Multiple Scales Research Articles

Multi-Scale methodology of breakup and atomization for liquid jets in crossflow

Liquid jets in crossflow represent a highly complex phenomenon, characterized by intricate interactions across multiple temporal and spatial scales spanning orders of magnitude. To better understand the atomization mechanisms and characteristics of liquid jets in crossflow, a hybrid multi-scale methodology, integrating volume of fluid and discrete phase models with an enhanced coupling algorithm based on Large Eddy Simulation, has been developed and implemented as a multiphase solver in OpenFOAM. This methodology enables the comprehensive reproduction of the atomization process, including primary breakup and secondary atomization, obviating the requirement for complex tunable atomization models. For further reducing computational costs, a parallel acceleration technology is proposed by integrating the adaptive mesh refinement with dynamic load balancing into the solver. The multi-scale methodology is validated against experimental data regarding the trajectory of the liquid jet, as well as the distribution of droplet sizes and velocities. The validated methodology is then used to predict the behavior of Jet-A in crossflow. Simulation results demonstrate that, under the multi-mode breakup regime, large droplets are formed through the deformation of ligaments, small droplets are stripped from threads, and even smaller droplets are generated after secondary breakup. In contrast, in the shear breakup mode, large droplets are directly squeezed from the sides of the liquid column, while smaller droplets are stripped from ligaments and threads. Additionally, compared to the multi-mode breakup, droplet diameters are smaller in the downstream field for the shear breakup mode.

Occupancy dynamics of free ranging American mink (Neogale vison) in Greece.

Identifying the environmental factors that determine the occurrence of invasive species is essential in defining and implementing effective control campaigns. Here, we applied multi-season occupancy models to analyze American mink (Neogale vison) track data collected using 121 floating rafts, as a function of factors occurring at multiple spatial scales. Our overall aim was to identify those factors that determine the use, colonization or abandonment of rafts by free ranging individuals found in western Macedonia, Greece. We found that increasing values of shrubs and rock cover at the micro-habitat scale were positively associated with the species' probability of raft use, as was the density of medium-sized rivers at the landscape scale. Colonization was found to increase with increasing amounts of shrub and reed cover; however, both variables were not informative. Conversely, the distance from the nearest fur farm was highly informative in predicting raft abandonment by the species. Effective control actions may require removal by trapping along rocky or densely vegetated riverbanks or lake shores located in the vicinity of the established fur farms in the area. Habitat management, although possible, may be difficult to implement due to the ability of the species to adapt. Finally, fur farms should maximize security and establish an early warning and rapid eradication system in case of future escapes.

Electroelastic wave dispersion in the rotary piezoelectric NEMS sensors/actuators via nonlocal strain gradient theory

This article introduces a computational means for investigating the electroelastic nonlinear wave dispersion traits of the nano-dimension sandwich pipe, which is composed of a core formed of a bi-directional functionally graded (Bi-FG) material, together with a piezoelectric sensor/actuator. A combination of Hamilton’s principle, first-order shear deformation, along with Von-Karman nonlinearity, is used for modeling and obtaining the nonlinear governing equations of the nano-sized sandwich pipe connected to a piezoelectric part. The nonlinear governing equations to obtain the nonlinear phase velocity of the current system are determined using a combination of analytical and multiple scales approaches. Due to some computational cost for choosing the precise values of both the nonlocality factor and length scale of the nanopipe in the laboratory, for the first time in this research, with the aid of COMSOL multi-physics finite element simulation, the results are verified, and new outcomes for obtaining the exact functions for nonlocal and length scale factors is presented. In addition, an artificial neural network (ANN) is utilized in this study for the prediction of the results. The mathematical and finite element results were used to train the ANN. A newly presented optimization algorithm is exploited for the first time for optimizing the ANN parameters concluding higher accuracy of the ANN predictions. Consequently, to explore the influences of the location of the piezoelectric patch, nonlocality and length scale factors, and applied voltage parameter on the phase velocity characteristics of the nano-dimension sandwich pipe made of a Bi-FG core and electrically patch, an effort is performed. As an applicable result that can be useful in the related nano-industries, the current work presents exact nonlocal and length scale functions for different conditions.

