Search Results (274)

In response to the problem that sensors cannot be directly installed at key local positions on the surface of ship hull structures during the transient strong shock process of underwater explosions due to spatial constraints or large plastic deformations, this paper investigates the chaotic-like nonlinear transient behavior of structural dynamic response systems under strong shock and proposes a key position structural response reconstruction method based on dynamic inversion. Since the structural response under a transient strong shock exhibits significant non-stationarity and nonlinearity, signals from neighboring measurement points cannot directly characterize the dynamic behavior at key positions. Therefore, the shock response signals are discretized in both time and space dimensions. The phase space reconstruction method is employed to characterize the motion trajectory of acceleration responses in a two-dimensional phase space, establish mapping functions for system motion evolution, and use their control parameters to characterize the system’s nonlinear dynamic behavior. Furthermore, based on the spatiotemporal dynamic equations, a spatiotemporal coupled mapping model for spatial state points is established to achieve the theoretical inversion of acceleration responses at key positions. This method provides theoretical support for analyzing the dynamic characteristics of structures at key positions under strong shock environments, characterizing the shock environment, and assessing and designing equipment for shock safety. However, the current validation is based on high-fidelity numerical simulations rather than physical prototype tests; therefore, the predictive capability of this method in actual physical environments requires further validation through subsequent physical model tests. Full article

This paper investigates a five-level active neutral-point-clamped (5L-ANPC) converter operating in rectifier mode with unbalanced DC-side loads, where neutral-point (NP) deviation may deteriorate grid-current quality. Conventional space-vector pulsewidth modulation (SVPWM) is typically derived under the split-capacitor-voltage symmetry assumption; when NP deviation occurs, fixed sector boundaries and ideal volt–second balance calculations can lead to sector misclassification and synthesis errors. To address this issue, an NP-aware SVPWM scheme is proposed by reconstructing sector criteria using real-time capacitor voltages and correcting the vector dwelling-time computation to improve modulation accuracy under imbalance. Based on the power-transfer mechanism, an average-power boundary condition is further derived to quantify the admissible upper/lower load power ratio that allows NP regulation without additional hardware, and its validity is examined under resistive-load cases. Moreover, for battery-type loads exhibiting voltage-source characteristics, the control objective is extended from voltage symmetry to controllable power/charge allocation by establishing a mapping between the small-vector duty ratio and the branch average-power ratio, with constrained online solution and smoothing to mitigate coefficient jitter. Experimental validation is conducted on an OPAL-RT OP5707-based hardware-in-the-loop platform, where both single-phase and three-phase 5L-ANPC systems are implemented according to different verification objectives. The derived boundary condition for resistive loads is examined in the single-phase system, while the proposed modulation and battery-load power-allocation strategy are verified in the three-phase system. The three-phase arrangement is adopted for the battery-load case in order to avoid the second-order power ripple inherent to single-phase operation. Full article

Non-Intrusive Load Monitoring (NILM) enables appliance-level classification from aggregate electrical measurements and supports efficient energy management in smart buildings. However, the accuracy of existing NILM methods is often limited by the inability of conventional feature extraction techniques to capture nonlinear steady-state behavior. This study proposes a novel feature extraction framework for appliance classification, which integrates phase-space reconstruction (PSR) with 2-D Fourier series to derive geometry-based descriptors of appliance current waveforms. Unlike traditional signal-processing methods, the proposed approach utilizes the nonlinear geometric structure revealed by PSR and encodes it through Fourier descriptors, offering a discriminative, low-dimensional feature space suitable for classification using supervised machine learning algorithms. The method is evaluated on the high-resolution controlled single-appliance recordings from the COOLL dataset using the K-Nearest Neighbor (KNN) classifier. Extension to aggregated multi-appliance NILM scenarios would require additional stages such as event detection and load separation. Sensitivity analysis demonstrates that classification performance depends strongly on the choice of time delay and harmonic order, with optimal settings yielding an accuracy of up to 99.52% using KNN. The results confirm that larger time delays and a small number of harmonics effectively capture appliance-specific signatures. The findings highlight the effectiveness of PSR–Fourier-based geometric features as a robust alternative to conventional NILM feature extraction strategies. Full article

