Cross-correlation Research Articles

ESPRESSO reveals blueshifted neutral iron emission lines on the dayside of WASP-76 b

Context. Ultra hot Jupiters (gas giants with Teq > 2000 K) are intriguing exoplanets due to the extreme physics and chemistry present in their atmospheres. Their torrid daysides can be characterised using ground-based high-resolution emission spectroscopy. Aims. We search for signatures of neutral and singly ionised iron (Fe I and Fe II, respectively) in the dayside of the ultra hot Jupiter WASP-76 b, as these species were detected via transmission spectroscopy in this exoplanet. Furthermore, we aim to confirm the existence of a thermal inversion layer, which has been reported in previous studies, and attempt to constrain its properties. Methods. We observed WASP-76 b on four epochs with ESPRESSO at the VLT, at orbital phases shortly before and after the secondary transit, when the dayside is in view. We present the first analysis of high-resolution optical emission spectra for this exoplanet. We compare the data to synthetic templates created with petitRADTRANS, using cross-correlation function techniques. Results. We detect a blueshifted (−4.7 ± 0.3 km s−1) Fe I emission signature on the dayside of WASP-76 b at 6.0σ. The signal is detected independently both before and after the eclipse, and it is blueshifted in both cases. The presence of iron emission features confirms the existence of a thermal inversion layer. Fe II was not detected, possibly because this species is located in the upper layers of the atmosphere, which are more optically thin. Thus the Fe II signature on the dayside of WASP-76 b is too weak to be detected with emission spectroscopy. Conclusions. We propose that the blueshifted Fe I signature is created by material rising from the hot spot to the upper layers of the atmosphere, and discuss possible scenarios related to the position of the hotspot. This work unveils some of the dynamic processes ongoing on the dayside of the ultra hot Jupiter WASP-76 b through the analysis of the Fe I signature from its atmosphere, and complements previous knowledge obtained from transmission studies. It also highlights the ability of ESPRESSO to probe the dayside of this class of exoplanets.

Time Series of Magnetic Field Parameters of Merged MDI and HMI Space-weather Active Region Patches as Potential Tool for Solar Flare Forecasting

Solar flare prediction studies have been recently conducted with the use of Space-Weather MDI (Michelson Doppler Imager on board Solar and Heliospheric Observatory) Active Region Patches (SMARPs) and Space-Weather HMI (Helioseismic and Magnetic Imager on board Solar Dynamics Observatory) Active Region Patches (SHARPs), which are two currently available data products containing magnetic field characteristics of solar active regions (ARs). The present work is an effort to combine them into one data product, and perform some initial statistical analyses in order to further expand their application in space-weather forecasting. The combined data are derived by filtering, rescaling, and merging the SMARP and SHARP parameters, which can then be spatially reduced to create uniform multivariate time series. The resulting combined MDI–HMI data set currently spans the period between 1996 April 4 and 2022 December 13, and may be extended to a more recent date. This provides an opportunity to correlate and compare it with other space-weather time series, such as the daily solar flare index or the statistical properties of the soft X-ray flux measured by the Geostationary Operational Environmental Satellites. Time-lagged cross correlation indicates that a relationship may exist, where some magnetic field properties of ARs lead the flare index in time. Applying the rolling-window technique makes it possible to see how this leader–follower dynamic varies with time. Preliminary results indicate that areas of high correlation generally correspond to increased flare activity during the peak solar cycle.

Powder-diffraction-based structural comparison for crystal structure prediction without prior indexing

