Complex Scaling Research Articles

Dietary Variation Among Herculaneum's Victims of Mt. Vesuvius via Dental Microwear Texture Analysis.

The current study addresses three primary hypotheses: (1) Subadult diets changed as children aged, (2) adult diets differed from subadult diets, and (3) male and female diets differed as they age. This study includes dental microwear texture data from 58 adults (age 16+) and 23 subadults (age 0-16) recovered by Sarah Bisel from within and near to storage rooms adjacent to Herculaneum's beach. The adults include 27 females and 31 males. Dental microwear texture analysis (DMTA) employed standard procedures: data collection used a white-light confocal profiler at 100×. The DMTA variables were complexity, anisotropy, textural fill volume, and scale of maximum complexity. Statistical methods used Bayesian versions of analysis of variance and correlation (Bayes factors above 1.5 were considered meaningful and above 3.0 significant) as well as discriminant function analysis and binary logistic regression. No differences emerged among the children. Adult diets were significantly lower than the subadults for anisotropy. Among the adults, age affected the females more, particularly for anisotropy. Subadult diets did not vary by age, but they did vary. The lower adult anisotropy indicates each adult ate a greater variety of foods compared to the subadults. As females aged, however, their diets became more restricted compared to the males. Overall, age and sex affected the Herculaneum diet.

From chimeras to extensive chaos in networks of heterogeneous Kuramoto oscillator populations.

Populations of coupled oscillators can exhibit a wide range of complex dynamical behavior, from complete synchronization to chimera and chaotic states. We can, thus, expect complex dynamics to arise in networks of such populations. Here, we analyze the dynamics of networks of populations of heterogeneous mean-field coupled Kuramoto-Sakaguchi oscillators and show that the instability that leads to chimera states in a simple two-population model also leads to extensive chaos in large networks of coupled populations. Formally, the system consists of a complex network of oscillator populations whose mesoscopic behavior evolves according to the Ott-Antonsen equations. By considering identical parameters across populations, the system contains a manifold of homogeneous solutions where all populations behave identically. Stability analysis of these homogeneous states provided by the master stability function formalism shows that non-trivial dynamics might emerge on a wide region of the parameter space for arbitrary network topologies. As examples, we first revisit the two-population case and provide a complete bifurcation diagram. Then, we investigate the emergent dynamics in large ring and Erdös-Rényi networks. In both cases, transverse instabilities lead to extensive space-time chaos, i.e., irregular regimes whose complexity scales linearly with the system size. Our work provides a unified analytical framework to understand the emergent dynamics of networks of oscillator populations, from chimera states to robust high-dimensional chaos.

Adaptive sensor placement selection and response reconstruction for bridge structural health monitoring

Abstract Bridge structural health monitoring (SHM) measures the real-time responses of bridges via instrumented sensors. This paper focuses on the bridge displacement reconstruction at unmeasured locations of interest (LoIs) using measurements from sensors. Increasing the number of sensors on the bridge can capture more structural information. However, considering the complex structure and scale of bridges, minimizing the number of sensors and selecting the critical sensing locations (CSL) to place sensors for measurements are significant in bridge SHM. To achieve efficient bridge SHM under limited number of sensors, this paper proposes an adaptive sensor selection and reconstruction neural network (ASSRNN) for determining the CSLs and reconstructing at all LoIs. In particular, a novel adaptive location selection mechanism (ALSM) as well as a unified network model and its learning process is proposed. In the experiments, the proposed model is validated based on a real-world case study of a long-span bridge in California, USA. Through the comparison of different sensor placement schemes. The experimental results show that the proposed model is effective and accurate in reconstructing the structural displacement responses via adaptive sensor selection for bridge SHM.

