Intrinsic Modes Research Articles

Tornado Occurrence in the United States as Modulated by Multidecadal Oceanic Oscillations Using Empirical Model Decomposition

Studies have analyzed U.S. tornado variability and correlated F1–F5 tornado occurrence with various natural climate oscillations and anthropogenic factors. Using a relatively new empirical mode decommission (EMD) method that extracts time-frequency modes adaptively without priori assumptions like traditional time-series analysis methods, this study decomposes U.S. tornado variability during 1954–2022 into intrinsic modes on specific temporal scales. Correlating the intrinsic mode functions (IMFs) of EMD with climate indices found that 1. the U.S. overall tornado count is negatively (positively) correlated with the Atlantic Multidecadal Oscillation (AMO) index (the Southern Oscillation Index (SOI)); 2. the negative (positive) correlation tends to be more prevalent in the western (eastern) U.S.; 3. the increase in weak (F1–F2) and decrease in strong (F3–F5) tornadoes after around 2000, when both the AMO and the Pacific Decadal Oscillation (PDO) shifted phases, are likely related to their secular trends and low-frequency IMFs; and 4. the emerging Dixie Tornado Alley coincides with an amplifying intrinsic mode of the SOI that correlates positively with the eastern U.S. and Dixie Alley tornadoes. The long-term persistence of these climate indices can offer potential guidance for future planning for tornado hazards.

Dynamic properties of the magnetic skyrmion driven by electromagnetic waves with spin angular momentum and orbital angular momentum

Abstract We theoretically studied the dynamic properties of the skyrmion driven by electromagnetic (EM) waves with spin angular momentum (SAM) and orbital angular momentum (OAM) using micromagnetic simulations. First, the guiding centers of the skyrmion driven by EM waves with SAM, i.e., left-handed and right-handed circularly polarized EM waves, present circular trajectories, while present elliptical trajectories under linear EM waves driving due to the superposition of oppositely polarized wave components. Second, the trajectories of the skyrmion driven by EM waves with OAM demonstrate similar behavior to that driven by linearly polarized EM waves. Because the wave vector intensity varies with the phase for both linearly polarized EM waves and EM waves with OAM, the angular momentum is transferred to the skyrmion non-uniformly, while the angular momentum is transferred to the skyrmion uniformly for left-handed and right-handed circularly polarized EM driving. Third, the dynamic properties of the skyrmion driven by EM waves with both SAM and OAM are investigated. It is found that the dynamic trajectories exhibit more complex behavior due to the contributions or competition of SAM and OAM. We investigate the characteristics of intrinsic gyration modes and frequency-dependent trajectories. Our research may provide insight into the dynamic properties of skyrmion manipulated by EM waves with SAM or OAM and provide a method for controlling skyrmion in spintronic devices.

Machine Learning Prediction of Fuel Cell Remaining Life Enhanced by Variational Mode Decomposition and Improved Whale Optimization Algorithm

In predicting the remaining lifespan of Proton Exchange Membrane Fuel Cells (PEMFC), it is crucial to accurately capture the multi-scale variations in cell performance. This study employs Variational Mode Decomposition (VMD) to decompose performance data into intrinsic modes, elucidating critical multi-scale dynamics vital for understanding the complex degradation processes in fuel cells. In addition to VMD, this research utilizes an Improved Whale Optimization Algorithm (IWOA) to optimize a Back Propagation (BP) Neural Network. The IWOA focuses on precise adjustments of weights and biases, enabling the BP network to effectively interpret complex nonlinear relationships within the dataset. This optimization enhances the predictive model’s reliability and stability. Extensive experimental evaluations demonstrate that the integration of VMD, and the learning capabilities of the IWOA-optimized BP network significantly improves the model’s accuracy and stability across multiple predictions, thereby increasing the reliability of lifespan predictions for PEMFCs. This methodology offers a robust framework for extending the operational life and efficiency of fuel cells.

Computational biomechanics for a standing human body: Modal analysis and simulation.

