Search Results (397)

In most amorphous materials, the concentration of Urbach tail states is larger than the concentration of dangling bond states. However, absorption accounting for the Urbach tail while disregarding the dangling bonds is commonly used or derived by spectroscopic characterizations of amorphous films from a single spectrum, mostly due to the insufficient accuracy of such characterizations. This paper proposes an advanced envelope method (AEM) for transmittance spectrum T(λ), aiming to resolve this problem. The novelties in AEM are: improved preprocessing of T(λ), extending the envelopes deeper into the region of strong absorption (RSA), enhanced determination of the refractive index n(λ) in the region of weak absorption, optimization of both n(λ) and the extinction coefficient k(λ) in RSA, as well as analysis of the types of electron transitions and calculation of their energy gaps. Three single magnetron sputtered a-Si films deposited on glass substrates are characterized by AEM, and three other relevant methods that disregard deep-levels. The best accuracy is achieved when these films are characterized by AEM. It is demonstrated that the absorption coefficient α(λ) of each of these films distinguishes electron transitions via dangling bond states from those via tails states, and the DOS corresponds to the Mott–Davis model of amorphous materials. Full article

Emery–Dreifuss muscular dystrophy (EDMD) is a rare inherited neuromuscular disorder within the spectrum of nuclear envelope diseases, classically characterized by early musculo-tendinous contractures, slowly progressive myopathy, and cardiac involvement dominated by conduction disease and arrhythmias, with variable evolution toward cardiomyopathy and heart failure. This narrative review provides a comprehensive and clinically actionable synthesis of cardiovascular manifestations across EDMD genotypes and phenotypes, outlining pragmatic diagnostic and therapeutic pathways for real-world care. A targeted literature search was performed in PubMed, Embase, and Web of Science, focusing on studies addressing cardiovascular involvement in EDMD. Relevant original studies, case series, registries, guideline documents, and high-quality reviews were selected and synthesized narratively, with particular emphasis on diagnostic strategies, risk stratification, and management approaches. Cardiac involvement in EDMD encompasses a broad and heterogeneous spectrum, including atrial disease and conduction disturbances, ventricular arrhythmias, dilated cardiomyopathy, thromboembolic complications, and sudden cardiac death. Phenotypic expression varies according to the underlying genetic substrate, with distinct atrial- and ventricular-dominant trajectories. Early recognition and structured cardiovascular surveillance are essential to guide timely intervention, including anticoagulation, device therapy, and heart failure management. Despite growing awareness, significant gaps remain in risk prediction and standardized management strategies. EDMD represents a paradigmatic model of cardiomyopathy characterized by prominent electrical instability and systemic involvement. A structured, genotype- and phenotype-informed approach centered on early surveillance, proactive arrhythmia and thromboembolic risk management and timely device therapy may improve clinical decision-making in real-world settings. Future perspectives include the integration of precision medicine and the development of gene- and pathway-targeted therapies, with the potential to shift from symptomatic management toward disease-modifying strategies. Full article

Monitoring the condition of rolling bearings is critical for industrial reliability, yet traditional contact-based accelerometers can be impractical in confined or hazardous environments. This study investigates the use of microphones as a non-invasive diagnostic alternative, focusing on the impact of sensor distance and spatial placement on fault detection sensitivity across various rotational speeds and load conditions. Using an accelerometer mounted directly on the bearing as a benchmark, acoustic data were acquired on a test bench under different speed and load conditions. The experimental setup evaluated three distinct microphone positions and five distances relative to the source to assess spatial influence. Analysis was conducted comparing scalar indicators, such as Root Mean Square (RMS), kurtosis and Crest Factor (CF) values, with advanced diagnostic techniques, specifically the High-Frequency Resonance Technique (HFRT) for envelope spectrum extraction. Results indicate that while the signal-to-noise ratio (SNR) predictably decreases with distance, diagnostic performance is significantly compromised by acoustic shielding effects caused by bearing housing. Moreover, while simple statistical factors (RMS, kurtosis, CF) show limited reliability across varying distances and noise floors, HFRT-based envelope analysis yields robust fault identification even at the maximum sensor distance. The study concludes that optimal microphone placement is essential for reliable remote monitoring. Particularly, these findings suggest that a preliminary spatial characterization of the acoustic field can significantly enhance the effectiveness of non-contact diagnostic systems in industrial applications. Full article

