive hydrogels with lamellar confinement cellular structure for programmable multimodal locomotion

Biological organisms can perform flexible and controllable multimodal motion under external stimuli owing to the hierarchical assembly of anisotropic structures across multiple length scales. However, artificial soft actuators exhibit the limited response speed, deformation programmability and movement capability especially in harsh environments because of insufficient anisotropic hierarchy and precision in structural design. Here, we report a programmed assembly directed confinement polymerization method for the fabrication of environmentally tolerant and fast responsive hydrogels with lamellar assembly-confined cellular structure interpenetrated with highly aligned nanopillars by the directional freezing-assisted polymerization in the predesigned anisotropic laminar scaffold. The obtained hydrogel exhibits ultrafast responsiveness and anisotropic deformation exposed to temperature/light/solvent stimulation, maintaining highly consistent responsive deformation capability in all-polarity solvents over 100 days of soaking. Moreover, the hydrogels implement photoactive programmable multi-gait locomotion whose amplitude and directionality are precisely regulated by the intrinsic structure, including controlled crawling and rotation in water and non-polar solvents, and 3D self-propulsion floating and swimming in polar solvents. Thus, this hydrogel with hierarchically ordered structure and dexterous locomotion may be suitable for flexible intelligent actuators serving in harsh solvent environments.

Cross-material battery capacity estimation using hybrid-model fusion transfer learning

Evaluating battery health involves navigating the intricate interplay of physical, chemical, and electrochemical processes across multiple scales—a task that becomes even more complex with the introduction of new battery materials. This necessitates substantial development, modeling, and recalibration of boundary conditions. Our study introduces a hybrid fusion model that combines convolutional neural networks (CNNs) with self-attention mechanisms to enhance battery health assessments. In total, three datasets are involved—covering 77 LFP, 20 NMC, and 18 NCA batteries—encompassing over 170,000 cycles across a broad spectrum of battery materials and operational conditions for pre-training the base model and for transfer learning. Our findings reveal that, when transferring aging knowledge from LFP to ternary batteries (NMC and NCA) under diverse chemistries, temperatures, and operational strategies, the model achieved root mean square errors (RMSEs) of 7.47 mAh and 12.4 mAh, mean absolute percentage errors (MAPEs) of 0.67 % and 1.14 %, and coefficients of determination (R2) of 0.922 and 0.918, respectively. These results demonstrate the effectiveness of our hybrid fusion model, which uses deep transfer learning and combines CNNs with self-attention mechanisms to accurately diagnose battery capacity across various types by analyzing short cycle sequences and integrating insights throughout the cell operational history.

Scalable protein design using optimization in a relaxed sequence space.

Machine learning (ML)-based design approaches have advanced the field of de novo protein design, with diffusion-based generative methods increasingly dominating protein design pipelines. Here, we report a "hallucination"-based protein design approach that functions in relaxed sequence space, enabling the efficient design of high-quality protein backbones over multiple scales and with broad scope of application without the need for any form of retraining. We experimentally produced and characterized more than 100 proteins. Three high-resolution crystal structures and two cryo-electron microscopy density maps of designed single-chain proteins comprising up to 1000 amino acids validate the accuracy of the method. Our pipeline can also be used to design synthetic protein-protein interactions, as validated experimentally by a set of protein heterodimers. Relaxed sequence optimization offers attractive performance with respect to designability, scope of applicability for different design problems, and scalability across protein sizes.

EMB-YOLO: A Lightweight Object Detection Algorithm for Isolation Switch State Detection

In power inspection, it is crucial to accurately and regularly monitor the status of isolation switches to ensure the stable operation of power systems. However, current methods for detecting the open and closed states of isolation switches based on image recognition still suffer from low accuracy and high edge deployment costs. In this paper, we propose a lightweight object detection model, EMB-YOLO, to address this challenge. Firstly, we propose an efficient mobile inverted bottleneck convolution (EMBC) module for the backbone network. This module is designed with a lightweight structure, aimed at reducing the computational complexity and parameter count, thereby optimizing the model’s computational efficiency. Furthermore, an ELA attention mechanism is used in the EMBC module to enhance the extraction of horizontal and vertical isolation switch features in complex environments. Finally, we proposed an efficient-RepGDFPN fusion network. This network integrates feature maps from different levels to detect isolation switches at multiple scales in monitoring scenarios. An isolation switch dataset was self-built to evaluate the performance of the proposed EMB-YOLO. The experimental results demonstrated that the proposed method achieved superior detection performance on our self-built dataset, with a mean average precision (mAP) of 87.2%, while maintaining a computational cost of only 6.5×109 FLOPs and a parameter size of just 2.8×106 bytes.

