Search Results (644)

Conjugate heat transfer in non-Newtonian fluids is a fundamental phenomenon in thermal management systems. This study investigates the combined effects of magnetic field topology, heat absorption/generation, the thermal conductivity ratio, enclosure inclination, and power-law rheology using the lattice Boltzmann method. The parametric analysis shows that increasing the heat generation coefficient from −5 to +5 reduces the average Nusselt number by up to 97% for the pseudo-plastic fluids and up to 29% for the Newtonian fluids, while entropy generation increases by 44–86% depending on the thermal conductivity ratio. Increasing the inclination angle from 0° to 90° weakens convection and reduces heat transfer by nearly 77%. Magnetic field strengthening (Ha = 0–45) decreases the Nusselt number by 20–55% depending on the barrier temperature. Among all tested conditions, the highest thermal performance (maximum heat transfer and minimum entropy generation) occurs when using a pseudo-plastic fluid (n = 0.75), exhibiting high wall conductivity (TCR = 50) and heat absorption (HAPC = −5), a cold obstacle (θ_b = 0), and zero inclination (λ = 0°), as well as in the absence of the magnetic field effects. These quantitative insights highlight the controllability of the conjugate heat transfer and irreversibility in the power-law fluids under coupled magnetothermal conditions. Full article

To obtain an excellent electromagnetic wave (EMW) absorption material, a strategy was proposed in this study with the aid of in-situ growth of carbon nanotubes (CNTs) on the surface of a metal–organic framework (MOF)-derived FeCoNiMnMg high-entropy alloy (HEA). The HEA@CNT composite was successfully prepared via a solvothermal method combined with a one-step pyrolysis process. With the pyrolysis temperature increasing from 600 °C to 800 °C, the length of CNTs grew from 200 nm to about 600 nm approximately, while the defect density of CNTs was enhanced. This structural evolution significantly improved the dielectric properties and impedance matching. Consequently, the sample prepared at 800 °C (HEA@CNT-800) exhibited outstanding microwave absorption performances, achieving a minimum reflection loss (RL_min) of −57.52 dB at a matched thickness of 2.3 mm and an effective absorption bandwidth (EAB) of 4.4 GHz at a thinner thickness of 1.9 mm. This work provides a novel perspective for designing high-performance MOF-derived absorption materials. Full article

With the growing deployment of Flying Ad hoc Networks (FANETs) in military and civilian applications, constructing a stable and efficient communication backbone has become a critical challenge. This paper tackles the Cluster Head (CH) optimization problem in large-scale and highly dynamic FANETs by formulating it as a Minimum Connected Dominating Set (MCDS) problem. However, since MCDS is NP-complete on general graphs, existing heuristic and exact algorithms suffer from limited coverage, poor connectivity, and high computational cost. To address these issues, we propose TGN-MCDS, a novel algorithm built upon the Temporal Graph Network (TGN) architecture, which leverages graph neural networks for cluster head selection and efficiently learns time-varying network topologies. The algorithm adopts a multi-objective loss function incorporating coverage, connectivity, size control, centrality, edge penalty, temporal smoothness, and information entropy to guide model training. Simulation results demonstrate that TGN-MCDS rapidly achieves near-optimal CH sets with full node coverage and strong connectivity. Compared with Greedy, Integer Linear Programming (ILP), and Branch-and-Bound (BnB) methods, TGN-MCDS produces fewer and more stable CHs, significantly improving cluster stability while maintaining high computational efficiency for real-time operations in large-scale FANETs. Full article

