Search Results (930)

The development of a geomechanical model is aimed at enhancing the safety of mining operations through the determination of optimal slope angles and the probabilistic assessment of pit wall stability. For the conditions of open-pit mining, three-dimensional geomechanical models were constructed based on the calculation of the slope stability factor using the Rocscience Slide2/Slide3 (v.9.027, 2023) software package. The stress–strain state of the rock mass at the final stage of extraction was evaluated using the finite element method. Strength reduction factors (SRF) were determined considering the physico-mechanical properties of the rocks forming the near-contour zone of the massif. The stability of the pit slopes was assessed along individual geological cross-sections in accordance with the design contours of the Northern Katpar open pit. Calculations performed using several methods confirmed the overall stability of the pit walls. The final design parameters of the projected open pit were determined. For the first time, it was established that in the southern and southwestern sectors of the Northern Katpar pit, within the elevation range of +700 to +400 m, a reduction in the SFR (from 1.18 to 1.41) occurs due to the predominance of siltstones and the presence of tectonic disturbances. The generalized results of numerical slope stability analyses for the design pit contour, together with the developed geological–structural model of the deposit, provide a basis for ensuring the safe conduct of mining operations at the site. Full article

In comparison to internal combustion engines, which usually have low frequency, broadband excitations, in electric vehicles, tonal excitations from the electric drivetrain are noticeable and disturbing. As the acoustic and structural dynamic behavior, often referred to as noise, vibration, and harshness (NVH), strongly influences customers’ quality perceptions, optimizing it is a key challenge in development. This study investigates the influence of static rotor–stator eccentricity on the NVH behavior of an electric drivetrain using a transient elastic multibody simulation (eMBS) model incorporating non-linear gear meshing, bearing contact, and electromagnetic forces. The analysis identifies the 36th order excitation of the electric machine as the dominant source, leading to a maximum total acceleration level of 152 dB. Two specific excitation directions were found to reduce this amplitude most effectively. However, varying the amount of static eccentricity in these directions resulted in only minor vibration reductions (<1.5 dB). The findings indicate that the symmetric mode shapes of the cylindrical housing govern the response, indicating that addressing the excitability of housing modes by developing asymmetric housing designs could offer a more effective approach for NVH optimizations of electric drivetrains. Full article

Dredged sludge is characterized by a high water content, low permeability, and poor load-bearing capacity, which hinder its sustainable utilization as an engineering filler. During the stabilization process using vacuum preloading (VP), fine-grained sludge readily clogs drainage channels, thereby prolonging consolidation duration and compromising drainage efficiency. To address these persistent challenges, this study proposes an improved method that combines electroosmosis, VP, and polyacrylamide (PAM) to enhance the consolidation performance of dredged sludge. Column settling experiments demonstrated that the optimal application dosages of anionic polyacrylamide (APAM) and calcium chloride (CaCl₂) were 0.25% and 4.0% of dry sludge mass, respectively. Excessive dosage of either APAM or CaCl₂ disturbed the agglomeration and sedimentation of fine-grained particles due to surface charge inversion. Electroosmotic VP (EVP) facilitated the directional movement of pore water, which increased the cumulative water discharge mass by 37.3%. The combination of APAM and CaCl₂ enhanced particle flocculation via adsorption and bridging effects, significantly improving soil permeability and dewatering performance. Driven by an electric field, Ca²⁺ ions transported water molecules toward the cathode. Subsequently, these Ca²⁺ ions participated in reactions to generate cementitious agents. Compared with VP, this integrated method increased the sludge shear strength by 108.1% and produced a much denser microstructure. Full article

The accurate acquisition and real-time calculation of the attitude angle of precision firefighting extinguishing projectiles are essential for ensuring stable flight and precise extinguishing agent release. However, measuring the roll attitude angle in such projectiles is challenging due to their highly dynamic nature and environmental disturbances such as fire smoke, high temperature, and electromagnetic interference. Traditional methods for measuring attitude angles rely on multi-sensor fusion schemes, which suffer from complex structure and high cost. This paper proposes a single-gyro attitude calculation method based on micro-electromechanical inertial measurement units (MIMUs). This method integrates Fourier transform time-frequency analysis with a second-order Infinite Impulse Response (IIR) bandpass filtering algorithm optimized by dynamic coefficients. Unlike conventional fixed-coefficient filters, the proposed algorithm adaptively updates filter parameters according to instantaneous roll angular velocity, thereby maintaining tracking capability under time-varying conditions. This theoretical contribution provides a general framework for adaptive frequency-tracking filtering, beyond the specific engineering case of firefighting projectiles. Through joint time-frequency domain processing, it achieves high-precision dynamic decoupling of the roll angle, eliminating the dependency on external sensors (e.g., radar/GPS) inherent in conventional systems. This approach drastically reduces system complexity and provides key technical support for low-cost and high-reliability firefighting projectile attitude control. The research contributes to enhancing the effectiveness of urban firefighting, forest fire suppression, and public safety emergency response. Full article

