Search Results (2,065)

Rolling bearings are prone to failure, meaning that research on intelligent fault diagnosis is crucial in relation to this key transmission component in rotating machinery. The application of deep learning (DL) has significantly advanced the development of intelligent fault diagnosis. This paper proposes a novel method for rolling bearing fault diagnosis based on the fractional constant Q non-stationary Gabor transform (FCO-NSGT) and VMamba-Conv. Firstly, a rolling bearing fault experimental platform is established and the vibration signals of rolling bearings under various working conditions are collected using an acceleration sensor. Secondly, a kurtosis-to-entropy ratio (KER) method and the rotational kernel function of the fractional Fourier transform (FRFT) are proposed and applied to the original CO-NSGT to overcome the limitations of the original CO-NSGT, such as the unsatisfactory time–frequency representation due to manual parameter setting and the energy dispersion problem of frequency-modulated signals that vary with time. A lightweight fault diagnosis model, VMamba-Conv, is proposed, which is a restructured version of VMamba. It integrates an efficient selective scanning mechanism, a state space model, and a convolutional network based on SimAX into a dual-branch architecture and uses inverted residual blocks to achieve a lightweight design while maintaining strong feature extraction capabilities. Finally, the time–frequency graph is inputted into VMamba-Conv to diagnose rolling bearing faults. This approach reduces the number of parameters, as well as the computational complexity, while ensuring high accuracy and excellent noise resistance. The results show that the proposed method has excellent fault diagnosis capabilities, with an average accuracy of 99.81%. By comparing the Adjusted Rand Index, Normalized Mutual Information, F1 Score, and accuracy, it is concluded that the proposed method outperforms other comparison methods, demonstrating its effectiveness and superiority. Full article

Copper alloys are critical heat sink materials for fusion reactor divertors due to their high thermal conductivity (TC) and strength, yet their performance under extreme particle bombardment and heat fluxes in future tokamaks requires enhancement. While neutron-induced transmutation helium affects the properties of copper, the atomistic mechanisms linking helium bubble size to thermal transport remain unclear. This study employs non-equilibrium molecular dynamics (NEMD) simulations to isolate the effect of bubble diameter (10, 20, 30, 40 Å) on TC in copper, maintaining a constant He-to-vacancy ratio of 2.5. Results demonstrate that larger bubbles significantly impair TC. This reduction correlates with increased Kapitza thermal resistance and pronounced lattice distortion from outward helium diffusion, intensifying phonon scattering. Phonon density of states (PDOS) analysis reveals diminished low-frequency peaks and an elevated high-frequency peak for bubbles >30 Å, confirming phonon confinement and localized vibrational modes. The PDOS overlap factor decreases with bubble size, directly linking microstructural evolution to thermal resistance. These findings elucidate the size-dependent mechanisms of helium bubble impacts on thermal transport in copper divertor materials. Full article

The complex topology of shipboard DC microgrids and the strong coupling between positive and negative poles during faults pose significant challenges for faulted-pole identification, especially under high-resistance conditions. To address these issues, this paper proposes a novel faulted-pole identification method based on S-Transformation and convolutional neural networks (CNNs). Single-ended voltage and current measurements from the generator side are used to generate time–frequency spectrograms via S-Transformation, which are then processed by a CNN trained to classify the faulted pole. This approach avoids reliance on complex threshold settings. Simulation results on a representative shipboard DC microgrid demonstrate that the proposed method achieves high accuracy, fast response, and strong robustness, even under high-resistance fault scenarios. The method significantly enhances the selectivity and reliability of fault protection, offering a promising solution for advanced marine DC power systems. Compared to conventional fault-diagnosis techniques, the proposed model achieves notable improvements in classification accuracy and computational efficiency for line-fault detection. Full article