Nuclear lamins are the core molecular bridge linking the extracellular mechanical microenvironment to intranuclear gene regulation, and play a central regulatory role in cellular mechanosensation and mechanotransduction. Here, we systematically integrate the latest global research progress on nuclear lamins, delineating the cascade regulatory mechanism by which lamins mediate the transmission of mechanical signals across the nuclear envelope and the subsequent regulation of chromatin remodeling and epigenetic modification, with a focus on the molecular characteristics and functional specificity of distinct nuclear lamin subtypes and their interaction modes with the Linker of Nucleoskeleton and Cytoskeleton complex (LINC complex) and chromatin. Existing studies have established that nuclear lamins are mainly divided into three categories: A-type lamins (Lamin A/C), B-type lamins (Lamin B1, B2), and germ cell-specific subtypes. Among these, A-type lamins directly determine the mechanical stiffness of the nucleus and serve as the core mediators of intranuclear mechanical signal transduction. Each subtype of B-type nuclear lamins has a well-defined, non-redundant functional division: Lamin B1 and Lamin B2 indirectly maintain nuclear structural stability and regulate epigenetic status by anchoring facultative heterochromatin and constitutive heterochromatin, respectively. Notably, Lamin A/C distributed in the nucleoplasm also bears significant mechanical tension, which challenges the long-standing view that the mechanical functions of nuclear lamins are restricted to the nuclear envelope region. After mechanical force is transmitted across the nuclear envelope to nuclear lamins via the LINC complex, it can regulate the spatial conformation of chromatin and epigenetic modifications, thereby determining core cellular life activities including proliferation, differentiation, and migration. Dysregulation of this pathway is closely associated with a wide spectrum of human diseases, including cardiovascular diseases, progeria, muscular dystrophy, and neurodevelopmental disorders. Taken together, this review systematically delineates the hierarchical regulatory network of the “LINC complex–nuclear lamina–chromatin” axis, advances our understanding of the fundamental principles of cellular mechanobiology, and provides a theoretical framework for deciphering the pathological mechanisms and developing targeted therapeutic drugs for related diseases. Full article

Early fault vibration signals from rolling bearings are typically nonlinear, non-stationary, and heavily obscured by background noise, which severely impedes the accurate extraction of fault features. To overcome the limitations of traditional stochastic resonance (SR)—specifically the small-parameter restriction for high-frequency signals and the subjectivity in parameter selection—this paper proposes an adaptive SR envelope spectroscopy method based on particle swarm optimization (PSO) and local mean decomposition (LMD). First, a variable-scale transformation is introduced to compress the high-frequency fault signals into the effective frequency band required by the adiabatic approximation theory. Second, utilizing the global search capability of PSO, the potential well parameters of the bistable system are adaptively optimized by maximizing the output signal-to-noise ratio (SNR), thereby achieving optimal matching between the nonlinear system and the input signal. Finally, the enhanced signal is decomposed by LMD, and the sensitive components are selected for envelope spectrum analysis to identify fault characteristics. Experimental validation using the Case Western Reserve University bearing dataset demonstrates that the proposed method effectively amplifies weak fault signals under strong noise conditions, exhibiting superior feature extraction accuracy and noise robustness compared to traditional methods. Full article

This paper proposes a novel integrated spectral kurtosis (ISK) technology, which is a new conceptualization for fault diagnosis, and compares it with conventional spectral kurtosis technology. The vibration signals from a gearbox are processed by time synchronous averaging (TSA) and analysed using the spectral kurtosis (SK). The ISK feature is estimated across the entire frequency domain, while the envelope is obtained through SK-based filtering and a Hilbert demodulation. The ISK technology demonstrates the ability to distinguish between healthy and defected gearbox cases, achieving a total probability of correct diagnosis (TPCD) of 91.5% for pinions and 96.1% for gears, whereas the SK-based squared envelope technology provides a limited diagnosis effectiveness, with a maximum TPCD of 80%. The motor current signals are also analysed through harmonic amplitude tracking within the current spectrum. A comparison of the ISK and motor current technologies is also made, showing that the motor current technology reaches a maximum of 90% TPCD for gears, which remains lower than the TPCD for the ISK technology. Full article