We develop computational mechanical modeling and methods for the analysis and simulation of the motions of a human body. This type of work is crucial in many aspects of human life, ranging from comfort in riding, the motion of aged persons, sports performance and injuries, and many ergonomic issues. A prevailing approach for human motion studies is through lumped parameter models containing discrete masses for the parts of the human body with empirically determined spring, mass, damping coefficients. Such models have been effective to some extent; however, a much more faithful modeling method is to model the human body as it is, namely, as a continuum. We present this approach, and for comparison, we choose two digital CAD models of mannequins for a standing human body, one from the versatile software package LS-DYNA and another from open resources with some of our own adaptations. Our basic view in this paper is to regard human motion as a perturbation and vibration from an equilibrium position which is upright standing. A linear elastodynamic model is chosen for modal analysis, but a full nonlinear viscoelastoplastic extension is possible for full-body simulation. The motion and vibration of these two mannequin models is analyzed by modal analysis, where the normal vibration modes are determined. LS-DYNA is used as the supercomputing and simulation platform. Four sets of low-frequency modes are tabulated, discussed, visualized, and compared. Higher frequency modes are also selectively displayed. We have found that these modes of motion and vibration form intrinsic basic modes of biomechanical motion of the human body. This view is supported by our finding of the upright walking motion as a low-frequency mode in modal analysis. Such a "walking mode" shows the in-phase and out-of-phase movements between the legs and arms on the left and right sides of a human body, implying that this walking motion is spontaneous, likely not requiring any directives from the brain. Dynamic motions of CAD mannequins are also simulated by drop tests for comparisons and the validity of the models is discussed through Fourier frequency analysis. All computed modes of motion are collected in several sets of video animations for ease of visualization. Samples of LS-DYNA computer codes are also included for possible use by other researchers.

Bright and Dark ILSM in a bilinear and biquadratic antiferromagnetic spin lattice

In the semiclassical limit, we investigate the intrinsic localized spin-wave modes (ILSM) in an antiferromagnetic spin system with bilinear, biquadratic and D-M interactions. Derivation of the nonlinear Schrödinger equation for the coherent-state amplitude involves the introduction of the multiple scale approximation paired with a quasi discreteness approximation. Consequently, we discover that the oscillatory frequency of the Bright–Dark stationary type ILSM’s can appear below the linear spin wave spectrum and the Bright–Dark type kink lies below the upper of the linear spin-wave spectrum. The effect of the interaction parameters in the system is also examined.

Cross-Scale Phase Relationship of the Ca II K Index with Solar Wind Parameters: A Space Climate Focus

The solar wind, representing one of the most impacting phenomena in the circum-terrestrial space, constitutes one of the several manifestations of the magnetic activity of the Sun. With the aim of shedding light on the scales beyond the rotational period of the Sun (i.e., Space Climate scales), this study investigates the phase relationship of a solar activity physical proxy, the Ca II K index, with solar wind properties measured near the Earth, over the whole space era (last five solar cycles). Using a powerful tool such as the Hilbert–Huang transform, we investigate the dependence of their phase coherence on the obtained time scale components. Phase coherence at the same time scales is found between all the components and is also preserved between adjacent components with time scales ≳ 2 yrs. Finally, given the availability of the intrinsic modes of oscillation, we explore how the relationship of Ca II K index with solar wind parameters depends on the time scale considered. According to our results, we hypothesize the presence of a bifurcation in the phase-space Ca II K index vs. solar wind speed (dynamic pressure), where the time scale seems to act as a bifurcation parameter. This concept may be pivotal for unraveling the complex interplay between solar activity and solar wind, bearing implications from the prediction and the interpretation point of view in Space Climate studies.

Modal analysis of taipei 101 building

In this paper, a multi-stage modeling and simulation approach incorporating different deformation conditions is used to analyze the structure of the Taipei 101 building. The modeling process mainly involves the formation of an initial simplified model based on the basic geometry, the determination of an accurate model based on the actual dimensions, and finally the introduction of a hollow structure design to mimic the density and mass distribution of the real building. After the model is established, mesh creation and mesh independence study are carried out. The simulation is carried out using Ansys software, which mainly performs the process of selecting boundary conditions, determining the equivalent density, dividing the mesh for modal analysis, and obtaining the modal vibration pattern as well as the corresponding intrinsic frequency. By analyzing the intrinsic frequencies and vibration modes, the structure of Taipei 101 Building is prone to resonance under certain circumstances, and this study provides some significance to the geology of local seismic activities.

