Multiple Dynamic Factors Research Articles

Supramolecular Spring-Like Fe(II) Spin-Crossover Complexes Experiencing Giant and Anisotropic Thermal Expansion Across Two Distinct Temperature Regimes.

Dynamic molecules with tunable chemical and physical properties in response to external stimuli hold great potential for applications in various fields such as information storage, smart molecular machines, and biomimetics. Among them, supramolecular springs and spin-crossover (SCO) complexes can both undergo visible macroscopic changes under heat or light stimulation. In this study, we synthesized a unique trinuclear Fe(II)-SCO complex, [(R-L)FeII{Au(CN)2}2] (R 1), using a chiral chelating ligand decorated with rotatable benzyl rings. The [FeAu2] trinuclear molecules form a 21-helical supramolecular chain via elastic Au Au contacts. Interestingly, the synergy between the multiple dynamic factors (SCO event, rotation of the rings, and flexibility in Au Au distance) endows the complex with multiple switchings in both magnetism and structure, as well as the most intriguing characteristic of giant and anisotropic "breathing" feature in thermal expansion within two distinct temperature regimes. Specifically, complex R 1 undergoes two hysteretic magnetic transitions: a non-spin transition between 360 and 380 K and an unsymmetric SCO transition in the region of 160-280 K, associated with a symmetry-breaking event between the non-polar and polar space groups (P212121↔P21). Both transitions are triggered/accompanied by the rotation (inward vs. outward) of the benzyl rings. Correspondingly, reversible spring-like motions of the helical chains with the helical pitches varying from 11.345140 K to 12.509280 K then back to 11.630380 K Å are observed in the two distinct temperature regimes. This work demonstrates a significant success in incorporating both SCO and spring-like motion in one system, paving the way for designing multifunctional dynamic materials for future devices.

Supramolecular Spring‐Like Fe(II) Spin‐Crossover Complexes Experiencing Giant and Anisotropic Thermal Expansion Across Two Distinct Temperature Regimes

AbstractDynamic molecules with tunable chemical and physical properties in response to external stimuli hold great potential for applications in various fields such as information storage, smart molecular machines, and biomimetics. Among them, supramolecular springs and spin‐crossover (SCO) complexes can both undergo visible macroscopic changes under heat or light stimulation. In this study, we synthesized a unique trinuclear Fe(II)‐SCO complex, [(R−L)FeII{Au(CN)2}2] (R 1), using a chiral chelating ligand decorated with rotatable benzyl rings. The [FeAu2] trinuclear molecules form a 21‐helical supramolecular chain via elastic Au Au contacts. Interestingly, the synergy between the multiple dynamic factors (SCO event, rotation of the rings, and flexibility in Au Au distance) endows the complex with multiple switchings in both magnetism and structure, as well as the most intriguing characteristic of giant and anisotropic “breathing” feature in thermal expansion within two distinct temperature regimes. Specifically, complex R 1 undergoes two hysteretic magnetic transitions: a non‐spin transition between 360 and 380 K and an unsymmetric SCO transition in the region of 160–280 K, associated with a symmetry‐breaking event between the non‐polar and polar space groups (P212121↔P21). Both transitions are triggered/accompanied by the rotation (inward vs. outward) of the benzyl rings. Correspondingly, reversible spring‐like motions of the helical chains with the helical pitches varying from 11.345140 K to 12.509280 K then back to 11.630380 K Å are observed in the two distinct temperature regimes. This work demonstrates a significant success in incorporating both SCO and spring‐like motion in one system, paving the way for designing multifunctional dynamic materials for future devices.

Strategies for choosing the best living donor: A review of the literature and a proposal of a decision-making paradigm.

Transplantation remains the gold-standard treatment for pediatric end-stage kidney disease. While living donor transplant is the preferred option for most pediatric patients, it is not the right choice for all. For those who have the option to choose between deceased donor and living donor transplantation, or from among multiple potential living donors, the transplant clinician must weigh multiple dynamic factors to identify the most optimal donor. This review will cover the key considerations when choosing between potential living donors and will propose a decision-making algorithm.

