Frequency Characteristics Research Articles

A Gaussian Process Regression and Wavelet Transform Time Series approaches to modeling Influenza A.

The global spread of Influenza A viruses is worsening economic and social challenges. Various mechanistic models have been developed to understand the virus's spread and evaluate intervention effectiveness. This study aimed to model the temporal dynamics of Influenza A using Gaussian Process Regression (GPR) and wavelet transform approaches. The study employed Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) and Wavelet Power Spectrum to analyze time-series data from 2009 to 2023. The GPR model, known for its non-parametric Bayesian nature, effectively captured non-linear trends in the Influenza A data, while wavelet transforms provided insights into frequency and time-localized characteristics. The integration of GPR with DWT denoising techniques demonstrated superior performance in forecasting Influenza A cases compared to traditional models like Auto Regressive Integrated Moving Averages (ARIMA) and Exponential Smoothing (ETS) using Holt-Winter method. The study identified significant anomalies in Influenza A cases, corresponding to known pandemic events and seasonal variations. These findings highlight the effectiveness of combining wavelet transform analysis with GPR in understanding and predicting infectious disease patterns, offering valuable insights for public health planning and intervention strategies. The research recommends extending this approach to other respiratory viruses to assess its broader applicability.

Comparative analysis of methods for regulating the frequency characteristics of simulators of electrical characteristics of spacecraft power supply systems

Comparative analysis of methods for regulating the frequency characteristics of simulators of electrical characteristics of spacecraft power supply systems

Wavelet transform-based mode decomposition for EEG signals under general anesthesia

Background Mode decomposition methods are used to extract the characteristic intrinsic mode function (IMF) from various multidimensional time series signals. We analyzed an electroencephalogram (EEG) dataset for sevoflurane anesthesia using two wavelet transform-based mode decomposition methods, comprising the empirical wavelet transform (EWT) and wavelet mode decomposition (WMD) methods, and compared the results with those from the previously reported variational mode decomposition (VMD) method. Methods To acquire the EEG data, we used the software application EEG Analyzer, which enabled the recording of raw EEG signals via the serial interface of a bispectral index (BIS) monitor. We also created EEG mode decomposition software to perform empirical mode decomposition (EMD), VMD, EWT, and WMD operations. Results When decomposed into six IMFs, the EWT enables narrow band separation of the low-frequency bands IMF-1 to IMF-3, in which all central frequencies are less than 10 Hz. However, in the upper IMF of the high-frequency band, which has a center frequency of ≥ 10 Hz, the dispersion within the frequency band covered was widespread among the individual patients. In WMD, a narrow band of clinical interest is specified using a bandpass filter in a Meyer wavelet filter bank within a specific mode-decomposition discipline. When compared with the VMD and EWT methods, the IMF that was decomposed via WMD was accommodated in a narrow band with only a small variance for each patient. Multiple linear regression analyses demonstrated that the frequency characteristics of the IMFs obtained from WMD best tracked the changes in the BIS upon emergence from general anesthesia. Conclusions The WMD can be used to extract subtle frequency characteristics of EEGs that have been affected by general anesthesia, thus potentially providing better parameters for use in assessing the depth of general anesthesia.

Learning spatial‐frequency interaction for generalizable deepfake detection

AbstractIn recent years, face forgery detection has gained significant attention, resulting in considerable advancements. However, most existing methods rely on CNNs to extract artefacts from the spatial domain, overlooking the pervasive frequency‐domain artefacts present in deepfake content, which poses challenges in achieving robust and generalized detection. To address these issues, we propose the dual‐stream frequency—spatial fusion network is proposed for deepfake detection. The dual‐stream frequency‐spatial fusion network consists of three components: the spatial forgery feature extraction module, the frequency forgery feature extraction module, and the spatial–frequency feature fusion module. The spatial forgery feature extraction module employs spatial‐channel attention to extract spatial domain features, targeting artefacts in the spatial domain. The frequency forgery feature extraction module leverages the focused linear attention to detect frequency domain anomalies in internal regions, enabling the identification of generated content. The spatial–frequency feature fusion module then fuses forgery features extracted from both the spatial and frequency domains, facilitating accurate detection of splicing artefacts and internally generated forgeries. This approach enhances the model's ability to more accurately capture forgery characteristics. Extensive experiments on several widely‐used benchmarks demonstrate that our carefully designed network exhibits superior generalization and robustness, significantly improving deepfake detection performance.

