Search Results (4,902)

Bacterial β-barrel pore-forming toxins, including Staphylococcus aureus α-toxin (Hla) and Bacillus cereus toxins hemolysin II (HlyII) and cytolytic toxin K2 (CytK-2), are secreted by bacterial cells as water-soluble monomers. These monomers assemble within lipid bilayers to form cylindrical pores, leading to lysis of target eukaryotic cells. We created mutant forms of these toxins that, based on the results of X-ray structural analysis of Hla and the prediction of the 3D structure of HlyII and CytK2, can form intramolecular disulfide bonds in monomers. The substitutions were made in the region responsible for toxin insertion into the target membrane. The mutant forms reversibly altered their hemolytic activity depending on the presence of reducing reagents and were non-toxic when injected into experimental animals. The immune response to injection of the mutant forms of Hla and CytK-2 toxins resulted in higher antibody titers against the wild-type toxins and a higher level of immunological memory than with injection of the HlyII mutant. The mutant form of CytK-2 demonstrates the properties of a prototype vaccine, as immunization with this protein protects animals against the effects of the wild-type toxin. Full article

Classifying power quality disturbances (PQDs) under strong noise conditions remains challenging for existing deep learning models. These models typically separate denoising from feature extraction, often rely on attention mechanisms that operate along only a single dimension, and tend to achieve high accuracy at the cost of high complexity, which limits their performance under low signal-to-noise ratio conditions and hinders practical deployment. To address these limitations, this paper proposes AD-PDAF-Net, which organically integrates three key mechanisms through a co-design strategy. Unlike conventional methods that depend on preprocessing, an adaptive soft thresholding denoising layer is embedded into a lightweight residual network to progressively suppress noise during feature extraction, thereby unifying denoising with feature learning. A parallel dual attention module independently refines features along the channel and temporal dimensions, then adaptively fuses them using learnable weights to capture both frequency domain and temporal characteristics of disturbances. The lightweight network entry replaces aggressive downsampling with small convolutions to preserve transient details, and a bidirectional long short-term memory network (BiLSTM) efficiently captures temporal dependencies. Evaluated on a dataset of 25 disturbance categories defined in IEEE Std 1159-2019, the model achieves a classification accuracy of 97.26% and a Kappa coefficient of 97.02% under 20 dB white Gaussian noise, along with an accuracy of 98.78% under mixed noise conditions. The model has only 0.36 million parameters and a computational cost of just 1.50 GFLOPS. Through this co-design, AD-PDAF-Net achieves both high noise robustness and high classification accuracy with minimal computational overhead, offering an effective solution for time series signal recognition in resource constrained environments. Full article

With increasing port automation and operational intensity, the gearboxes of gantry cranes widely used in bulk cargo terminals are prone to bearing and gear failures under prolonged heavy loads, intense vibrations, and complex operating conditions. Since fault samples often exhibit imbalanced distributions, this imposes two higher requirements on diagnostic methods—first, the ability to effectively address sample imbalance and, second, the capability to simultaneously identify multiple fault categories. To address these challenges, this paper proposes a joint diagnostic method integrating an improved Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP) and Multi-Task Learning (MTL). First, the modified CWGAN-GP performs conditional augmentation for minority fault classes, evaluating synthetic sample authenticity and diversity through multiple metrics. Subsequently, a multi-channel diagnostic network is constructed, in which vibration signals are fed into two parallel sub-networks: time–frequency features are extracted from the Short-Time Fourier Transform (STFT)-based time–frequency representations via a residual-block Convolutional Neural Network (CNN), while temporal features are captured from the raw time-domain signal using a Bidirectional Long Short-Term Memory (Bi-LSTM) with an attention mechanism. An attention fusion layer then integrates these two feature types, enabling joint classification of bearings and gears within a multi-task learning framework. Experimental validation on public gearbox datasets and port gantry crane gearbox datasets demonstrates that this method achieves an average diagnostic accuracy exceeding 97%. The proposed method reduces the impact of class imbalance, thereby improving the accuracy and stability of multi-task fault identification. Full article

