Weighted Mean Frequency Research Articles

Splenic Embolism in Infective Endocarditis: A Systematic Review of the Literature with an Emphasis on Radiological and Histopathological Diagnoses.

Infective endocarditis (IE) is characterised by fever, heart murmurs, and emboli. Splenic emboli are frequent in left-sided IE. A systematic review of the literature published on splenic embolism (SE) between 2000 and 2023 was conducted. Search strategies in electronic databases identified 2751 studies published between 1 January 2000 and 4 October 2023, of which 29 were finally included. The results showed that the imaging tests predominantly used to detect embolisms were computed tomography (CT), magnetic resonance imaging, positron emission tomography (PET)/CT, single-photon emission computed tomography/CT, ultrasound, and contrast-enhanced ultrasound. More recent studies typically used 18F-FDG PET-CT. The proportion of SE ranged from 1.4% to 71.7%. Only seven studies performed systematic conventional CT screening for intra-abdominal emboli, and the weighted mean frequency of SE was 22% (range: 8-34.8%). 18F-FDG PET-CT was performed systematically in seven studies, and splenic uptake was found in a weighted mean of 4.5%. There was a lack of uniformity in the published literature regarding the frequency and management of splenic embolisation. CT scans were the most frequently used method, until recently, when 18F-FDG PET-CT scans began to predominate. More data are necessary regarding the frequency of SE, especially focusing on their impact on IE management and prognosis.

Spectral analysis of cross-hole sonic logging data for pile integrity assessment

Ensuring the safety of foundations requires advanced non-destructive testing techniques. Cross-hole sonic logging (CSL) is a widely adopted method for evaluating the integrity of deep foundation elements, such as bored piles, barrettes, and diaphragm walls. This method involves analyzing the propagation time and relative energy of ultrasonic pulses transmitted and registered by probes inserted into pre-installed access tubes. However, in certain cases, standard analyses may not effectively distinguish anomalous signals arising from defects and other factors unrelated to concrete quality. Our study explores the potential of an alternative frequency-domain approach for CSL data analysis. We propose new attributes to quantify ultrasonic signal spectra, including spectrum area, normalized spectrum area, weighted mean frequency, and maximum frequency. These attributes are automatically calculated, eliminating the need for additional data processing time and minimizing the risk of human error. The proposed approach was applied to CSL data collected from two bored piles of 46 m length and successfully identified signals classified as anomalous through standard time-domain analysis. Further research is deemed necessary to fully explore the potential of the frequency-domain approach in enhancing the information content and reliability of CSL pile integrity testing

Epileptic Seizure Detection in EEG Signal Using Optimized Convolutional Neural Network with Selected Feature Set

This article implements novel epileptic seizure detection by EEG signal that includes various phases: Feature extraction, Feature selection, and Classification. Originally, the input EEG signal is subjected to the preprocessing phase, and from the preprocessed signal, certain features are extracted in which the features like TDFs, FDFs, and TFDFs are extracted. Here, the Time Domain Features (TDFs) include Non-linear energy (NE), Permutation Entropy (PE), and Weighted Permutation Entropy (WPE); the Frequency Domain Features (FDFs) consists of Intensity Weighted Mean Frequency (IWMF); and the Time-Frequency Domain Features (TFDFs) consists of the “Weighted Multi-scale Rényi Permutation Entropy (WMRPE)”. Moreover, the extracted features are subjected to the feature selection phase. An improved chi-square model is proposed for selecting the appropriate features. Then, the chosen features are subjected to the classification process. Here, the classification is performed using an Optimized Convolutional Neural Network (CNN). To make the detection more precise, the CNN weights are tuned optimally through a Weighting Factor Based Shark Smell Optimization (IWFSSO) model. The MCC of the implemented CNN+IWFSSO method achieved a better value for a learning percentage 90%; nevertheless, the compared existing models accomplish a smaller value for dataset 2.

Electronic Structure and Hole Transfer of All B-DNA Dimers and Homopolymers, via the Fishbone-Wire Model.

