Search Results (453)

Audio steganalysis has been little explored due to its complexity and randomness, which complicate the analysis. Audio files generate marks in the frequency domain; these marks are known as fingerprints and make the files unique. This allows us to differentiate between audio vectors. In this work, the use of the Goertzel algorithm as a steganalyzer in the frequency domain is combined with the proposed sliding window adaptation to allow the analyzed audio vectors to be compared, enabling the differences between the vectors to be identified. We then apply linear prediction to the vectors to detect any modifications in the acoustic signatures. The implemented Goertzel algorithm is computationally less complex than other proposed stegoanalyzers based on convolutional neural networks or other types of classifiers of lower complexity, such as support vector machines (SVD). These methods previously required an extensive audio database to train the network, and thus detect possible stegoaudio through the matches they find. Unlike the proposed Goertzel algorithm, which works individually with the audio vector in question, it locates the difference in tone and generates an alert for the possible stegoaudio. In this work, we apply the classic Goertzel algorithm to detect frequencies that have possibly been modified by insertions or alterations of the audio vectors. The final vectors are plotted to visualize the alteration zones. The obtained results are evaluated qualitatively and quantitatively. To perform a double check of the fingerprint of the audio vectors, we obtain a linear prediction error to establish the percentage of statistical dependence between the processed audio signals. To validate the proposed method, we evaluate the audio quality metrics (AQMs) of the obtained result. Finally, we implement the stegoanalyzer oriented to AQMs to corroborate the obtained results. From the results obtained for the performance of the proposed stegoanalyzer, we demonstrate that we have a success rate of 100%. Full article

The residual stress field induced by interference-fit riveting in aircraft panel structures significantly affects the fatigue performance around the rivet holes. Common residual stress analytical models often overlook the non-uniformity of interference between the rivet and the hole, which impacts the applicability of these models. Addressing this issue, an analytical model of residual stress around the rivet hole is proposed for a typical single-riveted structure based on the thick-walled cylinder theory and Lame’s equations, considering the non-uniform interference along the axis of the rivet hole. This novel model is then extended to multi-riveted structures in fuselage panels. Using vector synthesis, analytical models for single-row double-rivets and double-row quadruple-rivets configurations were derived. The established analytical models provide a three-dimensional characterization of the residual stress field in typical riveted structures. Finally, the accuracy of the model is verified through X-ray diffraction experiments and FEM simulation results. Full article

Aberrant expression of the double homeobox 4 (DUX4) gene in skeletal muscle predominantly drives the pathogenesis of facioscapulohumeral muscular dystrophy (FSHD). We recently demonstrated that berberine, an herbal extract known for its ability to stabilize guanine–quadruplex structures, effectively downregulates DUX4 expression in FSHD patient-derived myoblasts and in mice overexpressing exogenous DUX4 after viral vector-based treatment. Here, we sought to confirm berberine’s inhibitory efficacy on DUX4 in the widely used FSHD-like transgenic mouse model, ACTA1-MCM/FLExDUX4, where DUX4 is induced at pathogenic levels using tamoxifen. Animals repeatedly treated with berberine via intraperitoneal injections for 4 weeks exhibited significant reductions in both mRNA and protein levels of DUX4, and in mRNA expression of murine DUX4-related genes. This inhibition translated into improved forelimb muscle strength and positive alterations in important FSHD-relevant cellular pathways, although its impact on muscle mass and histopathology was less pronounced. Collectively, our data confirm the efficacy of berberine in downregulating DUX4 expression in the most relevant FSHD mouse model. However, further optimization of dosing regimens and new studies to enhance the bioavailability of berberine in skeletal muscle are warranted to fully leverage its therapeutic potential for FSHD treatment. Full article

In the speed-sensorless vector control of induction motors (IMs), the speed estimation accuracy suffers from the deteriorated current measurement caused by the current sensor faults, such as open circuit in one phase, DC bias, and odd harmonics. In this paper, a novel speed estimation strategy based on the current sensor fault-tolerant control is proposed to improve the speed estimation accuracy under the current sensor faults. First, to detect the current sensor faults in real time, the sequential probability ratio test is introduced to the system by using the innovations of the extended Kalman filter (EKF). Second, to ensure speed estimation accuracy, a double-cascading second-order generalized integrator (DSOGI) is employed to reconstruct the faulty current information when a fault is identified. Finally, the reconstructed current information is fed back to the sequential probability extended Kalman filter (SPEKF), which estimates the rotor speed of the IM, and high-accuracy speed estimation under the condition of current sensor faults is achieved. The effectiveness of the proposed strategy is validated by a series of experiments, which were conducted on a 3 kW induction motor drive platform. Full article