Nonlinear combination resonance analysis of parametric-forced excitation for an axially moving piezoelectric rectangular thin plate in thermal- electromechanical coupling field

In this paper, the combination resonance of parametric-forced excitation characteristics for an axially moving rectangular piezoelectric plate under a thermal-electromechanical field is studied. Based on Kirchhoff-Love plate theory and Von Karman theory, the transverse vibration governing equations are derived from Hamilton's principle. The equations are discretized to ordinary differential equations by the Galerkin method. Then, the multiple scales method is applied to solve the system combination resonance equation, two different resonance states and corresponding amplitude-frequency response equations are obtained by eliminating the secular term, respectively. Additionally, the stability of the steady-state responses is analyzed by Lyapunov stability. Based on the numerical analysis, the influence of axial velocity, external voltage, central temperature difference, structural damping, and other parameters on nonlinear combination resonance response are investigated. The effect of parameter variation on period-doubling bifurcation and the chaotic motion of the system are also discussed by the system global bifurcation diagram.

An online long-term load forecasting method: Hierarchical highway network based on crisscross feature collaboration

In order to ensure the efficient operation of the power system, accurate online load forecasting is necessary. Existing studies face challenges in addressing unconventional load changes in long-term load forecasting (LTLF) and are unsuitable for online forecasting due to their reliance on measured Forecast Day Data (FDD). The development of a model independent of prior knowledge is essential to improve the interpretability and applicability of load forecasting methods. In this study, a hybrid model based on Crisscross Feature Collaboration (CFC) and Hierarchical Highway Network (HHN) is introduced. During the horizontal feature learning, the HHN is employed to learn temporal information from annual load curves across multiple time scales, reducing time complexity in LTLF. Subsequently, the prediction results of various time scales are combined to approximate FDD, addressing data leakage problems. In the vertical feature matching phase, the similar day data are screened by the multi-modal information of FDD and the Toeplitz Inverse Covariance Based Clustering (TICC) method, enabling the model to be independent of prior knowledge. Finally, through the HHN, all similar day data are utilized to reconstruct the forecasting load curve. In LTLF, the Root Mean Square Error (RMSE) of the proposed model reduces by 94.35 %, 85.74 %, 84.02 %, 90.72 %, 85.17 % and 87.76 % respectively in different scenarios compared to the direct forecasting. This signifies the effectiveness of our model in predicting the unconventional load change and achieving accurate online LTLF.

R-LRBPNet: A Lightweight SAR Image Oriented Ship Detection and Classification Method

Synthetic Aperture Radar (SAR) has the advantage of continuous observation throughout the day and in all weather conditions, and is used in a wide range of military and civil applications. Among these, the detection of ships at sea is an important research topic. Ships in SAR images are characterized by dense alignment, an arbitrary orientation and multiple scales. The existing detection algorithms are unable to solve these problems effectively. To address these issues, A YOLOV8-based oriented ship detection and classification method using SAR imaging with lightweight receptor field feature convolution, bottleneck transformers and a probabilistic intersection-over-union network (R-LRBPNet) is proposed in this paper. First, a CSP bottleneck with two bottleneck transformer (C2fBT) modules based on bottleneck transformers is proposed; this is an improved feature fusion module that integrates the global spatial features of bottleneck transformers and the rich channel features of C2f. This effectively reduces the negative impact of densely arranged scenarios. Second, we propose an angle decoupling module. This module uses probabilistic intersection-over-union (ProbIoU) and distribution focal loss (DFL) methods to compute the rotated intersection-over-union (RIoU), which effectively alleviates the problem of angle regression and the imbalance between angle regression and other regression tasks. Third, the lightweight receptive field feature convolution (LRFConv) is designed to replace the conventional convolution in the neck. This module can dynamically adjust the receptive field according to the target scale and calculate the feature pixel weights based on the input feature map. Through this module, the network can efficiently extract details and important information about ships to improve the classification performance of the ship. We conducted extensive experiments on the complex scene SAR dataset SRSDD and SSDD+. The experimental results show that R-LRBPNet has only 6.8 MB of model memory, which can achieve 78.2% detection accuracy, 64.2% recall, a 70.51 F1-Score and 71.85% mAP on the SRSDD dataset.