The aims of this research were to characterize neuromuscular control features within the gait cycle in Parkinson’s disease (PD) from the perspectives of muscle synergies and cross-modal coupling and to propose a joint representation of the relationship between muscle activation patterns and kinematic dynamic outputs. PD participants (n = 19) were included. Lower-limb surface electromyography (EMG) and kinematic dynamic channels, including pelvic/hip, knee, and ankle angular acceleration, were collected during level-ground natural walking. EMG signals were first decomposed using non-negative matrix factorization (NMF) to extract muscle synergies, and the number of synergies was evaluated using reconstruction performance (R²). Multimodal matrix factorization (MMF) was then applied to jointly decompose the EMG and angular-acceleration channels, yielding a cross-modal synergy representation comprising a shared temporal structure (H) and modality-specific weight structures (W): non-negativity was imposed on EMG weights, whereas kinematic weights were allowed to take positive and negative values to encode directional contributions. Under the current task and muscle set, NMF achieved high EMG reconstruction performance with four synergies (R² = 0.882). The synergy weights showed an ankle-dominant pattern: tibialis anterior (TA) consistently carried high weights across multiple synergies, while lateral gastrocnemius (LG) and soleus (SOL) contributed prominently to another synergy. The synergy activation profiles exhibited phase-dependent fluctuations with multiple rises and falls across the gait cycle, suggesting that synergy output was primarily characterized by continuous modulation rather than single-peak recruitment. MMF further identified eight cross-modal synergies, simultaneously capturing the shared contributions of key muscle groups (e.g., RF, TA, and SOL) and pelvic/hip and knee/ankle angular-acceleration channels within the same decomposition framework and summarizing their descriptive co-variation through the shared temporal structure (H). Overall, A low-dimensional synergy analysis combining EMG-only NMF with cross-modal MMF enables simultaneous characterization of cohort-level modular organization of muscle activity during gait and its descriptive association with pelvis-to-lower-limb dynamic output. This joint framework provides a methodological basis for quantitatively describing gait-related modular organization and temporal modulation patterns in this PD cohort under natural level-ground walking and lays the groundwork for subsequent testing of associations between synergy features and gait phenotypes, clinical severity, and rehabilitation responses. Full article

Objectives: The aim of this systematic review was to evaluate the current evidence on photon-counting detector computed tomography (PCCT) in hepatocellular carcinoma (HCC) imaging. Methods: A systematic literature search was performed in PubMed and Scopus, and five articles were finally included. Results: Four studies focused on the optimization of acquisition and reconstruction parameters such as slice thickness, kernels, virtual monoenergetic imaging (VMI), and quantum iterative reconstruction (QIR), with 50 keV reconstructions consistently associated with improved lesion conspicuity. QIR demonstrated significant noise reduction compared with filtered back projection, enhancing overall image quality, while one proof-of-concept study investigated dual-contrast PCCT, showing feasibility for simultaneous arterial and portal-phase acquisition. According to QUADAS-2, most studies presented a low or unclear risk of bias, with only one study rated at high risk for patient selection. Conclusions: In conclusion, PCCT shows promising technical advances and potential for improved HCC detection and characterization. Current evidence remains preliminary and focused on image quality rather than clinical outcomes; PCCT applications in routine practice are still largely unexplored. Full article

This work investigates the structural evolution and electrocatalytic activity of the amorphous metal alloy Al₈₇Y₄Gd₁Ni₄Fe₄ during short-term annealing and its effect on the kinetics of the hydrogen evolution reaction (HER) in 1 M KOH. It is shown that a 5 min heat treatment at 647 ± 2 K initiates controlled nanocrystallisation with the formation of AlFe₂Ni, GdFe₂ and Al(X) (X = Gd, Ni, Y, Fe) phases, which are uniformly dispersed in the amorphous matrix. According to XRD, DSC and HRTEM data, it was established that the formation of intermetallic nanodomains leads to a decrease in charge transfer energy barriers and the appearance of additional active centres of H* adsorption. Electrochemical studies have shown an increase in cathode current density, an increase in i₀ by 2–3 orders of magnitude, and a decrease in R_ct after annealing, confirming the improvement in HER kinetics. Potentiostatic tests showed an increase in the volumetric hydrogen evolution rate from 35.1 to 106.0 mL/(g·min) during the first immersion and up to 217.9 mL/(g·min) during reuse. SEM/EDS analysis revealed surface reconstruction and Ni enrichment after HER, which contributes to the acceleration of the H* recombination stage. The synergy of the amorphous matrix and nanophases ensures high electrocatalytic activity and stability of the system, making annealed AMA a promising low-cost catalyst for alkaline hydrogen evolution. Full article