Comparison of the asymmetric multifractal behavior of green and U.S. bonds against benchmark financial assets

Presently, financial portfolio managers lack a solid basis for building a reliable risk management strategy for green debt instrument investments due to the lack of compelling growth and resilience data. Therefore, this study assesses the role of green bonds in financial markets by assessing and correlating their complex scaling behaviors across multiple periods with those of key benchmark assets (e.g., conventional bonds, high-yield bonds, Euro-Dollar exchange, Dow Jones Industrial Index, Bitcoin, and Gold). Specifically, we explore linear and nonlinear correlation patterns using cross-correlation tests and the dynamic conditional correlation model, focusing on bond interactions under various degrees of freedom. Our analysis reveals that although most assets exhibit nonlinear correlations, Bitcoin uniquely aligns linearly with U.S. bonds under certain conditions. Green bonds, however, display nonlinear correlations with Bitcoin and stand out for their distinct upward financial persistence. We find also that green bonds are primary drivers in the financial domain, highlighted by their pronounced interactions and the consistent cross-correlation with the Euro-Dollar exchange rate. Moreover, green bonds have the lowest multifractality, showing persistent upward trends and antipersistent downward trends, rendering them quite resilient during periods of high volatility. These results imply that green bonds may be advantageous to portfolio risk management strategies, especially during crises when diversification and hedging tactics are needed.

Quantum Contextual Hypergraphs, Operators, Inequalities, and Applications in Higher Dimensions.

Quantum contextuality plays a significant role in supporting quantum computation and quantum information theory. The key tools for this are the Kochen-Specker and non-Kochen-Specker contextual sets. Traditionally, their representation has been predominantly operator-based, mainly focusing on specific constructs in dimensions ranging from three to eight. However, nearly all of these constructs can be represented as low-dimensional hypergraphs. This study demonstrates how to generate contextual hypergraphs in any dimension using various methods, particularly those that do not scale in complexity with increasing dimensions. Furthermore, we introduce innovative examples of hypergraphs extending to dimension 32. Our methodology reveals the intricate structural properties of hypergraphs, enabling precise quantifications of contextuality. Additionally, we investigate several promising applications of hypergraphs in quantum communication and quantum computation, paving the way for future breakthroughs in the field.

STGLR: A Spacecraft Anomaly Detection Method Based on Spatio-Temporal Graph Learning.

Anomalies frequently occur during the operation of spacecraft in orbit, and studying anomaly detection methods is crucial to ensure the normal operation of spacecraft. Due to the complexity of spacecraft structures, telemetry data possess characteristics such as high dimensionality, complexity, and large scale. Existing methods frequently ignore or fail to explicitly extract the correlation between variables, and due to the lack of prior knowledge, it is difficult to obtain the initial relationship of variables. To address these issues, this paper proposes a new method, namely spatio-temporal graph learning reconstruction (STGLR), for spacecraft anomaly detection. STGLR employs a dynamic graph learning module to infer the initial relationships among telemetry variables. It then constructs a spatio-temporal feature extraction module to capture complex spatio-temporal dependencies among variables, leveraging a graph sample and aggregation network to learn embedded features and incorporating an attention mechanism to adaptively select salient features. Finally, a reconstruction module is used to learn the latent representations of features, capturing the normal patterns in telemetry data and achieving anomaly detection. To validate the effectiveness of the proposed method, experiments were conducted on two public spacecraft datasets, and the results demonstrate that the performance of the STGLR method surpasses existing anomaly detection methods, with an average F1 score exceeding 0.97.

Experienced cognitive load in the emergency department. A prospective study.

The emergency department (ED) is a hectic place, where many critically ill patients are treated. For residents working in the ED, this environment may be demanding. The aim of this study was to investigate the [1] cognitive load experienced by residents working in the ED, and [2] differences in cognitive load during the day. In this multicentre, prospective study in three EDs in the Netherlands, the experienced cognitive load was graded by residents on three scales, agreed upon during Delphi meetings: the complexity (low-high: 1-3), mental effort (low-high: 1-9) and comfortability scale (low-high: 0-100%). We applied the scores per decision, 1-hour and 2-hour intervals, patient and shift. We observed 14 residents and analysed 74 1-hour and 45 2-hour intervals, 79 patients, 24 shifts and 592 separate decisions. The experienced cognitive load per decision was low. In contrast, the cognitive load was higher per 2-hour interval (mental effort: median 4.0 (IQR 4.0) and comfortability 80% (IQR 20)) and per shift (mental effort: median 5.5 (IQR 4.0) and comfortability 80% (IQR 20). Complexity was low for all measurements. Mental effort rose from 17h onwards higher values, whereas a decrease in comfortability was seen from 21h onwards. From 17h onwards, residents working in the ED experienced rather high mental effort and reported feeling not optimally comfortable when making decisions. The mental effort was highest between 21-23h. This was found when cognitive load was measured per 2-hour interval and per shift, but not per decision. This study may provide an insights to optimise cognitive load by reorganisation of the ED.