Nurses' models of spiritual care: Predictors of spiritual care competence.

Previous studies have shown that nurses' spiritual care competence is related to characteristics of personal spirituality, training adequacy, and comfort, confidence, and frequency of provision of spiritual care. However, these studies assumed that all participants understood spiritual care in the same way, and used self-ratings of spiritual care competence, which are problematic. Our previous study found that spiritual care was understood in 4 qualitatively different ways that can be arranged in order of competence. This study aimed to re-examine the relationships between nurse characteristics and spiritual care competence, using spiritual care understanding as a proxy for competence. Data was collected from a convenience sample of nurses who completed an anonymous, online survey. The survey provided qualitative data about what spiritual care means for them. The survey also provided quantitative data regarding nurse characteristics. This study created sub-groups of nurses based on their understanding of spiritual care, and used the quantitative data to construct a profile of nurse characteristics for each sub-group. Kruskal-Wallis statistical tests determined whether nurse characteristics differed across the 4 sub-groups. Spiritual care competence was not related to confidence or comfort in providing spiritual care. Relationships with spirituality, training adequacy, and frequency of provision of spiritual care were not linear; i.e., higher competence did not always correspond with higher scores of these characteristics. The results raise concerns about the construct validity of using comfort and confidence as estimates of spiritual care competence. That the relationships between competence and spirituality, training adequacy, and frequency of spiritual care provision was not as linear as portrayed in extant literature, suggests that outcomes of training may depend on the type of spiritual care understanding subscribed to by training participants. The findings offer insights about how nurses could achieve high levels of spiritual care performance.

Wave Propagation in Prestressed Structures with Geometric Nonlinearities Through Carrera Unified Formulation

This paper deals with the analysis of wave propagation characteristics in various prestressed structures with geometric nonlinearities using the Carrera Unified Formulation (CUF). CUF provides a versatile platform to model a wide range of structures and nonlinearities that can take care of all wave propagation aspects. In this work, different geometric nonlinearities for which representative governing equations have been derived and numerical solutions have been obtained through a unified approach are considered. The study investigates in detail the effect of prestress and geometric nonlinearity on wave propagation behavior. The results indicate that prestress has a very influential effect on modal frequency and dispersion characteristics for wave propagation. Specifically, three CUF-modeled beams are considered herein, having a sandwich, metallic portal, and metallic box cross section, respectively. Initially, the principal cross-sectional modal shapes of the unstressed, linear, and full nonlinear (i.e., full three-dimensional Green–Lagrange strain matrix) beam with a prestress are investigated, among which torsional and flexural modes can be recognized. Afterward, the equilibrium curves of such structures for various geometrical nonlinear approximations are traced, highlighting that most types of nonlinearity induce a hardening behavior in the system, which increases with the preload, directly leading to a variation in modal frequencies. The dispersion relations of the full nonlinear structure examined as a function of the applied preload are further compared, enriching the investigation by exploiting wave finite element method capabilities. This knowledge paves the way toward the design and optimization of prestressed systems with enhanced acoustic performance, and that fosters the development of sound absorption, noise insulation, and structural isolation.

Application of Resonance Method in Composite Fault Diagnosis of Bearings

Poisson white noise is a more accurate representation than ideal Gaussian white noise for simulating the noise background during early bearing failures. In the context of strong Poisson white noise interference, the failure mode of rolling bearings usually remains undetermined. This paper proposes a new method for diagnosing unknown bearing faults in bearings, diagnosis is proposed, which effectively identifies unknown faults across various components. The theoretical characteristics of Poisson white noise are initially analyzed and discussed. Then, to extract potential fault characteristic information, a false characteristic frequency of bearing is constructed in the response spectrum, and the response results at this false fault characteristic are obtained. Finally, coherent resonance (CR) is introduced, and false frequencies caused by false faults are eliminated by defining a quality factor. To verify the effectiveness of the method, the experimental and simulation results were compared with the decomposition results of the SVMD algorithm. The SNR of the experimental signals for outer and inner ring faults under variable speed conditions increased to 8.62[Formula: see text]dB and 11.74[Formula: see text]dB, respectively. Results indicate show that this method not only successfully identifies fault features, but also exhibits a strong noise reduction effect.

