Analysis Of Time Evolution Research Articles

Analyzing the channels of information dissemination: Investigating abrupt transitions in resource investment.

Investment in resources is essential for facilitating information dissemination in real-world contexts, and comprehending the influence of resource allocation on information dissemination is, thus, crucial for the efficacy of collaborative networks. Nonetheless, current studies on information dissemination frequently fail to clarify the complex interplay between information distribution and resources in network contexts. In this work, we establish a resource-based information dissemination model to identify the complex interplay by examining the propagation threshold and equilibriums. We assess the model's efficacy by juxtaposing the mean-field method with Monte Carlo simulations across three author collaboration networks. In addition, we define the function of resources in information dissemination and evaluate the model's applicability using propagating threshold, time evolution, and parametric analyses. Our findings indicate that an increase in available resources accelerates and expands the distribution of information. Notably, we identify abrupt transition phenomena concerning available resources and demonstrate that the information self-learning rate and the information review rate hasten this transition, while information decline and re-diffusion rates decelerate it.

Time Evolution Analysis of Riders’ Preference Attention and Satisfaction on Real-Time Crowdsourcing Logistics Platform

This study examines the evolutionary trends in preference focus and satisfaction among real-time crowdsourced logistics platform riders (hereafter referred to as riders) in China since the outbreak of the COVID-19 pandemic. By analyzing real-time comments from 5,652 riders and applying the Latent Dirichlet Allocation (LDA) model to identify riders’ preferences and calculate their attention levels, combined with riders’ actual ratings to infer satisfaction levels (ranging from very dissatisfied to satisfied), this research for the first time explores the changing patterns of riders’ preferences and their attention and satisfaction trends in the pandemic context from a long-term dynamic perspective. The findings reveal that riders prioritize interaction quality; the range of fluctuation in average attention levels is large when high and small when low; attention to platform management, system utility, and rider relationships is on the rise, whereas interest in other preferences is declining; there is a significant correlation between rider attention and platform policies; and social media and related public opinion influence riders’ preferences. These insights are instrumental for the platform to adjust the policy direction duly and meet the core demands of riders according to the limited priority.

Spatial Constraints on Economic Interactions: A Complexity Approach to the Japanese Inter-Firm Trade Network

The trade distance is an important constraining factor underpinning the emergence of social and economic interactions of complex systems. However, agent-based studies supported by the granular analysis of distances are limited. Here, we present a complexity method that places the actual geographical locations of individual firms in Japan at the epicentre of our research. By combining methods derived from network science together with information theory measures, and by using a comprehensive dataset of Japanese inter-firm business transactions, we evaluate the effects of spatial features on the structural patterns of the economy. We find that the normalised probability distributions of the distances between interacting firms obey a power law like decay concomitant with the sizes of firms and regions. Furthermore, small firms would reach large distances to become customers of large firms, while trading between either only small firms or only large firms tends to be at smaller distances. Furthermore, a time evolution analysis suggests a reduction in the overall average trading distances in last 20 years. Lastly, our analysis concerning the trading dynamics among prefectures indicates that the preference to trade with neighbouring prefectures tends to be more pronounced at rural regions as opposed to the larger central conurbations.

Predictive insights into plant-based compounds as fibroblast growth factor receptor 1 inhibitors: a combined molecular docking and dynamics simulation study

The discovery of novel therapeutic agents with potent anticancer activity remains a critical challenge in drug development. Natural products, particularly bioactive phytoconstituents derived from plants, have emerged as promising sources for anticancer drug discovery. In this study, we used virtual screening techniques to explore the potential of bioactive phytoconstituents as inhibitors of fibroblast growth factor receptor 1 (FGFR1), a key signaling protein implicated in cancer progression. We used virtual screening techniques to analyze phytoconstituents extracted from the IMPPAT 2.0 database. Our primary objective was to discover promising inhibitors of FGFR1. To ensure the selection of promising candidates, we initially filtered the molecules based on their physicochemical properties. Subsequently, we performed binding affinity calculations, PAINS, ADMET, and PASS filters to identify nontoxic and highly effective hits. Through this screening process, one phytocompound, namely Mundulone, emerged as a potential lead. This compound demonstrated an appreciable affinity for FGFR1 and exhibited specific interactions with the ATP-binding site residues. To gain further insights into the conformational dynamics of Mundulone and the reference FGFR1 inhibitor, Lenvatinib, we conducted time-evolution analyses employing 200 ns molecular dynamics simulations (MDS) and essential dynamics. These analyses provided valuable information regarding the dynamic behavior and stability of the compounds in complexes with FGFR1. Overall, the findings indicate that Mundulone exhibits promising binding affinity, specific interactions, and favorable drug profiles, making it a promising lead candidate. Further experimental analysis will be necessary to confirm its effectiveness and safety profiles for therapeutic advancement in the cancer field. Communicated by Ramaswamy H. Sarma

Graphene as the anti-oxidation protective layer: How good or bad can it be?

