Search Results (8)

(1) Introduction: Dilated cardiomyopathy (DCM) mainly affects young individuals and is the main indication of heart transplantation. The variant c.77T>C (p.Val26Ala) of the gene coding for emerin (EMD) in chromosome Xq28 has been catalogued as a pathogenic variant for the development of DCM, exhibiting an X-linked inheritance pattern. (2) Methods: A retrospective study was conducted covering the period 2015–2023 in patients with DCM of genetic origin. The primary endpoint was patient age at onset of the first composite major cardiac event, in the form of a first episode of heart failure, malignant ventricular arrhythmia, or end-stage heart failure, according to the presence of truncating variant in titin gene (TTNtv) versus the p.Val26Ala mutation in the EMD protein. (3) Results: A total of 31 and 22 patients were included in the EMD group and TTNtv group, respectively. The primary endpoint was significantly higher in the EMD group, with a hazard ratio of 4.16 (95% confidence interval: 1.83–9.46; p = 0.001). At 55 years of age, all the patients in the EMD group had already presented heart failure, nine presented malignant ventricular arrhythmia (29%), and 13 required heart transplantation (42%). (4) Conclusions: DCM secondary to the c.77T>C (p.Val26Ala) mutation in the EMD gene is associated to an increased risk of major cardiac events compared to patients with DCM due to TTNtv, with a large proportion of transplanted patients in the fifth decade of life. Full article

The fatigue problem of hangers is fatal for the safety of the whole bridge structure. The objective of this study is to present a strain monitoring-based method to assess the fatigue performance of stiff hangers in highway arch bridges and predict their remaining life. A vehicle–bridge interaction system was constructed to analyze the dynamic behavior in the area close to the key welding line where the hanger was connected to the deck slab. Then, the empirical mode decomposition (EMD) algorithm and rain-flow counting algorithm were used in signal preprocessing and statistical analysis of field monitoring data. Finally, the fatigue life was assessed according to the standards in the Chinese Code for the Design of Steel Structures, as well as the Eurocode 3 and AASHTO codes. Differences were found in the fatigue behavior of hangers, and the shortest hanger was shown to surfer more serious fatigue damage. The influence of vehicle volume growth and low-stress amplitude on the fatigue performance was also discussed. Full article

This paper presents a novel method for condition monitoring using the RMS residual of vibration signal reconstruction based on trained dictionaries through sparse representation theory. Measured signals were firstly decomposed into intrinsic mode functions (IMFs) for training the initial dictionary. In this step, an adaptive variational mode decomposition (VMD) was proposed for providing information with higher accuracy, and the decompositions were used as discriminative atoms for sparse representation. Then, the overcomplete dictionary for sparse coding was learned from IMFs to reserve the highlight feature of the signals. As the dictionaries were trained, newly measured signals could be directly reconstructed without any signal decompositions or dictionary learning. This meant errors likely introduced by signal process techniques, such as VMD, EMD, etc., could be excluded from the condition monitoring. Moreover, the efficiency of the fault diagnosis was greatly improved, as the reconstruction was fast, which showed a great potential in online diagnosis. The RMS of the residuals between the reconstructed and measured signals was extracted as a feature of condition. A case study on operating condition identification of a diesel engine was carried out experimentally based on vibration accelerations, which validated the availability of the proposed feature extraction and condition-monitoring approach. The presented results showed that the proposed method resulted in a great improvement in the fault feature extraction and condition monitoring, and is a promising approach for future research. Full article

In global navigation satellite system (GNSS)-based positioning and applications, multipath is by far the most obstinate impact. To overcome paradoxical issues faced by current processing approaches for multipath, this paper employs an intrinsic method to identify and mitigate multipath based on empirical mode decomposition (EMD) and Hilbert–Huang transform (HHT). Frequency spectrum and power spectrum are comprehensively employed to identify and extract multipath from complex data series composed by combined GNSS observations. To systematically inspect the multipath from both code range and carrier phase, typical kinds of combinations of the GNSS observations including the code minus phase (CMP), differential correction (DC), and double differential (DD) carrier phase are selected for the suggested intrinsic approach to recognize and mitigate multipath under typical positioning modes. Compared with other current processing algorithms, the proposed methodology can deal with multipath under normal positioning modes without recourse to the conditions that satellite orbits are accurately repeated and surrounding environments of observing sites remain intact. The method can adaptively extract and eliminate multipath from solely the GNSS observations using intrinsic decomposition mechanism. From theoretical discussions and validating tests, it is found that both code and carrier phase multipath can be identified and distinguished from ionospheric delay and other impacts using the EMD based techniques. The resultant positioning accuracy is therefore improved to an obvious extent after the removal of the multipath. Overall, the proposed method can form an extensive and sound technical frame to enhance localization accuracy under typical GNSS positioning modes and harsh multipath environments. Full article