Bismuth oxychloride (BiOCl) has garnered significant research interest owing to its non-toxicity, affordability, and distinct layered structure. Although BiOCl possesses promising photocatalytic potential, its large band gap and rapid photocarrier recombination restrict its practical use. In this work, a natural tourmaline mineral was effectively integrated with BiOCl to form a composite (TBO). Comprehensive characterization and photocatalytic assessments revealed that the intrinsic electric field of tourmaline notably strengthened both the adsorption capacity and the light-driven catalytic efficiency of BiOCl. Under visible-light irradiation, ofloxacin (OFX, 10 ppm) was eliminated by approximately 98% within 60 min. The apparent reaction rate constant (k) of TBO was 0.0407 min⁻¹, which was approximately 184.8 and 2.26 times those of tourmaline alone and pristine BiOCl, respectively. Furthermore, both the visible-light absorption and the separation efficiency of photogenerated electron–hole pairs were significantly enhanced. After evaluating its behavior under various simulated natural environmental conditions, TBO displayed strong potential for practical application. Reactive species trapping and analysis identified singlet oxygen (¹O₂) and superoxide radicals (·O₂⁻) as the primary active species in photocatalysis. Moreover, the degradation route of ofloxacin and the toxicity of its intermediates were systematically examined. These findings offer meaningful guidance for improving photocatalytic materials by utilizing naturally occurring minerals. Full article

In this work, a novel approach for implementing a resonant micro-accelerometer is demonstrated that may extend the operating frequency of such devices to several tens of MHz, which may enable direct wireless signal transfer. The proposed resonant accelerometer consists of a hybrid structure: a piezoelectric micro-resonator and a capacitive mass-spring (CMS) system (that are mechanically separated but electrically interconnected). The sensor utilizes the piezoelectric stiffening mechanism, which translates the acceleration-induced displacement of the capacitive mass-spring (CMS) structure into a shift in the resonance frequency of the interconnected resonator. The operating principle is elaborated upon in detail, supported by simulation and experimental results. Additionally, a novel fabrication technique is presented to realize a suspended fixed bi-layer electrode for the CMS in which a hardened layer of photoresist is utilized as a sacrificial layer. The experimental sensitivity of a fully functional device is reported to be ~6 Hz/g at 25 MHz (~0.23 ppm/g), which closely matches the simulated sensitivity of ~7 Hz/g (~0.278 ppm/g) for the fabricated capacitive gap of ~7 µm. Full article

This paper proposes a non-overlapping planar cross-arranged ultra-wideband shared-aperture base station antenna array targeting the 2 to 6 GHz application bandwidth. The low-frequency module (double-layer parasitic coupling) and the high-frequency module (chamfered slotted patch) are independently designed, and metal baffles are introduced around the antenna elements to reshape the boundary conditions and physically block the electromagnetic coupling paths. Both simulation and experimental results demonstrate that the fabricated prototype successfully exceeds the targeted 2–6 GHz spectrum, achieving an actual continuous coverage from 1.84 to 6.3 GHz. Specifically, the antenna achieves a gain higher than 5.9 dBi in the measured low-frequency band (1.84–3.72 GHz) and higher than 6.1 dBi in the high-frequency band (3.63–6.3 GHz), with a voltage standing wave ratio (VSWR) below 2 across the entire band. The metal baffles successfully correct the high-frequency radiation pattern distortion and ensure stable directional radiation over the full operating bandwidth. This design provides an efficient, robust, and manufacturable solution for 5G offshore wind power multi-band base station antennas. Full article