The southeastern Yunnan region in the southwestern Nanpanjiang Basin is one of the most important manganese enrichment zones in China. Manganese mineralization is mainly confined to marine mud–sand–carbonate interbeds of the Middle Triassic Ladinian Falang Formation (T₂f), which contains several medium to large deposits such as Dounan, Baixian, and Yanzijiao. However, the geological processes that control manganese mineralization in this region remain insufficiently understood. Understanding the tectonic evolution of the basin is therefore essential to unravel the mechanisms of Middle Triassic metallogenesis. This study investigates how rift-related tectonic activity influences manganese ore formation. This study integrates global gravity and magnetic field models (WGM2012, EMAG2v3), audio-frequency magnetotelluric (AMT) profiles, and regional geological data to investigate ore-controlling structures. A distinct gravity low–magnetic high belt is delineated along the basin axis, indicating lithospheric thinning and enhanced mantle-derived heat flow. Structural interpretation reveals a rift system with a checkerboard pattern formed by intersecting NE-trending major faults and NW-trending secondary faults. Four hydrothermal plume centers are identified at these fault intersections. AMT profiles show that manganese ore bodies correspond to stable low-resistivity zones, suggesting fluid-rich, hydrothermally altered horizons. These findings demonstrate a strong spatial coupling between hydrothermal activity and mineralization. This study provides the first identification of the internal rift architecture within the Nanpanjiang Basin. The basin-scale rift–graben system exerts first-order control on sedimentation and manganese metallogenesis, supporting a trinity model of tectonic control, hydrothermal fluid transport, and sedimentary enrichment. These insights not only improve our understanding of rift-related manganese formation in southeastern Yunnan but also offer a methodological framework applicable to similar rift basins worldwide. Full article

High-viscosity stratified strata, characterized by complex geotechnical properties such as strong cohesion, low permeability, and pronounced layered structures, exhibit significant lateral friction resistance and high-end resistance during steel sheet pile installation. These factors substantially increase construction difficulty and may even cause structural damage. This study addresses two critical mechanical challenges during vibratory pile driving in Fujian Province’s hydraulic engineering project: prolonged high-frequency driving durations, and severe U-shaped steel sheet pile head damage in high-viscosity stratified soils. Employing the Coupled Eulerian–Lagrangian (CEL) numerical method, a systematic investigation was conducted into the penetration resistance, stress distribution, and damage patterns during vibratory pile driving under varying conditions of cohesive soil layer thickness, predrilled hole spacing, and aperture dimensions. The correlation between pile stress and penetration depth was established, with the influence mechanisms of key factors on driving-induced damage in high-viscosity stratified strata under multi-factor coupling effects elucidated. Finally, the feasibility of predrilling techniques for resistance reduction was explored. This study applies the damage prediction model based on the CEL method to U-shaped sheet piles in high-viscosity stratified formations, solving the problem of mesh distortion in traditional finite element methods. The findings provide scientific guidance for steel sheet pile construction in high-viscosity stratified formations, offering significant implications for enhancing construction efficiency, ensuring operational safety, and reducing costs in such challenging geological conditions. Full article

Global warming has resulted in an increase in the frequency of extreme high-temperature events. High temperatures can increase cell membrane permeability, elevate levels of osmotic adjustment substances, reduce photosynthetic capacity, impair plant growth and development, and even result in plant death. Under high-temperature stress, plants mitigate damage through physiological and biochemical adjustments, heat signal transduction, the regulation of transcription factors, and the synthesis of heat shock proteins. However, different plants exhibit varying regulatory abilities and temperature tolerances. Investigating the heat-resistance and regulatory mechanisms of plants can facilitate the development of heat-resistant varieties for plant genetic breeding and landscaping applications. This paper presents a systematic review of plant physiological and biochemical responses, regulatory substances, signal transduction pathways, molecular mechanisms—including the regulation of heat shock transcription factors and heat shock proteins—and the role of plant hormones under high-temperature stress. The study constructed a molecular regulatory network encompassing Ca²⁺ signaling, plant hormone pathways, and heat shock transcription factors, and it systematically elucidated the mechanisms underlying the enhancement of plant thermotolerance, thereby providing a scientific foundation for the development of heat-resistant plant varieties. Full article

