Search Results (342)

Unmanned Aerial Vehicles (UAVs) face significant challenges in global path planning within complex terrains, as traditional algorithms (e.g., A*, PSO, APF) struggle to balance computational efficiency, path optimality, and safety. This study proposes a Voronoi–A* fusion algorithm, combining Voronoi-vertex-based rapid trajectory generation with A* supplementary expansion for enhanced performance. First, an adaptive DEM layering strategy divides the terrain into horizontal planes based on obstacle density, reducing computational complexity while preserving 3D flexibility. The Voronoi vertices within each layer serve as a sparse waypoint network, with greedy heuristic prioritizing vertices that ensure safety margins, directional coherence, and goal proximity. For unresolved segments, A* performs localized searches to ensure complete connectivity. Finally, a line-segment interpolation search further optimizes the path to minimize both length and turning maneuvers. Simulations in mountainous environments demonstrate superior performance over traditional methods in terms of path planning success rates, path optimality, and computation. Our framework excels in real-time scenarios, such as disaster rescue and logistics, although it assumes static environments and trades slight path elongation for robustness. Future research should integrate dynamic obstacle avoidance and weather impact analysis to enhance adaptability in real-world conditions. Full article

In the context of intelligent coal mine safety monitoring, an integrated prediction and early-warning method named MTGNN-Bayesian-IF-DBSCAN (Multi-Task Graph Neural Network–Bayesian Optimization–Isolation Forest–Density-Based Spatial Clustering of Applications with Noise) is proposed to address the challenges of gas concentration prediction and anomaly detection in coal mining faces. The MTGNN (Multi-Task Graph Neural Network) is first employed to model the spatiotemporal coupling characteristics of gas concentration and wind speed data. By constructing a graph structure based on sensor spatial dependencies and utilizing temporal convolutional layers to capture long short-term time-series features, the high-precision dynamic prediction of gas concentrations is achieved via the MTGNN. Experimental results indicate that the MTGNN outperforms comparative algorithms, such as CrossGNN and FourierGNN, in prediction accuracy, with the mean absolute error (MAE) being as low as 0.00237 and the root mean square error (RMSE) maintained below 0.0203 across different sensor locations (T0, T1, T2). For anomaly detection, a Bayesian optimization framework is introduced to adaptively optimize the fusion weights of IF (Isolation Forest) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Through defining the objective function as the F1 score and employing Gaussian process surrogate models, the optimal weight combination (w_if = 0.43, w_dbscan = 0.52) is determined, achieving an F1 score of 1.0. By integrating original concentration data and residual features, gas anomalies are effectively identified by the proposed method, with the detection rate reaching a range of 93–96% and the false alarm rate controlled below 5%. Multidimensional analysis diagrams (e.g., residual distribution, 45° diagonal error plot, and boxplots) further validate the model’s robustness in different spatial locations, particularly in capturing abrupt changes and low-concentration anomalies. This study provides a new technical pathway for intelligent gas warning in coal mines, integrating spatiotemporal modeling, multi-algorithm fusion, and statistical optimization. The proposed framework not only enhances the accuracy and reliability of gas prediction and anomaly detection but also demonstrates potential for generalization to other industrial sensor networks. Full article

This paper proposes a speed-adaptive autonomous driving path-tracking framework based on the soft actor–critic (SAC) and pure pursuit (PP) methods, named the SACPP controller. The framework first analyzes the obstacles around the vehicle and plans an obstacle-free reference path with the minimum curvature using the hybrid A* algorithm. Next, based on the generated reference path, the current state of the vehicle, and the vehicle motor energy efficiency diagram, the optimal speed is calculated in real time, and the vehicle dynamics preview point at the future moment—specifically, the look-ahead distance—is predicted. This process relies on the learning of the SAC network structure. Finally, PP is used to generate the front wheel angle control value by combining the current speed and the predicted preview point. In the second layer, we carefully designed the evaluation function in the tracking process based on the uncertainties and performance requirements that may occur during vehicle driving. This design ensures that the autonomous vehicle can not only quickly and accurately track the path, but also effectively avoid surrounding obstacles, while keeping the motor running in the high-efficiency range, thereby reducing energy loss. In addition, since the entire framework uses a lightweight network structure and a geometry-based method to generate the front wheel angle, the computational load is significantly reduced, and computing resources are saved. The actual running results on the i7 CPU show that the control cycle of the control framework exceeds 100 Hz. Full article