Background: The Kingdom of Saudi Arabia is adjacent to two vital marine ecosystems; the semi-enclosed Arabian Gulf and the largely landlocked Red Sea. Dinoflagellates are repeatedly found in these bodies of marine water, which serve as significant routes for cargo ships. Through these ships and ballast water, invasive dinoflagellate species and their cysts are introduced. They compete with indigenous species for nutrients and space, cause massive fish kill-off and disturb the ecological balance and biodiversity. To address these threats, the International Convention for the Control and Management of Ships’ Ballast Water and Sediments (BWM Convention) set forth guidelines intended to curtail the dissemination of such detrimental organisms. The Kingdom of Saudi Arabia was one of the co-signatory countries to this Convention. Methods of detection and monitoring include microscopy, molecular characterization and remote sensing, which are employed for the detection and monitoring of these harmful algae, in order to avert disasters such as fish die-offs. The results of several reports confirmed the presence of number of dinoflagellates in both the Arabian Gulf and the Red Sea, some of which are toxin producers, with certain species being highlighted as invasive species whose presence requires a high level of alert. Discussion: The monitoring, the change in engineering of cargo ships and the introduction of advanced surveillance methods, together with the proper treatments of ballast water, are all important security elements that ensure the safe disposal of ballast water without introducing harmful species. Full article

Aiming at the challenge of balancing the accuracy and cost of the initial state calibration of traditional MEMS inertial navigation systems, as well as the current situation of the lack of high-precision three-axis turntables in engineering practice, this paper proposes a practical and innovative systematic error calibration and compensation scheme, which effectively suppresses the deterministic errors of MEMS-INS and enhances its applicability in high-precision and long-duration tasks. By analyzing the coordinate transformation characteristics of the MEMS-INS solution process under small-angle disturbances, a deterministic error model based on the device’s zero bias, scale factor, and cross-coupling errors is constructed. A twelve-position dual-axis calibration method, combined with a high-precision orthogonal fixture, is designed to excite errors on a dual-axis turntable, converting originally unobservable error terms into observable periodic signals. Experimental results show that the installation error calibration accuracy reaches 0.03°, an improvement of about 25% compared to the traditional dual-axis method, breaking through the limitations of dual-axis turntables in cross-coupling error calibration, achieving an initial error ≤ 1 μrad, and reducing the navigation error by 90% within one hour. This method eliminates reliance on expensive three-axis turntables while enabling multi-error calibration, addressing the cost–accuracy trade-off in engineering applications. Full article

The coordinated scheduling of diesel generators, photovoltaic (PV) systems, and energy storage systems (ESS) is essential for improving the reliability and resilience of islanded microgrids in remote and mission-critical applications. This review systematically analyzes diesel–PV–ESSs from an “energy symbiosis” perspective, emphasizing the complementary roles of diesel power security, PV’s clean generation, and ESS’s spatiotemporal energy-shifting capability. A technology–time–performance framework is developed by screening advances over the past decade, revealing that coordinated operation can reduce the Levelized Cost of Energy (LCOE) by 12–18%, maintain voltage deviations within 5% under 30% PV fluctuations, and achieve nonlinear resilience gains. For example, when ESS compensates 120% of diesel start-up delay, the maximum disturbance tolerance time increases by 40%. To quantitatively assess symbiosis–resilience coupling, a dual-indicator framework is proposed, integrating the dynamic coordination degree (ζ ≥ 0.7) and the energy complementarity index (ECI > 0.75), supported by ten representative global cases (2010–2024). Advanced methods such as hybrid inertia emulation (200 ms response) and adaptive weight scheduling enhance the minimum time to sustain (MTTS) by over 30% and improve fault recovery rates to 94%. Key gaps are identified in dynamic weight allocation and topology-specific resilience design. To address them, this review introduces a “symbiosis–resilience threshold” co-design paradigm and derives a ζ–resilience coupling equation to guide optimal capacity ratios. Engineering validation confirms a 30% reduction in development cycles and an 8–12% decrease in lifecycle costs. Overall, this review bridges theoretical methodology and engineering practice, providing a roadmap for advancing high-renewable-penetration islanded microgrids. Full article

