Search Results (361)

This paper investigates the controllability of vessel berthing systems assisted by multiple tugboats under actuator faults or failures. In such interconnected systems, a failure of an individual tugboat can potentially compromise the berthing operation, or even lead to the collapse of the entire system. To address this challenge, the dynamic model of the multi-tug-assisted vessel system is first derived, followed by a controllability analysis under various fault scenarios to identify tolerable fault configurations. Then, a robust controller is proposed, integrating an adaptive disturbance observer with finite-time sliding mode control. This design ensures effective rejection of maritime environmental disturbances, practical finite-time stability, and bounded trajectory tracking errors. To accommodate different fault conditions, a switching control allocation strategy is developed to redistribute control efforts among the remaining healthy tugboats, thereby maintaining system reliability and efficiency. Simulation results under various faulty conditions demonstrate the effectiveness and robustness of the proposed control approach. Full article

Background/Objectives: Accurate drug allergy documentation is essential for patient safety; however, documentation quality remains poor worldwide. In resource-limited settings that rely on paper records, allergy information may become fragmented across multiple forms, and evidence on concordance between paper-based documentation systems is limited. This audit assessed concordance between clinical notes and drug Kardex records, and completeness of drug allergy documentation entries, in a manual hospital system. Methods: This retrospective clinical audit, reported in accordance with SQUIRE 2.0 guidelines, examined 88 randomly selected patient records from 525 consecutive admissions to a general medicine ward in Pakistan during June–July 2024, retrospectively reviewed in August 2024. The audit assessed allergy status documentation in clinical notes and the drug Kardex, evaluated completeness against five internationally recommended elements (drug name, reaction description, severity, date, and treatment), and measured inter-system concordance using McNemar’s test and Cohen’s kappa. Results: Drug allergy status was documented in 25.0% of clinical notes (95% CI: 16.5–35.4%) versus 94.3% of drug Kardex records (95% CI: 87.2–98.1%), representing a 69.3 percentage-point gap (McNemar χ² = 59.06, p < 0.001). Inter-system agreement was poor (κ = 0.0079; 95% CI: −0.046 to 0.062), with an overall concordance of 28.4%. Discordant pairs showed that undocumented allergy status was far more likely in clinical notes than in the drug Kardex (OR = 62.00). Kardex-only documentation occurred in 62 of 88 patients (70.5%). Among nine patients with documented allergy history in at least one source, none met the five-element completeness standards (0%; 95% CI: 0.0–33.6%). Recorded entries were generic statements such as “drug allergy” or “allergic to antibiotics” without clinically actionable details. Conclusions: Drug allergy documentation showed two major quality failures: poor concordance between parallel paper records and lack of actionable detail in recorded entries. The two systems functioned independently rather than as complementary safety checks, with allergy information often present in the drug Kardex but absent from clinical notes. This Kardex-only failure mode may be a practical target for quality improvement through structured five-element templates, prompts for clinicians to review the drug Kardex, and interdisciplinary allergy-reconciliation workflows. These strategies require prospective evaluation in this setting. Full article

As the most prevalent type of existing building in China, masonry structures are susceptible to cracking due to the low tensile strength of the masonry material. In the event of a sudden, strong earthquake, they are highly prone to brittle collapse, leaving occupants little time and space to escape. Based on this, combining the advantages of the elastoplastic mechanical theory and the nonlinear finite element (FE) method, this study adopts different modeling methods: integral modeling (IM), contact element discrete modeling (CEDM), spring element discrete modeling (SEDM), and co-node discrete modeling (CNDM). FE models of unreinforced masonry walls (UMWs) are established, respectively, and a monotonic pushover mechanical performance analysis is carried out. The accuracy of the adopted modeling methods is verified against existing test results for UMW specimens. Through parametric analysis of aspect ratios (0.5, 0.75, 1.0, and 1.25), axial compression ratios (0.1, 0.3, 0.5, 0.7, and 0.8), and mortar strengths (M5, M7.5, and M10), the characteristic mechanical performance factors of UMWs are determined. A novel strength index is proposed to discriminate between failure modes and elucidate the damage mechanism of UMWs. The results indicate that the ultimate load and its corresponding displacement change systematically with variations in aspect ratios, axial compression ratios, and mortar strengths. Furthermore, integrating stress cloud maps with the proposed strength index provides a quantitative basis for discriminating between flexural and shear failure modes in UMWs. All four modeling methods can, to varying degrees, capture the pushover behavior of UMWs, and quantifiable selection schemes are provided to balance analysis accuracy and computational cost. The analytical methods and findings presented in this work can be applied to performance assessment, seismic design, and engineering practice of UMWs. Full article