The advent of induced pluripotent stem cell (iPSC) technology has brought about transformative advancements in regenerative medicine, offering novel avenues for disease modeling, drug testing, and cell-based therapies. Patient-specific iPSC-based treatments hold the promise of mitigating immune rejection risks. However, the intricacies and costs of producing autologous therapies present commercial challenges. The hair follicle is a multi-germ layered versatile cell source that can be harvested at any age. It is a rich source of keratinocytes, fibroblasts, multipotent stromal cells, and the newly defined Hair Follicle-Associated Pluripotent Stem Cells (HAP). It can also be obtained non-invasively and transported via regular mail channels, making it the ideal starting material for an autologous biobank. In this study, cryopreserved hair follicle-derived iPSC lines (HF-iPS) were established through integration-free vectors, encompassing a diverse cohort. These genetically stable lines exhibited robust expression of pluripotency markers, and showcased tri-lineage differentiation potential. The HF-iPSCs effectively differentiated into double-positive cKIT⁺/CXCR4⁺ definitive endoderm cells and NKX6.1⁺/PDX1⁺ pancreatic progenitor cells, affirming their pluripotent attributes. We anticipate that the use of plucked hair follicles as an accessible, non-invasive cell source to obtain patient cells, in conjunction with the use of episomal vectors for reprogramming, will improve the future generation of clinically applicable pancreatic progenitor cells for the treatment of Type I Diabetes. Full article

In this paper, we present a novel approach to state estimation in dynamic systems by combining Physics-Informed Neural Networks (PINNs) with an adaptive Unscented Kalman Filter (UKF). Recognizing the limitations of traditional state estimation methods, we refine the PINN architecture with hybrid loss functions and Monte Carlo Dropout for enhanced uncertainty estimation. The Unscented Kalman Filter is augmented with an adaptive noise covariance mechanism and incorporates model parameters into the state vector to improve adaptability. We further validate this hybrid framework by integrating the enhanced PINN with the UKF for a seamless state prediction pipeline, demonstrating significant improvements in accuracy and robustness. Our experimental results show a marked enhancement in state estimation fidelity for both position and velocity tracking, supported by uncertainty quantification via Bayesian inference and Monte Carlo Dropout. We further extend the simulation and present evaluations on a double pendulum system and state estimation on a quadcopter drone. This comprehensive solution is poised to advance the state-of-the-art in dynamic system estimation, providing unparalleled performance across control theory, machine learning, and numerical optimization domains. Full article

The superconductivity of CaC₆ as a function of pressure and Ca isotopic composition was revisited using DFT calculations on a 2c–double hexagonal superlattice. The introduction of superlattices was motivated by previous synchrotron absorption and Raman spectroscopy results on other superconductors that showed evidence of superlattice vibrations at low (THz) frequencies. For CaC₆, superlattices have previously been invoked to explain the ARPES data. A superlattice along the hexagonal c-axis direction is also illustrative of atomic orbital symmetry and periodicity, including bonding and antibonding s-orbital character implied by cosine-modulated electronic bands. Inspection of the cosine band revealed that the cosine function has a small (meV) energy difference between the bonding and antibonding regions, relative to a midpoint non-bonding energy. Fermi surface nesting was apparent in an appropriately folded Fermi surface using a superlattice construct. Nesting relationships identified phonon vectors for the conservation of energy and for phase coherency between coupled electrons at opposite sides of the Fermi surface. A detailed analysis of this Fermi surface nesting provided accurate estimates of the superconducting gaps for CaC₆ with the change in applied pressure. The recognition of superlattices within a rhombohedral or hexagonal representation provides consistent mechanistic insight on superconductivity and electron−phonon coupling in CaC₆. Full article

Dogs are considered the main reservoir of several zoonoses endemic to the Mediterranean Basin. In this study, a prevalence of infections and coinfections of canine vector-borne diseases was analyzed in apparently healthy dogs of different canine pure breeds in Sicily (Italy), where these diseases are endemic. The seroprevalence of Leishmania infantum, Ricketsia ricketsii, Anaplasma phagocytophilum, and Erlichia canis was assessed, as single and coinfections. Biochemical and hematological parameters were evaluated, and epidemiological factors, including sex, age, and canine breed, were recovered. The most frequent infection was L. infantum (45.61%), following R. ricketsii (36.84%), both as single, double, or triple coinfections. Coinfections change the biochemical and hematological parameters of the host, and canine breeds are related to the infection frequency and the parameters observed during infections. Changes in the complete blood count (CBC) and biochemical values also differ between canine breeds, with the Cirneco dell’Etna dogs infected with L. infantum being the animals presenting the most interesting results in our study. High values of RBC, hemoglobin, hematocrit, mean corpuscular hemoglobin (MCH), the albumin/globulin (A/G) ratio, and albumin and low levels of β-2 globulin and γ-globulin were found only in this canine breed, suggesting some resistance to infection in these dogs. Future studies about the immune response of this canine breed could be interesting to determine their possible resistance to zoonotic pathogens, such as L. infantum. Full article