The complex configuration of the internal flow field in aero-engines leads to limitations on sensor installation positions, and how to accurately identify the disturbances of the installation influence parameters under this constraint has long been a significant challenge. To address this issue, this study proposes an optimization algorithm to identify the optimal sensor layout. This is achieved by employing mutually distinct integer encoding, which ensures the uniqueness of each sensor position and prevents duplication. More importantly, an objective evaluation system incorporating tracking error, sensor comprehensiveness, and spatial coverage is integrated into the fitness function design, thereby overcoming the one-sidedness and limitations of single-indicator evaluation. Building upon this foundation, a sensor optimization scheme is proposed for identifying installation influence parameters. This scheme integrates the rapid search capability of the Tabu Bee Colony Random Variation Dimension Algorithm with the global optimization capability of an Improved Genetic Random Variation Dimension Algorithm, resulting in a Tabu Bee Colony–Improved Genetic Random Variation Dimension Optimization Algorithm (TBC-IG-RVDOA). For each installation influence parameter, different perturbation conditions were established, and the selected optimal sensor combination was then validated using the Extended Kalman Filter (EKF). Experimental studies show that, under all perturbation scenarios, the TBC-IG-RVDOA demonstrates strong convergence, high computational efficiency, and fitness function values consistently exceeding 0.92, thereby accurately capturing the changes in each installation influence parameter. Full article

In the context of multi-organ involvement in sepsis, cardiac toxicity is manifested as sepsis-induced cardiomyopathy (SICM). To date, no unified SICM definition exists, though a left ventricular ejection fraction ≤ 50% and/or an absolute drop ≥ 10% from baseline are the most widely accepted components. Several molecular pathways have been associated with SICM, including (i) pro-inflammatory mediator-induced cardiac depression; (ii) sarcolemmal membrane dysfunction; (iii) autonomic nervous system (ANS) imbalance; (iv) blunted cardiovascular response to catecholamines; (v) dysfunctional intracellular calcium handling; (vi) mitochondrial dysfunction; (vii) metabolic reprogramming; and (viii) disturbed endothelial and microcirculatory function. Atrial and ventricular arrhythmias—particularly atrial fibrillation—commonly complicate disease management and are associated with adverse outcomes. Key mechanisms outlining sepsis-induced arrhythmogenesis are (i) inflammation; (ii) electrolyte imbalances; (iii) myocardial ischemia; (iv) QT prolongation/dispersion; (v) adrenergic overactivation; (vi) calcium mishandling; and (vii) fever-induced arrhythmogenesis in Brugada. Established therapeutic approaches include prompt treatment with antibiotics, hemodynamic optimization, and/or selective use of beta-blockers. Furthermore, several molecules are currently being investigated targeting numerous pathways activated in sepsis. Vitamin C, ginsenoside Rc, Schistosoma Japonicum cystatin, and gasmerdin-D inhibitor Y2 exert anti-inflammatory actions, while melatonin and α-ketoglutarate regulate mitochondrial homeostasis. Triiodothyronine targets microcirculatory optimization and regulates protective pathways against stress-related cell death. Engineered exosomes may facilitate targeted drug delivery, inflammatory response modulation, and activation of pathways related to cell survival, while sodium octanoate exhibits anti-inflammatory actions coupled with improved energy metabolism. Finally, gene-regulating therapies aiming at inflammatory response optimization have also been proposed and are currently under development. Future research should aim to standardize the SICM definition, translate emerging therapeutics into clinical practice, identify novel molecular targets, and implement personalized treatment strategies for SICM. Full article