We employ the Tight Binding Fishbone-Wire Model to study the electronic structure and coherent transfer of a hole (the absence of an electron created by oxidation) in all possible ideal B-DNA dimers as well as in homopolymers (one base pair repeated along the whole sequence with purine on purine). The sites considered are the base pairs and the deoxyriboses, with no backbone disorder. For the time-independent problem, we calculate the eigenspectra and the density of states. For the time-dependent problem after oxidation (i.e., the creation of a hole either at a base pair or at a deoxyribose), we calculate the mean-over-time probabilities to find the hole at each site and establish the frequency content of coherent carrier transfer by computing the Weighted Mean Frequency at each site and the Total Weighted Mean Frequency of a dimer or polymer. We also evaluate the main oscillation frequencies of the dipole moment along the macromolecule axis and the relevant amplitudes. Finally, we focus on the mean transfer rates from an initial site to all others. We study the dependence of these quantities on the number of monomers that are used to construct the polymer. Since the value of the interaction integral between base pairs and deoxyriboses is not well-established, we treat it as a variable and examine its influence on the calculated quantities.

Association between Soft Drink Consumption and Aggressive Behaviour among a Quarter Million Adolescents from 64 Countries Based on the Global School-Based Student Health Survey (GSHS)

Soft drink consumption has become a significant public health concern that is associated with various adverse health outcomes. We aim to examine the association between soft drink consumption and aggressive behavior among adolescents. We used open access data from 79 studies in 64 countries, including 263,890 adolescents aged 12–18 years who completed the global school-based student health survey (GSHS). Self-reported data on past 30-day carbonated soft drink consumption (number of times per day) and past 12-month physical fighting were utilized for analysis. Of the 263,890 participants (48% boys) aged 12–18 years, the weighted mean frequency of soft drink consumption varied from 0.5 in Kiribati to 2.5 times/day in Surname, while the weighted prevalence of frequent aggressive behavior varied from to 2.7% in Laos to 49.2% in Tuvalu. We found that each increment of soft drink consumption (time/day) was associated with an 11% (95%CI 10–13%) increase of the likelihood of frequent physical fighting. This result remained significant after adjusting for various covariates. In this large pooled sample of multinational data, there is a significant positive association between soft drink consumption and aggressive behavior among adolescents. Reducing soft drink consumption may help reduce aggressive behavior, a major risk factor for violence.

Quasi-Periodic and Fractal Polymers: Energy Structure and Carrier Transfer.

We study the energy structure and the coherent transfer of an extra electron or hole along aperiodic polymers made of N monomers, with fixed boundaries, using B-DNA as our prototype system. We use a Tight-Binding wire model, where a site is a monomer (e.g., in DNA, a base pair). We consider quasi-periodic (Fibonacci, Thue–Morse, Double-Period, Rudin–Shapiro) and fractal (Cantor Set, Asymmetric Cantor Set) polymers made of the same monomer (I polymers) or made of different monomers (D polymers). For all types of such polymers, we calculate the highest occupied molecular orbital (HOMO) eigenspectrum and the lowest unoccupied molecular orbital (LUMO) eigenspectrum, the HOMO–LUMO gap and the density of states. We examine the mean over time probability to find the carrier at each monomer, the frequency content of carrier transfer (Fourier spectra, weighted mean frequency of each monomer, total weighted mean frequency of the polymer), and the pure mean transfer rate k. Our results reveal that there is a correspondence between the degree of structural complexity and the transfer properties. I polymers are more favorable for charge transfer than D polymers. We compare of quasi-periodic and fractal sequences with that of periodic sequences (including homopolymers) as well as with randomly shuffled sequences. Finally, we discuss aspects of experimental results on charge transfer rates in DNA with respect to our coherent pure mean transfer rates.

Periodic polymers with increasing repetition unit: Energy structure and carrier transfer

We study the energy structure and the transfer of an extra electron or hole along periodic polymers made of $N$ monomers, with a repetition unit made of $P$ monomers, using a Tight-Binding wire model, where a site is a monomer (e.g., in DNA, a base pair), for $P$ even, and deal with two categories of such polymers: made of the same monomer (GC..., GGCC..., etc) and made of different monomers (GA..., GGAA..., etc). We calculate the HOMO and LUMO eigenspectra, density of states and HOMO-LUMO gap and find some limiting properties these categories possess, as $P$ increases. We further examine the properties of the mean over time probability to find the carrier at each monomer. We introduce the weighted mean frequency of each monomer and the total weighted mean frequency of the whole polymer, as a measure of the overall transfer frequency content. We study the pure mean transfer rates. These rates can be increased by many orders of magnitude with appropriate sequence choice. Generally, homopolymers display the most efficient charge transfer. Finally, we compare the pure mean transfer rates with experimental transfer rates obtained by time-resolved spectroscopy.