Identification robust inference for the risk premium in term structure models

We propose identification robust statistics for testing hypotheses on the risk premia in dynamic affine term structure models. We do so using the moment equation specification proposed in Adrian et al. (2013). Statistical inference based on their three-stage estimator requires knowledge of the risk factors’ quality and can be misleading when the β’s are weak, which results when sampling errors are of comparable order of magnitude as the risk factor loadings. We extend the subset (factor) Anderson–Rubin test from Guggenberger et al. (2012) to models with multiple dynamic factors and time-varying risk prices. It provides a computationally tractable manner to conduct identification robust tests on a few risk premia when a larger number is present. We use it to analyze potential identification issues arising in the data from Adrian et al. (2013) for which we show that some factors, though potentially weak, may drive the time variation of risk prices, and weak identification issues are more prominent in multi-factor models.

AARGNN: An Attentive Attributed Recurrent Graph Neural Network for Traffic Flow Prediction Considering Multiple Dynamic Factors

Traffic flow prediction is a fundamental part of ITS (Intelligent Transportation System). Since the correlations of traffic data are complicated and are affected by various factors, traffic flow prediction is a challenging task. Existing traffic flow prediction methods generally take limited static factors (e.g., the distance between sensors and road network topological structure) into consideration and model the correlations of the traffic data separately to predict the future traffic. In this paper, we propose AARGNN (Attentive Attributed Recurrent Graph Neural Network), a GNN (graph neural network) based method considering multiple dynamic factors to predict short-term traffic flow. With multi-source urban data (e.g., POI, road network, incident, weather, etc.), AARGNN considers both static factors and dynamic factors (e.g., spatial distance, semantic distance, road characteristic, road situation, and global context) to predict the short-term traffic flow. Specifically, AARGNN constructs an attributed graph and encodes various factors into the attributes. The correlations of the traffic data are modeled by utilizing the GNN combined with LSTM (long short-term memory). In addition, AARGNN specifies the contributions of each factor based on attention mechanism. Experiments on real-world datasets show that the proposed method outperforms all baseline methods.

A Union of Dynamic Hydrological Modeling and Satellite Remotely-Sensed Data for Spatiotemporal Assessment of Sediment Yields

(1) The existing frameworks for water quality modeling overlook the connection between multiple dynamic factors affecting spatiotemporal sediment yields (SY). This study aimed to implement satellite remotely sensed data and hydrological modeling to dynamically assess the multiple factors within basin-scale hydrologic models for a realistic spatiotemporal prediction of SY in watersheds. (2) A connective algorithm was developed to incorporate dynamic models of the crop and cover management factor (C-factor) and the soil erodibility factor (K-factor) into the Soil and Water Assessment Tool (SWAT) with the aid of the Python programming language and Geographic Information Systems (GIS). The algorithm predicted the annual SY in each hydrologic response unit (HRU) of similar land cover, soil, and slope characteristics in watersheds between 2002 and 2013. (3) The modeled SY closely matched the observed SY using the connective algorithm with the inclusion of the two dynamic factors of K and C (predicted R2 (PR2): 0.60–0.70, R2: 0.70–0.80, Nash Sutcliffe efficiency (NS): 0.65–0.75). The findings of the study highlight the necessity of excellent spatial and temporal data in real-time hydrological modeling of catchments.

How Clinical Supervisors Conceptualize Procedural Entrustment: An Interview-Based Study of Entrustment Decision Making in Endoscopic Training.

Entrustment is central to assessment in competency-based medical education (CBME). To date, little research has addressed how clinical supervisors conceptualize entrustment, including factors they consider in making entrustment decisions. The aim of this study was to characterize supervisors' decision making related to procedural entrustment, using gastrointestinal endoscopy as a test case. Using methods from constructivist grounded theory, the authors interviewed 29 endoscopy supervisors in the United States and Canada across multiple specialties (adult and pediatric gastroenterology, surgery, and family medicine). Semistructured interviews, conducted between April and November 2019, focused on how supervisors conceptualize procedural entrustment, how they make entrustment decisions, and what factors they consider. Transcripts were analyzed using constant comparison to generate an explanatory framework and themes. Three themes were identified from the analysis of interview transcripts: (1) entrustment occurs in varying degrees and fluctuates over time; (2) entrustment decisions can transfer within and across procedural and nonprocedural contexts; (3a) persistent static factors (e.g., supervisor competence, institutional culture, legal considerations) influence entrustment decisions, as do (3b) fluctuating, situated dynamic factors (e.g., trainee skills, patient acuity, time constraints), which tend to change from one training encounter to the next. In the process of making procedural entrustment decisions, clinical supervisors appear to synthesize multiple dynamic factors against a background of static factors, culminating in a decision of whether to entrust. Entrustment decisions appear to fluctuate over time, and assessors may transfer decisions about specific trainees across settings. Understanding which factors supervisors perceive as influencing their decision making has the potential to inform faculty development, as well as competency committees seeking to aggregate faculty judgments about trainee unsupervised practice. Those leading CBME programs may wish to invest in optimizing the observed static factors, such that these foundational factors are tuned to facilitate trainee learning and achievement of entrustment.

