Multiple Scales Research Articles

The Role of the Built Environment in Supporting Older Adults' Engagement: A Narrative Literature Review.

This literature review aimed to summarize the existing knowledge surrounding the role of the built environment in supporting older adults' physical and social engagement and identify opportunities for future research and design that facilitates older adults' engagement at multiple environmental scales: from interior spaces to neighborhoods. Physical environments can support or impede older adults' physical and social engagement: willful, motivated involvement in meaningful activity or occupation. Research shows that engagement is a core component of health and well-being and relates to positive health outcomes, including reduced disease risk, better mental health, and improved physical and cognitive function. Thus, designing supportive built environments for engagement can yield significant, positive health and well-being impacts. A systematized search of five databases, a hand search, and an iterative screening process identified 55 studies for inclusion in this review. Through inductive thematic analysis, this review summarizes findings regarding the built environment's role in physical and social engagement in older age and design strategies to facilitate engagement and support health and well-being. Evidence indicates that built environment characteristics can influence older people's physical and social engagement in homes, neighborhoods, and care settings. The thematic analysis of the included studies identified three key themes concerning the relationships of built environment characteristics to older adults' engagement across multiple environmental scales: connection, access, and security. Built environments influence older people's physical and social engagement, significantly affecting their health, well-being, abilities, and longevity. Numerous design strategies can support older adults' engagement, but more research is needed.

Multi-scale analysis of virgin/aged/recycled asphalt self-healing performance based on molecular simulation and micro-macro tests

Conventional macro-micro experiments have not fully elucidated the thermal self-healing mechanism of virgin, aged, and recycled asphalt at the molecular level at different temperatures. Molecular dynamics and macro-micro experiments were used to investigate the multi-scale mechanism of thermal self-healing of nano-cracks in three asphalt types. The molecular characteristics (concentration, energy changes, and molecular diffusion properties) of the three asphalt types were simulated during self-healing using molecular dynamics simulations Macro-micro experimental tests were conducted to validate the simulation results. The self-healing process was analyzed at multiple scales, and correlation analysis between the self-healing parameters and the macroscopic healing characteristics was performed at nano- and microscopic scales. The results showed that the relative concentration increased significantly from 0 mol/L to 0.6 mol/L in the 60–80 Å range during the long-distance self-healing phase (0–40 ps), promoting the rapid reorganization of molecules around cracks and the formation of self-healing networks. The van der Waals forces were the dominant forces affecting self-healing, and the transition from repulsive to attractive forces promoted the self-healing of cracks. This study clarifies the thermal self-healing mechanism of asphalt cracks at multiple scales, ensuring the sustainable use of asphalt materials.

Large-scale overlap and fine-scale avoidance: assessing interactions between coyotes, bobcats, and cougars at multiple scales

Abstract In multi-carnivore systems individuals must forage and reproduce while also competing with other carnivores and avoiding intraguild predation. These interactions may vary by strength and scales across different ecosystems. We used occupancy analyses and attraction–avoidance indices to assess large- and fine-scale interactions, respectively, between Cougars (Puma concolor), Bobcats (Lynx rufus), and Coyotes (Canis latrans) in northeast Oregon based on data from camera traps set during the summer and fall of 2016 and 2017. To determine the importance of habitat preferences and spatial overlap, we compared occupancy (probability of use) models that included habitat covariates with and without species co-occurrence terms. Detection was different for all species and was influenced by roads, game trails, and canopy cover. Terrain ruggedness was important for all species; Bobcats showed the strongest preference for ruggedness, Cougars showed a weak preference, and Coyotes significantly but weakly avoided rugged areas. The top probability of use model showed that Bobcats and Coyotes were 1.88 times more likely to spatially overlap on roads. We also found evidence that interactions among carnivores were scale-dependent. At larger scales, habitat preference was more important than interactions with other carnivores, whereas at finer scales, most carnivores avoided each other. At fine spatiotemporal scales, our attraction–avoidance analyses showed that Bobcats avoided Coyotes and Coyotes avoided both Bobcats and Cougars. These findings suggest that carnivores in our study system were adept at minimizing conflict through fine-scale avoidance in space and time, that the human footprint can influence carnivore interactions, and that studying carnivore interactions at multiple scales is important for understanding the effects of spatial overlap and potential competition.