Based on the Direct Probability Integral Method (DPIM), this study investigates the global seismic reliability of reinforced concrete (RC) frame structures considering the randomness of material parameters and the non-stationarity of ground motions. A doubly non-stationary ground motion model is established using evolutionary power spectrum theory combined with the spectral representation–stochastic function method. A dimensionality reduction technique is adopted to generate ground motion samples compatible with the design response spectrum. A finite element model of the RC frame is developed in Abaqus. Modal analysis and deterministic time history analysis are conducted to obtain the dynamic characteristics and seismic responses of the structure. Based on 600 representative ground motion time histories generated using the maximum frontier (MF) discrepancy sampling method, nonlinear time history analyses are performed. The DPIM is then employed to calculate the statistical characteristics of structural responses and quantify response variability, enabling a rational evaluation of the structural safety margin. Finally, based on the equivalent extreme value event theory and DPIM, the reliability of the structure under a single failure mode and the global reliability under multiple failure modes are computed. The results show that the global reliability of the structure is 82.088%, which is significantly lower than that of any single failure mode. This study provides a quantitative reference for evaluating the global seismic reliability of RC frame structures subjected to nonstationary seismic excitation. Full article

In this paper, a cold standby repairable system comprising two heterogeneous components, each characterized by multiple types of mutually independent failure modes, is investigated. The operational lifetimes of the components follow exponential distributions, while their repair times after failure are governed by general distributions. By applying the theory of the Markov renewal process together with the Laplace and the Laplace–Stieltjes transform techniques, we derive analytical expressions for the time to the first system failure, system availability, and the rate of occurrence of system failures. Some results for these reliability measures under several special cases are also presented. Finally, numerical examples are provided under different repair time distributions to analyze the influence of model parameters on the system’s reliability performance. Full article

Grain-scale heterogeneity strongly influences hydraulic fracture initiation and trajectory in crystalline rocks, yet its contributions to non-planar growth and the interaction of multiple nearby cracks remain insufficiently quantified. To address this gap, we perform numerical experiments on a model containing two parallel pre-existing cracks using a hydro-mechanical phase-field framework, systematically quantifying how mineral distribution and axial compression govern non-planar hydraulic fracture growth and inter-fracture competition. The results demonstrate that mineral distribution is the primary driver of fracture complexity. Even within the same Voronoi tessellation, redistributing minerals alone yields markedly different trajectories, deflections, branching patterns, and final morphologies. Furthermore, non-planar growth follows a stepwise, energy-threshold-driven mechanism. When cracks penetrate strong grains or undergo large-angle deflections, propagation is impeded, and injection pressure builds up. Once a critical energy threshold is reached, accumulated energy is rapidly released along the path of minimum incremental energy, manifested as abrupt pressure drops and rapid crack advance. Additionally, the two nearby fractures exhibit strong mechanical competition. Despite negligible hydraulic interference in low-permeability granite, early growth of one fracture redistributes stresses and suppresses the driving force of the other, resulting in asymmetric development. Finally, axial compression primarily governs the overall propagation orientation and influences local failure modes but has a limited effect on peak pressure relative to mineral distribution. Full article

