Multiscale Analysis Research Articles

Multi-scale analysis of the Monoceros OB 1 star-forming region. III. Tracing the dense gas in the massive clump G202.3+2.5

Young massive clumps are relatively rare objects and are typically found at large distances. The G202.02+2.85 (hereafter, G202) massive clump was identified in the Monoceros OB 1 molecular complex at a distance of about 700 pc. It was found to be undergoing active star formation and located at the junction point between two colliding filaments. We aim to further clarify the evolutionary stage of the clump and the nature of the collision and of six dense cores in the area; specifically, we investigate whether the clump is collapsing as a whole and/or whether it shows signs of shocks. To this end, we examined the dense gas properties, notably through NH_3 and mathrm N_ H^ and their deuterated counterparts. We examined the evolutionary stages of the cores through deuterium fractionation values. We performed a mapping of the clump and deeper pointed molecular line observations towards the dense cores with the IRAM 30-m and Effelsberg 100-m telescopes in the 3-mm and centimetre ranges, respectively. The clump internal dynamics was examined using tracers of various gas densities (CO isotopologues, CS, ammonia, and diazenylium), along with a classical infall diagnosis with HCO^+ and diazenylium. Furthermore, SiO and methanol were used to characterise the shock properties. The evolutionary stages of the dense cores were evaluated from the deuterium fractionation of ammonia and diazenylium. The clump seen in dust continuum emission was detected in all dense-gas molecular tracers, including deuterated ammonia and diazenylium, contrasting with the distributions in shock tracers SiO and CH_3OH. These latter include both features compatible with protostellar outflows and a more diffuse emission in the clump, all with SiO line width corresponding to relatively low velocity shocks (lesssim 10 ). This could arise from multiple, blended outflows or be a signature of the filament collision. All the dense cores, except for the source 1446, were found to be in early evolutionary stages, the most massive one, the source 1450, being at most a Class 0 object. This is consistent with the idea that they originate in the same clump-compression event. They all present virial parameters indicating gravitational instability, while source 1450 and its surroundings show blue-shift asymmetry in HCO^+ compatible with gravitational infall, suggesting that this star formation activity came out of the collision. We find that, in contrast to NH_3 deuterium fractionation, the N_2H^+ deuterium fractionation values are likely to be correlated with the source evolutionary stage. Our results provide additional evidence that the star-forming cores in the G202 clump originate in the clump compression due to filament collision or convergence. Based on its physical parameters, we find that the source 1454 in the northern clump of G202 may represent the physical state of the region before the collision of the two filaments that make up the junction region. Determining the origin of the collision will require the examination of the large-scale motion of the gas.

Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion

Low-spatial-resolution measurements from contact sensors and excessive measurement noise have impeded the implementation of vibration-based damage detection. To tackle these challenges, we propose a novel vision-based damage detection method combining multi-scale signal analysis theory and data fusion algorithm. For high-spatial-resolution vibration measurements, phase-based optical flow estimation algorithm is adopted to deploy virtual sensors on the structure, yielding reliable mode shapes. We then introduce the concept of entropy into damage detection. A novel damage index, defined in Gaussian multi-scale space and named multi-scale local information entropy (MS-LIE), is proposed. The MS-LIE integrates the multi-scale analysis component and the entropy analysis component, addressing both the issue of detection sensitivity and noise immunity, thereby showcasing enhanced performance. Moreover, a data fusion technique for multi-scale damage information is developed to further mitigate the noise-induced uncertainty and pinpoint damage locations. A series of numerical and experimental scenarios are designed to validate the method, and the results indicate that the proposed method accurately detects single and multiple damages in noisy environments, obviating the need for baseline data as a reference.

GoChem: A Composable Library for Multi-Scale Computational Chemistry Data Analysis.

Data analysis is a major task for Computational Chemists. The diversity of modeling tools currently available in Computational Chemistry requires the development of flexible analysis tools that can adapt to different systems and output formats. As a contribution to this need, we report the implementation of goChem, a versatile open-source library for multiscale analysis of computational chemistry data. The library, written in and for the Go programming language, allows for easy integration of different levels of theory, in an easy-to-use API, allowing the development of both one-use and complex analysis programs in Go. We describe the library and detail some selected applications that illustrate the capabilities and potential of this tool. The library is available at http://gochem.org.