We investigate magnetic instabilities toward multiple-Q states in centrosymmetric bilayer triangular-lattice systems. By focusing on the interplay between the layer-dependent Dzyaloshinskii–Moriya interaction and layer-independent bond-dependent anisotropic interaction, both of which originate from the relativistic spin-orbit coupling, we construct a low-temperature phase diagram based on an effective spin model that also includes frustrated isotropic exchange interactions. Employing simulated annealing, we reveal the stabilization of three distinct double-Q phases in the absence of an external magnetic field, each characterized by noncoplanar spin textures with spatially modulated local scalar spin chirality. Under applied magnetic fields, we identify field-induced phase transitions among single-Q, double-Q, and triple-Q states, some of which exhibit a finite net scalar spin chirality indicative of topologically nontrivial order. These findings highlight centrosymmetric systems with sublattice-dependent Dzyaloshinskii–Moriya interactions as promising platforms for realizing a variety of multiple-Q spin textures. Full article

Traditional project scheduling tools like Gantt charts struggle with dynamic adjustments and real-time optimization in complex construction projects, leading to inefficiencies and delays. This study addresses this challenge by proposing a dynamic optimization framework that integrates Building Information Modeling (BIM) and Artificial Intelligence (AI) to enhance schedule management. The framework comprises three layers: a data layer for collecting BIM and real-time site data, an analysis layer powered by AI algorithms for predictive analytics and optimization, and an application layer for visualizing progress and supporting decision-making. Through a case study on a large-scale water reservoir tunnel project in China, the framework demonstrated significant improvements in identifying schedule risks, optimizing resource allocation, and enabling real-time adjustments. Key innovations include a 4-in-1 Network Diagram Engine and a Blueprint Engine, which facilitate intuitive progress monitoring and automated task management. However, limitations in personnel skill matching, interface complexity, and mobile system performance were identified. This research advances the theoretical foundation of BIM-AI integration and provides practical insights for improving scheduling efficiency and project outcomes in the construction industry. Future work should focus on enhancing human resource management modules and refining system usability for broader adoption. Full article

This paper proposes a mathematical description of nitriding atmospheres obtained from a one-component ammonia ingoing atmosphere and a two-component ammonia inlet nitrogen-diluted atmosphere. The Fe-N phase equilibrium diagrams of the nitriding atmosphere in the hydrogen content-temperature (Q-T) system for selected NH₃/N₂ atmosphere compositions are presented. The nitriding atmosphere obtained with different degrees of nitrogen dilution of the ingoing atmosphere was characterized. It has been shown that in processes carried out in nitriding atmospheres obtained from a two-component atmosphere with nitrogen, there is no direct relationship between the value of the nitrogen potential and the degree of dilution of the ingoing atmosphere with nitrogen. It has been shown analytically and confirmed experimentally that with changes in the degree of dilution of ammonia with nitrogen, the hydrogen content of the nitriding atmosphere and, consequently, the nitrogen availability of the nitriding atmosphere change. Using the example of nitriding AISI 52100 steel, it has been experimentally demonstrated that the change in nitrogen availability, caused by a change in the degree of dilution of the ingoing atmosphere with nitrogen, is not accompanied by a change in the value of the nitrogen potential. It has also been shown that the change in the nitrogen availability of the nitriding atmosphere, induced by the change in the composition of the aNH₃/bN₂ ingoing atmosphere, affects the kinetics of nitrogen mass gain in the nitrided layer and the distribution of nitrogen mass between the iron nitride layer and the solution zone. It has also been shown that with the change in nitrogen availability, what changes in addition to the thickness of the iron nitride layer is also the phase composition of the layer. Using gravimetric tests, the mass of nitrogen in the iron nitride layer and the solution zone has been determined. To describe the equilibrium between the NH₃/H₂ atmosphere and nitrogen in the different iron phases, a modified Lehrer diagram in the coordinate system of temperature and hydrogen content in the nitriding atmospheres (T-Q) has been proposed. Full article