Lightweight and conformal acousto‐ultrasonic sensing network for multi‐scale structural health monitoring: A review

AbstractStructural health monitoring (SHM) has been increasingly investigated for decades. Different physical principles have been developed for damage identification, such as electronics, mechanics, magnetics, etc., with different coverage (i.e., global, large‐area, and local monitoring) and sensitivity. Mechanical acousto‐ultrasonic‐based methods have formed a big family in SHM technologies. Multiple wave/resonance modes have been utilized for versatile SHM tasks. The permanently integrated sensing networks play a significant role in achieving a cost‐effective and reliable SHM system, with major concerns including weight increase for large‐scale deployment and conformity for complex geometry structures. In this review, typical acousto‐ultrasonic sensors made of different material systems are discussed, along with advantages and limitations. Moreover, advanced network installation methods have been introduced, including surface‐mounting with pre‐integrated networks on substrates and in situ printing, and embedding with composite layup and metal additive manufacturing. Sensor versatility and usage in multi‐scale SHM techniques are then highlighted. Different wave/resonance modes are transmitted and received with corresponding elements and network designs. In conclusion, this systematic review mainly covers a collection of acousto‐ultrasonic sensors, two modalities of network installation, and their employment with various SHM methods, hopefully providing a useful guide to building lightweight and conformal networks with passive or active‐passive sensors, and developing complete and reliable SHM strategies by integrating different damage identification methods on multiple scales.

The Effect of Emotion Priming on Risk Attitudes among Southern Chinese College Students

Abstract: The correlation between emotions and risk attitudes has been thoroughly substantiated in various disciplines such as behavioral economics, finance, and psychology. Evidence has revealed that emotions, influenced by a range of emotional factors, have the potential to significantly affect risk attitudes. Building upon prior research, this study investigates the impact of certain emotional triggers on risk attitudes in a sample of university students in southern China. A total of 55 valid subjects, including 42 females (76.4%), were enrolled in the experiment. Their risk attitudes were assessed using multiple scales, and they were randomly assigned into three groups to receive positive, neutral, and negative emotional stimuli before making risky decisions separately. The findings demonstrated that individuals who were emotionally primed with a positive emotion video were more inclined to undertake hazardous decisions compared to those exposed to negative emotional priming. This outcome aligns with earlier studies indicating that positive emotions are linked with a heightened fondness for risky behavior. In conclusion, this study corroborates the relevance of this theory to a group of undergraduates in the south of China.

Using eDNA to Supplement Population Genetic Analyses for Cryptic Marine Species: Identifying Population Boundaries for Alaska Harbour Porpoises.

Isolation by distance and biogeographical boundaries define patterns of population genetic structure for harbour porpoise along the Pacific coast from California to British Columbia. Until recently, inadequate sample sizes in many regions constrained efforts to characterise population genetic structure throughout the coastal waters of Alaska. Here, tissue samples from beachcast strandings and fisheries bycatch were supplemented with targeted environmental DNA (eDNA) samples in key regions of Alaska coastal and inland waters. Using a geographically explicit, hierarchical approach, we examined the genetic structure of Alaska harbour porpoises, using both mitochondrial DNA (mtDNA) sequence data and multilocus SNP genotypes. Despite a lack of evidence of genetic differentiation from nuclear SNP loci, patterns of relatedness and genetic differentiation from mtDNA suggest natal philopatry at multiple geographic scales, with limited gene flow among sites possibly mediated by male dispersal. A priori clustering of sampled areas at an intermediate scale (eastern and western Bering Sea, Gulf of Alaska and Southeast Alaska) best explained the genetic variance (12.37%) among regions. In addition, mtDNA differentiation between the Gulf of Alaska and eastern Bering Sea, and among regions within the Gulf of Alaska, indicated significant genetic structuring of harbour porpoise populations in Southeast Alaska. The targeted collection of eDNA samples from strata within Southeast Alaska was key for elevating the statistical power of our mtDNA dataset, and findings indicate limited dispersal between neighbouring strata within coastal and inland waters. These results provide evidence supporting a population boundary within the currently recognised Southeast Alaska Stock. Together, these findings will prove useful for ongoing management efforts to reduce fisheries conflict and conserve genetic diversity in this iconic coastal species.