Effects of Wave–Current Interaction on Storm Surge in the Pearl River Estuary: A Case Study of Super Typhoon Mangkhut

This study explored the effects of interactions between waves and current on storm surge in the Pearl River Estuary (PRE) using a fully coupled wave–current model. The model was validated based on in situ observations during the traverse of super typhoon Mangkhut. The results indicated that the model could reproduce the storm surge and wave setup processes. Numerical experiments showed that simulations of storm surge are minimally affected by wave setup. The wave setup during super typhoon Mangkhut reached up to 0.23 m and contributed to the total near shore storm surge by up to 8%. The simulations of the coupled model showed a better correlation with observations compared to those of an uncoupled model. The storm surge increased with transport upstream in a tidal-dominated outlet, whereas it decreased in a river-dominated outlet. The storm surge and wave setup increased and decreased, respectively, during spring tide as compared to that during a neap tide. The storm surge increased with increasing runoff in the upper river reaches, whereas there was little change in the tidal-dominated lower river reaches. This research emphasizes the importance of integrating the effects of multiple dynamic factors in the forecasting of storm surge and provides a reference for similar studies in other estuaries with multiple outlets and a complex river network.

Forearm Orientation and Contraction Force Independent Method for EMG-Based Myoelectric Prosthetic Hand

An accurate and robust pattern recognition system is crucial for advanced electromyography-based upper limb prosthetics. Variations in the user’s forearm orientation and contraction force varies the electromyography features for the same movement. It degrades the performance of electromyography-based movement classification. Few attempts reported to increase the classification accuracy under such dynamic factors include utilizing multi-stage classifiers, multi-modal sensory data, high-dimensional data, or set of power spectral descriptors. This work aims to overcome limitations in the existing methods through a novel feature set. The combined effect of forearm orientations and contraction force levels on the classification of six hand movements are considered in this study. In this paper, a novel feature set extracted from electromyography’s wavelet bispectrum, obtained as the combination of continuous wavelet transform and bispectrum, is proposed. The proposed method remains independent of the multiple dynamic factors due to its invariant and uniqueness properties. Linear discriminant analysis classifier with the proposed wavelet bispectrum feature set showed superiority (classification accuracy=86.43±3.67%) over conventional time-domain feature set (classification accuracy=75.92±5.41%) when trained with data from single orientation and force level. When trained with data from appropriate variations (three orientations and medium force), the accuracy with the proposed method increased to 90.35±2.01%. Further, by training with data from all three orientations and force levels, this method achieved an accuracy of 96.11% ±1.34%. The statistical significance of the higher classification accuracy achieved with the proposed feature set compared to other conventional feature sets is proven (repeated measure one-way ANOVA, p < 0.05).

Appropriate Feature Set and Window Parameters Selection for Efficient Motion Intent Characterization towards Intelligently Smart EMG-PR System

The constantly rising number of limb stroke survivors and amputees has motivated the development of intelligent prosthetic/rehabilitation devices for their arm function restoration. The device often integrates a pattern recognition (PR) algorithm that decodes amputees’ limb movement intent from electromyogram (EMG) signals, characterized by neural information and symmetric distribution. However, the control performance of the prostheses mostly rely on the interrelations among multiple dynamic factors of feature set, windowing parameters, and signal conditioning that have rarely been jointly investigated to date. This study systematically investigated the interaction effects of these dynamic factors on the performance of EMG-PR system towards constructing optimal parameters for accurately robust movement intent decoding in the context of prosthetic control. In this regard, the interaction effects of various features across window lengths (50 ms~300 ms), increments (50 ms~125 ms), robustness to external interferences and sensor channels (2 ch~6 ch), were examined using EMG signals obtained from twelve subjects through a symmetrical movement elicitation protocol. Compared to single features, multiple features consistently achieved minimum decoding error below 10% across optimal windowing parameters of 250 ms/100 ms. Also, the multiple features showed high robustness to additive noise with obvious trade-offs between accuracy and computation time. Consequently, our findings may provide proper insight for appropriate parameter selection in the context of robust PR-based control strategy for intelligent rehabilitation device.