In this study, the tensile properties, low-cycle fatigue behavior, and microscopic fatigue-failure mechanisms of 316H stainless steel in the temperature range of 600–800 °C were systematically investigated by means of tensile tests, high-temperature low-cycle fatigue tests, and scanning electron microscopy (SEM) analysis of fatigue fracture surfaces. Based on experimental data fitting, a life prediction model for the material in the high-temperature regime was established. The results indicate that the mechanical behavior of 316H stainless steel under both static and cyclic loading is significantly influenced by temperature and strain amplitude. Compared with its room-temperature properties, at 800 °C, the elastic modulus of 316H stainless steel decreases by approximately 30%, the tensile strength drops by about 60%, while the elongation after fracture increases by roughly 100%. Within the temperature range of 600–800 °C, the fatigue performance deteriorates with the increasing temperature, and the cyclic hardening rate accelerates as the temperature rises. The fracture mode in the instantaneous fracture zone of the fatigue fracture surface transitions from predominantly transgranular fracture to a mixed mode of transgranular and intergranular fracture as the temperature increases to 800 °C. Under higher strain amplitudes (around 0.6%), 316H stainless steel exhibits Masing behavior and dynamic strain aging (DSA). Correspondingly, the crack-initiation mode on the fatigue fracture surface shifts from a single surface source to multiple surface sources. A three-parameter model was employed to fit the strain–amplitude versus fatigue–life relationships of 316H stainless steel in the 600–800 °C range, showing good agreement with the experimental data, with most data points falling within a factor-of-two error band. Full article

Battery state-of-health (SoH) estimation is central to transportation electrification because it conditions safety limits, warranty accounting, power capability management, and long-horizon fleet optimization. Although deep temporal architectures can achieve high laboratory accuracy, field deployment is frequently limited by laboratory (Lab)-to-field (L2F) domain shift that alters input statistics, feature definitions, and noise regimes. Under such a shift, predictors may remain strongly monotonic, preserving degradation ordering and become operationally unreliable due to systematic output distortion (e.g., compression/warping of the SoH scale). A deployment-complete L2F transfer learning pipeline is presented, built around a gated Temporal Convolutional Network (TCN)–Transformer fusion backbone, domain-specific adapters and heads, alignment-regularized fine-tuning, and row-level inference via sliding-window overlap averaging. To address the dominant deployment failure mode, a Safe Calibration stage robustly filters calibration pairs and selects among candidate calibrators under a strict do-no-harm criterion. On an unseen deployment stream (2154 labeled rows), overlap-averaged raw inference achieves MAE = 0.0439, RMSE = 0.0501, and R² = 0.7451, consistent with mid-to-high SoH range compression, while Safe Calibration (Isotonic-Balanced selected) corrects nonlinear scaling without violating monotonic structure, improving to MAE = 0.0188, RMSE = 0.0252, and R² = 0.9357 to obtain a complete understanding of the challenges due to domain shifts, evaluation is extended to include other architecture baselines such as TCN-only, Transformer-only, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), and a Ridge regression baseline. Also added is explicit alignment and calibration ablations that include CORAL off/on, that is, none vs. Safe-Global vs. Context-Aware under identical leakage-safe splits and the same overlap-averaged deployment inference operator. This work goes beyond peak-score reporting and looks at the robustness of a pipeline under domain shift, which is quantified across four random seeds and multiple deployment streams, with uncertainty summarized via mean ± std and bootstrap confidence intervals for Mean of Absolute value of Errors (MAE)/Root of the Mean of the Square of Errors (RMSE) computed from per-example absolute errors. Full article

The proliferation of Unmanned Aerial Vehicles (UAVs) poses a significant challenge for ISAR imaging. Conventional multi-target imaging methods, such as sequential CLEAN-based techniques, are often hindered by error propagation and sensitivity to noise, leading to degraded performance or even imaging failure, especially at low SNR. To address these issues, this paper proposes a novel robust imaging framework. The framework is built upon two key innovations: a partitioned block-wise compensation mechanism integrated with PSO for simultaneous and precise motion parameters estimation of multiple targets, which avoids local optima and error accumulation; and the application of Sequential Variational Mode Decomposition (SVMD) to adaptively separate and reconstruct signals, thereby suppressing inter-target aliasing and noise interference overlooked in prior studies. Simulations and measured-data experiments confirm that the proposed method maintains clear focusing and superior image quality even at low SNR, outperforming existing techniques in terms of image entropy, contrast, and resolution. This paper provides a robust and effective solution for high-resolution radar surveillance in complex multi-target scenarios. Full article