Generating electricity from enormous energy contained in oceans is an important means to develop and utilize marine sustainable energy. An offshore marine current generator set (MCGS) is a system that runs in seas to produce electricity from tremendous energy in tidal streams. MCGSs operate in oceanic environments with high humidity, saline-alkali water, and impacts of marine organisms and waves, and consequently malfunctions can happen along with the need for expensive inspection and maintenance. In order to achieve effective fault diagnosis of MCGSs in events of failure, this paper focuses on fault detection and diagnosis (FDD) of MCGSs based on five-phase permanent magnet synchronous generators (FP-PMSGs) with the third harmonic windings (THWs). Firstly, mathematical models were built for a hydraulic turbine and the FP-PMSG with THWs; then, a fault detection method based on empirical mode decomposition (EMD) and Hilbert transform (HT) was studied to detect different open-circuit faults (OCFs) of the generator; afterwards, a variable-parameter particle swarm optimization (VPSO) was designed to optimize the penalty and kernel function parameters of a support vector machine (SVM), which was named the VPSO-SVM method in this paper and used to perform fault diagnosis of the FP-PMSG. Finally, simulation blocks were built with MATLAB/Simulink to realize the mathematical models of the MCGS, and the proposed FDD method was coded with MATLAB. The effectiveness of the proposed VPSO-SVM method was validated by simulation results analysis and comparisons. Full article

Data hiding in videos has been a big concern as their rich redundancy can be used for embedding a lot of secret information. Further, as high efficiency video coding (HEVC) introduces many innovative technologies compared with the previous standard, H.264, it has gradually become the mainstream. Therefore, it is valuable to develop new information hiding algorithms by using novel features of HEVC. A HEVC video data hiding algorithm based on prediction unit (PU) partition modes from inter prediction process is proposed in this paper. Firstly, code units (CUs) in two sizes of 8 × 8 and 16 × 16 are selected for embedding, then the PU partition modes in these CUs are coded by a spatial coding method. After that, two specific hiding algorithms by modifying coded PU partition modes in CUs of 8 × 8 and 16 × 16 are proposed, respectively. Experimental results show that the proposed algorithm has achieved excellent performance with high visual quality, and high embedding capacity and low bitrate increase in both high- and low-resolution videos compressed with different quantization parameters (QPs). Compared with the state-of-the-art work, the proposed algorithm achieves a much higher capacity while keeping quite high visual quality with little increase of bitrate. Full article

Emerin is an essential LEM (LAP2, Emerin, MAN1) domain protein in metazoans and an integral membrane protein associated with inner and outer nuclear membranes. Mutations in the human EMD gene coding for emerin result in the rare genetic disorder: Emery–Dreifuss muscular dystrophy type 1 (EDMD1). This disease belongs to a broader group called laminopathies—a heterogeneous group of rare genetic disorders affecting tissues of mesodermal origin. EDMD1 phenotype is characterized by progressive muscle wasting, contractures of the elbow and Achilles tendons, and cardiac conduction defects. Emerin is involved in many cellular and intranuclear processes through interactions with several partners: lamins; barrier-to-autointegration factor (BAF), β-catenin, actin, and tubulin. Our study demonstrates the presence of the emerin fraction which associates with mitotic spindle microtubules and centrosomes during mitosis and colocalizes during early mitosis with lamin A/C, BAF, and membranes at the mitotic spindle. Transfection studies with cells expressing EGFP-emerin protein demonstrate that the emerin fusion protein fraction also localizes to centrosomes and mitotic spindle microtubules during mitosis. Transient expression of emerin deletion mutants revealed that the resulting phenotypes vary and are mutant dependent. The most frequent phenotypes include aberrant nuclear shape, tubulin network mislocalization, aberrant mitosis, and mislocalization of centrosomes. Emerin deletion mutants demonstrated different chromatin binding capacities in an in vitro nuclear assembly assay and chromatin-binding properties correlated with the strength of phenotypic alteration in transfected cells. Aberrant tubulin staining and microtubule network phenotype appearance depended on the presence of the tubulin binding region in the expressed deletion mutants. We believe that the association with tubulin might help to “deliver” emerin and associated membranes to decondensing chromatin. Preliminary analyses of cells from Polish patients with EDMD1 revealed that for several mutations thought to be null for emerin protein, a truncated emerin protein was present. We infer that the EDMD1 phenotype may be strengthened by the toxicity of truncated emerin expressed in patients with certain nonsense mutations in EMD. Full article

The performance evaluation of wind power forecasting under commercially operating circumstances is critical to a wide range of decision-making situations, yet difficult because of its stochastic nature. This paper firstly introduces a novel TRSWA-BP neural network, of which learning process is based on an efficiency tabu, real-coded, small-world optimization algorithm (TRSWA). In order to deal with the strong volatility and stochastic behavior of the wind power sequence, three forecasting models of the TRSWA-BP are presented, which are combined with EMD (empirical mode decomposition), PSR (phase space reconstruction), and EMD-based PSR. The error sequences of the above methods are then proved to have non-Gaussian properties, and a novel criterion of normalized Renyi’s quadratic entropy (NRQE) is proposed, which can evaluate their dynamic predicted accuracy. Finally, illustrative predictions of the next 1, 4, 6, and 24 h time-scales are examined by historical wind power data, under different evaluations. From the results, we can observe that not only do the proposed models effectively revise the error due to the fluctuation and multi-fractal property of wind power, but also that the NRQE can reserve its feasible assessment upon the stochastic predicted error. Full article