Exploring Trends and Patterns of Popularity Stage Evolution in Social Media

The popularity of online contents in social media frequently experiences ebb and flow, and thus its evolution often involves different stages, such as burst and valley. Exploring the patterns of popularity evolution, especially how burst forms and decays, and even further, predicting the trends of popularity evolution is both an important research topic and beneficial to support decision making for many applications, such as emergency management, business intelligence, and public security. Previous work on popularity prediction has focused on predicting the popularity volume of online contents, and at most, popularity burst and ignored the exploration of popularity evolution and the prediction of its stages. To fill this gap, in this paper, we propose our method for the popularity stage prediction problem both at the microscopic level and macroscopic level. At the microscopic level, we first extract multiple dynamic factors and infer future evolution stage by considering the contributions of different dynamic factors. At the macroscopic level, we extract the overall evolution patterns of popularity stages and adopt a pattern matching-based method to predict future popularity stages. We evaluate the proposed approach using tweets in SinaWeibo, the most popular Twitter-like social media platform in China. The experimental results show the effectiveness of our proposed approach in predicting popularity evolution stages.

Analysis of Super-Gentrification Dynamic Factors Using Interpretative Structure Modeling

The driving force of super-gentrification shapes a complex system in which multiple dynamic factors interact with each other. This paper takes the dynamic factor system of super-gentrification as the research object and uses the Interpretative Structure Modeling (ISM) to analyze these dynamic factors. The levels of these dynamic factors and the interaction between them are determined. The Cross Impact Matrix Multiplication Applied to a Classification (MICMAC) analysis is also conducted to determine the dependence power and driving power of these dynamic factors. Through analysis, it is concluded that the dynamic factors of super-gentrification are distributed on six levels. Among these dynamic factors, Transformation of Industrial Structure and Occupational Structure in Urban Central Areas, Housing Needs of Overseas Elites, Investment Needs, Development of the Real Estate Market, and Unique Areas and Lifestyle Preferences are the fundamental dynamic factors affecting super-gentrification. The findings of this paper can enrich the existing theoretical research on the driving force of super-gentrification and can provide a reference for policy makers to promote urban landscape sustainability to some extent.

Towards resolving the co-existing impacts of multiple dynamic factors on the performance of EMG-pattern recognition based prostheses

Background and ObjectiveMobility of subject (MoS) and muscle contraction force variation (MCFV) have been shown to individually degrade the performance of multiple degrees of freedom electromyogram (EMG) pattern recognition (PR) based prostheses control systems. Though these factors (MoS-MCFV) co-exist simultaneously in the practical use of the prosthesis, their combined impact on PR-based system has rarely been studied especially in the context of amputees who are the target users of the device. MethodsTo address this problem, this study systematically investigated the co-existing impact of MoS-MCFV on the performance of PR-based movement intent classifier, using EMG recordings acquired from eight participants who performed multiple classes of targeted limb movements across static and non-static scenarios with three distinct muscle contraction force levels. Then, a robust feature extraction method that is invariant to the combined effect of MoS-MCFV, namely, invariant time-domain descriptor (invTDD), was proposed to optimally characterize the multi-class EMG signal patterns in the presence of both factors. ResultsExperimental results consistently showed that the proposed invTDD method could significantly mitigate the co-existing impact of MoS-MCFV on PR-based movement-intent classifier with error reduction in the range of 7.50%~17.97% (p<0.05), compared to the commonly applied methods. Further evaluation using 2-dimentional principal component analysis (PCA) technique, revealed that the proposed invTDD method has obvious class-separability in the PCA feature space, with a significantly lower standard error (0.91%) compared to the existing methods. ConclusionThis study offers compelling insight on how to develop accurately robust multiple degrees of freedom control scheme for multifunctional prostheses that would be clinically viable. Also, the study may spur positive advancement in other application areas of medical robotics that adopts myoelectric control schemes such as the electric wheelchair and human-computer-interaction systems.