Modeling Thermal Impedance of IGBT Devices Based on Fractional Calculus Techniques

The thermal impedance characteristics of insulated gate bipolar transistor (IGBT) modules are critical for the thermal management and design of electronic devices. This paper proposes a fractional-order equivalent thermal impedance model, which is inspired by the correlation between multi-time-scale dissipation characteristics of heat conduction processes and fractional calculus. The fractional-order equivalent thermal impedance model is derived based on the connection between fractional-order calculus and the Foster thermal network model in mathematical operations, with only two parameters to be identified: heat capacity C and fractional order α. Moreover, this paper provides a parameter identification method for the proposed fractional-order equivalent thermal impedance model based on the multi-objective particle swarm optimization (MOPSO) algorithm. In order to validate the effectiveness and superiority of this work, experiments and comparative works are provided in this paper. The results indicate that the fractional-order equivalent thermal impedance model can accurately describe the frequency domain characteristic curves of the thermal impedance of the Foster thermal network model for IGBT modules, with the difference between the amplitude frequency characteristics not exceeding 1 dB and the difference between the phase frequency characteristics not exceeding 1° within the operating frequency range of (1 kHz, 1 MHz).

Triple-Stacked FET Distributed Power Amplifier Using 28 nm CMOS Process

A broadband 28 nm complementary metal–oxide–semiconductor (CMOS) power amplifier was implemented using a distributed amplification design. To develop a model library for high-frequency design, various test patterns for active and passive elements were fabricated and compared through measurements. As a result, a symmetrical n-channel field-effect transistor (NFET) was used as the active device, and a co-planar waveguide (CPW) with floating bottom metal layers was chosen as the transmission line for the passive element. These choices demonstrated superior radio frequency (RF) characteristics at high frequencies compared to other device candidates. Furthermore, to address the low breakdown voltage of CMOS, a triple-stacked FET structure was designed as the gain cell of the distributed power amplifier (DPA). The fabricated DPA showed a maximum small-signal gain of 22 dB and a minimum of 10 dB from DC to 56 GHz, with a maximum saturated output power of 20 dBm and a minimum of 13 dBm from 1 to 39 GHz. Notably, these results were achieved on the first attempt by designing solely based on measurement data from the test patterns.

Characteristic spatial and frequency distribution of mutations in SCN1A

BackgroundSCN1A is the most well-recognized and commonly mutated gene related to epilepsy. This study analyzed the characteristic spatial and frequency distributions of SCN1A mutations, aiming to provide important insight into the mutagenesis etiopathology of SCN1A-associated epilepsy.MethodsEpilepsy-associated SCN1A variants were retrieved from the SCN1A mutation database, the HGMD database, and literature reviews. The base substitutions, mutation frequencies in CpG dinucleotides, and spatial distributions of mutations in terms of exons and structural domains were analyzed.ResultsA total of 2621 SCN1A variants were identified in 5106 unrelated cases. The most common type was missense mutation, followed by frameshift mutations and splice site mutations. Among the missense mutations, transitions within CpG dinucleotides were much more recurrently identified than transitions within non-CpG dinucleotides, and the most common type was the G > A transition. Among the nonsense mutations, the most predominant type of single-base substitution was the C > T transition, among which 75.3% (235/312) were within CpG sites. The most common “hotspot” codons for missense mutations were codons 101, 946, and 1783; while for nonsense mutations it was codon 712. One-base deletion or insertion was the most common type of frameshift mutation, causing protein truncation. The three most common frameshift mutations were c.5536_5539delAAAC, c.4554dupA, and c.5010_5013delGTTT. Splice mutations were the most frequently identified in exon 4 with a hotspot site c.602 + 1G > A. The spatial distribution of missense mutations showed that exons 22 and 4 had the highest mutation density (111 and 84 mutations per 100 bp, respectively), and exon 12 had the lowest mutation density, with 4 mutations per 100 bp. Further distribution analysis of the protein domains revealed that missense mutations were more common in the pore region and voltage sensor (231 mutations per 100 amino acids, respectively), and the protein truncation mutations were distributed evenly among the domains.ConclusionsSCN1A mutations tend to cluster at distinct sites, depending on the characteristic CpG dinucleotides, exons, and functional domains. Higher mutation density in particular regions, such as exon 22 and exon 4, offers promising targets for therapeutic genetic interventions.