Lebanese cannabis oil as a potential treatment for acute myeloid leukemia: In vitro and in vivo evaluations

The Cannabis sativa L. ssp. indica (Lam.) plant has been historically utilized as a natural herbal remedy for the treatment of several ailments. In Lebanon, cannabis extracts have long been traditionally used to treat arthritis, diabetes, and cancer. The current study aims to investigate the anti-cancer properties of Lebanese cannabis oil extract (COE) on acute myeloid leukemia using WEHI-3cells, and a WEHI-3-induced leukemia mouse model. WEHI-3cells were treated with increasing concentrations of COE to determine the IC50 after 24, 48 and 72-h post treatment. Flow cytometry was utilized to identify the mode of cell death. Western blot assay was performed to assess apoptotic marker proteins. In vivo model was established by inoculating WEHI-3cells in BALB/c mice, and treatment commencing 10 days post-inoculation and continued for a duration of 3 weeks. COE exhibited significant cytotoxicity with IC50 of 7.76, 3.82, and 3.34μg/mL at 24, 48, and 72h respectively post-treatment. COE treatment caused an induction of apoptosis through an inhibition of the MAPK/ERK pathway and triggering a caspase-dependent apoptosis via the extrinsic and intrinsic modes independent of ROS production. Animals treated with COE exhibited a significantly higher survival rate, reduction in spleen weight as well as white blood cells count. COE exhibited a potent anti-cancer activity against AML cells, both in vitro and in vivo. These findings emphasize the potential application of COE as a chemotherapeutic adjuvant in treatment of acute myeloid leukemia.

Pan-Cancer, Genome-Scale Metabolic Network Analysis of over 10,000 Patients Elucidates Relationship between Metabolism and Survival.

Despite the high variability in cancer biology, cancers nevertheless exhibit cohesive hallmarks across multiple cancer types, notably dysregulated metabolism. Metabolism plays a central role in cancer biology, and shifts in metabolic pathways have been linked to tumor aggressiveness and likelihood of response to therapy. We therefore sought to interrogate metabolism across cancer types and understand how intrinsic modes of metabolism vary within and across indications and how they relate to patient prognosis. We used context specific genome-scale metabolic modeling to simulate metabolism across 10,915 patients from 34 cancer types from The Cancer Genome Atlas and the MMRF-COMMPASS study. We found that cancer metabolism clustered into modes characterized by differential glycolysis, oxidative phosphorylation, and growth rate. We also found that the simulated activities of metabolic pathways are intrinsically prognostic across cancer types, especially tumor growth rate, fatty acid biosynthesis, folate metabolism, oxidative phosphorylation, steroid metabolism, and glutathione metabolism. This work shows the prognostic power of individual patient metabolic modeling across multiple cancer types. Additionally, it shows that analyzing large-scale models of cancer metabolism with survival information provides unique insights into underlying relationships across cancer types and suggests how therapies designed for one cancer type may be repurposed for use in others.

Stringlet excitation model of the boson peak.

The boson peak (BP), a low-energy excess in the vibrational density of states over the Debye contribution, is often identified as a characteristic of amorphous solid materials. Despite decades of efforts, its microscopic origin still remains a mystery. Recently, it has been proposed, and corroborated with simulations, that the BP might stem from intrinsic localized modes involving one-dimensional (1D) string-like excitations ("stringlets"). We build on a theory originally proposed by Lund that describes the localized modes as 1D vibrating strings, but we specify the stringlet size distribution to be exponential, as observed in simulations. We provide an analytical prediction for the BP frequency ωBP in the temperature regime well below the observed glass transition temperature Tg. The prediction involves no free parameters and accords quantitatively with prior simulation observations in 2D and 3D model glasses based on inverse power law potentials. The comparison of the string model to observations is more uncertain when compared to simulations of an Al-Sm metallic glass material at temperatures well above Tg. Nonetheless, our stringlet model of the BP naturally reproduces the softening of the BP frequency upon heating and offers an analytical explanation for the experimentally observed scaling with the shear modulus in the glass state and changes in this scaling in simulations of glass-forming liquids. Finally, the theoretical analysis highlights the existence of a strong damping for the stringlet modes above Tg, which leads to a large low-frequency contribution to the 3D vibrational density of states, observed in both experiments and simulations.