Gravitational wave turbulence: A multiple time scale approach for quartic wave interactions

Gravitational wave turbulence: A multiple time scale approach for quartic wave interactions

Extracting Features from Satellite Imagery to Understand the Size and Scale of Housing Sub-Markets in Madrid

The following paper proposes a novel machine learning approach to the segmentation of urban housing markets. We extract features from globally available satellite imagery using an unsupervised machine learning model called MOSAIKS, and apply a k-means clustering algorithm to the extracted features to identify sub-markets at multiple intra-urban scales within a case study of Madrid (Spain). To systematically explore scale effects on the resulting clusters, the analysis is repeated with varying sizes of satellite image patches. We assess the resulting clusters across scales using several internal cluster-evaluation metrics. Additionally, we use data from online listings portal Idealista to measure the homogeneity of housing prices within the clusters, to understand how well sub-markets can be differentiated by the image features. This paper evaluates the strengths and weakness of the method to identify urban housing sub-markets, a task which is important for planners and policy makers and is often limited by a lack of data. We conclude that the approach seems useful to divide large urban housing markets according to different attributes and scales.

Global warming significantly increases the risk of Pierce’s disease epidemics in European vineyards

Pierce’s disease (PD) is a vector-borne disease caused by the bacteria Xylella fastidiosa, which affects grapevines in the Americas. Currently, vineyards in continental Europe, the world’s largest producer of quality wine, have not yet been affected by PD. However, climate change may alter this situation. Here we incorporate the latest regional climate change projections into a climate-driven epidemiological model to assess the risk of PD epidemics in Europe for different levels of global warming. We found a significant increase in risk above +2∘C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$+\\,2\\,^{\\circ }\\hbox {C}$$\\end{document} in the main wine-producing regions of France, Italy and Portugal, in addition to a critical tipping point above +3∘C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$+\\,3\\,^{\\circ }\\hbox {C}$$\\end{document} for the possible spread of PD beyond the Mediterranean. The model identifies decreasing risk trends in Spain, as well as contrasting patterns across the continent with different velocities of risk change and epidemic growth rates. Although there is some uncertainty in model projections over time, spatial patterns of risk are consistent across different climate models. Our study provides a comprehensive analysis of the future of PD at multiple spatial scales (country, Protected Designation of Origin and vineyard), revealing where, why and when PD could become a new threat to the European wine industry.

Approximate analytical solutions of the 1:3 internal resonance response of piezoelectric energy harvester considering two types of coupled frequency components

The 1:3 internal resonance effect in the L-shaped piezoelectric vibration energy harvester leads to an expanded frequency band for energy harvesting and better matching of external excitation frequency in the environment. The electromechanical-coupled governing equations for this vibration energy harvester have been validated in our previous work (Nie et al., 2019). The effect of load resistance on the energy harvester’s natural frequency and damping ratio is addressed. The quadratic and cubic nonlinearities in this system leads to two types of coupled frequency components appear, they ultimately affect the approximate solution of the voltage response. The method of multiple scales is used to derive the two types of coupled frequency components of the system along with their contribution to the output responses. The revised approximate analytical solutions of the output responses are then presented. The system’s stability is assessed through the Jacobi matrix of modulation equations. The nonlinear softening phenomenon that favors the broadening of the bandwidth of the energy harvester is analyzed. The vibration patterns in regions with nonlinear softening and non-softening behavior are investigated through frequency spectra, phase trajectories, and Poincaré cross-sections. The findings reveal that the load resistance significantly affects the damping ratio of the harvester. Inclusion of quadratic and cubic nonlinearities results in three frequency components in the output responses spectrum, with an approximate ratio of 1:2:3. Two types of coupled frequency components appeared in the spectrum of the system’s output responses. The contribution of coupled frequency components to the output response proves crucial, and the revised approximate solutions are in good agreement with that of numerical solutions. The output responses are significantly affected by the initial conditions in the nonlinear softening region, the large and small initial conditions yield solutions in the upper and lower branches, respectively.

What drives wild pig (Sus scrofa) movement in bottomland and upland forests?