This study investigates the conditional anti-oxidation layer of graphene on Cu and its variation of morphology and strain state, demonstrating that the oxidation process could be divided into two stages. The chemical vapor deposition (CVD) growth and a high-humidity environment were exploited to observe the Cu foil oxidizing with the protection layer of graphene. The observation of optical microscopy (OM) and scanning electron microscopy (SEM) reveals the sequence process of the anti-oxidation layer of graphene on the diverse Cu orientations. Except for the wrinkles on graphene and orientations of Cu, the vacancies in the graphene play a crucial role in anti-oxidation. Furthermore, the oxidation of underlying Cu deteriorates the quality of graphene and reduces the strength of interaction at the graphene/Cu interface. The oxidation of graphene-capped Cu is divided into two stages: (i) The attenuated graphene/Cu interaction due to the Cu oxidation beneath, and (ii) the generation and enlargement of CuO particles damage the adjacent graphene, as clearly observed in the OM and SEM images. The time evolution analysis using angular-resolved photoelectron spectroscopy (ARPES) offers robust evidence to support the worse quality of graphene and the decoupling between graphene and Cu foil after the oxidation process. X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy express that the defects are created by the degradation of graphene rather than the generation of external functional groups. Thus, the Cu oxidation simultaneously influences both the substrate (Cu) and the protective layer (graphene), substantially weakening the graphene/Cu interaction.

Quantitative Analysis of the Complex Time Evolution of a Camphor Boat

The motion of a camphor boat on the water’s surface is a long-studied example of the direct transformation of chemical energy into a mechanical one. Recent experimental papers have reported a complex character of boat motion depending on the location of the camphor source. If the source is close to the stern, the boat moves at a constant speed. When it is shifted towards the boat center, oscillations of speed are observed. When the source is close to the boat center, pulses of speed followed by oscillations appear. Here, we focus on numerical simulations of camphor boat motion. We discuss approximations that allow us to reduce the numerical complexity of the problem and formulate a model in which the equation for boat velocity is coupled with a one-dimensional reaction–diffusion equation for camphor surface concentration. We scanned the phase space of model parameters and found the values that give qualitative agreement with the experiments. The model predicts all types of boat motion (continuous, oscillating, and pulsating) observed in experiments. Moreover, the model with selected parameter values shows that for specific locations of the camphor source, a spike in speed is followed by transient oscillations, which are an inherent part of speed relaxation.

Analysis of Viral Advertising Research Hotspots and Trends Based on Bibliometric Methods

With the popularity and development of social media, viral advertising has also gained tremendous development. Viral advertising as a useful promotional strategy can increase the publicity effectiveness of a brand or product. The purpose of this study is to better understand the research and progress of viral advertising and to explore future research directions. This study examined the viral advertising literature on the Web of Science database from 2004-2022, using bibliometric methods to analyze the data in terms of the annual number of publications, source journals, citations, authors, countries and institutions, and keywords. The results reveal the publishing trend of viral advertising research, the most influential literature and journals, as well as the most productive countries, institutions, and authors, including collaborative partnerships among them. The keyword co-occurrence analysis, cluster analysis, and time evolution analysis identified the research hotspots, themes, and evolution trends of viral advertising. This bibliometric study presents an overall profile of research in the field and proposes a research framework for the study of viral advertising that can serve as a reference, providing valuable guidance for advancing research in this area.