Developing Performance in Sports Teams Through a Collective Homeostasis Model Supports Regulation, Adaptation, and Evolution in Competition

The collective homeostasis model, grounded in ecological dynamics, has recently been proposed to explain the self-regulating individual-environment interactions that are necessary to conceptualizing sports teams as collective homeostatic systems. Based on these insights and given the capacity of humans to construct and adapt their ecological niches, this article explores the creation of enriching sporting contexts for the development of adaptive player-environment interactions in competitive team sports. This model of adaptation emphasizes the importance of creating a sports niche rich in shared affordances to enable team coordination through the development of individual and collective homeostasis in different sub-phases of competitive performance. In a homeostatic sports system, principles of play advocated by coaches allow players to intentionally coordinate and adapt their actions to shape the collective identity of the team. The team’s identity is shaped by the synergetic development of collective attractors (stable system states) emerging at specific scales of complexity. This is facilitated through the manipulation of constraints by the coaching and support staff. These ideas have important implications for implementing team sports training and performance preparation, helping to transform the self-regulating tendencies of players through adapting their behaviors to dynamic performance contexts.

Testing the Universality of Self-organized Criticality in Galactic, Extragalactic, and Black Hole Systems

In this study, we test whether the power law slopes (α F ) for fluxes (F), and (α E ) for energies (E) are universal in their size distributions, N(F)∝F−αF and N(E)∝E−αE , in astrophysical observations of galactic, extragalactic, and black hole systems. This is a test of fundamental importance for self-organized criticality (SOC) systems. The test decides whether (i) power laws are a natural consequence of the scale-freeness and inherent universality of SOC systems, or (ii) if they depend on more complex physical scaling laws. The former criterion allows quantitative predictions of the power-law-like size distributions, while the latter criterion requires individual physical modeling for each SOC variable and data set. Our statistical test, carried out with 61 published data sets, is consistent with the former option, which implies that observed power laws can simply be derived from the scale-freeness and do not require specific physical models to understand their statistical distributions. The observations show a mean and standard deviation of α F = 1.78 ± 0.29 for SOC fluxes and α E = 1.66 ± 0.22 for SOC fluences, and thus are consistent with the prediction of the fractal-diffusive SOC model, with α F = 1.80 and α E = 1.67.

Amalgamating Urban Scaling and Economic Complexity for New Metrics of Urban Fitness

AbstractWe propose a synthesis of the independent scientific frameworks of economic complexity and urban scaling into a consistent mechanism—city complexity—to measure multi-dimensional fitness of cities. Essentially, we use deviations from scaling law for a given set of urban outcomes as the basis to construct a bipartite city-outcome matrix. This matrix forms the input into the economic complexity methodology, which iterates over a pair of coupled non-linear maps, computing fitness of cities and complexity of outcomes. We test our algorithm with data from American cities and find that the emergent city fitness measure is consistent with expected behavior across the set of outcomes studied. We also find temporal evolution of city fitness and outcome complexity to be in agreement with theoretical expectation. Finally, we perturb the metrics to conditions of widespread socioeconomic stress and find that outcomes are indicative of the robustness of the city complexity algorithm. Overall, these findings suggest that the city complexity mechanism proposed here produces systematic, self-consistent metrics and can be applied across any set of diverse outcomes, irrespective of the specifics of national urban contexts.