The electrical protection of power networks with fault contribution from inverter-based power sources imposes new application challenges that have to be dealt with by protection engineers. This paper describes the development of a study case model of an HVDC-MMC link for testing the protection behaviour of connected converter transformers. The paper summarises the implementation and validation of the converter control as well as enhancements to provide Fault Ride-Through capability and fast fault current injection as required by the German Technical Connection Rules for HVDC. The grid code standard requires positive- and negative-sequence reactive current injection in the case of grid faults. A Doubled Decoupled Synchronous Reference Frame Phase Locked Loop (DDSRF-PLL) for Vector Current Control (VCC) is implemented. Additionally, a Fault Detection and Fault Ride-Through Reference Generator with a Current Limitation strategy is introduced. Though these techniques are well described in the literature, the DDSRF is improved for current control stability. The relationship between the parameters of the PLL and the control, as well as the behaviour of the protection system, are demonstrated. Grid faults with large voltage dips pose a significant challenge to the stability of the control system. Nevertheless, it is shown that with the developed model, it is possible to make general statements about the protection behaviour in an inverter-based environment. Full article

The study focuses on the four-strand tundish as the research object, aiming at the phenomenon of fewer strand casting (stable blockage) and sudden blockage of the tundish in industrial production. Numerical simulation methods are employed to compare the velocity vectors, flow fields, residence time distribution (RTD) curves, and outflow percentage curves under stable blockage and sudden blockage of the tundishes with a double-weir structure, U-shaped weir structure, and U-shaped weir structure with holes in the front. The results indicate that, after sudden blockage of the tundish strands, the flow field transitions from an unstable four-strand flow field to a stable three-strand flow field. Both the double-weir tundish and the U-shaped weir tundish reach a stable state after 200 s, while the U-shaped weir tundish with holes in the front reaches stability after 150 s. Additionally, compared to other structures, the tundish strands of the U-shaped weir with holes in the front are less affected by blockage, showing better consistency among strands and better adaptability under non-standard casting conditions. Full article

Range-spread target (RST) detection is an important issue for high-resolution radar (HRR). Traditional detectors relying on manually designed detection statistics have their performance limitations. Therefore, in this work, two deep learning-based detectors are proposed for RST detection using HRRPs, i.e., an NLS detector and DFCW detector. The NLS detector leverages domain knowledge from the traditional detector, treating the input HRRP as a low-level feature vector for target detection. An interpretable NLS module is designed to perform noise reduction for the input HRRP. The DFCW detector takes advantage of the extracted high-level feature map of the input HRRP to improve detection performance. It incorporates a feature cross-weighting module for element-wise feature weighting within the feature map, considering the channel and spatial information jointly. Additionally, a nonlinear accumulation module is proposed to replace the conventional noncoherent accumulation operation in the double-HRRP detection scenario. Considering the influence of the target spread characteristic on detector performance, signal sparseness is introduced as a measure and used to assist in generating two datasets, i.e., a simulated dataset and measured dataset incorporating real target echoes. Experiments based on the two datasets are conducted to confirm the contribution of the designed modules to detector performance. The effectiveness of the two proposed detectors is verified through performance comparison with traditional and deep learning-based detectors. Full article

To address problems that traditional two-stage inverters suffer such as high cost, low efficiency, and complex control, this study adopts a quasi-Z-source cascaded multilevel inverter. Firstly, the quasi-Z-source inverter utilizes a unique impedance network to achieve single-stage boost and inversion without requiring a dead zone setting. Additionally, its cascaded multilevel structure enables independent control of each power unit structure without capacitor voltage sharing problems. Secondly, this study proposes a current-predictive control strategy to reduce current harmonics on the grid side. Moreover, the feedback model of current and system state is established, and the fast control of grid-connected current is realized with the deadbeat control weighted by the predicted current deviation. And a grid-side inductance parameter identification is added to improve control accuracy. Also, an improved multi-carrier phase-shifted sinusoidal PWM method is adopted to address the issue of switching frequency doubling, which is caused by the shoot-through zero vector in quasi-Z-source inverters. Finally, the problems of switching frequency doubling and high harmonics on the grid side are solved by the improved deadbeat control strategy with an improved MPSPWM method. And a seven-level simulation model is built in MATLAB (2022b) to verify the correctness and superiority of the above theory. Full article