The objective of crystal structure prediction (CSP) is to predict computationally the thermodynamically stable crystal structure of a compound from its stoichiometry or its molecular diagram. Crystal similarity indices measure the degree of similarity between two crystal structures and are essential in CSP because they are used to identify duplicates. Powder-based indices, which are based on comparing X-ray diffraction patterns, allow the use of experimental X-ray powder diffraction data to inform the CSP search. Powder-assisted CSP presents two unique difficulties: (i) the experimental and computational structures are not entirely comparable because the former is subject to thermal expansion from lattice vibrations, and (ii) experimental patterns present features (noise, background contribution, varying peak shapes etc.) that are not easily predictable computationally. This work presents a powder-based similarity index (GPWDF) based on a modification of the index introduced by de Gelder, Wehrens & Hageman [J. Comput. Chem. (2001), 22, 273–289] using cross-correlation functions that can be calculated analytically. Based on GPWDF, a variable-cell similarity index (VC-GPWDF) is also proposed that assigns a high similarity score to structures that differ only by a lattice deformation and which takes advantage of the analytical derivatives of GPWDF with respect to the lattice parameters. VC-GPWDF can be used to identify similarity between two computational structures generated using different methods, between a computational and an experimental structure, and between two experimental structures measured under different conditions (e.g. different temperature and pressure). VC-GPWDF can also be used to compare crystal structures with experimental patterns in combination with an automatic pre-processing step. The proposed similarity indices are simple, efficient and fully automatic. They do not require indexing of the experimental pattern or a guess of the space group, they account for deformations caused by varying experimental conditions, they give meaningful results even when the experimental pattern is of very poor quality, and their computational cost does not increase with the flexibility of the molecular motif.

ARIMAX-Threshold 모형을 이용한 20대 이하 마약류 사범 검거 수 시계열 분석

In recent years, the number of drug offenders under 20s has surged, accounting for approximately 35.5% of all drug offenders in 2023, which becomes a serious social issue in Korea. The purpose of this work is to study and compare time series models for statistical analysis and prediction for the number of drug offenders under 20s. ARIMA and ARIMAX model is applied to the data of the number of drug offenders arrested under 20s. In order to further improve the predictability, a model added by threshold, reflecting the presence or absence of the exogenous variable, is proposed and compared. As for the exogenous variable, dark web data that is directly related to the number of drug offenders arrested, as a main drug dealer, is used. Based on cross-correlation coefficient analysis, the ARIMAX-Threshold model including indicator variables and threshold is proposed. Out-of-sample forecasting analysis is performed to compare the forecasting performance, and it is revealed that the ARIMAX-Threshold model proposed in this study improves the forecasting errors by up to 9.4%. The predicted values and prediction intervals of the data on the number of drug offenders under 20s until April 2025 are presented, which will contribute to help in policy development for drug crime prevention.

Impact of potential and temperature fluctuations on charge and heat transport in quantum Hall edges in the heat Coulomb blockade regime

We present a broad study of charge and heat transport in a mesoscopic system where one or several quantum Hall edge channels are strongly coupled to a floating Ohmic contact (OC). It is well-known that charge-current fluctuations emanating from the OC along the edge channels are highly susceptible to the OC charge capacitance in the heat Coulomb blockade regime (an impeded ability of the OC to equilibrate edge channels). Here, we demonstrate how potential and temperature fluctuations due to finite OC charge and heat capacities impact the heat-current fluctuations emitted from the OC. First, by assuming an infinite OC heat capacity, we show that the output heat-current noise is strongly dependent on the OC charge capacitance, following from a close relation between one-dimensional charge and heat currents. When also the OC heat capacity is finite, an interplay of potential and temperature fluctuations influences the heat transport. Concretely, we find that the effect of the charge capacitance on heat transport manifests in terms of a strongly increased energy relaxation time in the heat Coulomb blockade regime. Furthermore, we find expressions for a broad set of output observables, such as charge and heat auto- and cross correlations, as functions of input and OC fluctuations, depending on the relation between charge and energy relaxation times compared to the frequency of fluctuations and inverse (local) temperatures as well as on the number of edge channels attached to the OC. Finally, we show that a finite OC heat capacity transforms the full counting statistics of the output charge from Gaussian to non-Gaussian. Our findings provide opportunities to experimentally probe and harness the quantum nature of heat transport in strongly coupled electron circuits. Published by the American Physical Society 2024

A novel approach to estimating harmonic/interharmonic parameters

The conventional fast Fourier transforms(FFT) approach is susceptible to noise and has a low-frequency resolution with short sampling windows. This is a bottleneck problem for accurately monitoring harmonic and interharmonic. A new method is proposed to detect the parameters of harmonic and interharmonic in this study. At the beginning, the cross-correlation function and FFT estimate the rough values of frequency and phase. Next, the error between the rough and true values is derived. Then, the incoherence of signal and noise is used to create the cost function, and the optimal initial value is computed from the rough values and the error. Finally, the initial value is used in cost function iteration, the so-called Newton–Raphson method, to calculate the frequency and phase of harmonic and interharmonic. The least squares method is used to compute the amplitudes of harmonic and interharmonic. The proposed method allows adaptively selecting the basis function for the cost function instead of using the traditional orthogonal basis. Therefore, it achieves the super-resolution evaluation of harmonic and interharmonic frequency. The simulation results demonstrate that the proposed method provides high accuracy of parameter estimation and strong noise immunity.