Efficient methodology for electrocardiogram beat classification

Electrocardiogram (ECG) beat behaves as a non-linear and non-stationary signal. Since most of the existing data processing tools are poor alternatives for processing such signals, Hilbert–Huang transform (HHT) proves to be an efficient method as it deals with a time-varying frequency spectrum. In this study, a new and efficient methodology is proposed using HHT for feature selection which includes a set of essential features such as weighted mean frequency, Kolmogorov complexity and other statistical features (median, standard deviation, kurtosis, skewness and central moment) computed from the intrinsic mode functions extracted using the empirical mode decomposition (EMD) algorithm. Further, one-against-one multi-class support vector machine is employed for the classification of six generic ECG beats, namely: normal, left bundle branch block, right bundle branch block, premature ventricular contraction, paced beat and atrial premature beat. The classification process in this study yields better results than existing methodologies in terms of classification accuracy equal to 99.51% along with sensitivity, specificity and positive predictivity of 98.64, 99.77 and 98.17%, respectively.

Evaluation of flooded areas with satellite imagery using an objective hydrologic criterion

AbstractA method for computing flooded areas in relation to precedent climatic conditions in a plain area is evaluated. The studied area includes the most important production area for cattle breeding in Argentina; therefore, waterlogging periods create significant economic losses. In order to conduct this study, Landsat images that represent different hydrological conditions were used. The method is based on the frequency analysis of rainfall records of the 30, 60 and 120 days prior each image, thus obtaining a seasonally weighted mean frequency. In order to minimize biases of individual images, the images were combined linearly so as to obtain composed images with the desired antecedent precipitation frequency. Then, the flooded areas were associated with the frequency of the antecedent rainfall. Therefore, frequencies equal to or higher than 50% are associated with semi‐permanent or permanent lentic waterbodies. Consequently, lower frequencies are associated with waterlogged areas reaching 35% of flooded area for a 20% precipitation frequency.

Synchronization on overlapping community network

In this paper, we propose a simple random network model with overlapping communities controlled by several parameters, and investigate the influence of the overlapping community structure on the synchronization behavior under different parameters. It is found that the synchronizability of the network is mainly influenced by the overlapping size of the communities and the connectivity density of the overlapped group to the other interrelated communities, and has nothing to do with the intra-connectivity of the overlapped group. In addition, it is found that the highly interconnected communities can be almost synchronized in a given time scale, whereas the overlapped group is far from synchronization. Furthermore, the instantaneous frequencies of the nodes in the communities and their overlapped group are also investigated, which show that the nodes in the overlapped group will exhibit a remarkable oscillation with a weighted mean frequency of the other correlative communities.

O49 Autodyne and acoustic monitoring of the process of evaporating the multilayer biomaterials by CO 2 laser emission

Introduction: Laser ablation of biotissues is based on local heating results in phase transitions, intensive evaporation, substance mass emission in the form of drop-vapor mixture and tissue particles and laser-induced acoustic emission. These conditions create prerequisites for using of light scattering and optoacoustics techniques for monitoring of laser tissue ablation. Materials and Methods: Surgical CO2 laser was used to vaporize biomodels on the basis of gelatin with the different liquidwater content (60%-90%); biotissues of pig in vitro and of rat in vivo. Diagnostics of evaporating processes of the samples was accomplished by an autodyne method and by an optoacoustical method. The procedure of signal processing and extracting the informational component included fast Fourier transformation of the recorded signal and extraction the acoustic spectrum and the dependence of autodyne signal on the Doppler shift. As the quantitative characteristic of the obtained spectrum we used the spectral power of the signal and weighted mean frequency. According to the behavior of these values was made the conclusion about the approach of the moment the laser emission transmits through the boundary of the samples. Results: The autodyne signals, obtained during the cutting of biotissues by laser emission with the power density in the range from 1 to 10 kW/cm 2 were investigated. Tests on animals showed that it is possible to define the moment of the intersection the different biotissues by the laser beam. The analysis of the spectra of acoustical signals during the contact (piezoelectric ceramic) and remote (highly sensitive microphone) sounding of the laser evaporation zone of gelatin based model media and of biological tissues was made. It showed that during the laser cutting of multilayer gelatinous models a variation in the water content of layers leads to an abrupt change of the spectral power of the acoustical signal. Conclusion: It was established that interaction of CO2 laser radiation with the different biotissues possessed nonstationary nature and was manifested in a change in both the temporary form of autodyne and acoustic signals and their spectral characteristics. The developed algorithms for extracting information component of autodyne and acoustical signals allow to determine the moment of laser beam passage through the interface of biological tissues layers. The experiments have shown that the autodyne and acoustics methods can be used simultaneously to organizing the feedback in CO2 laser surgical systems. This work is performed with the support of Grant Russian Basic Research Fund No. 10 07 00605.