BackgroundThe wild pig (Sus scrofa) is an exotic species that has been present in the southeastern United States for centuries yet continues to expand into new areas dominated by bottomland and upland forests, the latter of which are less commonly associated with wild pigs. Here, we aimed to investigate wild pig movement and space use attributes typically used to guide wild pig management among multiple spatiotemporal scales. Our investigation focused on a newly invaded landscape dominated by bottomland and upland forests.MethodsWe examined (1) core and total space use using an autocorrelated kernel density estimator; (2) resource selection patterns and hot spots of space use in relation to various landscape features using step-selection analysis; and (3) daily and hourly differences in movement patterns between non-hunting and hunting seasons using generalized additive mixed models.ResultsEstimates of total space use among wild pigs (n = 9) were smaller at calculated core (1.2 ± 0.3 km2) and 90% (5.2 ± 1.5 km2) isopleths than estimates reported in other landscapes in the southeastern United States, suggesting that wild pigs were able to meet foraging, cover, and thermoregulatory needs within smaller areas. Generally, wild pigs selected areas closer to herbaceous, woody wetlands, fields, and perennial streams, creating corridors of use along these features. However, selection strength varied among individuals, reinforcing the generalist, adaptive nature of wild pigs. Wild pigs also showed a tendency to increase movement from fall to winter, possibly paralleling increases in hard mast availability. During this time, there were also increases in anthropogenic pressures (e.g. hunting), causing movements to become less diurnal as pressure increased.ConclusionsOur work demonstrates that movement patterns by exotic generalists must be understood across individuals, the breadth of landscapes they can invade, and multiple spatiotemporal scales. This improved understanding will better inform management strategies focused on curbing emerging invasions in novel landscapes, while also protecting native natural resources.

Deciphering Photoinduced Catalytic Reaction Mechanisms in Natural and Artificial Photosynthetic Systems on Multiple Temporal and Spatial Scales Using X-ray Probes.

Utilization of renewable energies for catalytically generating value-added chemicals is highly desirable in this era of rising energy demands and climate change impacts. Artificial photosynthetic systems or photocatalysts utilize light to convert abundant CO2, H2O, and O2 to fuels, such as carbohydrates and hydrogen, thus converting light energy to storable chemical resources. The emergence of intense X-ray pulses from synchrotrons, ultrafast X-ray pulses from X-ray free electron lasers, and table-top laser-driven sources over the past decades opens new frontiers in deciphering photoinduced catalytic reaction mechanisms on the multiple temporal and spatial scales. Operando X-ray spectroscopic methods offer a new set of electronic transitions in probing the oxidation states, coordinating geometry, and spin states of the metal catalytic center and photosensitizers with unprecedented energy and time resolution. Operando X-ray scattering methods enable previously elusive reaction steps to be characterized on different length scales and time scales. The methodological progress and their application examples collected in this review will offer a glimpse into the accomplishments and current state in deciphering reaction mechanisms for both natural and synthetic systems. Looking forward, there are still many challenges and opportunities at the frontier of catalytic research that will require further advancement of the characterization techniques.

Rail-STrans: A Rail Surface Defect Segmentation Method Based on Improved Swin Transformer

With the continuous expansion of the transport network, the safe operation of high-speed railway rails has become a crucial issue. Defect detection on the surface of rails is a key part of ensuring the safe operation of trains. Despite the progress of deep learning techniques in defect detection on the rails’ surface, there are still challenges related to various problems, such as small datasets and the varying scales of defects. Based on this, this paper proposes an improved encoder–decoder architecture based on Swin Transformer network, named Rail-STrans, which is specifically designed for intelligent segmentation of high-speed rail surface defects. The problem of a small and black-and-white rail dataset is solved using self-made large and multiple rail surface defect datasets through field shooting, data labelling, and data expansion. In this paper, two Local Perception Modules (LPMs) are added to the encoding network, which helps to obtain local context information and improve the accuracy of detection. Then, the Multiscale Feature Fusion Module (MFFM) is added to the decoding network, which helps to effectively fuse the feature information of defects at different scales in the decoding process and improves the accuracy of defect detection at multiple scales. Meanwhile, the Spatial Detail Extraction Module (SDEM) is added to the decoding network, which helps to retain the spatial detail information in the decoding process and further improves the detection accuracy of small-scale defects. The experimental results show that the mean accuracy of the semantic segmentation of the method proposed in this paper can reach 90.1%, the mean dice coefficient can reach 89.5%, and the segmentation speed can reach 37.83 FPS, which is higher than other networks’ segmentation accuracy. And, at the same time, it can achieve higher efficiency.