In industrial robots, harmonic drive flexible bearings are prone to faults, and fault diagnosis is essential for preventing unexpected downtime. However, vibration signals acquired from robot joints are often non-stationary and contaminated by strong multi-source interference, including motion-induced interference and vibrations induced by the deformation of flexible components. Such interference severely masks the subtle signatures of faults. To address this issue, this paper proposes a fault diagnosis framework that leverages multi-channel vibration signals to enhance fault-related features. First, angular resampling is applied to eliminate speed-induced non-stationarity. Second, envelope extraction is utilized to obtain demodulated signals suitable for independent component analysis (ICA). Subsequently, ICA is employed to extract fault-related components from the multi-channel signals. Finally, the fault-related independent component is identified and analyzed via envelope order spectrum analysis. Experimental validation on an industrial robot under both single-joint and multi-joint operating conditions demonstrates the effectiveness of the proposed framework. The method suppresses multi-source interference and achieves accurate fault diagnosis for flexible bearings under complex operating conditions, with quantitative validation confirming the diagnostic performance of the proposed framework. Full article

Edge intelligence enables significant latency reduction and enhances the timeliness of model-based fault diagnosis. However, existing deep learning-driven bearing fault diagnosis models are ill-suited for deployment on edge devices, primarily due to three critical limitations: (1) Lightweight models typically exhibit inadequate anti-noise performance, failing to meet the reliability requirements of real-world engineering scenarios. (2) Models with superior anti-noise capabilities often demand high-performance hardware for operation, thereby restricting their deployment on resource-constrained edge devices. (3) These models adopt a fixed input length, which makes it difficult to guarantee diagnostic accuracy across diverse application scenarios—attributed to variations in sampling frequencies, bearing parameters, and other relevant factors. To address these challenges, this paper proposes a lightweight anti-noise diagnostic framework (LADF) for edge-intelligent bearing fault diagnosis in complex engineering environments. The LADF comprises three core modules: a dynamic input module (DIM), a lightweight network module (LNM), and a denoising branch. Specifically, the DIM is designed based on the envelope spectrum, leveraging its inherent demodulation characteristics to dynamically adapt to input signals across diverse scenarios. Group convolution and layer normalization are employed to construct the LNM, ensuring robust diagnostic performance while achieving efficient computation. The denoising branch constrains the feature extractor via a loss function, enabling it to learn generalized fault features under varying noise environments and thereby enhancing the anti-noise capability of the framework. Finally, the proposed LADF is validated through test rig experiments on two datasets of train axle box bearings. Comparative analysis with state-of-the-art models demonstrates that the LADF achieves superior diagnostic stability and anti-noise performance while maintaining a more lightweight architecture, making it well-suited for edge deployment in railway bearing fault diagnosis. Full article

Wheel–rail coupled excitation introduces strong low-frequency modulation, random impact interference, and broadband background noise into the vibration system of locomotive gearboxes, causing early weak bearing fault features to become submerged and making traditional deconvolution methods insufficient for effective enhancement. To address this challenge, this study proposes an adaptive parameter optimization method for MCKD based on the weighted envelope spectrum factor (WESF). WESF integrates the Hoyer index, kurtosis, and envelope spectrum energy to jointly characterize sparsity, impulsiveness, and periodicity of signal components. By using WESF as the fitness function, the sparrow search algorithm (SSA) is employed to simultaneously optimize the key MCKD parameters L, T, and M, enabling optimal enhancement of weak periodic impacts. To further mitigate modal aliasing caused by wheel–rail excitation, the original signal is first adaptively decomposed using successive variational mode decomposition (SVMD), and modes with WESF values above the average are selected for signal reconstruction. The reconstructed signal is subsequently enhanced via SSA–MCKD, and fault characteristic frequencies are extracted using envelope spectrum analysis. Experimental validation using gearbox bearing data collected under 40, 50, and 60 Hz operating conditions shows that the proposed method achieves fault feature coefficient (FFC) values of 12.8%, 7.5%, and 7.2%, respectively—representing an average improvement of approximately 156% compared with traditional methods (average FFC of 3.6%). These results demonstrate that the proposed SVMD–WESF–SSA–MCKD approach can significantly enhance weak periodic impact features under strong background noise and wheel–rail excitation, exhibiting strong practical applicability for engineering implementation. Full article