Neuronal Coupling Modes Show Differential Development in the Early Cortical Activity Networks of Human Newborns.

The third trimester is a critical period for the development of functional networks that support the lifelong neurocognitive performance, yet the emergence of neuronal coupling in these networks is poorly understood. Here, we used longitudinal high-density electroencephalographic recordings from preterm infants during the period from 33 to 45 weeks of conceptional age (CA) to characterize early spatiotemporal patterns in the development of local cortical function and the intrinsic coupling modes [ICMs; phase-phase (PPCs), amplitude-amplitude (AACs), and phase-amplitude correlations (PACs)]. Absolute local power showed a robust increase with CA across the full frequency spectrum, while local PACs showed sleep state-specific, biphasic development that peaked a few weeks before normal birth. AACs and distant PACs decreased globally at nearly all frequencies. In contrast, the PPCs showed frequency- and region-selective development, with an increase of coupling strength with CA between frontal, central, and occipital regions at low-delta and alpha frequencies together with a wider-spread decrease at other frequencies. Our findings together present the spectrally and spatially differential development of the distinct ICMs during the neonatal period and provide their developmental templates for future basic and clinical research.

A deep learning based communication traffic prediction approach for smart monitoring of distributed energy resources in virtual power plants

AbstractVirtual power plants (VPPs) have been widely recognized as a key enabler for energy system neutrality. The communication traffic of a VPP fundamentally indicates its activeness in interacting with the power system, thus providing a new dimension in depicting the behaviour characteristics of distributed energy resources in VPPs. Therefore, the prediction of communication traffic is significant in improving the control efficiency of VPPs. However, due to the involvement of numerous interactive agents characterised by both individual randomness and coordinative characteristics, traditional prediction models are no longer capable of fitting VPP communication traffic effectively. Therefore, a novel prediction model is introduced that enhances the prediction accuracy by integrating long short‐term memory (LSTM) and variational mode decomposition (VMD). This model employs VMD as the initial step for extracting the intrinsic modes from the traffic sequence, thereby mitigating the impact of incidental noise. Then, LSTM is applied to fit each intrinsic mode individually. Additionally, considering the outer influencing factors, the attention mechanism is incorporated. Finally, all sub‐prediction algorithms are neatly integrated as a whole prediction model. The proposed model is evaluated through simulation prediction using realistic VPP communication traffic data, and the results demonstrate its effectiveness.

Intrinsically localized modes of bilinear FPU chains: Analytical study

Present study concerns the analysis of stationary intrinsically localized modes emerging in bilinear and symmetric Fermi–Pasta–Ulam chains. Each element of the chain is coupled to its nearest neighbors through identical and symmetric (tension/compression) bilinear springs. The intrinsically localized modes are the time-periodic, localized vibration states which are manifested by the extreme energy localization on one and two bonds of the chain for bond - centered and site - centered symmetries, respectively. Approximate and exact analytical solutions of intrinsic localized modes are derived for the infinite as well as the finite chains, correspondingly. The derived analytical approximations allow to describe explicitly the amplitude wave profiles of these special localized states as well as to establish their zones of existence in the space of system parameters. In the second part of the study, we derive the exact analytical solutions for the same type of site- and bond- centered intrinsically localized modes supported by the finite bilinear Fermi–Pasta–Ulam chains and analyze their stability properties using Fillipov method.