The threshold voltage (Vth) of p-GaN gate enhancement-mode (E-mode) high electron mobility transistors (HEMTs) on silicon substrates grown by metal–organic chemical vapor deposition (MOCVD) is often limited to 1.0–1.5 V. Apart from the low Mg acceptor activation rate, the non-uniform vertical Mg distribution in thin p-GaN layers is also a key bottleneck limiting Vth. This work reveals that the vertical distribution (not only magnitude) of Mg doping fundamentally influences Vth by modulating the charge centroid and electric field coupling to the heterointerface. Through bis(cyclopentadienyl)magnesium (Cp₂Mg) flow modulation, surfactant-assisted growth, and growth rate adjustment, the vertical Mg doping uniformity within the 80 nm p-GaN layer was improved while effectively suppressing Mg out-diffusion. A short-cycle gate-first self-aligned process was used to fabricate the devices, and the results showed that the improved Mg vertical distribution led to a significant Vth enhancement by 0.75 V. Technology Computer-Aided Design (TCAD) simulations further demonstrated that the uniform doping profile builds a stronger negative space charge field beneath the gate, raising the energy band and increasing Vth. This work not only presents practical strategies, but also establishes a direct physical link between vertical Mg doping distribution and Vth in Si-based E-mode HEMTs. Full article

As a key provider of Positioning, Navigation, and Timing (PNT) information, the characteristics of Global Navigation Satellite System (GNSS) signals, including types, Quality Indicators (QIs), and measurements, should be understood. This study employs temporally correlated deep learning models to classify GNSS signals as Line-of-Sight (LOS) or non-LOS using four QIs: the elevation angle, Carrier to Noise Ratio (C/N₀), code measurement’s standard deviation, and difference in azimuth angle. Autocorrelation analysis confirmed that these QIs exhibit significant temporal dependencies. The Bidirectional LSTM (Bi-LSTM) model, with four hidden layers, 64 units, and a sequence length of 18, achieved the best performance: 94.17% classification accuracy and a 2.61% False Positive (FP) rate. Positioning based on classified LOS signals significantly improved accuracy, reducing the mean errors in the horizontal, vertical, and 3D domain by 36.6%, 81.4%, and 59.6%, respectively, and reducing the Standard Deviation (STDEV) by 46.3%, 33.5%, and 45.5%, respectively. Moreover, the non-LOS probability output enables flexible signal selection and mitigates the issue of insufficient signal availability. These results highlight the effectiveness of temporally correlated models in GNSS signal classification and positioning performance. Full article

With grid-connected microgrids connected to power distribution networks, a hierarchical coordination scheduling framework is developed to solve the benefit allocation problem among different entities. Firstly, a bi-level master–slave game model with the power distribution network as the leader and the microgrids as the followers is proposed. For the leader, a two-stage robust optimization economic dispatch model considering wind power uncertainty is established for the power distribution network. For the followers, an optimal-scheduling model considering time-of-use pricing and load demand response is constructed. Secondly, the follower model is transformed into the equilibrium constraints of the leader model in light of the Karush–Kuhn–Tucker condition. As a result, the above bi-level master–slave game model can be converted into a single-layer robust optimization problem with mixed-integer recourse, which is solved by the nested column-and-constraint generation algorithm. Finally, the proposed model and solution method are validated via an improved IEEE 33-bus distribution network connected with three microgrids. The simulation results demonstrate that the proposed model can reduce the total operation cost by 12.42% compared with the centralized optimization model. Moreover, the load demand response and the regulation of ESSs at the real-time scheduling stage can prominently improve the operation flexibility and reduce the operation cost. Specifically, the operation cost of multiple microgrids has reduced by 21.55% when considering load demand response. In addition, the solving time for the proposed model is 627.3 s, which has the potential for practical engineering application. Full article

In this study we engineered bilayered electrospun scaffolds consisting of a hydrophobic PDLLA and hydrophilic PVP layer that incorporate either native HA or semi-synthetic HA-Gly-OH at concentrations of 1% and 3% w/w. Generally, bilayer scaffolds electrospun on different days delaminated, while herein they maintained their integrity because they were electrospun on the same day. Sequential electrospinning enabled the bilayer structure characterized via Scanning Electron Microscopy (SEM), Atomic Force Microscopy (AFM), and Young’s modulus measurements to assess morphology and mechanics. In vitro cytotoxicity and cell viability assays with fibroblast cells confirmed good biocompatibility for both the individual layers and the bilayer system. Among the tested formulations, the bilayer PDLLA/PVP–HA-Gly-OH 1% showed the most promising performance, attributed to the synergistic effects of HA and Gly-OH in promoting adhesion and proliferation. Full article