During the construction of deep underground soft rock strata, the adverse effects of high geostress, unfavorable geological conditions, and excavation disturbances are significant, easily triggering Material-Stress-Induced (MSI) large deformation disasters, leading to the failure of support structures or even collapse, thus posing great challenges to the safe construction of this type of underground engineering. Based on this, this study first classifies the large deformations, analyzes the instability mechanism of material-stress-induced large deformation in soft rock, and identifies the influencing factors of this type of large deformation from three aspects. Subsequently, a numerical investigation (FLAC3D 6.00) is utilized to examine the surrounding rock deformation characteristics under different material factors (uniaxial compressive strength and elastic modulus), stress factors (burial depth and lateral pressure coefficient), and construction factors (excavation method, support pattern, and timing of initial support installation). On this basis, a multi-factor sensitivity comparison analysis is conducted, which clarifies the differences and prioritization of parameter influences on large deformation, and reveals the dominant role of controlling factors such as elastic modulus. The analysis demonstrates a strong negative correlation between the examined material factors (uniaxial compressive strength and elastic modulus) and the magnitude of surrounding rock displacement, with both values eventually converging. A significant positive correlation between the examined stress factors and the magnitude of surrounding rock displacement was observed. A pronounced positive correlation was observed between stress factors and surrounding rock deformation. These factors distinctly have different effects on the peak displacement of different surrounding rock parts. Vault settlement demonstrates the most pronounced displacement, while arch bottom deformation is the least apparent. The three excavation methods exhibit relatively low sensitivity to surrounding rock displacement. Similarly, the support patterns demonstrate limited influence on surrounding rock deformation. The material factor of soft surrounding rock is the main controlling factor of the large deformation of soft surrounding rock in deep underground engineering. The elastic modulus has the strongest influence on the displacement of the surrounding rock. When the elastic modulus is less than 2 GPa, the sensitivity coefficient is much higher than the stress factors. The research results can provide some reference and guidance for similar underground projects. Full article

The reliability of ocean-going ship engine fuel systems is crucial for the safety and continuous operation of vessels. Failure of this system can lead to serious operational and economic consequences; therefore, effective diagnostics and failure prediction are essential elements of modern fleet management. This paper presents an analysis of the reliability of fuel systems based on operational data from ten bulk carriers operated by Polska Żegluga Morska in Szczecin. The analysis combined classical statistical methods with artificial intelligence algorithms to develop a hybrid diagnostic and forecasting framework. The Weibull lifetime distribution was applied to estimate time-to-failure parameters, revealing mixed failure mechanisms—random failures (k < 1) and aging-related processes (k > 1). Using the k-means algorithm, ships were automatically classified into two reliability groups: high-failure-rate units and stable operational vessels. Individual linear regression models were then developed for each ship to forecast the time to the next failure, achieving satisfactory predictive performance (R² > 0.75 for most vessels). Sensitivity analysis quantified model robustness under different disturbance scenarios, yielding mean Relative Prediction Deviation (RPD) values of approximately 65% for Missing Data, 60% for False Failure, and 26% for Data Noise. These results confirm that the proposed hybrid reliability–AI framework is resistant to random noise but sensitive to incomplete or erroneous historical data. The developed approach provides an interpretable and effective tool for predictive maintenance, supporting reliability management and operational decision-making in marine engine systems. The article presents a hybrid model that has been developed to enable the detailed characterization of emergency processes and the identification of the most important factors that influence damage forecasting. For systems with variable failure risk, it was found that both classical probabilistic models and machine learning methods must be considered to interpret damage patterns correctly. Implementing data filtration and validation procedures before using data in artificial intelligence models has been shown to improve forecast stability and increase the usefulness of forecasts for planning repairs. Full article

This study introduces a novel control strategy aimed at achieving projective synchronization in incommensurate fractional-order chaotic systems (IFOCS). The approach integrates the mathematical framework of fractional calculus with the recursive structure of the backstepping control technique. A key feature of the proposed method is the systematic use of the Mittag–Leffler function to verify stability at every step of the control design. By carefully constructing the error dynamics and proving their asymptotic convergence, the method guarantees the overall stability of the coupled system. In particular, stabilization of the error signals around the origin ensures perfect projective synchronization between the master and slave systems, even when these systems exhibit fundamentally different fractional-order chaotic behaviors. To illustrate the applicability of the method, the proposed fractional order backstepping control (FOBC) is implemented for the synchronization of two representative systems: the fractional-order Van der Pol oscillator and the fractional-order Rayleigh oscillator. These examples were deliberately chosen due to their structural differences, highlighting the robustness and versatility of the proposed approach. Extensive simulations are carried out under diverse initial conditions, confirming that the synchronization errors converge rapidly and remain stable in the presence of parameter variations and external disturbances. The results clearly demonstrate that the proposed FOBC strategy not only ensures precise synchronization but also provides resilience against uncertainties that typically challenge nonlinear chaotic systems. Overall, the work validates the effectiveness of FOBC as a powerful tool for managing complex dynamical behaviors in chaotic systems, opening the way for broader applications in engineering and science. Full article