CSIG-11. TUMOR-CELL INTRINSIC CALCIUM OSCILLATIONS DRIVE BRAIN METASTASIS

Abstract Calcium is a ubiquitous secondary messenger which regulates many cellular processes. Recently, we have shown that a subpopulation of glioblastoma cells can intrinsically generate calcium oscillations which drive proliferative signalling. Here, we set out to investigate whether such cancer cell autonomous oscillations are also present in secondary brain tumours. Employing longitudinal intravital two-photon microscopy in awake mice, we demonstrate that brain metastases of melanoma and lung cancer exhibit spontaneous calcium oscillations in vivo along the metastatic cascade. These oscillations are already observed during intravascular arrest and in in vitro monocultures, suggesting a tumour-cell intrinsic origin. We developed a fully automatic image analysis pipeline to accurately quantify calcium oscillations both in vivo and in vitro. Mathematical modelling of cytosolic and ER calcium stores can predict stable calcium oscillations with similar frequency and peak characteristics as those experimentally observed. We discovered that these oscillations are tightly regulated by an interplay of calcium-induced calcium release and store-operated calcium entry (SOCE). Similarly, SOCE-related ion channels are enriched in tissue from human melanoma brain metastases compared to healthy brain controls and calcium activity can also be observed ex vivo in patient-derived brain metastasis resections. Finally, by correlating calcium activity and EdU incorporation we discovered that the presence of calcium oscillations predicts their proliferative potential in vitro. Importantly, by intravital two-photon microscopy we show that calcium activity correlates with metastasis size in preclinical models, suggesting a potential new therapeutic angle from which to target brain metastases.

A Genetically Engineered Reporter System Designed for 2H-MRI Allows Quantitative In Vivo Mapping of Transgene Expression.

The ability to obtain quantitative spatial information on subcellular processes of deep tissues in vivo has been a long-standing challenge for molecular magnetic resonance imaging (MRI) approaches. This challenge remains even more so for quantifying readouts of genetically engineered MRI reporters. Here, we set to overcome this challenge with a molecular system designed to obtain quantitative 2H-MRI maps of a gene reporter. To this end, we synthesized deuterated thymidine, d3-thy, with three magnetically equivalent deuterons at its methyl group (-CD3), showing a singlet peak with a characteristic 2H-NMR frequency (δ = 1.7 ppm). The upfield 3.0 ppm offset from the chemical shift of the HDO signal (δ = 4.7 ppm) allows for spectrally resolving the two 2H NMR signals and quantifying the concentration of d3-thy based on the known concentration of a tissue's HDO. Following systemic administration of d3-thy, its accumulation as d3-thy monophosphate in cells expressing the human thymidine kinase 1 (hTK1) transgene was mapped with 2H-MRI. The data obtained in vivo show the ability to use the d3-thy/hTK1 pair as a reporter probe/reporter gene system to quantitatively map transgene expression with MRI. Relying on a structurally unmodified reporter probe (d3-thy) to image the expression of unmutated human protein (hTK1) shows the potential of molecular imaging with 2H-MRI to monitor gene reporters and other relevant biological targets.