Interferon-induced transmembrane proteins (IFITMs) are broad-spectrum antiviral factors that restrict the entry of many enveloped viruses, including HIV-1, by modifying host membrane properties and trapping fusion at the hemifusion stage. Beyond blocking entry in target cells, IFITMs also reduce the infectivity of virions produced from IFITM-expressing cells, a phenomenon termed “negative imprinting”. Conserved motifs, such as the amphipathic helix and oligomerization motifs, have been reported to be essential for IFITM-mediated protection of target cells from viral infection. Yet, the impact of IFITM incorporation on progeny virion infectivity remains poorly defined. Here, we show that IFITM3 mutants defective in target cell protection activity still markedly impair HIV-1 fusion/infection upon incorporating into virions, without affecting viral maturation or Env incorporation. Immunofluorescence studies suggest mislocalization of the IFITM3 mutants as the reason for the lack of antiviral activity in target cells. Testing the antiviral activity of chimeras between antiviral and non-antiviral IFITM orthologs failed to clearly identify a domain responsible for reduction of HIV-1 infectivity, suggesting that multiple domains may be required for negative imprinting. Interestingly, co-incorporation of non-antiviral dog IFITM1 with human IFITM3 did not interfere with IFITM3’s negative imprinting activity, despite forming mixed hetero-oligomers. This finding implies a dominant, oligomerization-independent antiviral phenotype of IFITM3 in virions. Our findings suggest that IFITMs may protect target cells and negatively imprint progeny virions through distinct mechanisms, underscoring the need to further characterize the molecular basis for the reduced fusion competence of IFITM-containing HIV-1 particles. Full article

The family Caliciviridae encompasses a diverse group of non-enveloped, positive-sense RNA viruses that are significant pathogens in veterinary medicine. This narrative review summarizes the current state of knowledge regarding the clinical, molecular, and epidemiological features of the three calicivirus genera identified in bovine hosts: Norovirus, Nebovirus, and Vesivirus. Bovine noroviruses and neboviruses are neglected enteric pathogens, frequently detected in association with neonatal calf diarrhea and often present in co-infections with other enteric agents. Clinical presentations for these enteric viruses range from severe, watery diarrhea to asymptomatic shedding, with distinct pathogenic profiles observed between norovirus genotypes GIII.1 and GIII.2. In contrast, the genus Vesivirus exhibits a broad host range, and bovine vesivirus strains are phylogenetically linked to vesiviruses identified in pigs and marine animals. Bovine vesivirus infections are associated with a broader spectrum of clinical manifestations, including respiratory disease, vesicular lesions, and abortion. Serological and virological surveys indicate that exposure to these viruses is ubiquitous in cattle populations globally. While direct evidence of human infection by bovine noroviruses and neboviruses remains limited, vesiviruses possess a confirmed capacity for cross-species transmission to humans. Significant knowledge gaps remain, particularly regarding in vitro culture systems, necessitating further research to facilitate vaccine development and clarify transmission dynamics. Full article

Cracks in planetary gearbox casings generate additional vibration responses, which may be used for monitoring structural degradations. This paper provides a signal processing framework to effectively track casing crack-related features in planetary gearboxes using the carrier synchronous signal average (C-SSA). The proposed algorithm is based on processing the hunting-tooth synchronous signal average (H-SSA) to extract the C-SSA which contains the cyclic interaction between the gear loadings and the corresponding casing response. The root mean square (RMS) of the C-SSA signal can then serve as a health condition indicator (CI) to track crack propagation. Further enhancement can be achieved by applying the Hilbert transform (HT) on the C-SSA using the full bandwidth to derive squared envelope signal, which enhances the trending capability. To remove cyclic temperature influences observed in the trends, singular spectrum analysis technique (SSAT) has been used to ensure that the trend reflects the changes purely due to the damage progression. Experiments using three casing-mounted sensors show good capability to track crack progression. Tests under 100%, 125%, and 150% load levels show consistent performance across these operating conditions, with better results seen at higher loads. The results demonstrate that C-SSA and its squared envelope signal effectively enhance the sensitivity and reliability of vibration-based casing crack detection, providing a practical tool for long-term structural health monitoring of planetary gearboxes. Full article