Titanium alloys are frequently subjected to surface treatments to enhance their biocompatibility and corrosion resistance in biological environments. Plasma electrolytic oxidation (PEO) is an environmentally friendly electrochemical technique capable of forming oxide layers characterized by high corrosion resistance, biocompatibility, and strong adhesion to the substrate. In this study, the PEO process was performed using a low-melting-point ternary eutectic electrolyte composed of Ca(NO₃)₂–NaNO₃–KNO₃ (41–17–42 wt.%) with the addition of ammonium dihydrogen phosphate (ADP). The use of this electrolyte system enables a reduction in the operating temperature from 280 to 160 °C. The effects of applied voltage from 200 to 400V, current frequency from 50 to 1000 Hz, and ADP concentrations of 0.1, 0.5, 1, 2, and 5 wt.% on the growth of titanium oxide composite coatings on a Ti-6Al-4V substrate were investigated. The incorporation of Ca and P was confirmed by phase and chemical composition analysis, while scanning electron microscopy (SEM) revealed a porous surface morphology typical of PEO coatings. Corrosion resistance in Hank’s solution, evaluated via Tafel plot fitting of potentiodynamic polarization curves, demonstrated a substantial improvement in electrochemical performance of the PEO-treated samples. The corrosion current decreased from 552 to 219 nA/cm², and the corrosion potential shifted from −102 to 793 mV vs. the Reference Hydrogen Electrode (RHE) compared to the uncoated alloy. These findings indicate optimal PEO processing parameters for producing composite oxide coatings on Ti-6Al-4V alloy surfaces with enhanced corrosion resistance and potential bioactivity, which are attributed to the incorporation of Ca and P into the coating structure. Full article

Background: Peptides are a class of molecules that can be presented as good antimicrobials and with mechanisms that avoid resistance, and the design of peptides with good activity can be complex and laborious. The study of their quantitative structure–activity relationships through machine learning algorithms can shed light on a rational and effective design. Methods: Information on the antimicrobial activity of peptides was collected, and their structures were characterized by molecular descriptors generation to design regression and classification models based on machine learning algorithms. The contribution of each descriptor in the generated models was evaluated by determining its relative importance and, finally, the antimicrobial activity of new peptides was estimated. Results: A structured database of antimicrobial peptides and their descriptors was obtained, with which 56 machine learning models were generated. Random Forest-based models showed better performance, and of these, regression models showed variable performance (R² = 0.339–0.574), while classification models showed good performance (MCC = 0.662–0.755 and ACC = 0.831–0.877). Those models based on bacterial groups showed better performance than those based on the entire dataset. The properties of the new peptides generated are related to important descriptors that encode physicochemical properties such as lower molecular weight, higher charge, propensity to form alpha-helical structures, lower hydrophobicity, and higher frequency of amino acids such as lysine and serine. Conclusions: Machine learning models allowed to establish the structure–activity relationships of antimicrobial peptides. Classification models performed better than regression models. These models allowed us to make predictions and new peptides with high antimicrobial potential were proposed. Full article

Magnetotelluric (MT) sounding is a crucial technique in mineral exploration. However, MT data are highly susceptible to various types of noise. Traditional data processing methods, which rely on the assumption of signal stationarity, often result in severe distortion when suppressing non-stationary noise. In this study, we propose a novel, adaptive, and less parameter-dependent signal decomposition method for MT signal denoising, based on time–frequency domain analysis and the application of modal decomposition. The method uses Variational Mode Decomposition (VMD) to adaptively decompose the MT signal into several intrinsic mode functions (IMFs), obtaining the instantaneous time–frequency energy distribution of the signal. Subsequently, robust statistical methods are introduced to extract the independent components of each IMF, thereby identifying signal and noise components within the decomposition results. Synthetic data experiments show that our method accurately separates high-amplitude non-stationary interference. Furthermore, it maintains stable decomposition results under various parameter settings, exhibiting strong robustness and low parameter dependency. When applied to field MT data, the method effectively filters out non-stationary noise, leading to significant improvements in both apparent resistivity and phase curves, indicating its practical value in mineral exploration. Full article