Integrated Study of X-Ray Spectrum and Time Lags for HBL Mrk 421 within the Framework of the Multiple-zone Leptonic Model

We present the timing analysis of 10 archived XMM-Newton observations with an exposure of >40 ks of Markarian 421. Mrk 421 is the brightest high-frequency-peaked BL Lac object emitting in X-rays produced by electrons accelerated in the innermost regions of a relativistic jet pointing toward us. For each observation, we construct averaged X-ray spectra in 0.5–10 keV band, as well as 100 s binned light curves (LCs) in various subbands. During these observations, the source exhibited various intensity states differing by close to an order of magnitude in flux, with the fractional variability amplitude increasing with energy through the X-ray band. Bayesian power spectral density analysis reveals that the X-ray variability can be characterized by a colored noise, with an index ranging from ∼ −1.9 to −3.0. Moreover, both the standard cross-correlation function and cross-spectral methods indicate that the amount of time lags increases with the energy difference between two compared LCs. A time-dependent two-zone jet model is developed to extract physical information from the X-ray emission of Mrk 421. In the model, we assume that the jet emission mostly comprises a quasi-stationary component and a highly variable one. Our results show that the two-zone model can simultaneously provide a satisfactory description for both the X-ray spectra and time lags observed in different epochs, with the model parameters constrained in a fully acceptable interval. We suggest that shocks within the jets may be the primary energy dissipation process responsible for triggering the rapid variability, although magnetic reconnection cannot be excluded.

Significance testing for cross correlation: A critical examination of correlations between ENSO and GRACE-derived terrestrial water storage variabilities

The cross correlation has a wide range of applications in geophysical fields for measuring linear connections or relationships among physical quantities. Nonetheless, there remains a dearth of comprehensive discourse regarding its statistical significance testing, which is crucial for differentiating meaningful outcomes from those merely stemming from fortuity or pure randomness. Conventional theoretical methods for significance testing, commonly used in statistical analysis tools such as SPSS and MATLAB, are only applicable when dealing with idealized circumstances such as white noise. In discretionary application of these methods to analyze geophysical signals with, say, red noise may result in potentially unjustified conclusions. This study aims to develop a comprehensive approach based on the t-distribution within a rigorous statistical context, aiming to facilitate significance tests of cross correlation for general signals with specified time shifts or ranges. Extensive Monte Carlo experiments substantiate its robustness, thereby paving the way for accurate and expeditious identification of statistically (hence potentially physically) meaningful correlations in general. As an example, we examine critically the previously purported significant correlations between ENSO (El Niño Southern Oscillation) and global terrestrial water storage variations derived from the GRACE (Gravity Recovery and Climate Experiment) satellite mission, demonstrating that they are subject to questioning in the absence of complete significance testing.

Ankle Moment Estimation Based on A Novel Distributed Plantar Pressure Sensing System.

Ankle moment plays an important role in human gait analysis, patients' rehabilitation process monitoring, and the human-machine interaction control of exoskeleton robots. However, current ankle moment estimation methods mainly rely on inverse dynamics (ID) based on optical motion capture system (OMC) and force plate. These methods rely on fixed instruments in the laboratory, which are difficult to be applied to the control of exoskeleton robots. To solve this problem, this paper developed a new distributed plantar pressure system and proposed an ankle plantar flexion moment estimation method using the plantar pressure system. We integrated eight pressure sensors in each insole to collect the pressure data of the key area of the foot and then used the plantar pressure data to train four neural networks to obtain the ankle moment. The performance of the models was evaluated using normalized root mean square error (NRMSE) and cross-correlation coefficient (ρ). During experiments, eight subjects were recruited for the overground walking tests, and OMC and force plate were used as the gold standard. The results indicate that the Genetic algorithm - Gated recurrent unit estimation algorithm (GA-GRU) was the best estimation model which achieved the highest accuracy in generalized ankle moment estimation (NRMSE = 7.23%, ρ = 0.85) compared with the other models. The designed novel distributed plantar pressure system and the proposed method could serve as a joint moment estimation approach in wearable robot control and human motion state monitoring.