This study experimentally compares classical PID and three adaptive control strategies (including a novel adaptive control strategy developed by the authors) for stabilizing the crankshaft speed of a diesel engine (ADCR Euro 4). The tests were performed on a dynamometer with alternator-induced step loads. All tests were performed at a constant engine crankshaft speed using National Instruments instrumentation and custom LabVIEW-based software for real-time monitoring. Metrics included response time (RT), overshoot (OV), and steady-state error (SSE), each based on ten repetitions with reported standard deviations. Results show that the competitive adaptive algorithm reduced RT by ~20%, OV by ~15%, and SSE by ~10% compared to PID. These results confirm that adaptive control, especially the competitive strategy, provides high precision and fast disturbance rejection, bridging the gap between simulation-based studies and industrial diesel engine applications. These results highlight the potential of adaptive control in applications such as air–fuel ratio control, turbocharger pressure control, knock detection, and fuel optimization. Full article

As carbon cycling and global environmental protection gain increasing attention, forest disturbance research has intensified worldwide. Constrained by limited data availability, existing frameworks often rely on extracting individual spectral bands for simple binary disturbance detection, lacking systematic approaches to visualize and classify causes of disturbance over large areas. Accurately identifying disturbance types is critical because different disturbances (e.g., fires, logging, pests) exhibit vastly different impacts on forest structure, successional pathways and, consequently, forest carbon sequestration and storage capacities. This study proposes an integrated remote sensing and deep learning (DL) method for forest disturbance type identification, enabling high-precision monitoring in Northeast China from 1992 to 2023. Leveraging the Google Earth Engine platform, we integrated Landsat time-series data (30 m resolution), Global Forest Change data, and other multi-source datasets. We extracted four key vegetation indices (NDVI, EVI, NBR, NDMI) to construct long-term forest disturbance feature series. A comparative analysis showed that the proposed convolutional neural network (CNN) model with six feature bands achieved 5.16% higher overall accuracy and a 6.92% higher Kappa coefficient than a random forest (RF) algorithm. Remarkably, even with only six features, the CNN model outperformed the RF model trained on fifteen features, achieving a 0.4% higher overall accuracy and a 0.58% higher Kappa coefficient, while utilizing 60% fewer parameters. The CNN model accurately classified forest disturbances—including fires, pests, logging, and geological disasters—achieving a 92.26% overall accuracy and an 89.04% Kappa coefficient. This surpasses the 81.4% accuracy of the Global Forest Change product. The method significantly improves the spatiotemporal accuracy of regional-scale forest monitoring, offering a robust framework for tracking ecosystem dynamics. Full article

This paper addresses the critical multiple-uncertainty challenge in laser link acquisition for space gravitational wave detection missions—a key bottleneck where spacecraft attitude alignment for laser link establishment is perturbed by inherent random disturbances in such missions, while also needing to balance ultra-high attitude precision, fuel efficiency, and compliance with engineering constraints. To tackle this, a convex optimization-based attitude control strategy integrating covariance control and free terminal time optimization is proposed. Specifically, a stochastic attitude dynamics model is first established to explicitly incorporate the aforementioned random disturbances. Subsequently, an objective function is designed to simultaneously minimize terminal state error and fuel consumption, with three key constraints (covariance constraints, pointing constraints, and torque saturation constraints) integrated into the convex optimization framework. Furthermore, to resolve non-convex terms in chance constraints, this study employs a hierarchical convexification method that combines Schur’s complementary theorem, second-order cone relaxation, and Taylor expansion techniques. This approach ensures lossless relaxation, renders the optimization problem computationally tractable without sacrificing solution accuracy, and overcomes the shortcomings of traditional convexification methods in handling chance constraints. Finally, numerical simulations demonstrate that the proposed method adheres to engineering constraints while maintaining spacecraft attitude errors below 1 μrad under environmental uncertainties. This study provides a convex optimization solution for laser link acquisition in space gravitational wave detection missions considering uncertainty conditions, and its framework can be extended to the optimal design of other stochastically uncertain systems. Full article