A Study on the Mechanisms Influencing Youth Intermittent Disengagement Behavior on Short Video PlatformsBased on the Moderating Role of Self-Control Ability

As a product of the technological revolution in information media, short video platforms feature an increasingly complex scale of information, functional settings, and social networks. These complexities pose risks of perceived overload on the user level, leading to negative usage behaviors such as usage fatigue and intermittent disengagement. Based on the Stimulus-Organism-Response (S-O-R) theoretical framework, this study constructs a model of the mechanisms influencing intermittent disengagement behaviors among young users on short video platforms. Innovatively, the study introduces the moderating role of self-control theory. Data were collected through an online questionnaire, yielding 423 valid samples, and analyzed using structural equation modeling, mediation effect modeling, and moderation effect modeling. Results indicate that perceived information overload has a significant positive impact on the occurrence of intermittent disengagement among youth; usage fatigue partially mediates the pathway from perceived information overload to intermittent disengagement and fully mediates the pathway from perceived functional overload to intermittent disengagement; self-control ability positively moderates the relationship between usage fatigue and intermittent disengagement.

AMS-Net: Modeling Adaptive Multi-Granularity Spatio-Temporal Cues for Video Action Recognition.

Effective spatio-temporal modeling as a core of video representation learning is challenged by complex scale variations in spatio-temporal cues in videos, especially different visual tempos of actions and varying spatial sizes of moving objects. Most of the existing works handle complex spatio-temporal scale variations based on input-level or feature-level pyramid mechanisms, which, however, rely on expensive multistream architectures or explore multiscale spatio-temporal features in a fixed manner. To effectively capture complex scale dynamics of spatio-temporal cues in an efficient way, this article proposes a single-stream architecture (SS-Arch.) with single-input namely, adaptive multi-granularity spatio-temporal network (AMS-Net) to model adaptive multi-granularity (Multi-Gran.) Spatio-temporal cues for video action recognition. To this end, our AMS-Net proposes two core components, namely, competitive progressive temporal modeling (CPTM) block and collaborative spatio-temporal pyramid (CSTP) module. They, respectively, capture fine-grained temporal cues and fuse coarse-level spatio-temporal features in an adaptive manner. It admits that AMS-Net can handle subtle variations in visual tempos and fair-sized spatio-temporal dynamics in a unified architecture. Note that our AMS-Net can be flexibly instantiated based on existing deep convolutional neural networks (CNNs) with the proposed CPTM block and CSTP module. The experiments are conducted on eight video benchmarks, and the results show our AMS-Net establishes state-of-the-art (SOTA) performance on fine-grained action recognition (i.e., Diving48 and FineGym), while performing very competitively on widely used Something-Something and Kinetics.

An Improved YOLOv8 Network for Multi-Object Detection with Large-Scale Differences in Remote Sensing Images

Aiming to address the challenges of low object detection precision in remote sensing images due to high background complexity and significant target scale variations, a novel model for large-scale disparate object detection in remote sensing images is proposed based on the modified YOLOv8. The model incorporates a Context Aggregation Module (CAM) with an attention mechanism in the backbone network to exploit contextual information, enabling multi-scale feature fusion for effective small object detection. The Neck network utilizes GSConv modules and employs the Slim-Neck design paradigm to enhance model robustness, making it better suited for detecting objects in high-complexity backgrounds. Furthermore, the model adopts the Wise-IoU as the loss function, incorporating a dynamic nonmonotonic focusing mechanism and a gradient gain allocation strategy to enhance the overall performance of disparate object detection. The experimental results indicate that promising performance improvements in the face of large-scale variations in remote sensing image targets. Specifically, the model achieves mAP values of 71.4% and 90.3% on the DOTA and NWPU VHR-10 datasets, respectively, representing increases of 4.4% and 3.7% compared to the original model. Compared to other typical algorithms, it also has considerable advantages in both comprehensive detection accuracy and detection speed.

The Effect of Y Addition on Oxidation Resistance of Bulk W-Cr Alloys.

The self-passivating tungsten-based alloy W-11.4Cr-0.6Y (in wt.%) is a potential plasma-facing material for the first wall of future fusion reactors, which has been shown to suppress oxidation of tungsten and withstand temperatures of up to 1000 °C. In this study, the effect of Y addition on the microstructure and oxidation behavior of W-11.4Cr alloy at 1000 °C is analyzed by comparing it with W-11.4Cr-0.6Y, both prepared using identical synthesis routes. While the binary W-Cr alloy already exhibits improved oxidation resistance over pure W due to the formation of an outer Cr2WO6 layer, it still shows a tendency for spallation and, hence, is not protective. A continuous passivating chromia layer is only obtained with the addition of Y, and we demonstrate that it results in a 50-fold decrease in the oxide growth rate and eliminates the preferred growth of the oxide at edges seen in the binary alloy. Although a porous, complex oxide scale containing mixed oxide layers and WO3 is formed in both cases, the addition of Y results in lower porosity, which makes the oxide scale more adherent.