Wind power output sequences exhibit strong randomness and intermittency characteristics; traditional single forecasting models struggle to capture the internal features of sequences and are highly susceptible to interference from high-frequency noise and predictive accuracy is still notably poor at the peaks where the power curve undergoes abrupt changes. To address the poor fitting at peaks, a short-term wind power forecasting method based on the improved Informer model is proposed. First, the temporal convolutional network (TCN) is introduced to enhance the model’s ability to capture regional segment features along the temporal dimension, enhancing the model’s receptive field to address wind power fluctuation under varying environmental conditions. Next, a discrete cosine transform (DCT) is employed for adaptive modeling of frequency dependencies between channels, converting the time series data into frequency domain representations to extract its frequency features. These frequency domain features are then weighted using a channel attention mechanism to improve the model’s ability to capture peak features and resist noise interference. Finally, the Informer generative decoder is used to output the power prediction results, this enables the model to simultaneously leverage neighboring temporal segment features and long-range inter-temporal dependencies for future wind-power prediction, thereby substantially improving the fitting accuracy at power-curve peaks. Experimental results validate the effectiveness and practicality of the proposed model; compared with other models, the proposed approach reduces MAE by 9.14–42.31% and RMSE by 12.57–47.59%. Full article

This paper presents a novel approach employing localized annealing through Joule heating to enhance the performance of Thin-Film Piezoelectric-on-Silicon (TPoS) MEMS resonators that are crucial for applications in sensing, energy harvesting, frequency filtering, and timing control. Despite recent advancements, piezoelectric MEMS resonators still suffer from anchor-related energy losses and limited quality factors (Qs), posing significant challenges for high-performance applications. This study investigates interface modification to boost the quality factor (Q) and reduce the motional resistance, thus improving the electromechanical coupling coefficient and reducing insertion loss. To balance the trade-off between device miniaturization and performance, this work uniquely applies DC current-induced localized annealing to TPoS MEMS resonators, facilitating metal diffusion at the interface. This process results in the formation of platinum silicide, modifying the resonator’s stiffness and density, consequently enhancing the acoustic velocity and mitigating the side-supporting anchor-related energy dissipations. Experimental results demonstrate a Q-factor enhancement of over 300% (from 916 to 3632) and a reduction in insertion loss by more than 14 dB, underscoring the efficacy of this method for reducing anchor-related dissipations due to the highest annealing temperature at the anchors. The findings not only confirm the feasibility of Joule heating for interface modifications in MEMS resonators but also set a foundation for advancements of this post-fabrication thermal treatment technology. Full article

AlGaN-based high-electron-mobility transistors are critical for next-generation power electronics and radio-frequency applications, yet achieving stable enhancement-mode operation with a high threshold voltage remains a key challenge. In this work, we designed p-AlGaN superlattices with different structures and performed energy band structure simulations using the device simulation software Silvaco. The results demonstrate that thin barrier structures lead to reduced acceptor incorporation, thereby decreasing the number of ionized acceptors, while facilitating vertical hole transport. Superlattice samples with varying periodic thicknesses were grown via metal-organic chemical vapor deposition, and their crystalline quality and electrical properties were characterized. The findings reveal that although gradient-thickness barriers contribute to enhancing hole concentration, the presence of thick barrier layers restricts hole tunneling and induces stronger scattering, ultimately increasing resistivity. In addition, we simulated the structure of the enhancement-mode HEMT with p-AlGaN as the under-gate material. Analysis of its energy band structure and channel carrier concentration indicates that adopting p-AlGaN superlattices as the under-gate material facilitates achieving a higher threshold voltage in enhancement-mode HEMT devices, which is crucial for improving device reliability and reducing power loss in practical applications such as electric vehicles. Full article