As cyber systems increasingly converge with physical infrastructure and social processes, they give rise to Complex Cyber–Physical–Social Systems (C-CPSS), whose emergent behaviors pose unique risks to security and mission assurance. Traditional cyber–physical system models often fail to address the unpredictability arising from human and organizational dynamics, leaving critical gaps in how cyber risks are assessed and managed across interconnected domains. The challenge lies in building resilient systems that not only resist disruption, but also absorb, recover, and adapt—especially in the face of complex, nonlinear, and often unintentionally emergent threats. This paper introduces the concept of ‘responsible resilience’, defined as the capacity of systems to adapt to cyber risks using trustworthy, transparent agent-based models that operate within socio-technical contexts. We identify a fundamental research gap in the treatment of social complexity and emergence in existing the cyber–physical system literature. To address this, we propose the E3R modeling paradigm—a novel framework for conceptualizing Emergent, Risk-Relevant Resilience in C-CPSS. This paradigm synthesizes human-in-the-loop diagrams, agent-based Artificial Intelligence simulations, and ontology-driven representations to model the interdependencies and feedback loops driving unpredictable cyber risk propagation more effectively. Compared to conventional cyber–physical system models, E3R accounts for adaptive risks across social, cyber, and physical layers, enabling a more accurate and ethically grounded foundation for cyber defence and mission assurance. Our analysis of the literature review reveals the underrepresentation of socio-emergent risk modeling in the literature, and our results indicate that existing models—especially those in industrial and healthcare applications of cyber–physical systems—lack the generalizability and robustness necessary for complex, cross-domain environments. The E3R framework thus marks a significant step forward in understanding and mitigating emergent threats in future digital ecosystems. Full article

High concrete gravity dams, particularly Roller-Compacted Concrete (RCC) types, face long-term safety challenges due to weak interlayer formation and crack propagation. This study presented a comprehensive evaluation of safety monitoring indexes for the Guxian high RCC dam (currently under construction) using both numerical and mathematical models. A finite element method (FEM) is employed with a strength reduction approach to assess dam stability considering weak layers. In parallel, a fracture mechanical model is used to investigate the safety of the Guxian dam based on failure assessment diagrams (FADs) for calculating the safety factor and the residual strength curve for calculating critical crack depth for two different crack locations, single-edge and center-through crack, to investigate the high possible risk associated with crack location on the dam safety. Additionally, the Guxian dam’s resistance to hydraulic fracture is assessed under two fracture mechanic failure modes, Mode I (open type) and Mode II (in-plane shear), by computing the ultimate overload coefficient using a proposed novel derived formula. The results show that weak layers reduce the dam’s safety index by approximately 20%, especially in lower sections with extensive interfaces. Single-edge cracks pose greater risk, decreasing the safety factor by 10% and reducing critical crack depth by 40% compared to center cracks. Mode II demonstrates higher resistance to hydraulic fracture due to greater shear strength and fracture energy, whereas Mode I represents the most critical failure scenario. The findings highlight the urgent need to incorporate weak layer behavior and hydraulic fracture mechanisms into dam safety monitoring, and to design regulations for high RCC gravity dams. Full article

The present research proposes an Artificial Neural Network (ANN) model to predict the critical buckling load of six different types of metallic aerospace grid-stiffened panels: isogrid type I, isogrid type II, bi-grid, X-grid, anisogrid, and waffle, all subjected to compressive loading. Six thousand samples (one thousand per panel type) were generated using the Latin Hypercube Sampling method to ensure a diverse and comprehensive dataset. The ANN model was systematically fine-tuned by testing various batch sizes, learning rates, optimizers, dense layer configurations, and activation functions. The optimized model featured an eight-layer architecture (200/100/50/25/12/6/3/1 neurons), used a selu–relu–linear activation sequence, and was trained using the Nadam optimizer (learning rate = 0.0025, batch size = 8). Using regression metrics, performance was benchmarked against classical machine learning models such as CatBoost, XGBoost, LightGBM, random forest, decision tree, and k-nearest neighbors. The ANN achieved superior results: MSE = 2.9584, MAE = 0.9875, RMSE = 1.72, and R² = 0.9998, significantly outperforming all other models across all metrics. Finally, a Taylor Diagram was plotted to assess the model’s reliability and check for overfitting, further confirming the consistent performance of the ANN model across both training and testing datasets. These findings highlight the model’s potential as a robust and efficient tool for predicting the buckling strength of metallic aerospace grid-stiffened panels. Full article