Technical Framework and Preliminary Practices of Global Geographic Information Resource Construction

Abstract. High-precision and real-time global geographic information data are fundamental and strategic resources in various fields such as safeguarding global strategic interests, studying global environmental changes, and planning for sustainable development. However, due to challenges related to ground control and obtaining reference information, the development of global geographic information resources faces significant hurdles in terms of geometric positioning, information extraction, and data mining. This paper starts with the characteristics of domestically produced remote sensing images and proposes a comprehensive technical framework centered around "uncontrolled geometric positioning, intelligent interpretation of typical elements, mining of multi-source data from abroad, and intelligent hybrid collection and compilation of Digital Elevation Models (DEMs)." The paper elaborates on the key technical challenges that need to be overcome and their corresponding solutions. It also outlines the development of relevant data products and production technical specifications. Multiple production-oriented software tools were developed, leading to the creation of a variety of data products in multiple types and scales, including global 30-meter land cover data, DEM data, core vector data, and more.

Learning a general model of single phase flow in complex 3D porous media

Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures and fluid physics—in particular, confinement effects which vary across the nanoscale to the microscale. While numerical simulation is possible, the computational cost is prohibitive for realistic domains, which are large and complex. Although machine learning (ML) models have been proposed to circumvent simulation, none so far has simultaneously accounted for heterogeneous 3D structures, fluid confinement effects, and multiple simulation resolutions. By utilizing numerous computer science techniques to improve the scalability of training, we have for the first time developed a general flow model that accounts for the pore-structure and corresponding physical phenomena at scales from Angstrom to the micrometer. Using synthetic computational domains for training, our ML model exhibits strong performance (R 2 = 0.9) when tested on extremely diverse real domains at multiple scales.

A study of pedestrian wayfinding behavior based on desktop VR considering both spatial knowledge and visual information

Understanding pedestrian wayfinding behavior is crucial for traffic management and building design. The use of virtual reality technology presents an efficient approach for investigating pedestrian wayfinding behavior in large public spaces, offering numerous advantages for data collection. However, the impact of different scenario dimensions on pedestrian wayfinding behavior in large public spaces remains unclear. Additionally, the selection of virtual experiment scenario dimensions currently relies primarily on researchers’ experience and practical conditions, lacking sufficient evidence to support their rational. Another challenge is the limited focus on spatial knowledge’s effect on wayfinding behavior, with insufficient analysis of the utility of pedestrian visual information and a lack of precise methods to quantify visual field information accurately. This study addresses these gaps by incorporating spatial knowledge at multiple scales and pedestrian visual field information as influencing factors in the analysis of wayfinding behavior. Furthermore, it distinguishes between three-dimensional and two-dimensional scenarios to compare the impact of dimensional differences on pedestrian wayfinding behavior. By analyzing behavior data from non-immersive wayfinding experiments, this research employs statistical analysis methods and a deep learning framework to derive results regarding the factors influencing wayfinding behavior. The findings demonstrate that considering both spatial and visual field information effectively enhances the predictive ability of the wayfinding model. Additionally, dimensional differences significantly influence the pedestrian wayfinding process. These results offer empirical evidence to guide researchers in selecting experimental scenarios of pedestrian behavior and provide insights for public space layout, signage design, and improving pedestrian efficiency.

Different roads, same destination: The shared future of plant ecophysiology and ecohydrology.

Terrestrial water fluxes are substantially mediated by vegetation, while the distribution, growth, health, and mortality of plants are strongly influenced by the availability of water. These interactions, playing out across multiple spatial and temporal scales, link the disciplines of plant ecophysiology and ecohydrology. Despite this connection, the disciplines have provided complementary, but largely independent, perspectives on the soil-plant-atmosphere continuum since their crystallization as modern scientific disciplines in the late 20th century. This review traces the development of the two disciplines, from their respective origins in engineering and ecology, their largely independent growth and maturation, and the eventual development of common conceptual and quantitative frameworks. This common ground has allowed explicit coupling of the disciplines to better understand plant function. Case studies both illuminate the limitations of the disciplines working in isolation, and reveal the exciting possibilities created by consilience between the disciplines. The histories of the two disciplines suggest opportunities for new advances will arise from sharing methodologies, working across multiple levels of complexity, and leveraging new observational technologies. Practically, these exchanges can be supported by creating shared scientific spaces. This review argues that consilience and collaboration are essential for robust and evidence-based predictions and policy responses under global change.