Land subsidence and structural instability at the Slănic Prahova salt mine have evolved significantly over 190 years of underground extraction, particularly following the mine’s expansion in 1970. This study reconstructs the complete geomechanical history from 1835 to 2025 and forecasts deformation trajectories up to 2050 using a calibrated creep-based numerical model. A high-fidelity geological model was developed in Leapfrog Works, with the numerical mesh generated in Rhinoceros and converted to FLAC^3D format via the Griddle plug-in. Salt creep was characterized using a Norton power-law constitutive model, with initial parameters derived from the steady-state phases of laboratory creep tests, and subsequently with calibrated parameters identified at the mine scale as n = 2.03 and A = 3 × 10⁻²⁵ s⁻¹ MPa⁻ⁿ. The simulation results demonstrate a high degree of correlation with field observations. These parameters were subsequently refined at the mine scale by integrating surface leveling data (1994–2025) and underground displacement records (2004–2019). The simulation results demonstrate a high degree of correlation with field observations, highlighting critical deformation zones. Maximum surface subsidence increased from approximately −560 mm in 1970 to −1020 mm by 1992, reflecting the intensified impact of later mining phases. The current maximum cumulative displacement is estimated at −1640 mm (2025) and is projected to reach −2060 mm by 2050. Underground, the largest displacement rates are concentrated in the eastern sector, driven by the synergistic effects of overburden loading and regional horizontal stress. Full article

In the context of three-dimensional (3D) point cloud modeling for pillar-type insulators during the “post-production–pre-use” phase, current methodologies encounter challenges in achieving a balance between cost-effectiveness, comprehensive coverage, and high precision. This study introduces a novel 3D point cloud modeling approach that utilizes multi-view two-dimensional (2D) LiDAR technology. This method employs three 2D LiDAR sensors positioned at 120° intervals to conduct layer-by-layer scanning, thereby capturing the surface point cloud data of insulators from various heights and perspectives. This approach effectively mitigates the impact of occlusion and facilitates comprehensive 360° data acquisition. Based on this foundation, the skirt structure characteristics of pillar-type insulators were extracted, and a point cloud registration and stitching algorithm, grounded in structural constraints, was developed to facilitate a high-precision 3D reconstruction. The experimental findings indicate that the proposed approach in this study demonstrates substantial improvements in modeling accuracy compared with the baseline methods. In repeated experiments, the proposed method in this study showed an average distance error with a mean (${\mu }_{\mathrm{MDE}}$) ± standard deviation ($\sigma$) of 1.15 ± 0.07, while the root mean square error had a mean (${\mu }_{\mathrm{RMS}}$) ± standard deviation ($\sigma$) of 1.26 ± 0.11. This method offers several advantages, including a straightforward structure, low system cost, and excellent point cloud continuity (1 mm). The maximum measurement error for the disc diameter was 2.986 mm, which satisfies the engineering application requirement of ±5 mm, thereby confirming the feasibility and practical utility of the method in the 3D modeling of pillar-type insulators. Full article

Background: With the rapid advancement of artificial intelligence (AI), its applications in andrology are expanding across diagnostic assessment, preoperative planning, intraoperative assistance, and postoperative management. This narrative review aims to synthesize current evidence regarding AI applications across the spectrum of andrological surgery. Methods: A comprehensive literature search was conducted using the PubMed, Scopus and Web of Science databases to identify relevant studies published between January 2020 and October 2025. The search strategy utilized combinations of keywords including “artificial intelligence,” “andrology,” “erectile dysfunction,” “male infertility,” “microsurgery,” and “robotic-assisted surgery.” Original research and review articles published in English were selected based on their clinical relevance to surgical practice. Results: AI has shown promise in the evaluation and management of erectile dysfunction (ED), male infertility-related microsurgery, and complex reconstructive procedures. AI-based models can improve risk prediction and diagnosis of ED, standardize semen analysis, support individualized selection of surgical candidates for varicocele repair and other interventions, and augment microsurgery through enhanced visualization and decision support. In the postoperative phase, AI-driven tools are being explored for complication prediction, functional recovery monitoring, and long-term quality-of-life follow-up, enabling more patient-centered, continuous care. Conclusions: AI holds significant promise for advancing precision medicine in andrological surgery by enhancing objective assessment and intraoperative guidance. However, large-scale, standardized datasets and rigorous multi-institutional validation are needed. Establishing robust ethical and legal frameworks will be essential to ensure the safe and effective integration of AI into routine andrological care. Full article