Endometrial cancer (EC) is the most common gynecological malignancy in developed countries. Risk factors for EC include metabolic alterations (obesity, metabolic syndrome, insulin resistance), hormonal imbalance, age at menopause, reproductive factors, and inherited conditions, such as Lynch syndrome. For the inherited forms, several genes had been implicated in EC occurrence and development, such as POLE, MLH1, TP53, PTEN, PIK3CA, PIK3R1, CTNNB1, ARID1A, PPP2R1A, and FBXW7, all mutated at high frequency in EC patients. However, gene function impairment is not necessarily caused by mutations in the coding sequence of these and other genes. Gene function alteration may also occur through post-transcriptional control of messenger RNA translation, frequently caused by microRNA action, but transcriptional impairment also has a profound impact. Here, we review how chromatin modifications change the expression of genes whose impaired function is directly related to EC etiopathogenesis. Chromatin modification plays a central role in EC. The modification of chromatin structure alters the accessibility of genes to transcription factors and other regulatory proteins, thus altering the intracellular protein amount. Thus, DNA structural alterations may impair gene function as profoundly as mutations in the coding sequences. Hence, its central importance is in the diagnostic and prognostic evaluation of EC patients, with the caveat that chromatin alteration is often difficult to identify and needs investigations that are specific and not broadly used in common clinical practice. The different phases of the healthy endometrium menstrual cycle are characterized by differential gene expression, which, in turn, is also regulated through epigenetic mechanisms involving DNA methylation, histone post-translational modifications, and non-coding RNA action. From a medicolegal and policy-making perspective, the implications of using epigenetics in cancer care are briefly explored as well. Epigenetics in endometrial cancer is not only a topic of biomedical interest but also a crossroads between science, ethics, law, and public health, requiring integrated approaches and careful regulation. Full article

Osteosarcoma (OS), the most common primary bone cancer of mesenchymal origin in children and young adolescents, remains a challenge due to metastasis and resistance to chemotherapy. It displays severe aneuploidy and a high mutation frequency which drive tumor initiation and progression; however, recent studies have highlighted the role of epigenetic modifications as a key driver of OS pathogenesis, independent of genetic mutations. DNA and RNA methylation, histone modifications and non-coding RNAs are among the major epigenetic modifications which can modulate the expression of oncogenes. Abnormal activity of these mechanisms contributes to gene dysregulation, metastasis and immune evasion. Therapeutic targeting against these epigenetic mechanisms, including inhibitors of DNA and RNA methylation as well as regulators of RNA modifications, can enhance tumor suppressor gene activity. In this review, we examine recent studies elucidating the role of epigenetic regulation in OS pathogenesis and discuss emerging drugs or interventions with potential clinical utility. Understanding of tumor- specific epigenetic alterations, coupled with innovative therapeutic strategies and AI-driven biomarker discovery, could pave the way for personalized therapies based on the molecular profile of each tumor and improve the management of patients with OS. Full article

A steel modular building is a highly prefabricated form of steel construction. It offers rapid assembly, a high degree of industrialization, and an environmentally friendly construction site. To promote the application of multi-story steel modular buildings in earthquake fortification zones, it is imperative to conduct in-depth research on their seismic behavior. In this study, a seven-story modular steel building is investigated using shaking table tests. Three seismic waves (artificial ground motion, Tohoku wave, and Tianjin wave) are selected and scaled to four intensity levels (PGA = 0.035 g, 0.1 g, 0.22 g, 0.31 g). It is found that no residual deformation of the structure is observed after tests, and its stiffness degradation ratio is 7.65%. The largest strains observed during the tests are 540 × 10⁻⁶ in beams, 1538 × 10⁻⁶ in columns, and 669 × 10⁻⁶ in joint regions, all remaining below a threshold value of 1690 × 10⁻⁶. Amplitudes and frequency characteristics of the acceleration responses are significantly affected by the characteristics of the seismic waves. However, the acceleration responses at higher floors are predominantly governed by the structure’s low-order modes (first-mode and second-mode), with the corresponding spectra containing only a single peak. When the predominant frequency of the input ground motion is close to the fundamental natural frequency of the modular steel structure, the acceleration responses will be significantly amplified. Overall, the structure demonstrates favorable seismic resistance. Full article