Stochastic analysis of air–sea heat fluxes variability in the North Atlantic in 1979–2022 based on reanalysis data

A dynamic stochastic model based on the Langevin stochastic differential equation is introduced for the reanalysis data of the ERA5 database to model and analyze the behavior of latent and sensible air–sea heat fluxes in the North Atlantic for the period 1979–2022. The point estimates of the random coefficients (the drift vector and the diffusion matrix) of this type of equation for the entire period under consideration are presented. The numerical methods and software tools for statistical analysis of time evolution of the coefficients as well as determination their relationships and the behavior of their maxima, averages and minima at various time intervals (days, months, years), are developed. A strong seasonality for the coefficients of the equation is demonstrated. The spatiotemporal variability of the dynamic and stochastic components of the coefficients of the Langevin equation and their relationship with jet streams of different regions of the North Atlantic is analyzed. The presence of non-trivial positive trends in the drift and diffusion coefficients, especially for the latent fluxes, within the interannual variability is demonstrated. One indicates a quantitative increase in the air–sea interaction on the interannual scale. Numerical estimation was carried out using high-performance computing cluster with software implementation in the Python programming language. The tools for dynamic visualization of various quantities on geographical maps of the region under consideration are also presented.

Accelerating the concerted double proton transfer process of 2,2′-bipyridine-3,3′-diol-5,5′-dicarboxylate acid ethyl ester (BPDC) molecule by centrosymmetric dual intermolecular hydrogen bonds

This study has conducted a systematic investigation into the excited state double proton transfer (ESDPT) process of 2,2′-bipyridine-3,3′-diol-5,5′-dicarboxylate acid ethyl ester (BPDC) in cyclohexane (CYH) and water (H2O) by density functional theory (DFT) and time-dependent density functional theory (TDDFT) methods. The calculated geometrical parameters, reduced density gradient and infrared vibration spectra confirm that BPDC undergoes the ESDPT process in both CYH and H2O. In comparison to the CYH solvent, the intramolecular hydrogen bonds (IAHBs) are strengthened in the S0 state by the formation of centrosymmetric intermolecular hydrogen bonds (IEHBs) between H2O and the OH group of BPDC molecules. Furthermore, the constructed potential energy surfaces show that BPDC molecules undergo a concerted ESDPT process without potential barriers in both solvents. Time evolution analysis confirms that the ESDPT process of BPDC molecules in H2O solvent (19 fs) is faster than that in CYH solvent (62 fs). The results indicate that the formation of IEHBs causes zero disruption to the original I AHBs, but it can effectively promote the ESDPT process of BPDC molecules. Overall, this work serves as important theoretical guidance for the development of high-performance fluorescence materials.

Chaos in a two dimensional fractional discrete Hopfield neural network and its control

A novel two-dimensional fractional discrete Hopfield neural network is presented in this study, which is based on discrete fractional calculus. This network incorporates both constant and variable orders, and its behavior is examined using phase plots, time evolution, bifurcation, Lyapunov exponents, and complexity analysis. Compared to integer and constant fractional orders, the numerical simulations demonstrate that the proposed variable-order fractional HNN exhibits more complex characteristics, and by selecting different fractional variable orders, novel attractors with chaotic behavior can be obtained. Additionally, a control scheme is proposed to stabilize the suggested neural network by utilizing the stability theorem for fractional discrete time systems. This control scheme is applied to both states in the study.

Numerical analysis of bipartite entanglement evolution in simple cubic 1/2-spin system with additional spin 1 dopant

The system in consideration is a singular simple cubic cell of spins s = frac{1}{2} doped in centre with additional spin S = 1. The structure is located in external magnetic field and is undergoing dissipative, Markovian evolution. The analysis of system time evolution is focused on the entanglement evaluation and determination of its behaviour over time. We found out the distinct effect of doping the structure with additional spin S = 1 in the time evolution of the bipartite entanglement in vert W rangle state. We also distinguished the raising and lowering spin interaction with environment and determined its impact on decoherence. Furthermore, we have shown that the entanglement between spins is in the form of a damped superposition of Gaussian functions. Its pulsed nature results from the unitary evolution in spin structure, while the damping is caused by the influence of the environment.