Effects of Wheel Flats on Vibration and Stress Response of the Wheel-Rail System in High-Speed Trains

With the advancement of high-speed and lightweight vehicles, the occurrence of wheel tread wear, including wheel flats, has escalated. The resulting impact loads on axle-end components and the dynamic response of the axle cannot be overlooked. On-track tests were conducted on wheel flats to obtain wheel-rail forces, axle box vibration accelerations, and axle stresses during service. Time–frequency analysis was employed to uncover patterns of variation with speed. Additionally, a dynamic model of the high-speed train–track system was developed to replicate test conditions, investigating the characteristics of wheel–rail loads and structural elastic vibrations under high-frequency excitation for various flat sizes and higher operating speeds. The findings reveal that under wheel flats, the time-domain response of the wheel–rail system exhibits periodic impacts, while the frequency-domain shows high-frequency energy comprising multiple harmonics of the wheel rotation frequency. Wheel–rail forces, axle box vibration accelerations, and axle stresses exhibit inflection points with increasing speed, particularly within the 120–160 km/h range. In the high-frequency domain, axle stresses display characteristic frequencies that remain unchanged with speed, attributed to the coupled vertical bending vibration modes of the wheel–rail system. These research results offer foundational support for predicting fatigue damage to axle-end components, assessing axle stress, and establishing tolerances for tread defect limits.

Evaluation of the Heat Transfer Performance of a Device Utilizing an Asymmetric Pulsating Heat Pipe Structure Based on Global and Local Analysis

PHPs (pulsating heat pipes) are widely used as an efficient heat transfer element in equipment thermal management and waste heat recovery due to their flexibility. The purpose of this study was to design a heat transfer device that utilizes an asymmetric pulsating heat pipe structure by adjusting the lengths of selected pipes within the entire circulation pipeline. In the experiment, a constant temperature water bath was used as the heat source, with heat dissipated in the condensing section via natural convection. An infrared thermal imager was used to record the temperature of the condensing section, and the local wall temperature distribution was measured in different channels of the condensing section. Based on an in-depth analysis of the wavelet frequency, the following research conclusions are drawn: Firstly, as the heat source temperature increases, the start-up time of the pulsating heat pipe is shortened, the operating state changes from start–stop–start to stable and continuous oscillation, and the oscillation mode changes from high amplitude and low frequency to low amplitude and high frequency. These changes are especially pronounced when the heat source temperature is 80 °C, which is when the thermal resistance reaches its lowest value of 0.0074 K/W, and the equivalent thermal conductivity reaches its highest value of 666.29 W/(m·K). Secondly, the flow and oscillation of the working fluid can be effectively promoted by appropriately shortening the length of the condensing section of the pulsating heat pipes or the heat transfer distance between the evaporation and condensing sections. Third, under a low-temperature heat source, the oscillation frequency of each channel of a pulsating heat pipe is found to be low based on wavelet analysis. However, as the heat source temperature increases, the energy content of the temperature signal of the working fluid in each channel changes from a low- to a high-frequency value, gradually converging to the same characteristic frequency. At this point, the working fluid in the pipes no longer flows randomly in multiple directions but rather in a single direction. Finally, we determined that the maximum oscillation frequency of working fluid in a PHP is around 0.7 HZ when using the water bath heating method.

Frequency and clinical characteristics of children and young people with type 2 diabetes at diagnosis from five world regions between 2012 and 2021: data from the SWEET Registry.