Does the S-curve demonstrate an asymmetrical response to fluctuations in exchange rates?

PurposeThis study aims to examine the asymmetric S-curve between the trade balances of Pakistan and China at the commodity level using disaggregated data.Design/methodology/approachThis study focuses on Pakistan and China bilateral trade based on commodity-level data. This study delves into the S-curve phenomena by examining time series data from 1980 to 2023 across 32 three-digit industries/commodities.FindingsThe findings show significant evidence in favor of the “asymmetric S-curve” in 27 out of the 32 industries studied. This study confirms that the devaluation of home currency is not a viable solution always to improve trade balance.Research limitations/implicationsThis study considers 32 three-digit industries limiting the generalizability of findings. Due to data unavailability, the authors fail to consider other industries. In the absence of quarterly data on industry-level trade between Pakistan and China, annual data from 1980 to 2023 were used in generating the cross-correlation functions. Previous literature frequently resorted to the general consumer price index with its inherent aggregation issues, whereas this study has opted for commodity price indices to overcome the shortcomings in the estimation of S-curves at the commodity level.Practical implicationsThe findings have practical relevance in guiding policy decisions regarding commodity trade, whereas the industry-wise analysis enriches the understanding of the short-term effects of currency depreciation on trade balance dynamics.Originality/valueThe S-curve hypothesis predicts a negative cross-correlation between a country's current exchange rate and its past trade balance and a positive cross-correlation between the current exchange rate and its future trade balance. Previous empirical S-curve studies had the limitation of assuming symmetry in cross-correlation with both current and future trade balance values.

Arterial blood pressure monitoring in stroke cohorts: the impact of reduced sampling rates to optimise remote patient monitoring.

Remote patient monitoring (RPM) beat-to-beat blood pressure (BP) provides an opportunity to measure poststroke BP variability (BPV), which is associated with clinical stroke outcomes. BP sampling interval (SI) influences ambulatory BPV, but RPM BP SI optimisation research is limited. SI and RPM device capabilities require compromises, meaning SI impact requires investigation. Therefore, this study assessed healthy and stroke subtype BPV via optimised BP sampling, aiding sudden BP change identification and potentially assisting cardiovascular event (recurrent stroke) prediction. Leicester Cerebral Haemodynamic Database ischaemic [acute ischaemic stroke (AIS), n = 68] and haemorrhagic stroke (intracerebral haemorrhage, n = 12) patient and healthy control (HC, n = 40) baseline BP data were analysed. Intrasubject and interpatient SD (SDi/SDp) represented individual/population variability with synthetically altered SIs. Matched-filter approaches using cross-correlation function detected sudden BP changes. At SIs between 1 and 180 s, SBP and DBP SDi staticised while SDp increased at SI < 30 s. Mean BP and HR SDi and SDp increased at SI < 60s. AIS BPV, normalised to SI1s, increased at SI30s (26%-131%) and SI120s (1%-274%). BPV increased concomitantly with SI. Cross-correlation analysis showed HC and AIS BP sudden change detection accuracy reductions with increasing SI. Positive BP deviation detection fell 48.48% (SI10s) to 78.79% (SI75s) in HC and 67.5% (SI10s) to 100% (SI75s) in AIS. Negative BP deviation detection fell 50% (SI10s) to 82.35% (SI75s) in HC and 52.27% (SI10s) to 95.45% (SI75s) in AIS. Sudden BP change detection and BPV are relatively robust to SI increases within certain limits, but accuracy reductions generate unacceptable estimates, considerable within RPM device design. This research warrants further SI optimisation.

Parameter-free super-resolution structured illumination microscopy via a physics-enhanced neural network.