Post-thrombotic syndrome in children: a systematic review of frequency of occurrence, validity of outcome measures, and prognostic factors

Post-thrombotic syndrome is a manifestation of chronic venous insufficiency following deep venous thrombosis. This systematic review was conducted to critically evaluate pediatric evidence on frequency of occurrence, validity of outcome measures, and prognostic indicators of post-thrombotic syndrome. A comprehensive literature search of original reports revealed 19 eligible studies, totaling 977 patients with upper/lower extremity deep venous thrombosis. Calculated weighted mean frequency of post-thrombotic syndrome was 26% (95% confidence interval: 23-28%) overall, and differed significantly by prospective/non-prospective analysis and use/non-use of a standardized outcome measure. Standardized post-thrombotic syndrome outcome measures included an adaptation of the Villalta scale, the Clinical-Etiologic-Anatomic-Pathologic classification, and the Manco-Johnson instrument. Data on validity were reported only for the Manco-Johnson instrument. No publications on post-thrombotic syndrome-related quality of life outcomes were identified. Candidate prognostic factors for post-thrombotic syndrome in prospective studies included use/non-use of thrombolysis and plasma levels of factor VIII activity and D-dimer. Given that affected children must endure chronic sequelae for many decades, it is imperative that future collaborative pediatric prospective cohort studies and trials assess as key objectives and outcomes the incidence, severity, prognostic indicators, and health impact of post-thrombotic syndrome, using validated measures.

Fluctuations of the fractal dimension of the electroencephalogram during periodic breathing in heart failure patients

The physiological mechanisms responsible for periodic breathing (PB) in heart failure (HF) patients are still debated. A role for rhythmic shifts in the level of wakefulness has been suggested, but their existence has never been proven. In this study we investigated the existence of an oscillation in EEG activity during PB in these patients and assessed its relationship with the ventilatory oscillation. EEG activity was measured by the fractal dimension (FD) and by a spectral technique (weighted mean frequency, WMF) in 17 stable HF patients (mean age +/- SD: 57+/-10 yrs, NYHA class: 2.6 +/- 0.4, LVEF: 24 +/- 6%), with sustained PB during supine rest. The relationship between minute ventilation (MV) signal and FD and WMF was assessed by coherence analysis. Most patients (10/17) showed a well defined oscillation in FD and WMF at the frequency of PB closely linked (coherence > 0.7) with the oscillation of MV. In the remaining patients, neither FD nor WMF showed a clear oscillatory pattern synchronous with MV. Overall, the two EEG-derived parameters showed the same coherence with the ventilatory oscillation (mean coherence +/- SD: 0.65 +/- 0.25 vs 0.66 +/- 0.23, for FD and WMF respectively, p = 0.44). Our results provide evidence that during PB in HF patients, EEG activity often, but not always, fluctuates synchronously with the ventilatory oscillation. These fluctuations can be effectively detected by the fractal dimension, but classical spectral methods provide substantially the same information. Other mechanisms, particularly chemical instability in the respiratory control system, are likely to play a role in the genesis of PB.

The uncertainty associated with the weighted mean frequency of a phase-stabilized signal with white phase noise

The weighted mean is widely used in combining data sets of experimental measurements with a weight proportional to the value of the data number divided by the sample variance in a conventional method. However, this standard procedure is not appropriate for obtaining the weighted mean frequency of a phase-stabilized signal with white phase noise, since the data are autocorrelated. The autocorrelation is obtained in the case of white phase noise and a new weighting method is proposed. Using this, the uncertainty associated with the weighted mean frequency of a phase-stabilized signal with white phase noise is given. The effect of counter dead-time is also discussed.

Information regarding tactile sensation in friction signals with high uncertainty

An artificial tribosystem utilizing silicone-rubber sliding surfaces has been developed for simulating the friction phenomena that occur when human fingers are rubbed together in an attempt to acquire the tactile sensation of small particles. Although the friction signals acquired in the system have high uncertainty, two types of feature quantities, including the information about the particle size, were obtained—the elastic recovery in the normal signal as the slow information and the weighted mean frequency in the tangential signal as the fast information. The discrimination of particle size is realized by using one of the feature quantities to a certain extent, and a hybrid algorithm using both the feature quantities provides the discrimination with higher accuracy, which is comparable to or higher than the accuracy of human tactile systems. Consequently, the slow and fast information are complementary, and the relative reliability between the slow and fast information varies with a change in particle size.