Overcoming strength-ductility tradeoff with high pressure thermal treatment

Conventional material processing approaches often achieve strengthening of materials at the cost of reduced ductility. Here, we show that high-pressure and high-temperature (HPHT) treatment can help overcome the strength-ductility trade-off in structural materials. We report an initially strong-yet-brittle eutectic high entropy alloy simultaneously doubling its strength to 1150 MPa and its tensile ductility to 36% after the HPHT treatment. Such strength-ductility synergy is attributed to the HPHT-induced formation of a hierarchically patterned microstructure with coherent interfaces, which promotes multiple deformation mechanisms, including dislocations, stacking faults, microbands and deformation twins, at multiple length scales. More importantly, the HPHT-induced microstructure helps relieve stress concentration at the interfaces, thereby arresting interfacial cracking commonly observed in traditional eutectic high entropy alloys. These findings suggest a new direction of research in employing HPHT techniques to help develop next generation structural materials.

Photoresponsive Biomimetic Functions by Light-Driven Molecular Motors in Three Dimensionally Printed Liquid Crystal Elastomers.

Despite the fascinating developments in design and synthesis of artificial molecular machines operating at the nanoscales, translating molecular motion along multiple length scales and inducing mechanical motion of a three-dimensional macroscopic entity remains an important challenge. The key to addressing this amplification of motion relies on the effective organization of molecular machines in a well-defined environment. By taking advantage of long-range orientational order and hierarchical structures of liquid crystals and unidirectional rotation of light-driven molecular motors, we report here photoresponsive biomimetic functions of liquid crystal elastomers (LCEs) by the repetitive unidirectional rotation of molecular motors using 3D printing. Molecular motors were built in the main chain of liquid crystals oligomers to serve as photoactuators. The oligomers were then used as the ink, and liquid crystal elastomers with different morphologies were printed. The obtained LCEs are able to conduct multiple types of motions including bending, helical coiling, closing of petals, and flipping of wings of a butterfly upon UV illumination, which paves the way for future design of responsive materials with enhanced complex actuating functions.

Dynamics of a New Delayed Glucose–Insulin Model with Obesity

In this work, a new glucose–insulin model incorporating time delay and obesity is developed to gain insights of its dynamical mechanisms. Through the method of multiple scales, we theoretically demonstrate that time delay can drive the system to yield Hopf bifurcation, thereby producing oscillating solutions that are consistent with the simulation results. Moreover, obesity changes the level of glucose, but cannot induce oscillations. In particular, it is found that under the combined effect of obesity and time delay, obesity delays the appearance of Hopf bifurcation which is induced by time delay. Results show that a low calorie diet can achieve therapeutic effects including reducing blood glucose fluctuations and insulin resistance, which can be used as an adjuvant for the treatment of diabetes. In addition, our results indicate that the delay, together with an optimal rate of model parameters can cause a variety of dynamics and induce glucose oscillations. The result obtained in this paper may help to better understand the obesity, diabetes, and the interaction between glucose and insulin, so that control strategies can be designed to better regulate blood glucose levels and fluctuations and mitigate the occurrence of type-2 diabetes.

Adaptive governance of disaster preparedness? The case of regional networks in Finland

Adaptive governance (AG) has emerged as a theoretical alternative to traditional forms of governance, which often fail to address complexity and rapid changes in socioecological systems. Frequently recognized attributes of AG include learning and experimentation, collaboration and networks, polycentric institutions and multilevel governance at multiple appropriate scales, as well as reflexivity, flexibility and adaptive capacity. To provide more examples of the practical implementation of AG, we examine whether a Finnish regional preparedness network model could be an example of a tangible way to implement AG. According to our results, the regional networks embody many features of AG: they facilitate person- and organization-level networking and peer learning, and thus help their members to grasp the overall picture of preparedness and safety in the region, and reveal ambiguities concerning the division of responsibilities. The networks also support nestedness and polycentricity through cross-sector and multilevel cooperation. At the same time, the voluntary nature of the networks is perceived as problematic considering the overall preparedness of society. Notably, although networks often include non-state actors, citizens remain outside of them, which may reduce the effectiveness of a network.