With full-field imaging and high photon efficiency advantages, structured illumination microscopy (SIM) is one of the most potent super-resolution (SR) modalities in bioscience. Regarding SR reconstruction for SIM, spatial domain reconstruction (SDR) has been proven to be faster than traditional frequency domain reconstruction (FDR), facilitating real-time imaging of live cells. Nevertheless, SDR relies on high-precision parameter estimation for reconstruction, which tends to suffer from low signal-to-noise ratio (SNR) conditions and inevitably leads to artifacts that seriously affect the accuracy of SR reconstruction. In this Letter, a physics-enhanced neural network-based parameter-free SDR (PNNP-SDR) is proposed, which can achieve SR reconstruction directly in the spatial domain. As a result, the peak-SNR (PSNR) of PNNP-SDR is improved by about 4 dB compared to the cross-correlation (COR) SR reconstruction; meanwhile, the reconstruction speed of PNNP-SDR is even about five times faster than the fast approach based on principal component analysis (PCA). Given its capability of achieving parameter-free imaging, noise robustness, and high-fidelity and high-speed SR reconstruction over conventional SIM microscope hardware, the proposed PNNP-SDR is expected to be widely adopted in biomedical SR imaging scenarios.

Spatiotemporal correlation enhanced real-time 4D-CBCT imaging using convolutional LSTM networks.

To enhance the accuracy of real-time four-dimensional cone beam CT (4D-CBCT) imaging by incorporating spatiotemporal correlation from the sequential projection image into the single projection-based 4D-CBCT estimation process. We first derived 4D deformation vector fields (DVFs) from patient 4D-CT. Principal component analysis (PCA) was then employed to extract distinctive feature labels for each DVF, focusing on the first three PCA coefficients. To simulate a wide range of respiratory motion, we expanded the motion amplitude and used random sampling to generate approximately 900 sets of PCA labels. These labels were used to produce 900 simulated 4D-DVFs, which in turn deformed the 0% phase 4D-CT to obtain 900 CBCT volumes with continuous motion amplitudes. Following this, the forward projection was performed at one angle to get all of the digital reconstructed radiographs (DRRs). These DRRs and the PCA labels were used as the training data set. To capture the spatiotemporal correlation in the projections, we propose to use the convolutional LSTM (ConvLSTM) network for PCA coefficient estimation. For network testing, when several online CBCT projections (with different motion amplitudes that cover the full respiration range) are acquired and sent into the network, the corresponding 4D-PCA coefficients will be obtained and finally lead to a full online 4D-CBCT prediction. A phantom experiment is first performed with the XCAT phantom; then, a pilot clinical evaluation is further conducted. Results on the XCAT phantom and the patient data show that the proposed approach outperformed other networks in terms of visual inspection and quantitative metrics. For the XCAT phantom experiment, ConvLSTM achieves the highest quantification accuracy with MAPE(Mean Absolute Percentage Error), PSNR (Peak Signal-to-Noise Ratio), and RMSE(Root Mean Squared Error) of 0.0459, 64.6742, and 0.0011, respectively. For the patient pilot clinical experiment, ConvLSTM also achieves the best quantification accuracy with that of 0.0934, 63.7294, and 0.0019, respectively. The quantification evaluation labels that we used are 1) the Mean Absolute Error (MAE), 2) the Normalized Cross Correlation (NCC), 3)the Structural Similarity Index Measurement(SSIM), 4)the Peak Signal-to-Noise Ratio (PSNR), 5)the Root Mean Squared Error(RMSE), and 6) the Absolute Percentage Error (MAPE). The spatiotemporal correlation-based respiration motion modeling supplied a potential solution for accurate real-time 4D-CBCT reconstruction.

FASHI: A Search for Extragalactic OH Megamasers with FAST

The FAST All Sky H i survey (FASHI) is broader in frequency band and sky volume, and deeper in detection sensitivity than the Arecibo Legacy Fast ALFA survey. To efficiently expand the sample of OH megamasers (OHMs), whose strongest line has a rest frequency of 1667.35903 MHz, we directly matched the IRAS Point Source Catalog Redshift (PSCz) catalog with the corresponding FASHI data cube. From 145 PSCz sources already covered by FASHI, we obtained 27 OHMs with a detection rate of 18.6%, including 9 previously known and 18 new ones, within a redshift range of 0.14314 ≲ z OH ≲ 0.27656. We also measured the hyperfine ratio of nine OHMs between the 1667 and 1665 MHz lines. The ratio ranges from 1.32 to 15.22, with an average of R 1667:1665 = 4.74. In a fit to the L OH versus L FIR relation, we have logLOH=(1.57±0.10)logLFIR−(15.80±1.19) , which is almost the same as derived from previous observations. As expected, since the OHM sample was selected by cross correlation with the IRAS-selected PSCz, our detected OHMs are (ultra)luminous infrared galaxies ((U)LIRGs). However, not all (U)LIRGs have detectable OH emission, suggesting that the OH emission may be triggered within a specific stage of the merger or can only be seen in specific orientations. In general, FAST, with its 19-beam array and Ultra-Wide Bandwidth receiver, will be a powerful tool for observing more OHMs and unraveling their mystery in the future.