Discrimination of Particle Diameter by using Tactile Information (Estimation Algorithm of an Artificial System including the Functions of Tactile Receptors)

Discrimination of particles has been investigated by using tactile information in an artificial system. Two kinds of time-series signals, i.e. normal and tangential fluctuations, were obtained experimentally when two silicone-gum specimens were rubbed with sands between the surfaces of specimens, in which the sands with sizes of 20-710 μm were classified into nine samples by sieves. The particle diameters of samples were found to be correlated with two quantities obtained from the time-series signals; one was the normal elastic recovery between before and after rubbing, and the other was the weighted mean frequency of dominant elements appearing in the spectrum of tangential fluctuation. By using the relationship between the two quantities and the particle diameters, the artificial system showed an ability of discrimination comparable with human subjects. Information of the size of sands appears in three ways. In case of larger-size samples, it appears strongly in the weighted mean frequency with lower-range frequency. On the contrary, it appears strongly in the elastic recovery for middle-size samples, and in the weighted mean frequency with higher-range frequency for smaller-size samples. By considering such characteristics, an algorithm has been constructed for discriminating the all samples with a higher accuracy, which has a similarity to the functions of three kinds of tactile receptors.

Discrimination of Particle Diameter by using Tactile Information (Construction of an Artificial System and Comparison with Human Subject Experiment)

Humans obtain some tactile feeling when they touch and rub objects with their fingers, which should be associated with some information about the objects. It means tactile information exists in such tribological actions of their fingers inevitably. In the present study, discrimination of particles has been investigated by using tactile information in an artificial system. Two kinds of time-series signals, i.e. normal and tangential fluctuations, were obtained experimentally when two silicone-gum specimens were rubbed with sands between the surfaces of specimens, in which the sands with sizes of 180-710 1.im were classified into five samples by sieves. The particle diameters of samples were found to be correlated with two quantities obtained from the time-series signals ; one was the normal elastic recovery between before and after rubbing, and the other was the weighted mean frequency of dominant elements appearing in the spectrum of tangential fluctuation. By using the relationship between the two quantities and the particle diameters, the artificial system showed an ability of discrimination comparable with human subjects.

PORE PRESSURE PREDICTION BASED ON SEISMIC ATTRIBUTES RESPONSE TO OVERPRESSURE

A seismic attribute-based quantitative methodology for remote prediction of effective stresses is proposed in this study. The algorithm uses neural networks as a nonlinear interpolator between a set of seismic attributes to predict changes in differential pressure. This method is based on the relationships between specific seismic attributes and differential pressures, which were established through a series of laboratory tests. A number of ultrasonic elastic measurements simulating normal compaction, fluid expansion and tectonic mechanisms of overpressure were performed on reservoir sandstone and shale core samples. The effects of different stress paths on seismic attributes derived from experimentally recorded wavelets were investigated. Positive relationships were established between differential pressure and several seismic attributes for the first time in these tests. It has been found that instantaneous frequency, weighted mean frequency, instantaneous phase, instantaneous pseudo-Q factor, instantaneous bandwidth, instantaneous dominant frequency and autocorrelation function are sensitive to differential pressure changes. The relationships between those attributes and differential pressure follow Eberhart- Phillips’ stress-velocity empirical relationship. This implies that seismic attributes could be used for a quantitative prediction of overpressure. Moreover, our experimental results show that frequency-based seismic attributes exhibit a greater sensitivity to changes in differential pressure than does seismic velocity. The proposed methodology has been tested on a 3D seismic dataset from the North West Shelf of Australia. Predicted pore pressure values are in good agreement with available borehole data.

705 AE法によるレーザ溶接部の溶込みに関する評価(GS-3 材料計測,研究発表講演)

The laser welding is easier to automate than the other welding method, so it is expected as the high quality and accuracy welding. The stainless steels were irradiated using YAG laser for examining the penetrating conditions during the laser irradiation. And the detected AE signals during laser irradiation were analyzed with "Weighted Mean Frequency Distribution Analysis Method" and "Wavelet Transform". In this paper, relevancy between the analysis results of the signals during laser irradiation and the penetrating condition of the specimen are reported.

Diagnostics of the laser perforation of biological tissues by the method of autodyne detection of backscattered radiation

The method of autodyne detection of backscattered radiation is used to study the passage of CO2 laser radiation through interfaces of model media and biological tissues in vitro during their laser perforation. It is shown that a stepwise change in the weighted mean frequency of the autodyne-signal power spectrum is a criterion for this passage in real time.