A New Dataset and Comparative Study for Aphid Cluster Detection and Segmentation in Sorghum Fields

Aphid infestations are one of the primary causes of extensive damage to wheat and sorghum fields and are one of the most common vectors for plant viruses, resulting in significant agricultural yield losses. To address this problem, farmers often employ the inefficient use of harmful chemical pesticides that have negative health and environmental impacts. As a result, a large amount of pesticide is wasted on areas without significant pest infestation. This brings to attention the urgent need for an intelligent autonomous system that can locate and spray sufficiently large infestations selectively within the complex crop canopies. We have developed a large multi-scale dataset for aphid cluster detection and segmentation, collected from actual sorghum fields and meticulously annotated to include clusters of aphids. Our dataset comprises a total of 54,742 image patches, showcasing a variety of viewpoints, diverse lighting conditions, and multiple scales, highlighting its effectiveness for real-world applications. In this study, we trained and evaluated four real-time semantic segmentation models and three object detection models specifically for aphid cluster segmentation and detection. Considering the balance between accuracy and efficiency, Fast-SCNN delivered the most effective segmentation results, achieving 80.46% mean precision, 81.21% mean recall, and 91.66 frames per second (FPS). For object detection, RT-DETR exhibited the best overall performance with a 61.63% mean average precision (mAP), 92.6% mean recall, and 72.55 on an NVIDIA V100 GPU. Our experiments further indicate that aphid cluster segmentation is more suitable for assessing aphid infestations than using detection models.

Using multiscale lidar to determine variation in canopy structure from African forest elephant trails

AbstractRecently classified as a unique species by the IUCN, African forest elephants (Loxodonta cyclotis) are critically endangered due to severe poaching. With limited knowledge about their ecological role due to the dense tropical forests they inhabit in central Africa, it is unclear how the Afrotropics are influenced by elephants. Although their role as seed dispersers is well known, they may also drive large‐scale processes that determine forest structure through the creation of elephant trails and browsing the understory, allowing larger, carbon‐dense trees to succeed. Multiple scales of lidar were collected by NASA in Lopé National Park, Gabon from 2015 to 2022. Utilizing two airborne lidar datasets in an African forest elephant stronghold, detailed canopy structural information was used in conjunction with elephant trail data to determine how forest structure varies on and off trails. Forest along elephant trails displayed different structural characteristics than forested areas off trails, with lower canopy height, canopy cover, and different vertical distribution of plant density. Less plant area density was found on trails at 1 m in height, while more vegetation was found at 12 m, compared to off trail locations. Trails in forest areas with previous logging history had lower plant area in the top of the canopy. Forest elephants can be considered as “logging light” ecosystem engineers, affecting canopy structure through browsing and movement. Both airborne lidar scales were able to capture elephant impact along trails, with the high‐resolution discrete return lidar performing higher than waveform lidar.

Two-loop non-planar four-point topology with massive internal loop

We study a set of two-loop non-planar master integrals needed for the NNLO QCD corrections to diphoton and dijet production at hadron colliders. The top-sector topology contains an internal massive fermion loop and is known to contain elliptic curves. Leveraging the method of differential equations, we provide a comprehensive discussion for deriving an ϵ-factorized differential equation related to the most intricate sector within the Feynman integral family. Despite the dependence on multiple scales and the presence of two elliptic sectors, we demonstrate how to leverage the properties of their maximal cuts and the factorization of the Picard-Fuchs operator to deal with the complexity of the analytic computation. In particular, we construct a transformation matrix that brings the differential equations into a format enabling the convenient expression of analytic results in terms of Chen’s iterated integrals.