Background: Visceral artery aneurysms (VAAs) are rare but potentially life-threatening vascular lesions often clinically silent until rupture. The widespread use of advanced imaging has increased incidental detection, highlighting the need for accurate, noninvasive diagnostic strategies. Dual-Energy Computed Tomography Angiography (DECTA) offers potential advantages over conventional CT across diagnostic and post-treatment settings; however, its role in VAAs remains incompletely defined. This narrative review summarizes current evidence on DECTA applications in VAAs, focusing on diagnosis, emergency evaluation, and post-treatment follow-up. Methods: A non-systematic literature search of PubMed and Embase focusing on English-language articles up to June 2025 was performed. The search included peer-reviewed original research articles, systematic reviews, and meta-analyses addressing dual-energy CT and spectral CT in vascular and aneurysmal imaging. Case reports without technical data and non-English articles were excluded. Results: In the diagnostic phase, DECTA enhances tissue differentiation through virtual monoenergetic images, iodine maps, and material decomposition reconstructions. In the post-treatment setting, DECTA supports assessment after endovascular procedures, including coil embolization or stent graft placement. In VAAs, these techniques may improve aneurysm delineation, reduce metal artifacts after endovascular treatment, enable accurate detection of endoleaks or residual perfusion, and support volumetric follow-up. Virtual Non-Contrast images may reduce radiation exposure without compromising diagnostic confidence. Conclusions: DECTA represents a versatile imaging modality with potential benefits across the diagnostic, emergency, and post-treatment phases of VAA management. Although many applications are extrapolated from aortic and peripheral vascular disease, emerging evidence supports its growing clinical relevance. Further dedicated studies are needed to define its role in VAA-specific decision-making and follow-up. Full article

Situated within the field of modern Chinese political history, this study investigates the Late Qing New Policies (1901–1911) as a pivotal transition from a traditional tributary empire to a modern multi-ethnic nation-state. A critical limitation in current scholarship is the tendency to reduce these reforms to mere expedients for dynastic preservation, thereby overlooking the complex mechanisms by which they fundamentally reconstructed national identity and interethnic power structures amidst the “triple crisis” of territory, sovereignty, and nationality. To address this, the article employs a comprehensive historical analysis to explore how institutional restructuring in administration, military, and ideology catalyzed the transformation from imperial autocracy toward a “responsible government” framework. The research is distinguished by its innovative application of Anthony D. Smith’s theories of “ethnic” versus “civic” nationalism to deconstruct the “myth-symbol complex” of the Chinese nation, bridging the theoretical divide between the “New Qing History” paradigm and empirical modernization narratives. Findings demonstrate that while the Manchu leadership aimed to secure formal primacy, the practical implementation of reforms engendered a de facto Han-supported power structure, compelling the reconceptualization of the state as a “multi-ethnic constitutional monarchy” and establishing the institutional logic for the “Five Races Under One Union” model. Consequently, this study offers significant academic value by redefining the New Policies as the foundational phase of modern China, providing a crucial theoretical framework for understanding the continuity of China’s multi-ethnic statehood and national identity beyond the dynastic collapse. Full article