FTPComplEx: A flexible time perspective approach to temporal knowledge graph completion

The dynamic nature of interconnected data evolving over time poses significant challenges for graph representation and reasoning, particularly as temporal knowledge graphs scale in size and complexity. Existing models like TPComplEx (Time Perspective Complex Embedding) leverage tensor decomposition techniques to capture temporal dynamics, but their static weighting approach often lacks the flexibility needed to adapt to the nuanced evolution of relationships and entities. This rigidity can lead to missed temporal dependencies and loss of valuable insights, especially in large-scale graphs comprising millions or even billions of factual entries. To overcome these limitations, we propose FTPComplEx (Flexible Time Perspective Complex Embedding), a novel embedding model that introduces adjustable weights to dynamically modulate the influence of temporal information. This flexibility enables FTPComplEx to more accurately capture the intricate interactions between entities, relations, and time, providing a more robust understanding of temporal dynamics within knowledge graphs. Our extensive evaluations on benchmark datasets, including YAGO15k, ICEWS, and GDELT, demonstrate that FTPComplEx achieves state-of-the-art results, outperforming TPComplEx and other existing models. Notably, on the YAGO15k dataset, FTPComplEx achieves a 9.04% improvement in Mean Reciprocal Rank (MRR) and an 11.35% increase in Hits@1, demonstrating its effectiveness in managing complex temporal relationships. Further analysis shows that FTPComplEx maintains strong performance even with lower-rank embeddings, significantly reducing computational costs while maintaining accuracy.

In Situ Probing Electrified Interfacial Molecular Anion Structures at Atomically Flat Gold Electrode

Electrochemical interfaces—buried between electrodes and electrolytes—are notoriously difficult to probe, and enormous efforts, both experimentally and computationally, have been devoted to this endeavour. Although some progress has been made, the microscopic structures of electrochemical interfaces remain elusive and a great challenge in physical sciences.[1] Answering this question is not only of fundamental interest but also of technological importance in a broad range of research areas in science and technology, to name a few, energy storage in supercapacitors, electrocatalysis of relevance to energy and environmental applications, self-assembly of colloidal particles, ion transport across biological membranes, and mineralization processes in earth science. Despite its significance, molecular-level understanding of Electrified Double Layer (EDL) is largely missing, owing to its complexity and small scale making it difficult to probe. Because of the advent of advanced experimental (e.g., synchrotron-based techniques and Raman spectroscopy) and computational methods [e.g., ab intio molecular dynamics (AIMD)], it is not until recently that the microscopic structures of EDL have started to be unveiled. [2,3] In this study, we employ in-situ Shell Isolated Nanoparticle Enhance Raman spectroscopy (SHINERS) to investigate the structures of electric double layers at electrochemical interfaces. Specifically, we focus on understanding the arrangement and surface interaction of anions on electrified gold single-crystal electrode surfaces under varying bias potentials. We discuss how these ions orient themselves and interact with the electrode surface, and how these interactions influence local permittivity and ion-surface attraction, ultimately impacting the structure and behavior of the EDL and electrochemical processes.[1] Le et al., Sci. Adv. 2020; 6[2] Li et al.,Nature Materials, 2020; 18[3] Wang et al., ,Nature, 2021;600

Efficient differential expression analysis of large-scale single cell transcriptomics data using dreamlet.

Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet (DiseaseNeurogenomics.github.io/dreamlet) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.