Multiple enhanced synchrosqueezing in the time–frequency–chirprate space

It is challenging to analyze multi-component signals with crossover instantaneous frequencies (IFs), both in producing high-resolution time–frequency representations and disentangling intrinsic modes. One of the most promising attempts is lifting the two-dimensional TFRs to three-dimensional time–frequency–chirprate (TFC) representations. However, the strong “blurring” effect in the chirp rate (CR) axis makes it rather challenging to estimate accurate IF and CR trajectories. Driven by this need, a method to provide highly energy-concentrated TFC representation, called multiple enhanced synchrosqueezing chirplet transform (MESCT), is proposed in this paper. MESCT is an enhanced iterative framework that combines multi-synchrosqueezing and multi-taper techniques. MESCT estimates IF and CR using phase information from different chirplet transform results with different windows, which provides more accurate estimates for reassignment. Then, three-dimensional multi-synchrosqueezing operations subsequently concentrate the ambiguous energy. As a result, MESCT accurately sharpens the TFC representations, even around the crossing points. Thus, accurate IF and CR trajectories can be jointly estimated by the three-dimensional ridge detection algorithm. Finally, crossover modes can be retrieved with high precision based on accurate trajectory estimation. A series of numerical experiments, both simulations and real-world cases, are considered to demonstrate the effectiveness and advantages of the proposed method.

Extraordinary mode conversion of elastic waves through asymmetric metaplates

Recently, the mode conversion of elastic waves has attracted much attention, due to its scientific significance and potential applications. The applications based on the high mode conversion efficiency were also explored in many fields. However, because of the complexity of elastic waves, the existing structures for the high efficient conversion of elastic waves are relatively complicated, and there are also some limitations in the practical design. Here, we report the extraordinary mode conversion of elastic waves through asymmetric brass plates partitioned by subwavelength cuts. It is demonstrated that high efficiencies (90%) and one-way conversions between transversal waves and longitudinal waves are achieved by the structured solid plate at the resonant frequency, which leads to the striking unidirectional transmission of elastic waves. Analyzing the resonant fields demonstrates that the intrinsic modes within the individual pieces derived by the cuts are responsible for this abnormal wave conversion. The simple scheme for wave conversion presented here may have potential applications, such as non-invasive flow sensing.

Prediction of Diverse Boreal Summer Intraseasonal Oscillation in the GFDL SPEAR Model

Abstract Boreal summer intraseasonal oscillation (BSISO) is a primary source of predictability for summertime weather and climate on the subseasonal-to-seasonal (S2S) time scale. Using the GFDL SPEAR S2S prediction system, we evaluate the BSISO prediction skills based on 20-yr (2000–19) hindcast experiments with initializations from May to October. It is revealed that the overall BSISO prediction skill using all hindcasts reaches out to 22 days as measured by BSISO indices before the bivariate anomalous correlation coefficient (ACC) drops below 0.5. Results also show that the northeastward-propagating canonical BSISO (CB) event has a higher prediction skill than the northward dipole BSISO (DB) event (28 vs 23 days). This is attributed to CB’s more periodic nature, resulting in its longer persistence, while DB events are more episodic accompanied by a rapid demise after reaching maximum enhanced convection over the equatorial Indian Ocean. From a forecaster’s perspective, a precursory strong Kelvin wave component in the equatorial western Pacific signifies the subsequent development of a CB event, which is likely more predictable. Investigation of individual CB events shows a large interevent spread in terms of their prediction skills. For CB, the events with weaker and fluctuating amplitude during their lifetime have relatively lower prediction skills likely linked to their weaker convection–circulation coupling. Interestingly, the prediction skills of individual CB events tend to be relatively higher and less scattered during late summer (August–October) than those in early summer (May–July), suggestive of the seasonal modulation on the evolution and predictability of BSISO. Significance Statement The advance of subseasonal-to-seasonal (S2S) prediction largely depends on dynamical models’ ability to predict some major intrinsic modes in the climate system, including the boreal summer intraseasonal oscillation (BSISO). Using a newly developed S2S prediction system, we thoroughly evaluated its performance in predicting BSISO, and revealed the skill dependence on the BSISO propagation diversity. Here we provide physical explanations of what influences the BSISO predictions and identify different precursory signals for two types of BSISO, which have important implications for operational forecasts.