As a core power supply component of the more electric aircraft (MEA), the reliability of the permanent magnet synchronous generator (PMSG) is of paramount importance. Phase current reconstruction technology can enhance the redundancy of current sensors, thereby improving system reliability. However, owing to the generally high engine speeds in MEAs, the employment of traditional d-axis current–zero control not only induces DC-link voltage fluctuations but also leads to inaccurate DC-link sampling points and distortion in the reconstructed current. In this paper, a lead-angle flux-weakening control strategy is introduced into the PMSG rectification system. This approach guarantees the normal operation of the current loop when the rotational speed exceeds the rated speed of the PMSG, ensuring the accuracy of the sampling points for phase current reconstruction. To further enhance the reconstruction accuracy, a phase current reconstruction technology based on a linear extended state observer (LESO) is proposed. The LESO not only filters the reconstructed current but also ensures that the observer performance remains robust against PMSG parameter perturbations. Finally, the effectiveness of the proposed method is validated through Hardware-in-the-Loop results. Full article

Unidirectional multilevel back-to-back (BTB) converters are widely employed in renewable energy generation systems and in motor drives for coal mining operations. However, the current zero-crossing distortion (CZCD) on the grid side and the neutral-point potential (NPP) imbalance on the common DC bus all restrict its applicability, such as in grids with stringent low harmonic requirements and in medium to high power situations. This paper proposes a coordinated control strategy to simultaneously address these issues theoretically. The study focuses on topology comprising a Vienna rectifier structure on the grid side and a three-level NPC inverter structure on the load side. In the proposed strategy, the current distortion angle, the manifestation of CZCD, is first eliminated by reactive current compensation on the Vienna rectifier side. Furthermore, the coupling between CZCD and NPP imbalance is resolved by reconstructing the neutral-point current target function. Ultimately, the optimal zero-sequence voltage (ZSV) is obtained using an interpolation function and then injected into the three-phase reference voltages of the inverter side to balance the NPP on the DC bus. The strategy transforms the influence of the rectifier on the NPP from an unknown coupling factor into a known disturbance and enables the inverter to actively compensate for variations in the overall converter system. An experimental platform was independently developed to verify the effectiveness of the proposed control strategy. Full article

Accurately predicting molten steel carbon content plays a crucial role in improving productivity and energy efficiency during the Basic Oxygen Furnace (BOF) steelmaking process. However, current data-driven methods primarily focus on endpoint carbon content prediction, while lacking sufficient investigation into real-time curve forecasting during the blowing process, which hinders real-time closed-loop BOF control. In this article, a novel Transformer-based framework is presented for real-time carbon content prediction. The contributions include three main aspects. First, the prediction paradigm is reconstructed by converting the regression task into a sequence classification task, which demonstrates superior robustness and accuracy compared to traditional regression methods. Second, the focus is shifted from traditional endpoint-only forecasting to long-term prediction by introducing a Transformer-based model for continuous, real-time prediction of carbon content. Last, spatial–temporal feature representation is enhanced by integrating an optical flow channel with the original RGB channels, and the resulting four-channel input tensor effectively captures the dynamic characteristics of the converter mouth flame. Experimental results on an independent test dataset demonstrate favorable performance of the proposed framework in predicting carbon content trajectories. The model achieves high accuracy, reaching 84% during the critical decarburization endpoint phase where carbon content decreases from 0.0829 to 0.0440, and delivers predictions with approximately 75% of errors within ±0.05. Such performance demonstrates the practical potential for supporting intelligent BOF steelmaking. Full article

Offshore aquaculture requires net cages that remain stable under strong currents and during submersion and emergence operations. In this study, we proposed a submersible net cage structure equipped with vertical buoyancy columns as an alternative to the conventional horizontal floating-frame cage and evaluated its stability using a net geometry and load analysis system (NaLA system). Model-scale cages were tested in a recirculating flume tank at two current velocities, and the three-dimensional cage geometry was reconstructed using the multicamera through direct linear transformation method to validate the simulated cage inclination. The NaLA system accurately reproduced the measured geometry and time-varying inclination. After validation, stability was compared over a range of current velocities by tracking the cage inclination during the emergence phase. When mooring lines were attached to the top of the cage, the conventional floating-frame cage exhibited a smaller inclination than the buoyancy-column cage. However, relocating the mooring attachment point on the columns significantly improved the stability; attaching the moorings near the bottom of the columns generated the smallest final inclination and yielded a higher stability than the conventional cage. The buoyancy columns can outperform those of conventional designs when paired with an appropriate mooring configuration, thus offering a promising structure for applications under harsh offshore conditions. Full article