Issues in the Expansion and Contraction of Operators

This paper explores fundamental issues in the correct contraction and expansion of operators, with a primary focus on the concept of symmetry within operator theory. Special attention is given to how symmetry influences the behavior of operators, particularly regarding their approximation and convergence properties. In the domains of quantum mechanics and condensed matter physics, such operators are essential for modeling phenomena like superconductivity, excitons, and surface states. The symmetric properties of operators have a profound impact on the physical interpretations and predictions these models generate. A rigorous analysis is provided regarding the existence of correct contractions and expansions for a specific class of nonlinear operators, demonstrating how symmetry affects the structural integrity of operators under natural conditions. The study presents a comprehensive description of the set of all correct contractions, expansions, and regular expansions, with an application to a third-order nonlinear differential expression. Additionally, a condition for the unique solvability of a Bitsadze–Samarskii-type problem is derived, showcasing how symmetry plays a crucial role in guiding the solution of complex physical models. Furthermore, the paper emphasizes the importance of preserving symmetry in the construction of operators, ensuring the consistency and accuracy of mathematical models. This has significant implications for both theoretical research and practical applications in various fields, including nuclear physics and quantum theory.

Multiscale Sensitivity Analysis of Landscape Fragmentation in Plantation Forests on the Loess Plateau

ABSTRACTEffectively quantifying plantation forest fragmentation is crucial for vegetation restoration and management on the Loess Plateau. Forest area density (FAD) effectively measures fragmentation, with multiscale sensitivity analysis essential for determining the appropriate window scale threshold. This study uses plantation forest data from different geomorphological types (hilly gully and plateau gully region) of the Loess Plateau. Various window sizes are applied to evaluate the FAD threshold curve and its derivative for determining a stable fragmentation threshold, while a structural equation model is employed to analyze the drivers of forest fragmentation. The results are as follows: (1) FAD variability decreases with increasing window size, leveling off after reaching the threshold. (2) Thresholds vary by forest type and county. (3) Two principal components explain 81.27% of the window stabilization threshold variation, with strong correlations between similar fragmentation types. (4) Fragmentation slowed between 2000 and 2022, with hilly gully region more fragmented than plateau gully region. (5) Stand structure mitigates fragmentation, with topographic and climate change influences showing heterogeneity. “Core” maintain stability, while “Islet” drive fragmentation. This study emphasizes the importance of scale‐specific analysis and adaptive management strategies in mitigating forest fragmentation. This study supports optimizing ecological restoration and targeted conservation strategies, promoting sustainable forest management on the Loess Plateau.

Multiscale analysis of the mechanical properties and interface damage mechanisms of carbon fiber reinforced polypropylene composites

AbstractIn this article, the mechanical properties of carbon fiber reinforced polypropylene (CF/PP) composites at different carbon fiber (CF) mass fractions were systematically investigated. A comprehensive examination is provided through the integration of experimental testing, numerical simulation, and theoretical analysis, revealing the enhancement effects of CF on the polypropylene (PP) matrix across multiple scales. Experimental results demonstrated that the incorporation of CF significantly impedes crack propagation and enhances the fracture resistance of the composites. Specifically, at a CF mass fraction of 15%, the elastic modulus of the composites is enhanced by 21.7% compared with pure PP, while the tensile strength increases by 40.9%–43.2%. The addition of maleic anhydride grafted polypropylene (MAH‐g‐PP) as a compatibilizer significantly improves the interfacial compatibility between CF/PP. Furthermore, finite element simulations revealed the damage evolution process at the CF/PP interface during tensile loading. Based on the Mori‐Tanaka model, the elastic modulus was predicted, aligning well with experimental and simulated results. This research offers a scientific basis for the design and application of CF/PP composites and contributes to the advancement of high‐performance composites.Highlights CF/PP composites show enhancements in mechanical properties with CF addition MAH improves interfacial adhesion between CF and PP, boosting tensile strength Multiscale experimental and FEA studies reveal CF/PP interface damage mechanisms Mori‐Tanaka model accurately predicts the elastic modulus of CF/PP composites

Multi-Scale Analysis of Urban Greenspace Exposure and Equality: Insights from a Population-Enhanced Vegetation Index (EVI)-Weighted Model in the West Side Straits Urban Agglomeration

Multi-Scale Analysis of Urban Greenspace Exposure and Equality: Insights from a Population-Enhanced Vegetation Index (EVI)-Weighted Model in the West Side Straits Urban Agglomeration

Persistent Topological Laplacians—A Survey

Persistent topological Laplacians constitute a new class of tools in topological data analysis (TDA). They are motivated by the necessity to address challenges encountered in persistent homology when handling complex data. These Laplacians combine multiscale analysis with topological techniques to characterize the topological and geometrical features of functions and data. Their kernels fully retrieve the topological invariants of corresponding persistent homology, while their non-harmonic spectra provide supplementary information. Persistent topological Laplacians have demonstrated superior performance over persistent homology in the analysis of large-scale protein engineering datasets. In this survey, we offer a pedagogical review of persistent topological Laplacians formulated in various mathematical settings, including simplicial complexes, path complexes, flag complexes, digraphs, hypergraphs, hyperdigraphs, cellular sheaves, and N-chain complexes.