Robust proportional–integral control of submersible autonomous robotized vehicles by backstepping-averaged sub-gradient sliding mode control

This study presents a novel robust controller strategy for submersible autonomous robotized vehicles (SARV). This controller applies the averaged sub-gradient (ASG) descendant method to optimize the tracking of well-posed reference trajectories. The motion control form of the SARV is done in three stages (cascade-like control design). The phases consist of using the translation velocities as pseudo controllers to adjust the SARV position, regulating the angular velocities to attain the requested translation velocities, and using the proposed ASG to control the thrusters to get the needed angular velocities. ASG implementation optimizes a convex cost function depending on the integral of the tracking error using the ASG method. The application of Barbalat’s lemma justifies the tracking error is converging the origin asymptotically. A class of integral sliding mode controllers with a variable state-dependent gain solves the tracking of the designed reference trajectories in each of the three stages. The sliding surface depends on the tracking error for each pseudo-controller, its integral, and the cost function average. The optimization of the cost function can be done without complete knowledge of the SARV dynamics. A numerical example is presented in this study to confirm the suggested control design’s effectiveness based on the cost function’s time evolution analysis. The forced motion by the proposed controller is compared with the movement obtained by a proportional–integral–derivative (PID) controller. The proposed controller exhibits a better tracking of the reference trajectory than the PID version.

New Insights Into the Social Rumor Characteristics During the COVID-19 Pandemic in China.

BackgroundIn the early stage of the COVID-19 outbreak in China, several social rumors in the form of false news, conspiracy theories, and magical cures had ever been shared and spread among the general public at an alarming rate, causing public panic and increasing the complexity and difficulty of social management. Therefore, this study aims to reveal the characteristics and the driving factors of the social rumors during the COVID-19 pandemic.MethodsBased on a sample of 1,537 rumors collected from Sina Weibo's debunking account, this paper first divided the sample into four categories and calculated the risk level of all kinds of rumors. Then, time evolution analysis and correlation analysis were adopted to study the time evolution characteristics and the spatial and temporal correlation characteristics of the rumors, and the four stages of development were also divided according to the number of rumors. Besides, to extract the key driving factors from 15 rumor-driving factors, the social network analysis method was used to investigate the driver-driver 1-mode network characteristics, the generation driver-rumor 2-mode network characteristics, and the spreading driver-rumor 2-mode characteristics.ResultsResearch findings showed that the number of rumors related to COVID-19 were gradually decreased as the outbreak was brought under control, which proved the importance of epidemic prevention and control to maintain social stability. Combining the number and risk perception levels of the four types of rumors, it could be concluded that the Creating Panic-type rumors were the most harmful to society. The results of rumor drivers indicated that panic psychology and the lag in releasing government information played an essential role in driving the generation and spread of rumors. The public's low scientific literacy and difficulty in discerning highly confusing rumors encouraged them to participate in spreading rumors.ConclusionThe study revealed the mechanism of rumors. In addition, studies involving rumors on different emergencies and social platforms are warranted to enrich the findings.

Harmonic Generation and Inverse Cascade in the z-Pinch Driven, Preseeded Multimode, Magneto-Rayleigh-Taylor Instability.

The magneto-Rayleigh-Taylor instability (MRTI) plays an essential role in astrophysical systems and in magneto-inertial fusion, where it is known to be an important degradation mechanism of confinement and target performance. In this Letter, we show for the first time experimental evidence of mode mixing and the onset of an inverse-cascade process resulting from the nonlinear coupling of two discrete preseeded axial modes (400- and 550-μm wavelengths) on an Al liner that is magnetically imploded using the 20-MA, 100-ns rise-time Z Machine at Sandia National Laboratories. Four radiographs captured the temporal evolution of the MRTI. We introduce a novel unfold technique to analyze the experimental radiographs and compare the results to simulations and to a weakly nonlinear model. We find good quantitative agreement with simulations using the radiation magnetohydrodynamics code hydra. Spectral analysis of the MRTI time evolution obtained from the simulations shows evidence of harmonic generation, mode coupling, and the onset of an inverse-cascade process. The experiments provide a benchmark for future work on the MRTI and motivate the development of new analytical theories to better understand this instability.

Bioactive Phytoconstituents as Potent Inhibitors of Tyrosine-Protein Kinase Yes (YES1): Implications in Anticancer Therapeutics.

Tyrosine-protein kinase Yes (YES1) belongs to the Tyrosine-protein kinase family and is involved in several biological activities, including cell survival, cell–cell adhesion, cell differentiation, and cytoskeleton remodeling. It is highly expressed in esophageal, lung, and bladder cancers, and thus considered as an attractive drug target for cancer therapy. In this study, we performed a virtual screening of phytoconstituents from the IMPPAT database to identify potential inhibitors of YES1. Initially, the molecules were retrieved on their physicochemical properties following the Lipinski rule of five. Then binding affinities calculation, PAINS filter, ADMET, and PASS analyses followed by an interaction analysis to select safe and clinically better hits. Finally, two compounds, Glabrene and Lupinisoflavone C (LIC), with appreciable affinities and a specific interaction towards the AlphaFold predicted structure of YES1, were identified. Their time-evolution analyses were carried out using an all-atom molecular dynamics (MD) simulation, principal component analysis, and free energy landscapes. Altogether, we propose that Glabrene and LIC can be further explored in clinical settings to develop anticancer therapeutics targeting YES1 kinase.