The diagnosis of type 2 diabetes is increasing in young people worldwide. This study evaluated the frequency and clinical characteristics of young people presenting with type 2 diabetes from the multinational SWEET e.V Registry 2012-2021, including the first years of the COVID-19 pandemic. This is a longitudinal observational study based on the SWEET Registry, which collects demographic and clinical data on children and adolescents with diabetes from centres worldwide, with the diagnosis and classification of diabetes provided locally by each centre according to International Society for Paediatric and Adolescent Diabetes definitions. By July 2022, the SWEET Registry included 96,931 individuals from 130 centres with a total of 1,154,555 visits. Data were analysed by region: Europe (EU), Australia and New Zealand (AU/NZ), South America (SA), North America (NA) and Asia/Middle East and Africa (AS/AF). Trends in proportions for the two-year periods, calculated as cases with type 2 diabetes diagnoses over all cases with diabetes diagnoses, were estimated using logistic regression models adjusted for age at onset and sex. Overall, there were 2819 of 58,170 new cases (4.8%) with type 2 diabetes: 614 in EU, 293 in AU/NZ, 79 in SA, 1211 in NA and 622 in AS/AF. The proportion of type 2 diabetes increased from 3.2% to 6.0% from 2012/2013 to 2020/2021, a relative rate of increase of 9% per two-year period (95% CI 5.9, 12.3; p<0.001). In the two-year period of the COVID-19 pandemic, type 2 diabetes continued to follow the observed trend, with a proportion of 6.0% in 2020-2021 compared with 5.4% in 2018-2019. High variability in the proportion of type 2 diabetes was observed across regions, with the lowest values observed in EU and the highest in NA. A significant increase in the proportion of type 2 diabetes was observed in EU, AU/NZ and NA. The median HbA1c was not uniform and was highest in AS/AF (85 mmol/mol [9.9%]; IQR 55-111 [7.2-12.3%]) and lowest in EU (63 mmol/mol [7.9%]; IQR 48-99 [6.5-11.2%]), and the difference between EU and NA (median value 73 mmol/mol [8.8%]; IQR 50-105 [6.7-11.8%]) was statistically significant (p=0.047). There was also a difference in BMI SD score by region: the lowest median BMI SD score was 2.2 (IQR 1.4-2.7) in AS/AF and the highest was 3.1 (IQR 2.5-3.6) in AU/NZ. The multinational SWEET data from the years 2012 to 2021 inclusive support recent findings of a worldwide increase in type 2 diabetes in young people, albeit with regional differences. This increase highlights the need for ongoing preventive measures and available advanced treatment modalities worldwide.

Searching for similarities in the accretion flow of Seyfert 1 galaxies and cataclysmic variables based on the flare profiles of IRAS 13224-3809, 1H 0707-495, Mrk 766, and MV Lyr

We studied the fast variability of three selected active galactic nuclei (AGNs), IRAS\,13224-3809, 1H\,0707-495, and Mrk\,766, and the cataclysmic variable MV\,Lyr, which were observed by the and spacecrafts, respectively. Our goal is to search for the common origin of the variability and to test the so-called sandwich model, in which a geometrically thick corona surrounds a geometrically thin disc. We studied the substructures of the averaged flare profiles. The flare profile method identifies individual flares in the light curve, and averages them. The direct fitting of the profile substructures identified individual characteristic frequencies that are seen in standard power density spectra (PDS) as a break frequency or quasi-periodic oscillation. The credibility of the flare profile substructures is demonstrated by comparison with the autocorrelation function. We found that the flare profiles of AGNs are similar to those of a cataclysmic variable in the low state. We explain this as a consequence of a truncated inner disc in a sandwich model. The same scenario is also able to explain the characteristic break frequencies in X-ray PDS, which are not seen in the optical. We also searched for substructures in the flare profile of IRAS\,13224-3809. In addition to a permanently present main flare, we found that a transient side-lobe appears before the main flare and is only seen in a high-flux period. The complex flare profile of this AGN suggests that an additional source of X-rays appears during the high-flux period. We propose a scenario in which an accretion flow fluctuation enters the sandwich corona and propagates further to some very central part of the accretion disc.

Research on Damage Detection of Dual-Rotor Synchronous Excitation Mine Screen Beams Based on Strain Mode Difference Vibration Mode Analysis

The frame beam structure of the mine screen, subjected to various excitations, is a critical component of mining machinery. Its stress is intricate, the operational environment is severe, and damage can lead to catastrophic failures resulting in machinery destruction and fatalities. Based on the characterization of the vibration response of mine screen frame beams with varying degrees of damage at the same location and with the same degree of damage but at different locations, this paper develops a method of strain modal difference vibration pattern analysis and damage feature extraction for the detection of structural damage in beams. This method is based on the sensitivity of the sudden change in vibration strain modal difference to small deformations. This method solves the problem of using the conventional structural finite element analysis or experimental modal analysis methods to obtain the displacement mode, intrinsic frequency, and other characteristics, which make it difficult to effectively identify the actual engineering, with the damage conditions of the damage state and damage location of the mine screen frame beam problems. The feasibility and validity of the engineering application of the concept are demonstrated through instances.