Comparative analysis of parametric B-spline and Hermite cubic spline based methods for accurate ECG signal modeling

Analyzing Electrocardiogram (ECG) signals is imperative for diagnosing cardiovascular diseases. However, evaluating ECG analysis techniques faces challenges due to noise and artifacts in actual signals. Machine learning for automatic diagnosis encounters data acquisition hurdles due to medical data privacy constraints. Addressing these issues, ECG modeling assumes a crucial role in biomedical and parametric spline-based methods have garnered significant attention for their ability to accurately represent the complex temporal dynamics of ECG signals. This study conducts a comparative analysis of two parametric spline-based methods—B-spline and Hermite cubic spline—for ECG modeling, aiming to identify the most effective approach for accurate and reliable ECG representation. The Hermite cubic spline serves as one of the most effective interpolation methods, while B-spline is an approximation method. The comparative analysis includes both qualitative and quantitative evaluations. Qualitative assessment involves visually inspecting the generated spline-based models, comparing their resemblance to the original ECG signals, and employing power spectrum analysis. Quantitative analysis incorporates metrics such as root mean square error (RMSE), Percentage Root Mean Square Difference (PRD) and cross correlation, offering a more objective measure of the model's performance. Preliminary results indicate promising capabilities for both spline-based methods in representing ECG signals. However, the analysis unveils specific strengths and weaknesses for each method. The B-spline method offers greater flexibility and smoothness, while the cubic spline method demonstrates superior waveform capturing abilities with the preservation of control points, a critical aspect in the medical field. Presented research provides valuable insights for researchers and practitioners in selecting the most appropriate method for their specific ECG modeling requirements. Adjustments to control points and parameterization enable the generation of diverse ECG waveforms, enhancing the versatility of this modeling technique. This approach has the potential to extend its utility to other medical signals, presenting a promising avenue for advancing biomedical research.

Insight into structural dynamics involved in activation mechanism of full length KRAS wild type and P-loop mutants

KRAS protein is known to be frequently mutated in various cancers. The most common mutations being at position 12, 13 and 61. The positions 12 and 13 form part of the phosphate binding region (P-loop) of KRAS. Owing to mutation, the protein remains in continuous active state and affects the normal cellular process. Understanding the structural changes owing to mutations in GDP-bound (inactive state) and GTP-bound (active state) may help in the design of better therapeutics. To understand the structural flexibility due to the mutations specifically located at P-loop regions (G12D, G12V and G13D), extensive molecular dynamics simulations (24 μs) have been carried for both inactive (GDP-bound) and active (GTP-bound) structures for the wild type and these mutants. The study revealed that the local structural changes at the site of mutations allosterically guide changes in distant regions of the protein through hydrogen bond and hydrophobic signalling network. The dynamic cross correlation analysis and the comparison of the correlated motions among different systems manifested that changes in SW-I, SW-II, α3 and the loop preceding α3 affects the interactions of GDP/GTP with different regions of the protein thereby affecting its hydrolysis. Further, the Markov state modelling analysis confirmed that the mutations, especially G13D imparts rigidity to structure compared to wild type and thus limiting its conformational state in either intermediate state or active state. The study suggests that along with SW-I and SW-II regions, the loop region preceding the α3 helix and α3 helix are also involved in affecting the hydrolysis of nucleotides and may be considered while designing therapeutics against KRAS.

Wastewater Surveillance to Confirm Differences in Influenza A Infection between Michigan, USA, and Ontario, Canada, September 2022-March 2023.

Wastewater surveillance is an effective way to track the prevalence of infectious agents within a community and, potentially, the spread of pathogens between jurisdictions. We conducted a retrospective wastewater surveillance study of the 2022-23 influenza season in 2 communities, Detroit, Michigan, USA, and Windsor-Essex, Ontario, Canada, that form North America's largest cross-border conurbation. We observed a positive relationship between influenza-related hospitalizations and the influenza A virus (IAV) wastewater signal in Windsor-Essex (ρ = 0.785; p<0.001) and an association between influenza-related hospitalizations in Michigan and the IAV wastewater signal for Detroit (ρ = 0.769; p<0.001). Time-lagged cross correlation and qualitative examination of wastewater signal in the monitored sewersheds showed the peak of the IAV season in Detroit was delayed behind Windsor-Essex by 3 weeks. Wastewater surveillance for IAV reflects regional differences in infection dynamics which may be influenced by many factors, including the timing of vaccine administration between jurisdictions.