Cropland mapping using remote sensing data is the basis for effective crop monitoring, crop rotation control, and the detection of irrational land use. Classification using Normalized Difference Vegetation Index (NDVI) time series from multi-year data requires additional time costs, especially when sentinel data are sparse. Approximation by nonlinear functions was proposed to solve this problem. Time series of weekly NDVI composites were plotted using multispectral Sentinel-2 (Level-2A) images at a resolution of 10 m for sites in Khabarovsk District from April to October in the years 2021 and 2022. Missing values due to the lack of suitable images for analysis were recovered using cubic polynomial, Fourier series, and double sinusoidal function approximation. The classes that were considered included crops, namely, soybean, buckwheat, oat, and perennial grasses, and fallow. The mean absolute percentage error (MAPE) of each class fitting was calculated. It was found that Fourier series fitting showed the highest accuracy, with a mean error of 8.2%. Different classifiers, such as the support vector machine (SVM), random forest (RF), and gradient boosting (GB), were comparatively evaluated. The overall accuracy (OA) for the site pixels during the cross-validation (Fourier series restored) was 67.3%, 87.2%, and 85.9% for the SVM, RF, and GB classifiers, respectively. Thus, it was established that the best result in terms of combined accuracy, performance, and limitations in cropland mapping was achieved by composite construction using Fourier series and machine learning using GB. Similar results should be expected in regions with similar cropland structures and crop phenological cycles, including other regions of the Far East. Full article

The use of machine learning algorithms in healthcare can amplify social injustices and health inequities. While the exacerbation of biases can occur and be compounded during problem selection, data collection, and outcome definition, this research pertains to the generalizability impediments that occur during the development and post-deployment of machine learning classification algorithms. Using the Framingham coronary heart disease data as a case study, we show how to effectively select a probability cutoff to convert a regression model for a dichotomous variable into a classifier. We then compare the sampling distribution of the predictive performance of eight machine learning classification algorithms under four stratified training/testing scenarios to test their generalizability and their potential to perpetuate biases. We show that both extreme gradient boosting and support vector machine are flawed when trained on an unbalanced dataset. We then show that the double discriminant scoring of type 1 and 2 is the most generalizable with respect to the true positive and negative rates, respectively, as it consistently outperforms the other classification algorithms, regardless of the training/testing scenario. Finally, we introduce a methodology to extract an optimal variable hierarchy for a classification algorithm and illustrate it on the overall, male and female Framingham coronary heart disease data. Full article

Yunnan province in China shares its borders with three neighboring countries: Myanmar, Vietnam, and Laos. The region is characterized by a diverse climate and is known to be a suitable habitat for various arthropods, including midges which are notorious for transmitting diseases which pose significant health burdens affecting both human and animal health. A total of 431,100 midges were collected from 15 different locations in the border region of Yunnan province from 2015 to 2020. These midges were divided into 37 groups according to the collection year and sampling site. These 37 groups of midges were then homogenized to extract nucleic acid. Metatranscriptomics were used to analyze their viromes. Based on the obtained cytochrome C oxidase I gene (COI) sequences, three genera were identified, including one species of Forcipomyia, one species of Dasyhelea, and twenty-five species of Culicoides. We identified a total of 3199 viruses in five orders and 12 families, including 1305 single-stranded positive-stranded RNA viruses (+ssRNA) in two orders and seven families, 175 single-stranded negative-stranded RNA viruses (−ssRNA) in two orders and one family, and 1719 double-stranded RNA viruses in five families. Six arboviruses of economic importance were identified, namely Banna virus (BAV), Japanese encephalitis virus (JEV), Akabane virus (AKV), Bluetongue virus (BTV), Tibetan circovirus (TIBOV), and Epizootic hemorrhagic disease virus (EHDV), all of which are capable, to varying extents, of causing disease in humans and/or animals. The survey sites in this study basically covered the current distribution area of midges in Yunnan province, which helps to predict the geographic expansion of midge species. The complexity and diversity of the viral spectrum carried by midges identified in the study calls for more in-depth research, which can be utilized to monitor arthropod vectors and to predict the emergence and spread of zoonoses and animal epidemics, which is of great significance for the control of vector-borne diseases. Full article