Recent advances in characterizing the crustal stress field and future applications of stress data: perspectives from North America

Abstract The stress field controls patterns of crustal deformation, including which faults are likeliest to cause earthquakes or transmit fluids. Since the 1950s, maps of maximum horizontal stress ( S Hmax ) orientations have advanced dramatically, and the style of faulting (relative principal stress magnitudes) has recently been mapped in some regions as well. This perspectives paper summarizes developments in characterizing stress orientations and (relative) magnitudes, including new seismic and borehole methods, as well as progress in identifying the causes of stress variations. Despite these advances, adding far more spatiotemporal detail would allow geoscientists to address many of today's key challenges regarding natural hazards, energy development, and geodynamics. In particular, it is critically important to characterize stress heterogeneity at multiple scales while also recognizing the coherent variability of the stress field. The second part of the paper considers how more detailed stress datasets could prove essential to addressing some of the grand questions in geoscience, including deciphering the poorly understood feedbacks between crustal dynamics and surface processes, improving earthquake and eruption forecasts, and determining the origins and shared properties of plate boundaries.

Phase contrast micro-CT with adjustable in-slice spatial resolution at constant magnification

Objective. To report on a micro computed tomography (micro-CT) system capable of x-ray phase contrast imaging and of increasing spatial resolution at constant magnification. Approach. The micro-CT system implements the edge illumination (EI) method, which relies on two absorbing masks with periodically spaced transmitting apertures in the beam path; these split the beam into an array of beamlets and provide sensitivity to the beamlets’ directionality, i.e. refraction. In EI, spatial resolution depends on the width of the beamlets rather than on the source/detector point spread function (PSF), meaning that resolution can be increased by decreasing the mask apertures, without changing the source/detector PSF or the magnification. Main results. We have designed a dedicated mask featuring multiple bands with differently sized apertures and used this to demonstrate that resolution is a tuneable parameter in our system, by showing that increasingly small apertures deliver increasingly detailed images. Phase contrast images of a bar pattern-based resolution phantom and a biological sample (a mouse embryo) were obtained at multiple resolutions. Significance. The new micro-CT system could find application in areas where phase contrast is already known to provide superior image quality, while the added tuneable resolution functionality could enable more sophisticated analyses in these applications, e.g. by scanning samples at multiple scales.

Effects of reduced snowpack due to climate warming on abiotic and biotic soil properties in alpine and boreal forest systems

Reduction in snow cover, depth, onset, and duration of seasonal snow in mid-latitude regions due to climate warming has multiple global and local scale ecosystem impacts. These effects include modulations of the hydrological cycles and increases in land surface solar radiation absorption due to decreased albedo. Changes in snow cover characteristics also affect underlying soils. Snow has an insulating effect on soils by decoupling air and soil temperatures, thus seasonal snow cover reduction leads to overall lower soil temperatures and an increase in freeze-thaw cycles. This is especially prominent during the fall and spring thaw seasons when the snow cover is not as extensive. This in turn has downstream impacts on soil physical, chemical, and biological properties. Among these impacts are soil moisture reduction, temperature, frost regimes, soil pH shifts, and alteration in nutrient flux dynamics during winter, snowmelt period and the following summer growing season. These changes in soil physicochemical properties due to snowpack reduction can then impact the biological soil properties via increased plant root mortality, reduced abundance and diversity of soil arthropods, and shifts in composition, abundance and activity of soil microbial communities. All these soil biotic factors can in turn alter the dynamics of soil nutrient fluxes and future greenhouse gas emissions. Here, we integrate data on the effects of snow cover reduction on abiotic and biotic soil properties, with focus on temperate alpine and forest ecosystems and with an outlook on future impacts.

Nonlinear behaviors and chaos control of a fluid-conveying piezoelectric pipe in supercritical regime

The study investigated the steady state response of a piezoelectric pipe conveying fluid under external and internal resonances in the supercritical regime of fluid velocity. Internal resonance conditions between the first and second frequency modes were studied at a specific flow velocity in the supercritical regime. The Galerkin method and the multiple scale method were used to extract the vibration amplitude versus the excitation frequency and amplitude. The steady state solutions lost stability through saddle-node and Hopf bifurcations, leading to periodic, double periodic, multi-periodic, and chaotic behaviors in the forced and frequency response curves. Time response, FFT, phase portrait, and Lyapunov exponents were presented to predict the unstable regions of the piezo-pipe system between the Hopf points. The Wolf algorithm was utilized to evaluate the Lyapunov exponents. Additionally, a fuzzy terminal sliding mode controller was designed in the chaotic region of the piezoelectric pipe conveying fluid, and its parameters were optimized using the genetic algorithm. The controller effectively stabilized the chaotic motion of the system, demonstrating better performance than the sliding mode control in numerical results.