WC-based cermet coatings with a CoCrNiAlTi binder were fabricated on 04Cr13Ni5Mo stainless steel substrates using the atmospheric high-velocity air–fuel (HVAF) spraying process. The influence of the air-to-fuel ratio (AFR) on the microstructure, mechanical properties, and wear resistance of the WC-CoCrNiAlTi coatings was systematically investigated. The results indicate that the WC-CoCrNiAlTi coatings primarily consisted of WC, (Co, Ni)₃W₃C and a face-centered cubic (FCC) binder phase. As the AFR increased, the formation of the (Co, Ni)₃W₃C phase gradually decreased. Concurrently, the coating density improved, which was attributed to the enhanced particle melting state and increased flight velocity, leading to better flattening upon impact. The average microhardness of the WC-CoCrNiAlTi coatings gradually increased with an increasing AFR. The coating produced at an AFR of 1.130 exhibited the highest microhardness of 1355.68 HV_0.2. Both the friction coefficient and the wear rate of the coatings decreased progressively as the AFR increased. At the optimal AFR of 1.130, the coating demonstrated the lowest friction coefficient (0.6435) and wear rate (1.15 × 10⁻⁶ mm³·N⁻¹·m⁻¹), indicating a wear resistance 34.85 times that of the stainless steel substrate. Furthermore, the slurry erosion weight loss rate of the WC-CoCrNiAlTi coatings decreased gradually with increasing AFR. The coating sprayed at an AFR of 1.130 showed the minimum erosion rate (1.70 × 10⁻⁶ g·cm⁻²·min⁻¹), which was 24.04 times lower than that of the substrate. The erosion mechanism of the WC-CoCrNiAlTi coatings was identified as the fatigue-induced removal of WC particles under alternating stress. The ductile high-entropy alloy (HEA) binder effectively protects the brittle WC phase through adaptive deformation, thereby significantly mitigating coating damage. Full article

Aiming at the problem that existing intuitionistic fuzzy entropy measures fail to fully balance the interaction between intuition (determined by hesitation degree) and fuzziness (characterized by the difference between membership degree and non-membership degree), this paper proposes the concept of isentropic arc, reveals the mutual offset effect of the two in entropy composition, and provides a new theoretical perspective for the planar analysis of entropy measures. Further research finds that there are maximum and minimum entropy points in the intuitionistic fuzzy entropy plane. Based on this, two different types of isentropic arcs can be constructed. Combining this feature with the core logic of approaching the ideal solution, this paper constructs a new intuitionistic fuzzy entropy measure formula based on the TOPSIS method. This formula can characterize the synergistic influence of intuition and fuzziness at the same time, meets all the constraints of the axiomatic definition, and is more suitable for the needs of actual decision-making scenarios. Comparative analysis of numerical examples shows that the proposed new entropy measure has significantly better discrimination than existing methods for six groups of samples with a high hesitation degree and high fuzziness, and the entropy value ranking is consistent with the ranking of the uncertainty information contained in the samples. Finally, the weight decision-making model based on this entropy measure is applied to the evaluation of coal mine emergency rescue capability, verifying its practical value in solving complex uncertainty problems. Full article

Flywheel energy storage systems play an important role in frequency regulation and power quality control within modern power grids, yet the fault signals generated by defects in their rolling bearings are typically indistinct, making direct diagnosis difficult. Raw noisy signals often yield unsatisfactory diagnostic performance when directly processed by neural networks. Although MOMEDA (Multipoint Optimal Minimum Entropy Deconvolution Adjusted) can effectively extract impulsive fault components, its performance is highly dependent on the selected fault period and filter length. To address these issues, this paper proposes an improved fault diagnosis method that integrates MOMEDA-based periodic extraction with a neural network classifier. The Artificial Fish Swarm Algorithm (AFSA) is employed to adaptively determine the key parameters of MOMEDA using multi-point kurtosis as the optimization objective, and the optimized parameters are used to enhance impulsive fault features. The filtered signals are then converted into image representations and fed into a ResNet-18 network (a compact 18-layer deep convolutional neural network from the residual network family) to achieve intelligent identification and classification of bearing faults. Experimental results demonstrate that the proposed method can effectively extract and diagnose bearing fault signals. Full article