Lateral buckling is the governing failure mode affecting the strength of cold-formed steel purlins. In industrial roofing systems, these purlins are frequently restrained by two or three anti-sag bars within their spans. Previous research by the authors indicated that under wind suction, the buckling behaviour of purlins with multiple anti-sag bars differs significantly from those with fewer restraints, primarily due to the semi-rigid nature of the bracing. This paper investigates the lateral buckling of C-section purlins with two or three anti-sag bars, explicitly accounting for lateral restraints provided by both the roof sheeting and the bars. Simplified analytical solutions are derived to facilitate practical design. Notably, a novel parameter is introduced to identify the controlling buckling mode, which significantly simplifies the calculation procedure. The proposed solutions show excellent agreement with results obtained from both commercial and custom-developed finite element codes. Full article

This study presents a novel pneumatic seed-metering device for precision soybean breeding, engineered to deliver two seeds per hill with high operational reliability. Its design features a compartmentalized structure and an integrated seed-clearing mechanism, explicitly addressing the key limitations of conventional seeders, such as low automation levels and intervarietal contamination during seed switching. The seed-metering and clearing processes were analyzed using coupled discrete element method–computational fluid dynamics (DEM–CFD) simulations. The exploratory DEM–CFD analysis identified distinct operational thresholds for seeding failures: miss-seeding occurred at disc rotational speeds exceeding 2.55 rad s⁻¹, while multiple-seeding issues were frequent at applied vacuum pressures above 5.6 kPa. Following this, a Central Composite Design (CCD) experiment was conducted in a controlled laboratory setting to examine the effects of operational speed and vacuum pressure on seeding quality indices. A multi-objective numerical optimization identified an optimal operational compromise with a seed-metering disc speed of 2.65 rad s⁻¹ (approximately 1.82 km h⁻¹) and an applied negative pressure of 5.80 kPa. This operating point effectively balances the competing failure modes of multiple seeding and miss-seeding, resulting in rates of 2.95% and 0.85% respectively. Field validation in saline–alkali soil conditions confirmed the device’s high precision, with actual multiple and miss-seeding rates maintained below 2% and 0.5%, respectively. Overall, this device significantly enhances seeding efficiency and operational reliability, providing a practical and effective solution for high-throughput soybean breeding programmes. Full article

The structural design of unstiffened cylindrical shells under external hydrostatic pressure is critical for the safety of marine structures, such as submarine hulls and pressure vessels. Accurately assessing nonlinear buckling and collapse failure modes traditionally requires computationally intensive Finite Element Analysis (FEA), which creates a bottleneck in iterative design optimization. To address this, our research leverages a robust Deep Neural Network (DNN) model specifically trained and validated for AL-6061 aluminum alloy cylinders. This predictive model, focusing on unstiffened cylindrical shells within a valid domain ($2\le L/D\le 15$ and $20\le D/t\le 150$), integrates a high-speed surrogate model with a Differential Evolution (DE) algorithm. This predictive model was trained on a large-scale dataset of 46,060 points generated through FEA simulations and rigorously validated against 28 physical experimental data points. Building upon this foundation, the present study implements a novel optimization framework that integrates the pre-trained DNN as a high-speed surrogate model with a Differential Evolution (DE) algorithm for global optimization. The primary objective is to minimize structural weight while strictly satisfying collapse strength requirements. Additionally, a grid search component is incorporated to provide designers with multiple feasible design candidates almost instantaneously. Validation against independent FEA results confirms high fidelity, with error rates of less than 2%. This methodology transforms the design cycle from days to mere minutes, establishing a reusable digital asset that significantly enhances efficiency and structural safety in marine engineering. Full article