Integral resonant negative derivative feedback suppression control strategy for nonlinear dynamic vibration behavior model

One of the major problems in robotics research has been developing an actuator system for extremely dynamic-legged robots. High torque density and the capacity to control dynamic physical interactions are two design requirements for high-speed locomotion that are challenging for conventional actuators used in manufacturing applications to meet. To address this system and apply the desired control to reach the best stability position, the robot's foot was simulated with the Van der Pol equations, applied the required control, and studied that application. This work describes the actions of a new novel control mechanism known as the Integral Resonant Negative Derivative Feedback (IRNDF) controller, which reduces the vibration response of a double Van der Pol oscillator subjected to external excitations. This unique controller combines integral resonant control (IRC) and negative derivative feedback (NDF) controllers to provide a new controller effect for double Van der Pol oscillators. The multiple scale perturbation technique (MSPT) has been applied to solve the controlled system analytically. The MATLAB and MAPLE programs have been used to complete and clarify all of the numerical talks. The frequency response curves have been used to study the impact that altering the parameter values had on the amplitude. The controlled system vibration amplitude is governed by frequency-response equations (FREs), which have been constructed. In the vibration system, the IRC, NDF, and IRNDF controllers were compared to see which one was the best. Numerical results show that the unique IRNDF controller is the best at reducing oscillations and decreasing amplitude values. The effects of the effective parameters on the controlled system have been identified. The frequency-response equation that was derived has been used to plot the various response curves for the framework that show the stable and unstable zones when the controller is off and on. Lastly, excellent agreement between the derived numerical findings and the analytical ones was observed. Lastly, utilizing time histories and response curves to compare analytical and numerical solutions was fascinating and significant.

Unlocking self-discharge: Unveiling the mysteries of electrode-free Zn-MnO2 batteries with advanced in situ techniques in mild acid aqueous electrolytes

We introduce a novel approach to Zinc-MnO2 battery architecture utilizing a 3D network of carbon nanofibers as both current collector and electrode material, promising enhanced performance and longevity for large-scale energy storage. Employing mild aqueous electrolytes, we address the challenge of managing self-discharge, crucial for short-term energy storage. Advanced coupled characterization techniques, including in-situ EQCM (Electrochemical Quartz Crystal Microbalance) and high-resolution optical microscopy, elucidate self-discharge mechanisms across over multiple length scales. Findings reveal that the self-discharge is mainly at the zinc electrode due to concomitant dissolution of Zinc (corrosion) and HER (Hydrogen Evolution Reaction) phenomena. Interestingly, the corrosion current was estimated irrespective of charging protocol and remains consistent, indicating the independence of zinc corrosion kinetics from the length scale. Finally, the morphology of the zinc layer appears to be critical, suggesting that self-discharge is primarily a chemical process. This innovative design strategy offers the potential for high-performance Zinc-MnO2 batteries with extended cycle life to meet the requirements of large-scale energy storage applications.

Measuring the ecological outcomes of fire: metrics to guide fire management

BackgroundChanges to fire regimes threaten biodiversity worldwide and emphasize the need to understand the ecological consequences of fire management. For fire management to effectively protect biodiversity, it is essential to have ecologicallyrelevant metrics to plan and evaluate management interventions. Here, we describe a suite of metrics to guide fire management for enhanced biodiversity outcomes.ResultsWe define five metrics that collectively provide comprehensive and complementary insights into the effect of fire regimes on ecosystem resilience and components of biodiversity. These include (1) Species Habitat Availability, a measure of the amount of suitable habitat for individual species; (2) Fire Indicator Species Index, population trends for species with clear fire responses; (3) Vegetation Resilience, a measure of plant maturity and the capability of vegetation communities to regenerate after fire; (4) Desirable Mix of Growth Stages, an indicator of the composition of post-fire age-classes across the landscape; and (5) Extent of High Severity Fire, a measure of the effect of severe fire on post-fire recovery of treed vegetation communities. Each metric can be quantified at multiple spatial and temporal scales relevant to evaluating fire management outcomes. We present a case study from Victoria, Australia, in which two metrics are applied across spatially-nested management areas. Results highlight four characteristics of metrics that enhance their value for management: (1) they quantify both status and trends through time; (2) they are scalable and can be applied consistently across management levels (from individual reserves to the whole state); (3) most can be mapped, essential for identifying where and when to implement fire management; and (4) their complementarity provides unique insights to guide fire management for ecological outcomes.ConclusionsThese metrics reflect common relationships between fire and biodiversity and are relevant to management in fire-prone ecosystems worldwide. They facilitate consistent translation of management responsibilities (planning, evaluation, reporting) across administrative levels and enable managers to strategically plan on-ground actions and transparently evaluate outcomes against strategic goals. A key next step for fire managers globally is to define “desirable” states for ecological metrics, to enable target-setting and the evaluation of management outcomes.