The minimal membrane requirements for BAX-induced pore opening upon exposure to oxidative stress

Perforation of the outer mitochondrial membrane triggered by BAX and facilitated by its main activator cBID is a fundamental process in cell apoptosis. Here, we employ a newly designed correlative approach based on a combination of a fluorescence cross correlation binding with a calcein permeabilization assay to understand the involvement of BAX in pore formation under oxidative stress conditions. To mimic the oxidative stress, we enriched liposomal membranes by phosphatidylcholines with truncated sn-2 acyl chains terminated by a carboxyl or aldehyde moiety. Our observations revealed that oxidative stress enhances proapoptotic conditions involving accelerated pore-opening kinetics. This enhancement is achieved through increased recruitment of BAX to the membrane and facilitation of BAX membrane insertion. Despite these effects, the fundamental mechanism of pore formation remained unchanged, suggesting an all-or-none mechanism. In line with this mechanism, we demonstrated that the minimal number of BAX molecules at the membrane necessary for pore formation remains constant regardless of BAX activation by cBID or the presence of oxidized lipids. Overall, our findings give a comprehensive picture of the molecular mechanisms underlying apoptotic pore formation and highlight the selective amplifying role of oxidized lipids in triggering formation of membrane pores.

An online decoupling-whitening frequency domain filtered-error least mean square algorithm for active road noise control.

Active road noise control (ARNC) systems have been widely investigated for low-frequency road noise attenuation in vehicle cabins. Multiple reference and error sensors are usually required to ensure noticeable noise reduction. However, this tends to slow down the convergence speed of adaptive algorithms due to the coupling of secondary paths and the cross correlation of reference signals. Furthermore, the high computational burden of normally utilized multichannel control algorithms exacerbates the difficulty of practical implementations. In this paper, an online decoupling-whitening frequency domain filtered-error least mean square (ODW-FDFeLMS) algorithm is proposed to address the aforementioned problems. Secondary path decoupling through inner-outer product decomposition and online reference whitening through spectral factorization effectively accelerate the convergence rate. Additionally, the utilization of the filtered-error algorithm based on frequency domain processing mitigates the computational complexity. Simulations with measured road noise data confirm the superiority of the ODW-FDFeLMS algorithm over existing algorithms in terms of convergence speed and computational complexity. Real-time experiments in a vehicle cabin further confirm the effectiveness of the proposed algorithm.

A New Development of Cross-Correlation-Based Flow Estimation Validated and Optimized by CFD Simulation

The accurate measurement of mass flow rates is important in nuclear power plants. Flow meters have been invented and widely applied in several industries; however, the operating environment in advanced nuclear power plants is especially harsh due to high temperatures, high radiation, and potentially corrosive conditions. Traditional flow meters are largely limited to deployment at the outlet of pumps, on pipes, or in limited geometries. Cross-correlation function (CCF) flow estimation, on the other hand, can estimate the flow velocity indirectly without any specific instruments for flow measurement and in any geometry of the flow region. CCF flow estimation relies on redundant instruments, typically temperature sensors, in series in the direction of flow. One challenge for CCF flow estimation is that the accuracy of the flow measurement is mainly determined by inherent, common local process variation across the sensors, which may be small compared to the uncorrelated measurement noise. To differentiate the process variations from the uncorrelated noise, this research implements periodic fluid injection at a different temperature than the bulk fluid before the temperature sensors to amplify process variation. The feasibility and accuracy of this method are investigated through flow loop experiments and Computational Fluid Dynamics (CFD) simulations. This paper focuses on a CFD simulation model to verify the previous experimental results and optimize CCF flow estimation with different configurations. The optimization study is carried out to perform a grid search on the optimal location of the sensor pair under different flow rates. The CFD results show that the optimal sensor spacing depends on the flow rate being measured and provides guidance for sensor location implementation under various anticipated flow rates.