Abstract LB-275: U3-1402, a novel HER3-targeting antibody-drug conjugate, exhibits its antitumor activity through increased payload intracellular delivery via highly efficient drug internalization

Abstract Background: Human epidermal growth factor receptor 3 (HER3), a member of the ErbB receptor tyrosine kinase family, is overexpressed in a variety of human cancers and plays an important role in cell proliferation and survival. U3-1402 is a novel HER3-targeting antibody-drug conjugate (ADC) consisting of a fully human anti-HER3 antibody (patritumab), a tetrapeptide-based linker, and a topoisomerase I inhibitor payload. It is currently being investigated in clinical trials for HER3-positive breast cancer (phase 1/2) and NSCLC (phase 1). In principle, effective action of ADCs depends on multiple dynamic factors including antigen-specific binding, internalization, trafficking to lysosomes, and payload release, as well as other inherent characteristics such as payload potency and linker stability. Methods: To assess the targeted delivery potential of U3-1402, the HER3-mediated molecular dynamics of U3-1402 (cell surface binding, internalization, trafficking, and payload release) were investigated in 8 cancer cell lines with various HER3 expression levels, along with cell growth inhibition activity. In vitro molecular dynamics were compared between U3-1402 and trastuzumab (anti-HER2 antibody) using the MDA-MB-453 cell line, which expresses both HER2 and HER3. The activity of patritumab (naked antibody of U3-1402) was tested. In vivo efficacy of U3-1402 was also evaluated and immunostaining of HER3 and γH2AX (marker of DNA double-stranded breaks) as well as drug concentration assessment were performed. Results: U3-1402 bound to the cell surface of HER3-positive cell lines in HER3 expression level-dependent manner, and was then internalized into cells. Interestingly, the internalization rate of U3-1402 in MDA-MB-453 cells at 1 h was 64%, which was higher than that observed with trastuzumab (2%). Subsequently, U3-1402 was translocated to the lysosome, followed by payload release, leading to an improved cell growth inhibition compared to that noted with patritumab. In vivo antitumor efficacy was also demonstrated in a HER3-dependent manner in MDA-MB-453 xenograft model. In the study, internalization of HER3 from cell membrane into the intracellular compartment by U3-1402 treatment was observed, followed by increased free payload and γH2AX levels in tumor tissue. Conclusion: U3-1402 has a high internalization property for effective payload delivery to HER3-expressing cancer cells, resulting in a favorable ADC-driven efficacy. Citation Format: Kumiko Koyama, Yuuri Hashimoto, Yasuki Kamai, Yoshinobu Shiose, Manabu Abe, Yuki Kaneda, Naoyuki Maeda, Kenji Hirotani, Yusuke Ogitani, Tsuyoshi Karibe, Takashi Kagari, Kenichi Wakita, Suguru Ueno, Toshinori Agatsuma, Masato Murakami. U3-1402, a novel HER3-targeting antibody-drug conjugate, exhibits its antitumor activity through increased payload intracellular delivery via highly efficient drug internalization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-275.

Data-Driven Dynamic Probabilistic Reserve Sizing Based on Dynamic Bayesian Belief Networks

Due to the variability and partial unpredictability of renewable energy sources (RES), generation reserve sizing is key for managing operational risks and enhancing grid reliability. Most existing approaches for reserve sizing estimate the forecasting error for RES using either simple point forecasts, or probabilistic forecasts that assume a predefined parametric model of the underlying distribution, such as a Gaussian distribution. However, these approaches are unable to accurately model the probability of critical events that occur in the tails of the distributions, which is vital for operational risk management. Further, they do not account for multiple dynamic factors that may impact operational risk. In this paper, we introduce a data-driven dynamic probabilistic reserve sizing method based on the artificial intelligence technique of dynamic Bayesian belief networks. The method considers the actual underlying distribution of forecasting errors from the availability of conventional generators (forecasting errors of their available generation capacity), demand, RES, and prevailing conditions such as weather and market prices. In addition, we introduce a new dynamic metric for calculating the reliability level of the power grid in order to provide a real-time stochastic decision support tool for power system operators. The proposed method is demonstrated using seven years of real historical data with a granularity of 30 min from the power system in Australia, and has been implemented and is currently used by the Australian system operator.

A Deep Reinforcement Learning-Enabled Dynamic Redeployment System for Mobile Ambulances