InSAR stacking to detect active landslides and investigate their relation to rainfalls in the Northern Apennines of Italy

Interferometric stacking is a useful technique to detect active slope deformation over vast mountainous area. Compared to the analysis of single interferograms, the stacking approach improves the signal to noise ratio and deformation signals become clearer. In this work, we used stacked interferograms to detect active slow-moving landslides during the years 2015 to 2019 over a 1200 km2 portion of the Northern Apennines of Italy. C-band Sentinel 1 SAR images were used to create short temporal baseline (6 to 24 days) interferograms which limit decorrelation and maximize the range of measurable displacement rates. Then we operated a further selection of interferograms based on coherence and visual inspection, before stacking over multiple time scales (1 month to years). The products of the analysis are ground displacement maps where the presence of residual noise is inversely proportional to the duration of the stack and the deformation signal is clearly recognized. Results show that only a small fraction of the mapped landslide deposits are experiencing deformation that can be detected by differential interferometry. In particular, we identified 118 InSAR deformation signals corresponding to ongoing gravitational slope deformations over the 9916 landslides mapped in the area. Active movements are mostly located on landslides that have undergone catastrophic reactivation in relatively recent times. Annual interferometric stacks proved better suited to detect active slope movements, while <15 % of our deformation signals can only be detected by inspecting monthly stacks. Active slow-moving landslides show variable displacement rates in monthly stacks. Periods of dormancy alternate to accelerations that may lead to actual catastrophic failures or, more often, determine finite periods of sustained slow movement before the displacement rates drop below the detection limit. We compared the evolutionary trends of the phenomena to the occurrence of rainfall events. For this purpose, we use the probability of landslide occurrence which is associated with a rainfall event based on a territorial alert threshold. Results show that, at increasing landslide occurrence probabilities, an increasing fraction of actively deforming landslides can be detected by InSAR.

Enhanced performance of CMIP6 climate models in simulating historical precipitation in the Florida Peninsula

AbstractAssessing the performance of General Circulation Models (GCMs) in simulating historical regional precipitation is essential for climate assessments. This study analysed 18 GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 27 GCMs from Phase 6 (CMIP6) for the Florida Peninsula. Naïve combinations formed the CMIP5‐Ensemble and CMIP6‐Ensemble. The reference dataset consisted of global gridded precipitation data from National Oceanic and Atmospheric Administration. GCM‐simulated precipitation data were compared to the reference dataset across multiple time scales between 1951 and 2005. Most CMIP5 and CMIP6 GCMs exhibited a negative bias at the daily time scale, with an absolute value ranging between 0 and 2 mm day−1 at the median level, except for INM‐CM4 and MRI‐CGCM3 (CMIP5) and CNRM‐ESM2‐1 (CMIP6). Two performance metrics, that is, Pearson correlation and mean absolute error (MAE), were used to evaluate the GCMs. A correlation value above 0.3 is statistically significant at the significance level (α = 0.05). For the Pearson correlation between simulated and observed monthly precipitation, only 15% of CMIP5 GCMs (3 out of 19) had a median value above 0.3, whilst this increased to approximately 39% (11 out of 28) for CMIP6 GCMs. For monthly climatological precipitation, only 21% (4 out of 21) of GCMs in CMIP5 showed a correlation with a median value of 0.8 or higher. This percentage rose to 50% (14 out of 28) for CMIP6. A notable difference was observed in the MAE between CMIP5 and CMIP6 climate models. Lastly, GCM raw outputs were evaluated based on rainy season characteristics (onset, demise and duration). Significant improvements from the CMIP5 to the CMIP6 models were observed in capturing the rainy season in the Florida Peninsula. This study thoroughly compares CMIP5 and CMIP6 climate models in simulating historical precipitation at various time scales for the study region. Although the results are specific to the study area, the methods can be applied more broadly.