Enhanced complete ensemble EMD with superior noise handling capabilities: A robust signal decomposition method for power systems analysis

AbstractSignal decomposition is crucial in several domains, particularly in the dissection of complex signals present in electrical power systems. Understanding the oscillations and patterns within these signals can significantly influence energy resource management, grid stability, and efficient system operation. This paper presents an advanced enhanced decomposition method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to mitigate the inherent drawbacks of the conventional CEEMDAN and its improved version. Unlike CEEMDAN's generalized noise approach, the proposed method introduces adaptive noise, enhancing target signal noise handling by incorporating a tailored filtering and updating process after each iteration. This leads to more accurate signal decomposition compared to traditional methods. Comprehensive tests were conducted using artificially generated signals characterized by mode mixing, varying frequency oscillations, complex real‐world electrical demand signals, generator axis vibrations and partial discharge signals. The results demonstrate that the proposed method outperforms traditional techniques in two significant aspects. First, it provides superior spectral separation of the intrinsic modes (IMF) of the signal, thereby enhancing decomposition accuracy. Second, it significantly reduced the number of shifting iterations, thereby alleviating the computational load. These advancements have led to a more accurate and efficient framework that is essential for analyzing nonlinear and nonstationary signals.

Green in blue

The global commodification of jazz (and indeed culture more generally) has contributed to significant and enduring environmental consequences. The various physical formats through which it has been disseminated have each employed by-products of the petrochemical industry in their production and distribution, and the environmental cost of digital streaming platforms has more recently been observed as contributing further to the carbon footprints of music producers and consumers. International and regional travel by touring artists and festival audiences has similarly been recognized as having negative environmental impacts through associated air, sea, and land transport emissions and through increased burden on host-location infrastructures. The environmental implications of culture have come increasingly under scrutiny as society becomes conscious of individual and collective contributions to, and imperative mitigations against, critical anthropogenic climate change. This article seeks to explore the machineries of jazz dissemination, foregrounding the festival as a place of coming together in celebration of music and community, and considers how the road ahead may look as we attempt to green our engagement with culture while safeguarding its intrinsic values and modes of experience. It questions, therefore, whether the art form that spearheaded the ways in which we currently interact with live and recorded music can similarly lead us in addressing urgent and necessary paradigm shifts in the means and methods of cultural production and consumption.

Intrinsic Mode-Based Network Approach to Examining Multiscale Characteristics of Sea Surface Temperature Variability

Variability of sea surface temperature (SST), characterized by various spatiotemporal scales, is a proxy of climate change. A network analysis combined with empirical mode decomposition is newly presented for examining scale-dependent spatial patterns of SST variability. Our approach is applied to SST anomaly variability in the East/Japan Sea (EJS), consisting of satellite-derived daily datasets of 0.25° × 0.25° resolution from 1981 to 2023. Through the spatial distribution of instantaneous energy in intrinsic modes and features of intrinsic-mode networks, scale-dependent spatiotemporal features are found. The season-specific spatial pattern of energy density is observed only for weekly to semiannual modes, while a persistent high-energy distribution in the tongue-shaped region from East Korea Bay (EKB) to the Sub-Polar Front (SPF) is observed only for annual-to-decadal modes. The seasonality is apparent in the time evolution of energy only for weekly-to-annual modes, with a peak in summer and an increasing trend since the 2010s. Hubs of intrinsic-mode networks are observed in the whole southern area (some northern part) of EJS during the summer (winter), only for monthly to semiannual modes. Regional communities are observed only for weekly to seasonal modes, while there is an inter-basin community with annual-to-biennial modes, incorporating two pathways of East Sea Intermediate Water (ESIW).

Attention on the key modes: Machinery fault diagnosis transformers through variational mode decomposition

Machinery signals typically consist of multiple sub-signals in different frequency bands, while existing Transformer-based fault diagnosis methods often lack attention to key fault frequencies, causing interference in fault diagnosis. Therefore, an innovative Transformer structure for fault diagnosis based on variational mode decomposition (VMD) is proposed. First, to address the difficulty in identifying signal features arising from coupling of multiple frequency bands, a mode encoder based on VMD is proposed to decompose the coupled modes and calculate the key modes. Second, a position encoding method based on central frequency is proposed to address the lack of attention to signal’s frequency in existing position encoding methods. Third, fault characteristic frequency is used to verify the frequency band attention scores, improving the interpretability and reliability of the network in response to the lack of internal interpretability in fault diagnosis methods based on deep learning. Finally, the proposed method was validated on bearing vibration dataset and motor sound dataset. The results showed that the method has high diagnostic accuracy, and could capture the intrinsic modes of different faults.