Protecting citizens' lives from emergent accidents (e.g. traffic accidents) and diseases (e.g. heart attack) is of vital importance in urban computing. Every day many people are caught in emergent accidents or diseases and thus need ambulances to transport them to hospitals. In this paper, we propose a dynamic ambulance redeployment system to reduce the time needed for ambulances to pick up patients and to increase the probability of patients being saved in time. For patients in danger, every second counts. Specifically, whenever there is an ambulance becoming available (e.g. finishing transporting a patient to a hospital), our dynamic ambulance redeployment system will redeploy it to a proper ambulance station such that it can better pick up future patients. However, the dynamic ambulance redeployment is challenging, as when we redeploy an available ambulance we need to simultaneously consider each station's multiple dynamic factors. To trade off these multiple factors using handcrafted rules are almost impossible. To deal with this issue, we propose using a deep neural network, called deep score network, to balance each station's dynamic factors into one score, leveraging the excellent representation ability of deep neural networks. And then we propose a deep reinforcement learning framework to learn the deep score network. Finally, based on the learned deep score network, we provide an effective dynamic ambulance redeployment algorithm. Experiment results using data collected in real world show clear advantages of our method over baselines, e.g. comparing with baselines, our method can save ~100 seconds (~20%) of average pickup time of patients and improve the ratio of patients being picked up within 10 minutes from 0.786 to 0.838. With our method, people in danger can be better saved.

Phase-amplitude coupling and infraslow (

Resting state functional magnetic resonance imaging (fMRI) can identify network alterations that occur in complex psychiatric diseases and behaviors, but its interpretation is difficult because the neural basis of the infraslow BOLD fluctuations is poorly understood. Previous results link dynamic activity during the resting state to both infraslow frequencies in local field potentials (LFP) (<1 Hz) and band-limited power in higher frequency LFP (>1 Hz). To investigate the relationship between these frequencies, LFPs were recorded from rats under two anesthetics: isoflurane and dexmedetomidine. Signal phases were calculated from low-frequency LFP and compared to signal amplitudes from high-frequency LFP to determine if modulation existed between the two frequency bands (phase-amplitude coupling). Isoflurane showed significant, consistent phase-amplitude coupling at nearly all pairs of frequencies, likely due to the burst-suppression pattern of activity that it induces. However, no consistent phase-amplitude coupling was observed in rats that were anesthetized with dexmedetomidine. fMRI-LFP correlations under isoflurane using high frequency LFP were reduced when the low frequency LFP's influence was accounted for, but not vice-versa, or in any condition under dexmedetomidine. The lack of consistent phase-amplitude coupling under dexmedetomidine and lack of shared variance between high frequency and low frequency LFP as it relates to fMRI suggests that high and low frequency neural electrical signals may contribute differently, possibly even independently, to resting state fMRI. This finding suggests that researchers take care in interpreting the neural basis of resting state fMRI, as multiple dynamic factors in the underlying electrophysiology could be driving any particular observation.

Characterization of the Dynamic Thickness of the Aerobic Layer during Pig Manure Aerobic Composting by Fourier Transform Infrared Microspectroscopy

A new method for characterizing the aerobic layer thickness in pig manure based on Fourier transform infrared microspectroscopy (FTIRM) is presented to improve the anaerobic/aerobic co-process mechanism, to ensure adequate oxygen supply and, thus, minimize methane emissions during aerobic composting. Freeze-dried manure particles were microtomed into 10 μm thick sections; the spectral range, spectral resolution, and pixel dimensions in the transmission spectra were 4000-650 cm(-1), 16 cm(-1), and 6.25 × 6.25 μm, respectively. A mean spectrum of 16 scans was used for the second-derivative analysis with nine smoothing points. This is the first attempt at determining the oxidation profile of composting particles according to the radial variations in second-derivative spectra at 2856 and 1568 cm(-1), which are attributed to the reactants and products of the oxidation, respectively. In addition, an intermediate area is detected between the aerobic layer and anaerobic core. The experimental values of the aerobic layer thickness are consistent with the estimates, and an exponential increase is observed, which is influenced by multiple dynamic factors. However, the contribution of each factor to dynamic variations in the aerobic layer thickness should be investigated using available methods.

Auditory stream segregation by musical timbre.

A series of experiments examined the acoustic attributes of timbre that most contribute to auditory stream segregation. In the first experiment, listeners heard short sequences of repeated pairs of tones from orchestral instruments, and were asked to rate how strongly the tones formed different streams. These ratings were used as difference measures, and were fit to a two-dimensional space using multidimensional scaling. One dimension corresponded to average spectral frequency, and the other to multiple dynamic factors. These ratings were highly correlated with similarity judgments from a previous study. A second experiment assessed if the dynamic factors indirectly contributed to streaming by influencing perceptual attack time. The perceptual attack time of each tone was measured, and new sequences were recorded with the perceptual attacks isochronous. The results were highly correlated with the first experiment, and the effect of the dynamic factors was not diminished. These experiments indicate that streaming and similarity judgments are based on the same acoustic attributes. Implications for the segregation of melodic lines in music will be discussed.