Multiscale statistical progressive failure analysis of unbalanced woven composites

Multiscale statistical progressive failure analysis of unbalanced woven composites

Multi-scale Analysis Reveals Hippocampal Subfield Vulnerabilities to Chronic Cortisol Overexposure: Evidence from Cushing's Disease.

Chronic cortisol overexposure plays a significant role in the development of neuropathological changes associated with neuropsychiatric and neurodegenerative disorders. The hippocampus, the primary target of cortisol, may exhibit characteristic regional responses due to its internal heterogeneity. This study explores structural and functional alterations of hippocampal subfields in Cushing's disease (CD), an endogenous model of chronic cortisol overexposure. Utilizing structural and resting-state functional magnetic resonance imaging data from 169 participants (86 CD patients and 83 healthy controls) recruited from a single center, we investigated specific structural changes in hippocampal subfields and explored the functional connectivity alterations driven by these structural abnormalities. We also analyzed potential associative mechanisms between these changes and biological attributes, neuropsychiatric representations, cognitive function, and gene expression profiles. Compared to healthy controls, CD patients exhibited significant bilateral volume reductions in multiple hippocampal subfields. Notably, volumetric decreases in the left hippocampal body and tail subfields were significantly correlated with cortisol levels, Montreal Cognitive Assessment scores, and quality of life measures. Disrupted connectivity between the structurally abnormal hippocampal subfields and ventromedial prefrontal cortex may impair reward-based decision making and emotional regulation, with this dysconnectivity linked to structural changes in right hippocampal subfields. Additionally, another region exhibiting dysconnectivity was located in the left pallidum and putamen. Gene expression patterns associated with synaptic components may underlie these macrostructural alterations. Our findings elucidate the subfield-specific effects of chronic cortisol overexposure on the hippocampus, enhancing understanding of shared neuropathological traits linked to cortisol dysregulation in neuropsychiatric and neurodegenerative disorders.

Multi-scale analysis and hierarchical optimization design of a 2D twill woven composite front firewall for electric vehicles

Multi-scale analysis and hierarchical optimization design of a 2D twill woven composite front firewall for electric vehicles

Multi-scale finite element analysis of the strengthening and damage behavior of carbides with different characteristics in nickel-based superalloys

This study proposes a novel multi-scale numerical approach to explore the mechanical behavior and damage evolution in nickel (Ni)-based superalloys containing various carbide particles. At the nanoscale, the elastic properties of two distinct carbides are determined using first-principles calculations. At the microscale, finite element simulations (FEM) in ABAQUS are used to analyze the stress–strain relationship and local stress distribution within a three-dimensional representative volume element, as well as damage and fracture behavior. The model integrates the elastic–plastic response of the Ni matrix, the elastic-brittle fracture of micro-scale carbides, and the interface behavior between carbide and matrix. FEM findings are consistent with tensile test data, indicating that skeletal carbide promotes plasticity while blocky carbide elevates strength. The interface between blocky carbide and matrix is susceptible to cracking. When the carbide is oriented at 45° to the load direction, offering a balance of strength and plasticity. Stress concentration is reduced when carbides are uniformly distributed and present in high-volume fractions. The numerical method is well-suited for a thorough analysis of the comprehensive behavior of reinforced phase/metal-matrix composites.

Heat-related mortality in Mexico: A multi-scale spatial analysis of extreme heat effects and municipality-level vulnerability

Heat-related mortality in Mexico: A multi-scale spatial analysis of extreme heat effects and municipality-level vulnerability

Composite layer evaluation based on multi-scale finite element analysis

Composite layer evaluation based on multi-scale finite element analysis

A multi-scale finite element analysis method for dynamic simulation of the wire rope

A multi-scale finite element analysis method for dynamic simulation of the wire rope

A multiscale analysis of interfacial adhesion properties in asphalt mixtures incorporating recycled concrete aggregates

A multiscale analysis of interfacial adhesion properties in asphalt mixtures incorporating recycled concrete aggregates

Multi-scale analysis of the progressive failure behaviour of 2.5D woven SiC/SiC composites under compression

Multi-scale analysis of the progressive failure behaviour of 2.5D woven SiC/SiC composites under compression