Background/Objectives: Brain health monitoring is increasingly essential as modern cognitive load, stress, and lifestyle pressures contribute to widespread neural instability. The paper presents BrainTwin, a next-generation cognitive digital twin, as a patient-specific, constantly updating computer model that combines state-of-the-art MRI analytics for neuro-oncological assessment related to clinical study and management of tumors affecting the central nervous system (including their detection, progression, and monitoring) with real-time EEG-based brain health intelligence. Methods: Structural analysis is driven by an Enhanced Vision Transformer (ViT++), which improves spatial representation and boundary localization, achieving more accurate tumor prediction than conventional models. The extracted tumor volume forms the baseline for short-horizon tumor progression modeling. Parallel to MRI analysis, continuous EEG signals are captured through an in-house wearable skullcap, preprocessed using Edge AI on a Hailo Toolkit-enabled Raspberry Pi 5 for low-latency denoising and secure cloud transmission. Pre-processed EEG packets are authenticated at the fog layer, ensuring secure and reliable cloud transfer, enabling significant load reduction in the edge and cloud nodes. In the digital twin, EEG characteristics offer real-time functional monitoring through dynamic brainwave analysis, while a BiLSTM classifier distinguishes relaxed, stress, and fatigue states, which are probabilistically inferred cognitive conditions derived from EEG spectral patterns. Unlike static MRI imaging, EEG provides real-time brain health monitoring. The BrainTwin performs EEG–MRI fusion, correlating functional EEG metrics with ViT++ structural embeddings to produce a single risk score that can be interpreted by clinicians to determine brain vulnerability to future diseases. Explainable artificial intelligence (XAI) provides clinical interpretability through gradient-weighted class activation mapping (Grad-CAM) heatmaps, which are used to interpret ViT++ decisions and are visualized on a 3D interactive brain model to allow more in-depth inspection of spatial details. Results: The evaluation metrics demonstrate a BiLSTM macro-F1 of 0.94 (Precision/Recall/F1: Relaxed 0.96, Stress 0.93, Fatigue 0.92) and a ViT++ MRI accuracy of 96%, outperforming baseline architectures. Conclusions: These results demonstrate BrainTwin’s reliability, interpretability, and clinical utility as an integrated digital companion for tumor assessment and real-time functional brain monitoring. Full article

The development of multilayer coatings based on titanium carbides and nitrides remains one of the most active areas in materials science, owing to their ability to markedly enhance wear resistance and extend the service life of machine components. Particular interest is currently focused on tailoring conventional TiN/TiCN architectures through alloying metal additions. In this study, the tribological and mechanical performance of aluminum- and zirconium-doped TiN/TiCN multilayer coatings deposited by direct-current magnetron sputtering onto 41Cr4 steel was investigated. The morphology, elemental distribution, and phase constitution of the multilayer coatings were examined. It is shown that increasing the number of bilayers from two to four in TiN/TiCN–based multilayer coatings leads to improved tribomechanical characteristics. It was determined that zirconium provides a more pronounced beneficial effect than aluminum. The four-bilayer TiZrN/TiZrCN coating simultaneously exhibited the lowest coefficient of friction (0.11) and wear rate (10⁻⁶ mm³ m⁻¹ N⁻¹) at a hardness of 16.4 GPa. Full article

This paper explores the feasibility of early-season crop classification based on Sentinel-2-time series using the TimeSen2Crop dataset (≈1 million pixels, 16 crops). The aim of the study was to evaluate the spectral-phenological separability of crops during the season and compare the performance of classical tabular algorithms, deep sequence models, and a seasonally oriented hybrid stacking scheme. Based on multispectral observations, a feature set was formed from 9 optical channels and 13 vegetation indices for 30 dates. F-criteria were calculated, confirming a sharp increase in interclass separability during the active vegetative growth phase and substantiating three time series truncation scenarios (early, early + mid-season, and full season). Random Forest (macro-F1: 0.46/0.74/0.75) was used as the base tabular model. LSTM, BiLSTM, GRU, 1D-CNN, and Transformer were trained in parallel, with Transformer showing the best results among the deep architectures (0.42/0.68/0.78). The main contribution of the work is a hybrid multi-layer stacking scheme combining heterogeneous base algorithms and OOF meta-features, which provides the highest quality (0.51/0.83/0.86) in all scenarios. The obtained results confirm the effectiveness of phenology-oriented selection of time windows, informative indices, and hybrid ensemble learning for improving the accuracy of early-season crop monitoring. Full article