A Highly Resolved Speleothem δ13C Record from Central China and Its Manifestation on Multiple Time Scales during the Last Glacial

Speleothem δ13C in monsoonal China differs from speleothem δ18O, which is widely used as a climatic proxy for several complex control reasons. Nevertheless, δ13C records have the potential to reveal the implications of hydroclimatic changes. This study reports a speleothem δ13C record from Didonghe (DDH) Cave in central China spanning 34 to 13 kyr BP. After we investigated the factors that influence speleothem δ13C, we found that the δ13C record showed that DDH Cave can prompt directional shifts via local hydroclimatic changes, such as in vegetation types, biomass, and rock–water interaction processes, suggesting that δ13C is mainly controlled by the local hydroclimate. Ensemble empirical mode decomposition (EEMD) results revealed a coupling relationship between δ18O and δ13C on multiple timescales, which suggested that changes in precipitation caused by large-scale monsoonal circulation are controlled by regional hydrological conditions to a great extent. However, the hydrological conditions of the cave were relatively mild and humid during the last glacial maximum (LGM), which revealed the impact of evaporation on changes in the region’s hydrological conditions. We also found that the δ18O and δ13C profiles decoupled when δ13C changed with a shift in the location of the westerly during HS1. The δ13C record correlates well with other paleoclimate records, suggesting that regional hydrological conditions are also modulated by the Earth’s internal and external driving factors.

Residual stress and subsurface damage in sapphire ultra-precision grinding

This study conducted experiments on ultra-precision grinding of sapphire using various strategies. It evaluated surface and subsurface characteristics across multiple dimensions, including morphology, scale, mechanical properties, residual stresses, lattice distortions, in-service properties, and chemical components. The research aimed to explore the intrinsic relationship between these behaviours. The findings revealed that both semi-finish grinding and ultra-precision grinding achieved ductile and low-roughness surfaces, with surface hardness enhancement near the grinding surface. Additionally, Raman spectra wave shifts were able to distinguish residual stresses at different grinding strategies, with larger stresses observed in ultra-precision grinding compared to semi-finish grinding and rough grinding. Furthermore, dense etching dislocation pits were observed on the ultra-precision grinding surface, responding to the scale of residual stress. The presence of larger subsurface cracks in semi-finish grinding and rough grinding was found to contribute to the release of residual stress. Moreover, ultra-precision grinding significantly reduced subsurface damage compared to rough grinding, with a reduction from 23.8 μm to 4.3 μm. Photothermal absorption during rough grinding was noted to be over 10 times higher than in ultra-precision grinding. Transmission electron microscopy (TEM) analysis revealed that subsurface damage included microcrack-induced line defects and uniform plastic deformation areas. This damage involved lattice aberrations, dislocations, stacking faults, and thin amorphous layers. The hardness and modulus of the subsurface damage layer were observed to decrease. Additionally, Raman spectra wave shifts were able to distinguish the subsurface damage layer in ultra-precision grinding. Energy-dispersive X-ray spectroscopy (EDS) analysis showed no change in the chemical components of the subsurface damage layer. Furthermore, a model was proposed to evaluate subsurface damage based on residual stress, and the mechanism of residual stress inducing damage in ultra-precision grinding was further analysed.

Intrinsic conformal order revealed in geometrically confined long-range repulsive particles

Abstract Elucidating long-range interaction guided organization of matter is a fundamental question in physical systems covering multiple length scales. Here, based on the hexagonal disk model, we analyze the characteristic inhomogeneity created by long-range repulsions, and reveal the intrinsic conformal order in particle packings in equilibrium. Specifically, we highlight the delicate angle-preserved bending of the lattice to match the inhomogeneity condition. The revealed conformal order is found to be protected by the surrounding topological defects. These results advance our understanding on long-range interacting systems, and open the promising possibilities of using long-range forces to create particle packings not accessible by short-range forces, which may have practical consequences.