Rolling bearings are critical components in rotating machinery, and their failures may lead to significant economic losses and safety hazards. However, early fault signals are often weak and masked by strong background noise, making accurate fault diagnosis extremely challenging. To address this issue, this paper proposes a fault diagnosis method for rolling bearings based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), an autoregressive (AR) model, and observer-based eigenvalue extraction, combined with a particle swarm optimization-based kernel extreme learning machine (PSO-KELM). Targeting rotating machinery with rolling bearings, the approach begins by applying ICEEMDAN as a preprocessing step to decompose non-stationary vibration signals into multiple intrinsic mode functions (IMFs), from which all essential fault-related information is extracted. The preprocessed vibration signal is then reconstructed. Subsequently, an AR model is used to establish a state-space representation for the observer, which processes the reconstructed signal and generates a residual output by comparing it with the actual mechanical signal. Features are then extracted from the residual signal, including its mean, variance, maximum and minimum values, kurtosis, waveform factor, pulse factor, and clearance factor. These features serve as inputs to the PSO-KELM classifier for fault diagnosis. To validate the method, real vibration data from electric motor bearings were employed in a case study, covering normal conditions and three typical fault types: outer race fault, inner race fault, and rolling element fault. The results demonstrate that the proposed method effectively enables fault feature extraction and accurate identification of bearing conditions. Full article

Generative Adversarial Networks (GANs) perform well on natural images but often fail in domains governed by strict geometric or symbolic constraints. This work focuses on convolutional GANs and studies how their inductive biases interact with two contrasting types of synthetic image data: fractal patterns, characterized by self-similarity and scale-invariant local structure, and Euclidean shapes, defined by simple geometric primitives and rigid global constraints. Using multiple convolutional GAN architectures (DCGAN, WGAN-GP, and SNGAN), two resolutions (64 × 64 and 128 × 128), and a suite of evaluation metrics, we compare adversarial training behavior on these datasets under tightly controlled conditions. Fractal datasets yield stable training dynamics and perceptually plausible generations, whereas Euclidean shape datasets consistently exhibit structural failure modes that persist under higher resolution, smoother shape representations, and architectural stabilization. Geometry-aware metrics reveal severe violations of global shape consistency in Euclidean outputs that are not reliably captured by standard perceptual or distributional measures such as FID, SSIM, or LPIPS. We argue that these findings reflect a fundamental inductive bias of convolutional generative models toward a locally rich, scale-repeating structure rather than globally constrained geometry. Rather than indicating that fractals are intrinsically easier to model, our results show that Euclidean geometry exposes limitations of adversarial generative learning that remain hidden under conventional evaluation. From this perspective, fractal datasets serve as informative diagnostic benchmarks for probing how adversarially trained convolutional generators handle scale-invariant structure versus globally constrained geometry, and our results highlight the need for domain-aware metrics and alternative architectural biases when applying generative models to structured or symbolic data. Full article

Permanent ground deformation caused by fault movement threatens the safe operation of buried pipelines. Accurate fragility assessment of buried pipelines subjected to faulting is essential for pipeline design and risk management. However, buried pipelines exhibit nonlinear mechanical responses due to the coupled effects of multiple factors. Moreover, the effects of key parameters remain insufficiently quantified, limiting the accuracy and engineering applicability of existing fragility assessments. In this study, a three-dimensional finite element model incorporating large deformation and nonlinear pipe–soil interaction is developed and validated against representative experimental data. Using this model, numerical simulations are performed for 352 parameter combinations covering fault type, dip angle, burial depth, soil type, and pipe material. Nonlinear regression of the simulation results yielded predictive models for pipeline peak axial strain under normal-slip and reverse-slip faulting. A fragility framework is then established with fault displacement as the intensity measure, and fragility curves are derived for both faulting modes. The predicted peak axial strains agree with the finite element results: 78.6% (normal-slip) and 72.5% (reverse-slip) of predictions fall within ±20% error. The fragility curves enable quantitative estimation of fault-displacement thresholds. In the case study, the intact-to-damage displacement threshold is approximately 0.6 m for normal-slip faults but approximately 0.2 m for reverse-slip faults, indicating a higher failure likelihood under reverse-slip faulting. Within the investigated parameter ranges, the fault dip angle is the most significant factor affecting the pipeline failure probability for both normal-slip and reverse-slip faulting. Sandy soil and greater burial depth substantially increase the probability of moderate-to-severe damage, whereas higher steel grade increases the displacement threshold for transition from intact to failure. This study provides a rapid quantitative tool and a theoretical basis for pipeline design and risk quantification of buried pipelines in fault zones. Full article