This study examines the evolution, structure, and dynamic and thermodynamic mechanisms of a Mediterranean tropical-like cyclone (TLC), or medicane (from Mediterranean–Hurricane), that occurred in the central Mediterranean region from 15 to 19 September 2020. This event is considered an extreme meteorological phenomenon, particularly impacting the Greek area and affecting the country’s economic and social structures. It is one of the most significant recorded Mediterranean cyclone phenomena in the broader Mediterranean region. The synoptic and dynamic environment, as well as the thermodynamic structure of this atmospheric disturbance, were analyzed using thermodynamic parameters. The system’s development can be described through three distinct phases, characterized by its symmetrical structure and warm core, as illustrated in the phase space diagrams and further supported by dynamical analysis. During the first phase, on 15 September, the structure of the upper tropospheric layers began to strengthen the parent barometric low, which had been in the Sirte Bay region since 13 September. The influence of upper-level dynamical processes was responsible for the reconstruction of the weakened barometric low. In the second phase, during the formation of the Mediterranean cyclone, low-level diabatic processes determined the evolution of the surface cyclone without significant support from upper-tropospheric baroclinic processes. Therefore, in this phase, the system is characterized as barotropic. In the third phase, the system remained barotropic but showed a continuous weakening tendency as the sea surface pressure steadily increased. This comprehensive analysis highlights the intricate processes involved in the development and evolution of Mediterranean cyclones with tropical characteristics. Full article

Modeling of impurity diffusion processes in a multiphase randomly inhomogeneous body is performed using the Feynman diagram technique. The impurity diffusion equations are formulated for each of the phases separately. Their random boundaries are subject to non-ideal contact conditions for concentration. The contact mass transfer problem is reduced to a partial differential equation describing diffusion in the body as a whole, which accounts for jump discontinuities in the searched function as well as in its derivative at the stochastic interfaces. The obtained problem is transformed into an integro-differential equation involving a random kernel, whose solution is constructed as a Neumann series. Averaging over the ensemble of phase configurations is performed. The Feynman diagram technique is developed to investigate the processes described by parabolic partial differential equations. The mass operator kernel is constructed as a sum of strongly connected diagrams. An integro-differential Dyson equation is obtained for the concentration field. In the Bourret approximation, the Dyson equation is specified for a multiphase randomly inhomogeneous medium with uniform phase distribution. The problem solution, obtained using Feynman diagrams, is compared with the solutions of diffusion problems for a homogeneous layer, one having the coefficients of the base phase and the other having the characteristics averaged over the body volume. Full article

Based on the research on concrete-filled circular steel tubular columns, the influence of carbon-fiber-reinforced polymers (CFRPs) on the ultimate bearing capacity of concrete-filled steel tubes (CFSTs) was further explored in this paper. Ten different concrete-filled circular CFRP–steel middle long columns were made for an axial compression test, and the influence of the CFRP layers, the concrete strength grades, the steel tube wall thickness, and the slenderness ratio on the ultimate bearing capacity was discussed. Combined with theoretical analysis, the calculation method of ultimate bearing capacity of it was found. The load midspan deflection diagram was obtained by numerical simulation with finite element analysis software ANSYS2021R1, and the test results were compared. The results demonstrate that CFRP layers significantly enhance the ultimate bearing capacity of circular steel tube–CFRP confined concrete columns, with one to three layers increasing the capacity by 42.5%, 69.4%, and 88.4%, respectively, under identical conditions. In comparison, the concrete strength, the steel tube thickness, and the slenderness ratio showed lesser effects (<20% improvement), providing critical support for engineering applications of CFRP-confined circular steel tubular columns. Moreover, the error of ANSYS calculation results is small, which is in line with the test. This is of great significance to verify the correctness of the test of concrete-filled circular CFRP–steel middle long columns. Full article