Doubly heavy tetraquark bound and resonant states

We calculate the energy spectrum of the S-wave doubly heavy tetraquark systems, including the QQ(′)q¯q¯, QQ(′)s¯q¯, and QQ(′)s¯s¯ (Q(′)=b, c and q=u, d) systems within the constituent quark model. We use the complex scaling method to obtain bound states and resonant states simultaneously, and the Gaussian expansion method to solve the complex-scaled four-body Schrödinger equation. With a novel definition of the root-mean-square radii, we are able to distinguish between meson molecules and compact tetraquark states. The compact tetraquarks are further classified into three different types with distinct spatial configurations: compact even tetraquarks, compact diquark-antidiquark tetraquarks, and compact diquark-centered tetraquarks. In the I(JP)=0(1+) QQq¯q¯ system, there exists the D*D molecular bound state with a binding energy of −14 MeV, which is the candidate for Tcc(3875)+. The shallow B¯*B¯ molecular bound state is the bottom analog of Tcc(3875)+. Moreover, we identify two resonant states near the D*D* and B¯*B¯* thresholds. In the JP=1+ bbq¯q¯(I=0) and bbs¯q¯ systems, we obtain deeply bound states with a compact diquark-centered tetraquark configuration and a dominant χ3¯c⊗3c component, along with resonant states with similar configurations as their radial excitations. These states are the QCD analog of the helium atom. We also obtain some other bound states and resonant states with “QCD hydrogen molecule” configurations. Moreover, we investigate the heavy quark mass dependence of the I(JP)=0(1+) QQq¯q¯ bound states. We strongly urge the experimental search for the predicted states. Published by the American Physical Society 2024

Sequence modeling and design from molecular to genome scale with Evo.

The genome is a sequence that encodes the DNA, RNA, and proteins that orchestrate an organism's function. We present Evo, a long-context genomic foundation model with a frontier architecture trained on millions of prokaryotic and phage genomes, and report scaling laws on DNA to complement observations in language and vision. Evo generalizes across DNA, RNA, and proteins, enabling zero-shot function prediction competitive with domain-specific language models and the generation of functional CRISPR-Cas and transposon systems, representing the first examples of protein-RNA and protein-DNA codesign with a language model. Evo also learns how small mutations affect whole-organism fitness and generates megabase-scale sequences with plausible genomic architecture. These prediction and generation capabilities span molecular to genomic scales of complexity, advancing our understanding and control of biology.

Abstract 4113951: Calcification Alters Luminal Surface Complexity in Cadaveric Left Anterior Descending Arteries

Background: Understanding the intricacies of luminal surface topographic changes in pathologic coronary arteries may yield insight not only on the efficacy of existing clinical interventions (e.g. impact of surface changes on stent anchoring), but also on the subsequent hemodynamic changes and pathophysiology of thrombus formation. In this study, we aim to detect and quantify luminal surface alterations induced by the presence of calcification in cadaveric left anterior descending arteries (LADs) using a novel approach–surface metrology. Methods: LADs (n=10) were harvested from cadaveric hearts using a systematic dissection approach, scanned using the Bruker Skyscan 1173 microCT scanner, and then underwent threshold-based image segmentation on Dragonfly by Object Research Systems to quantify the total calcium volume present throughout the vessel. Each specimen was then splayed open and up to 15 scans were performed at 20X magnification using the Sensofar S Neox optical profiler. Each scan subsequently underwent surface metrologic scale-sensitive fractal analyses in SensoMap 10. Results: Extent of calcification was standardized and expressed using the following ratio: total calcium volume by total vessel length (TC/VL). We considered a TC/VL ratio of 0-0.25 as low calcification and 0.26-0.5 as high calcification. Interestingly, our findings demonstrated statistically significant increases in smooth-rough crossover (SRC) and scale of maximum complexity (Smfc) with positive correlation, but reductions in fractal complexity (Lsfc) and fractal dimension (Dls) with negative correlation, in highly calcified compared to lowly calcified LADs. Conclusions: Our data suggests that the presence of calcium in the walls of LADs may translate to increased roughness in luminal surfaces generally, but smoothness over calcified plaques, locally. Our proposed mechanism is increased endothelial tension directly over areas of calcification, thus reducing surface complexity. Future studies will involve modeling blood flow using smooth particle hemodynamics (SPH) based on complete luminal and surface topographical geometries to evaluate the impact of these surface changes on hemodynamics.