Effect of anisotropy field on the resonance frequency and reflection loss shift characteristics of barium hexaferrite ceramics

Abstract For this study, a modified barium hexaferrite based microwave and radar absorbing material was used. Synthesis and characterization of BaFe(12-2x)(MnTi)xO19 (0.1 ≤ x ≤ 1.0) have been prepared by solid state reaction method. The X-ray diffraction patterns reveal that all samples only crystallized into M-type barium hexaferrite after calcination at 1200 °C. Surface morphology images show that the hexagonal flake-like particle is formed at the initial stages of the Mn-Ti substitution level. The magnetic properties measurement reveals that the variation of magnetization values may vary based on preferable site occupation of Mn4+-Ti2+ cations to replace Fe3+ cations. The significant decrease in field coercivity (Hc) from 3.18 kOe to 0.51 kOe with the increase of Mn-Ti substitution affected the lowering values of field anisotropy (Ha). The maximum reflection loss (RL) positions of BaFe(12-2x)(MnTi)xO19 compositional series are shifted to lower frequency range following the Ha trendlines. The maximum RL value equals to 98.42% microwave absorption at 10.5 GHz has been obtained in x = 1.0 (BaFe10Mn0.5Ti0.5O19) composition.&amp;#xD;

An Enhanced Spectral Amplitude Modulation Method for Fault Diagnosis of Rolling Bearings

As a classic nonlinear filtering method, Spectral Amplitude Modulation (SAM) is widely used in the field of bearing fault characteristic frequency identification. However, when the vibration signal contains high-intensity noise interference, the accuracy of SAM in identifying fault characteristic frequencies is greatly reduced. To solve the above problems, a Data Enhancement Spectral Amplitude Modulation (DA-SAM) method is proposed. This method further processes the modified signal through improved wavelet transform (IWT), calculates its logarithmic maximum square envelope spectrum to replace the original square envelope spectrum, and finally completes SAM. By highlighting signal characteristics and strengthening feature information, interference information can be minimized, thereby improving the robustness of the SAM method. In this paper, this method is verified through fault data sets. The research results show that this method can effectively reduce the interference of noise on fault diagnosis, and the fault characteristic information obtained is clearer. The superiority of this method compared with the SAM method, Autogram method, and fast spectral kurtosis diagram method is proved.

Paediatric oral and maxillofacial biopsies: A retrospective institutional archival study.

This is a retrospective cross-sectional study of oral and maxillofacial biopsies in paediatric population, up to 16 years of age from 1991 to 2021. This study aimed to investigate the frequency, demographic and clinicopathological characteristics of paediatric biopsies according to the diagnosis categories of the main tertiary centre in Malaysia. A total of 17294 histopathological reports from Diagnostic Oral Pathology Unit between 1991 and 2021 were retrieved. One thousand eleven histopathological reports that fulfilled the inclusion and exclusion criteria were included. Information of interest was extracted from histopathological reports. The lesions were categorised into 10 diagnosis categories. Descriptive and correlation analysis were performed. Paediatric oral and maxillofacial biopsies accounted for 5.9% of the total biopsies received. The peak incidence of diagnosis was in age range of 12 to 16 years old and mean age of 11.6 years old (±3.5), with female preponderance. The most common category of diagnosis was salivary gland pathology with mucocele (30%) being the most frequently diagnosed lesions. Lip and labial mucosa were the commonest site of occurrence. Specimen sizes smaller than 1 cm were the most common specimen diameter with mean size of 1.6 cm (±1.5). Age, site of occurrence and specimen size were statistically significant (P < 0.05) with paediatric oral and maxillofacial biopsies. This is a large-scale study of paediatric oral and maxillofacial biopsies of a tertiary centre over three decades. We hope this data is useful for chairside assessment and aids in diagnosis making.