Marine lectins function as pattern recognition receptors in innate immunity through carbohydrate-binding mechanisms. However, mechanistic evidence detailing intracellular signaling cascades (e.g., MAPK/NF-κB/JAK-STAT activation linked to defined cytokine outputs) remains taxonomically uneven. Bivalve mollusks—particularly the Mytilectin family—represent the most extensively characterized group, whereas lectins from other marine phyla (echinoderms, cnidarians, fish, algae) have been studied primarily for structural and glycan-binding properties alongside phenotypic antimicrobial outcomes. Signaling-level resolution in native immune-cell contexts, while present in some cases, remains comparatively limited. This review synthesizes mechanistic insights dominated by bivalve-derived lectins, while integrating cross-taxa comparisons at evidence-supported levels. Specific bivalve lectins induce macrophage activation and pro-inflammatory cytokine production through reactive oxygen species-dependent activation of key signaling pathways including MAPK, NF-κB, and JAK-STAT cascades. These lectins exhibit context-dependent properties, promoting inflammatory responses in resting cells while inducing endotoxin tolerance in pre-activated macrophages through epigenetic reprogramming. Functional outcomes include broad-spectrum antiviral activity through viral envelope glycoprotein binding, anti-inflammatory effects in pain models, and cancer-associated immune responses through tumor glycan recognition and macrophage polarization. Critical gaps include uncharacterized effects on adaptive immunity, limited understanding of dendritic cell and natural killer cell interactions, and incomplete evaluation of cancer immunotherapy potential. Future research should prioritize mechanistic characterization of marine lectin-based immunotherapeutics. Full article

As a critical mechanical component, the operational integrity of rolling bearings is essential for equipment safety. However, under strong noise interference, the weak fault features in vibration signals are difficult to extract. To address this issue, a novel fault diagnosis method is proposed in this paper, which integrates an improved speed time-varying filtering empirical mode decomposition (ISTVF-EMD) with an adaptive sine–cosine optimization algorithm (A-SCA), enabling precise and efficient extraction of fault features. The core of the proposed method lies in improving the conventional time-varying filtering empirical mode decomposition (TVF-EMD) by setting a maximum decomposition layer limit, effectively addressing issues of excessive components and low computational efficiency during the decomposition of low signal-to-noise ratio (SNR) signals. Furthermore, a multi-characteristic frequency energy concentration centrality (MCFECC) index is employed as a fitness function to guide A-SCA in adaptively searching for the optimal bandwidth threshold and fitting order parameters of ISTVF-EMD, thereby extracting components with the most enriched fault information. Validated through simulation and multiple test bench cases, the results indicate that the proposed method can not only significantly enhance the fault characteristic frequencies and their harmonics in the envelope spectrum, successfully diagnosing outer race, inner race, and rolling element faults, but also, compared with the original method, ISTVF-EMD substantially reduces the computational time while ensuring or even improving the decomposition quality. The method presented in this paper provides an effective solution for achieving precise and adaptive fault diagnosis of rolling bearings under strong noise interference. Full article

This paper addresses the challenge of extracting weak early fault signals from rolling bearings under variable speed conditions, where strong background noise often obscures diagnostic features. We propose a novel fault diagnosis method that integrates variational mode decomposition (VMD) and maximum second-order cyclo-stationarity blind deconvolution (CYCBD). The proposed approach begins by converting non-stationary vibration signals into angular-domain stationary signals using computed order tracking (COT). Subsequently, the parameters of the VMD algorithm are optimized via the sine–cosine and Cauchy mutation sparrow search algorithm (SCSSA) to select the optimal modal components. A key contribution is the introduction of a composite index (CI), combining harmonic significance and the envelope spectrum crest factor, which serves as the fitness function for the SCSSA to optimize the critical parameters of CYCBD for enhanced feature enhancement. Finally, fault characteristics are extracted by analyzing the deconvolved signal with an order envelope spectrum. Both simulation and experimental results demonstrate the superior capability of the proposed VMD-CYCBD method in effectively identifying weak fault features submerged in strong noise under variable speed conditions. Full article