A novel uncertainty assessment protocol for integrated ecosystem services-life cycle assessments: A comparative case of nature-based solutions

Integrating ecosystem services and life cycle assessment is gaining increasing attention for the analysis of environmental costs and benefits associated with human activities covering multiple geographical scales and life cycle stages. Such integration is particularly relevant for evaluating the sustainability of nature-based solutions. However, merging these methods introduces additional uncertainties. This paper introduces a novel protocol to assess uncertainties in combined ecosystem services-life cycle assessment, focusing on ecosystem services accounting, life cycle inventory of foreground systems, and life cycle impact assessment characterisation factors. Applied to a nature-based solution case study compared to no-action and energy-intensive scenarios, the uncertainties were analysed using multi-method global sensitivity analysis. The robustness of the analysis results was assessed through convergence plots and statistical tests. Findings reveal significant uncertainties, especially in life cycle impact assessment characterisation factors, with the extent varying by impact category. Uncertainties in foreground life cycle inventory, particularly in land use of nature-based solutions scenario, are also notable. Compared to these, uncertainties associated with indicators of impact on ecosystem services (uncertainty arising from input variability in ecosystem services accounting) are relatively lower. This study underscores the critical role of uncertainty assessment in enhancing the reliability of integrated assessments for nature-based solutions, providing a framework to identify and quantify key uncertainties, thereby supporting more informed decision-making.

Hierarchically embedded scales of movement shape the social networks of vampire bats.

Social structure can emerge from hierarchically embedded scales of movement, where movement at one scale is constrained within a larger scale (e.g. among branches, trees, forests). In most studies of animal social networks, some scales of movement are not observed, and the relative importance of the observed scales of movement is unclear. Here, we asked: how does individual variation in movement, at multiple nested spatial scales, influence each individual's social connectedness? Using existing data from common vampire bats (Desmodus rotundus), we created an agent-based model of how three nested scales of movement-among roosts, clusters and grooming partners-each influence a bat's grooming network centrality. In each of 10 simulations, virtual bats lacking social and spatial preferences moved at each scale at empirically derived rates that were either fixed or individually variable and either independent or correlated across scales. We found that numbers of partners groomed per bat were driven more by within-roost movements than by roost switching, highlighting that co-roosting networks do not fully capture bat social structure. Simulations revealed how individual variation in movement at nested spatial scales can cause false discovery and misidentification of preferred social relationships. Our model provides several insights into how nonsocial factors shape social networks.

Virtual Reality for Spatial Planning and Emergency Situations: Challenges and Solution Directions

The notion of the smart city involves embedding Industry 4.0 technologies to improve the lives of inhabitants in urban environments. Within this context, smart city data layers (SCDLs) concern the integration of extra tiers of information for the purposes of improving communication potential. Under the Industry 4.0 technology grouping, advanced communication technologies, such as virtual reality (VR), further the opportunities to model, recreate, evaluate and communicate scenarios that potentially improve citizens lives at multiple SCDL scales in a smart cities context. The use of added contextual information in SCDLs is of special interest for emergency planning situations at the building scale. In this research, a literature review to understand the current approaches for the use, development and evaluation of VR applications in the context of emergency planning was conducted. The results indicated four main categories of relevant challenges for these types of applications, for which recommendations and a roadmap for VR development are presented. In total, the study identified 10 commonly occurring challenges (e.g., optimization and discomfort) and 19 solution directions (e.g., model construction and spatial directions) in related articles when considering the development of VR for spatial planning and emergency situations.

Whole-genome assembly of a novel invertebrate herpesvirus from the gastropod Babylonia areolata.

Molluscan herpesviruses cause disease in species of major importance to aquaculture and are the only known herpesviruses to infect invertebrates, which lack an adaptive immune system. Understanding the evolution of malacoherpesviruses in relation to their hosts will likely require comparative genomic studies on multiple phylogenetic scales. Currently, only two malacoherpesvirus species have genomes that have been fully assembled, which limits the ability to perform comparative genomic studies on this family of viruses. In the present study, we fully assemble a herpesvirus from Illumina and Nanopore sequence data that were previously used to assemble the genome of the gastropod Babylonia areolata. We tentatively assign this novel herpesvirus to the genus Aurivirus within the family Malacoherpesviridae based on a phylogenetic analysis of DNA polymerase. While structurally similar to other malacoherpesvirus genomes, a synteny analysis of the novel herpesvirus with another Aurivirus species indicates that genomic rearrangements might be an important process in the evolution of this genus. We anticipate that future complete assemblies of malacoherpesviruses will be a valuable resource in comparative herpesvirus research.