Local variations of the stellar velocity ellipsoid – II. The effect of the bar in the inner regions of Auriga galaxies

ABSTRACT Theoretical works have shown that off-plane motions of bars can heat stars in the vertical direction during buckling but is not clear how do they affect the rest of components of the stellar velocity ellipsoid (SVE). We study the 2D spatial distribution of the vertical, σz, azimuthal, σϕ, and radial, σr velocity dispersions in the inner regions of Auriga galaxies, a set of high-resolution magneto-hydrodynamical cosmological zoom-in simulations, to unveil the influence of the bar on the stellar kinematics. σz and σϕ maps exhibit non-axisymmetric features that closely match the bar light distribution with low-σ regions along the bar major axis and high values in the perpendicular direction. On the other hand, σr velocity dispersion maps present more axisymmetric distributions. We show that isophotal profile differences best capture the impact of the bar on the three SVE components providing strong correlations with bar morphology proxies although there is no relation with individual σ. Time evolution analysis shows that these differences are a consequence of the bar formation and that they tightly coevolve with the strength of the bar. We discuss the presence of different behaviours of σz and its connection with observations. This work helps us understand the intrinsic σ distribution and motivates the use of isophotal profiles as a mean to quantify the effect of bars.

HYDROTHERMAL BEHAVIOR OF FLUID FLOW AND HEAT TRANSFER THROUGH A BENDING SQUARE CHANNEL

The numerous applications in medical fields as well as in industrial areas have drawn substantial attention of the researchers to study the fluid flow and heat transfer (HT) through a bent duct. The present paper demonstrates a spectral-based numerical study of 2D flow in a bent square geometry for various curvature ratios. The numerical calculation has been conducted over Dn, and the curvature ranges from 0.001 to 0.5. The horizontal walls are thermally different where the bottom wall is heated while the ceiling wall cooled, the vertical walls being thermally insulated. After an extensive investigation, we found two branching structures of the solution, each consisting of two branches with 2- to 8-vortex solutions for small and medium curvatures while three branches of solution structure for large curvature. The instability of the flow is then calculated by performing time-evolution (TEv) analysis and by sketching the phase-space (PS) of the solutions. This study also demonstrates that the HT is significantly boosted with the effect of secondary flows (SF) and the increasing secondary vortices boost heat transfer more effectively than other physically realizable solutions.

Application of Cluster Analysis of Time Evolution for Magnetic Resonance Imaging -Derived Oxygen Extraction Fraction Mapping: A Promising Strategy for the Genetic Profile Prediction and Grading of Glioma.

Background: The intratumoral heterogeneity of oxygen metabolism and angiogenesis are core hallmarks of glioma, unveiling that genetic aberrations associated with magnetic resonance imaging (MRI) phenotypes may aid in the diagnosis and treatment of glioma.Objective: To explore the predictability of MRI-based oxygen extraction fraction (OEF) mapping using cluster analysis of time evolution (CAT) for genetic profiling and glioma grading.Methods: Ninety-one patients with histopathologically confirmed glioma were examined with CAT for quantitative susceptibility mapping and quantitative blood oxygen level–dependent magnitude-based OEF mapping and dynamic contrast-enhanced (DCE) MRI. Imaging biomarkers, including oxygen metabolism (OEF) and angiogenesis [volume transfer constant, cerebral blood volume (CBV), and cerebral blood flow], were investigated to predict IDH mutation, O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status, receptor tyrosine kinase (RTK) subgroup, and differentiation of glioblastoma (GBM) vs. lower-grade glioma (LGG). The corresponding DNA sequencing was also obtained. Results were compared with DCE-MRI using receiver operating characteristic (ROC) analysis.Results: IDH1-mutated LGGs exhibited significantly lower OEF and hypoperfusion than IDH wild-type tumors (all p < 0.01). OEF and perfusion metrics showed a tendency toward higher values in MGMT unmethylated GBM, but only OEF retained significance (p = 0.01). Relative prevalence of RTK alterations was associated with increased OEF (p = 0.003) and perfusion values (p < 0.05). ROC analysis suggested OEF achieved best performance for IDH mutation detection [area under the curve (AUC) = 0.828]. None of the investigated parameters enabled prediction of MGMT status except OEF with a moderate AUC of 0.784. Predictive value for RTK subgroup was acceptable by using OEF (AUC = 0.764) and CBV (AUC = 0.754). OEF and perfusion metrics demonstrated excellent performance in glioma grading. Moreover, mutational landscape revealed hypoxia or angiogenesis-relevant gene signatures were associated with specific imaging phenotypes.Conclusion: CAT for MRI-based OEF mapping is a promising technology for oxygen measurement and along with perfusion MRI can predict genetic profiles and tumor grade in a non-invasive and clinically relevant manner.Clinical Impact: Physiological imaging provides an in vivo portrait of genetic alterations in glioma and offers a potential strategy for non-invasively selecting patients for individualized therapies.