Hybrid electric mining trucks operating in open-pit environments encounter highly variable gradients and payload conditions that standard energy management strategies fail to address adequately. Existing approaches are predominantly calibrated for full-load scenarios and neglect the accelerated battery degradation resulting from sustained high-power cycling, undermining long-term operational viability. This study presents a multi-objective energy management framework that couples real-time driving condition recognition with dynamic programming (DP) optimization for a 130-tonne hybrid mining truck. Field data collected from an open-pit mine in Heilongjiang Province were used to construct six physically representative driving conditions via principal component analysis and K-means clustering. A Bidirectional Gated Recurrent Unit (Bi-GRU) network (2 layers, 128 hidden units per direction) was trained on a route-based temporal split, attaining 95.8% classification accuracy across all six conditions. Condition-specific powertrain modes were subsequently defined, and a DP formulation with a weighted-sum cost function was solved to jointly minimize diesel consumption and battery capacity fade—quantified through a semi-empirical effective electric quantity metric. A marginal rate of substitution (MRS) analysis was conducted to identify the optimal trade-off between fuel economy and battery life preservation. In the DP cost function, the weight coefficient μ (ranging from 0 to 1) governs the relative emphasis placed on battery degradation minimization versus fuel consumption minimization: μ = 0 corresponds to pure fuel minimization, whereas μ = 1 corresponds to pure battery degradation minimization. The MRS analysis identified μ = 0.1 as the knee point of the Pareto trade-off: relative to pure fuel minimization (μ = 0), this setting reduces effective electric quantity by 6.1% while increasing fuel consumption by only 1.4% (MRS = 4.36). Against a rule-based baseline, the proposed strategy improves fuel economy by 12.3% and extends battery service life by 15.7%. Co-simulation results were validated against onboard fuel-flow measurements; absolute simulated and measured fuel consumption values are reported route-by-route, with deviations within 4.5%. A three-layer BP neural network (3 inputs, two hidden layers of 20 and 10 neurons, 1 output) trained on the DP solution reproduces near-optimal performance—with fuel consumption and effective electric quantity increases below 1.0% and 1.1%, respectively—while reducing computation time by over 96% (from approximately 52,860 s to 1836 s for the 1800 s driving cycle), demonstrating practical feasibility for real-time deployment. Full article

Reliable sensor fault detection is critical for the safe and efficient operation of complex industrial systems, such as thermal power plants. However, traditional data-driven methods and standard deep learning models often struggle to detect incipient gradual drift faults under severe environmental noise, primarily because they ignore the inherent physical correlations among multivariate sensor signals. To address this challenge, this paper proposes a novel Physics-Coupled Deep Long Short-Term Memory Autoencoder (PC-Deep-LSTM-AE). Specifically, we integrate a deep LSTM architecture with an explicit non-linear information compression bottleneck and layer normalization to enhance robust feature extraction in high-noise environments. Furthermore, we innovatively introduce a Physics-Coupling Loss (PCC Loss) that jointly optimizes the mean squared reconstruction error and the Pearson correlation coefficient, forcing the model to strictly preserve the dynamic physical relationships among multivariable signals. Extensive experiments were conducted on a real-world thermal power plant dataset with severe noise injection. The results demonstrate that the proposed PC-Deep-LSTM-AE achieves an outstanding F1-score of over 0.98, significantly outperforming mainstream baseline models, including Vanilla LSTM-AE, GRU-AE, Bi-LSTM-AE, and CNN-AE. The proposed method exhibits exceptional robustness and high interpretability for root-cause analysis, highlighting its immense potential for real-world industrial deployment. Full article