Multi-scale analyses for adhesion improvement of cement emulsified asphalt mastic to aggregates by calcium carbide slag

Multi-scale analyses for adhesion improvement of cement emulsified asphalt mastic to aggregates by calcium carbide slag

Analyzing Drivers of Tropical Moist Forest Dynamics in the Kahuzi-Biega National Park Landscape, Eastern Democratic Republic of Congo from 1990 to 2022

The protected areas (PA) of the Democratic Republic of the Congo serve as vital carbon reservoirs and are crucial for biodiversity conservation and climate regulation. Despite their significance, these areas face escalating rates of deforestation and degradation, often poorly understood at the local level. This study focuses on the dynamics of tropical moist forest (TMF) and the relative importance of the driving factors in the landscape of Kahuzi-Biega National Park (KBNP), one of the country’s prominent PAs. Analyzing annual TMF dynamics from 1990 to 2022 using data classified by Vancutsem and his collaborators in 2021 from Landsat imagery alongside spatial datasets of deforestation and degradation drivers, we employed a comprehensive analytical approach. This included meshing, multi-scale analysis, principal component analysis, zoning, multiple linear regression, and relative importance analysis through bootstrapping. The findings indicate that the grid size considered does not significantly influence TMF dynamics in the KBNP landscape (p-value = 0.67, α = 0.05). The edge and outer zones experienced substantial dynamics, with approximately 30% forest cover loss in both areas, contrasting with the relatively stable TMF cover (~100%) in the inner zone. Fire emerged as the most influential driver, explaining TMF dynamics with a relative importance of approximately 55%, 30%, and 23% in the inner, edge, and outer zones, respectively. This study underscores KBNP’s efficacy in curbing TMF loss but highlights the need for enhanced protection around its periphery. Management efforts should prioritize sustainable land use practices, livelihood improvement, and the establishment of an officially recognized buffer zone.

Quantitative electroencephalography predicts postoperative delirium in adult cardiac surgical patients from a prospective observational study

The diagnostic and prognostic value of quantitative electroencephalogram (qEEG) in the the onset of postoperative delirium (POD) remains an area of inquiry. We aim to determine whether qEEG could assist in the diagnosis of early POD in cardiac surgery patients. We prospectively studied a cohort of cardiac surgery patients undergoing qEEG for evaluation of altered mental status. Delirium was assessed with the Confusion Assessment Method for the intensive care unit (CAM-ICU). The qEEG were interpreted by clinician, and reports were reviewed to identify features such as amplitude-integrated EEG (aEEG), relative band energy in ɑ/β/θ/δ frequencies, α variability and spectral entropy. The raw EEG was also preprocessed offline for nonlinear analysis including Multi-scale Entropy analysis (MSE) and Detrended Fluctuation Analysis (DFA). Linear regression was performed to quantify associations among EEG findings, delirium, and clinical outcomes. Receiver operating characteristic (ROC) analysis was used to assess the accuracy of the qEEG as POD prediction index. Meanwhile, a comprehensive comparison of dynamic complexity across time scales and DFA exponent α was conducted between the non-delirium and delirium groups. Among those recruited initially (n = 64), 60 patients were evaluated and 29 patients (48.3%) met delirium criteria. When comparing delirious and non-delirious participants, significant differences were found in terms of age (p = 0.03), APACHE II scores (p = 0.004), lactate (p = 0.03), and hospital days (p = 0.048). Multivariate regression analysis revealed that the first quartile (Q1) and fourth quartile (Q4) of peak or valley value of F3-P3/F4-P4 derivation (for example, Q1 of peak value for F3-P3 derivation: OR 12.4, 95% CI 1.72–89.76, p = 0.012) showed a higher association with the incidence of POD. ROC analysis demonstrated qEEG could predict POD with high sensitivity and specificity, yielding an overall good accuracy. For instance, the peak value of F3-P3 derivation (the area under the curve of 0.81), as a predictor of POD showed a sensitivity of 90% and specificity pf 72% (p < 0.001). Furthermore, the MSE curves indicated that the non-delirium group exhibited higher complexity values at fine scales, while the delirium group had significantly higher complexity at coarse scales. The DFA comparison results revealed that long-term fractal exponent alpha2 values were higher in delirium patients than in non-delirium patients, with significant differences observed at the F4-P4 electrodes (p = 0.04). The qEEG can reliably predict delirium after heart cardiac surgery. It is helpful for clinicians to early diagnose and manage these patients.Trial registration: Clinical Trials.gov Identifier, NCT03351985. Registered 1 December 2017.