The Spatiotemporal Evolution of MRI-Derived Oxygen Extraction Fraction and Perfusion in Ischemic Stroke.

PurposeThis study aimed to assess the spatiotemporal evolution of oxygen extraction fraction (OEF) in ischemic stroke with a newly developed cluster analysis of time evolution (CAT) for a combined quantitative susceptibility mapping and quantitative blood oxygen level-dependent model (QSM + qBOLD, QQ).MethodOne hundred and fifteen patients in different ischemic stroke phases were retrospectively collected for measurement of OEF of the infarcted area defined on diffusion-weighted imaging (DWI). Clinical severity was assessed using the National Institutes of Health Stroke Scale (NIHSS). Of the 115 patients, 11 underwent two longitudinal MRI scans, namely, three-dimensional (3D) multi-echo gradient recalled echo (mGRE) and 3D pseudo-continuous arterial spin labeling (pCASL), to evaluate the reversal region (RR) of the initial diffusion lesion (IDL) that did not overlap with the final infarct (FI). The temporal evolution of OEF and the cerebral blood flow (CBF) in the IDL, the RR, and the FI were assessed.ResultsCompared to the contralateral mirror area, the OEF of the infarcted region was decreased regardless of stroke phases (p < 0.05) and showed a declining tendency from the acute to the chronic phase (p = 0.022). Five of the 11 patients with longitudinal scans showed reversal of the IDL. Relative oxygen extraction fraction (rOEF, compared to the contralateral mirror area) of the RR increased from the first to the second MRI (p = 0.044). CBF was about 1.5-fold higher in the IDL than in the contralateral mirror area in the first MRI. Two patients showed penumbra according to the enlarged FI volume. The rOEF of the penumbra fluctuated around 1.0 at earlier scan times and then decreased, while the CBF decreased continuously.ConclusionThe spatiotemporal evolution of OEF and perfusion in ischemic lesions is heterogeneous, and the CAT-based QQ method is feasible to capture cerebral oxygen metabolic information.

Temporal clustering, tissue composition, and total variation for mapping oxygen extraction fraction using QSM and quantitative BOLD.

To improve the accuracy of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) based mapping of oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2 ) using temporal clustering, tissue composition, and total variation (CCTV). Three-dimensional multi-echo gradient echo and arterial spin labeling images were acquired from 11 healthy subjects and 33 ischemic stroke patients. Diffusion-weighted imaging (DWI) was also obtained from patients. The CCTV mapping was developed for incorporating tissue-type information into clustering of the previous cluster analysis of time evolution (CAT) and applying total variation (TV). The QQ-based OEF and CMRO2 were reconstructed with CAT, CAT+TV (CATV), and the proposed CCTV, and results were compared using region-of-interest analysis, Kruskal-Wallis test, and post hoc Wilcoxson rank sum test. In simulation, CCTV provided more accurate and precise OEF than CAT or CATV. In healthy subjects, QQ-based OEF was less noisy and more uniform with CCTV than CAT. In subacute stroke patients, OEF with CCTV had a greater contrast-to-noise ratio between DWI-defined lesions and the unaffected contralateral side than with CAT or CATV: 1.9 ± 1.3 versus 1.1 ± 0.7 (P = .01) versus 0.7 ± 0.5 (P < .001). The CCTV mapping significantly improves the robustness of QQ-based OEF against noise.