The test method for internal stress of titanium-based nitride was optimized via penetration depth and surface roughness. Through the test method, the variations in the mechanical properties due to the ratio of the carbon gradient layer were investigated in terms of internal stress. TiN coatings were deposited on SUS 304 using RF/DC magnetron sputtering, and the penetration depth was adjusted by varying the X-ray power of HR-XRD for test specimens with the same coating thickness of 1 μm. The gradient of diagram for internal stress remained constant regardless of the penetration depth, and this was attributed to the analysis of internal stress focusing on the preferred growth orientation of the coating and excluding the influence of the substrate. In addition, we tested different surface roughness values (0.01 Sa, 0.02 Sa, and 0.03 Sa) to observe the effect on internal stress measurement. The results showed negligible difference in internal stress, confirming that this measurement method is valid for coatings with a surface roughness of 0.03 Sa or less. The test method was applied to analyze the carbon-doped TiZrN coating. TiZrN coatings were deposited on SUS 304, and coating thicknesses of 0.5 μm, 1 μm, and 2 μm were used to control the ratio of the carbon gradient layer. After applying the carbon paste for carbon doping, the TiZrN coating was irradiated with a pulsed laser. The compressive internal stress increased from −1263 MPa to −1687 MPa at a coating thickness of 0.5 μm, where the ratio of the carbon gradient layer was the highest. It was confirmed that the increase in internal stress with the ratio of the carbon gradient layer improved the mechanical properties of the carbon-doped TiZrN coating by laser carburization. Full article

The issue of energy transfer is extremely important. In order to achieve the lowest possible energy consumption and the required thermal efficiency in energy-efficient buildings, it is necessary, among other things, to minimize the heat-transfer coefficient, which depends on the properties of the insulating material. Analyses of the relationship between the structure of a material and its thermal conductivity coefficient have shown that lower values of this coefficient can be achieved with a more complex structure that mimics natural forms. This paper presents a design method based on the Voronoi diagram to obtain a three-dimensional structure of a porous composite material. The method was found to be effective in producing structures with predefined and functionally graded porosity. The porous specimens were fabricated from a biodegradable soybean oil-based resin using mSLA additive technology. Analyses were performed to determine the thermal parameters of the anisotropic composites. Experimental results showed that both porosity and irregularity affect the thermal properties. The lowest thermal conductivity coefficients were obtained for a 100 mm-thick prototype composite with the following parameters: wall thickness D = 0.2 mm, cell size S = 4 mm, number of structural layers n = 2, and degree of irregularity R = 4. The lowest possible thermal conductivity of the insulation was 0.026 W/(m·K), and the highest possible thermal resistance was 3.92 (m²·K)/W. The method presented in this study provides an effective solution for nature-inspired design and topological optimization of porous structures. Full article

This study investigates the physical interactions and between forest fires and the atmosphere, which often lead to conditions favourable to instability and the formation of pyrocumulus (PyCu). Using the coupled atmosphere–fire spread modelling framework, WRF-SFIRE, the Portuguese October 2017 Quiaios wildfire, in association with tropical cyclone Ophelia, was simulated. Fire spread was imposed via burnt area data, and the fire’s influence on the vertical and surface atmosphere was analysed. Simulated local atmospheric conditions were influenced by warm and dry air advection near the surface, and moist air in mid to high levels, displaying an inverted “V” profile in thermodynamic diagrams. These conditions created a near-neutrally unstable atmospheric layer in the first 3000 m, associated with a low-level jet above 1000 m. Results showed that vertical wind shear tilted the plume, resulting in an intermittent, high-based, shallow pyroconvection, in a zero convective available potential energy environment (CAPE). Lifted parcels from the fire lost their buoyancy shortly after condensation, and the presence of PyCu was governed by the energy output from the fire and its updrafts. Clouds formed above the lifted condensation level (LCL) as moisture fluxes from the surface and released from combustion were lifted along the fire plume. Clouds were primarily composed of liquid water (1 g/kg) with smaller traces of ice, graupel, and snow (up to 0.15 g/kg). The representation of pyroconvective dynamics via coupled models is the cornerstone of understanding the phenomena and field applications as the computation capability increases and provides firefighters with real time extreme fire conditions or predicting ahead of time. Full article