Accurate identification of harmonic components is a prerequisite for addressing resonance risks in new energy power stations. Traditional Variational Modal decomposition (VMD) is susceptible to the influence of the modal decomposition order K and the penalty factor α when decomposing harmonic signals. This paper proposes an adaptive parameter selection method for VMD based on an improved Triangular Topology Aggregation Optimization (TTAO) algorithm. Firstly, the pre-set parameters of variational modal decomposition—modal order K and penalty factor α—exhibit strong coupling. Conventional optimization algorithms cannot effectively coordinate adjustments to both parameters. This paper employs an enhanced TTAO algorithm, whose triangular topology unit structure and dual aggregation mechanism enable simultaneous adjustment of modal order K and penalty factor α, effectively resolving their coupled optimization challenge. Using minimum envelope entropy as the fitness function, the algorithm obtains an optimized parameter combination for VMD to decompose the signal. Subsequently, dominant modal components are selected based on Pearson’s correlation coefficients for reconstruction, with harmonic parameters precisely identified using the Prony algorithm. Simulation results demonstrate that under a 20 dB noise environment, the proposed method achieves a signal-to-noise ratio (SNR) of 25.6952 for steady-state harmonics, with a root mean square error (RMSE) of 0.4889. The mean errors for frequency and amplitude identification are 0.055% and 3.085%, respectively, significantly outperforming methods such as PSO-VMD and EMD. Moreover, the runtime of our model is markedly shorter than that of the PSO-VMD algorithm, effectively resolving the symmetric trade-off between recognition accuracy and runtime inherent in variational modal decomposition. Full article

Roof water inrush is a major hazard threatening coal mine safety. This paper addresses the risk of roof water inrush during mining in the shallow-buried Jurassic coalfield of Northern Shaanxi, taking the Guojiawan Coal Mine as a case study. A systematic framework of “identification of main controlling factors–coupling of subjective and objective weighting–GIS-based spatial evaluation” is proposed. An integrated weighting system combining the Fuzzy Analytic Hierarchy Process (FAHP) and the Coefficient of Variation (CV) method is innovatively adopted. Four weight optimization models, including Linear Weighted Method, Multiplicative Synthesis Normalization Method, Minimum Information Entropy Method, and Game Theory Method, are introduced to evaluate 10 main controlling factors, including the fault strength index and sand–mud ratio. The results indicate that the GIS-based vulnerability evaluation model using the Multiplicative Synthesis Normalization Method achieves the highest accuracy, with a Spearman correlation coefficient of 0.9961. This model effectively enables five-level risk zoning and accurately identifies high-risk areas. The evaluation system and zoning results developed in this paper can provide a direct scientific basis for the design of water prevention engineering and precise countermeasures in the Guojiawan Coal Mine and other mining areas with similar geological conditions. Full article

The optimal allocation of funds within a portfolio is a central research focus in finance. Conventional mean-variance models often concentrate a significant portion of funds in a limited number of high-risk assets. To promote diversification, Shannon Entropy is widely applied. This paper develops a portfolio optimization model that incorporates Shannon Entropy alongside a risk diversification principle aimed at minimizing the maximum individual asset risk. The study combines empirical analysis with numerical simulations. First, empirical data are used to assess the theoretical model’s effectiveness and practicality. Second, numerical simulations are conducted to analyze portfolio performance under extreme market scenarios. Specifically, the numerical results indicate that for fixed values of the risk balance coefficient and minimum expected return, the optimal portfolios and their return distributions are similar when the risk is measured by standard deviation, absolute deviation, or standard lower semi-deviation. This suggests that the model exhibits robustness to variations in the risk function, providing a relatively stable investment strategy. Full article

CoCrNi medium-entropy alloys (MEAs) have emerged as a promising class of structural materials due to their exceptional strength–ductility synergy. However, the lack of composition-dependent predictive models severely hinders rational alloy design, forcing reliance on costly trial-and-error experimentation. This study develops a comprehensive theoretical model to predict the yield strength of single-phase face-centered-cubic (FCC) Co_(1-x-y)Cr_yNi_x MEAs by quantitatively evaluating the contributions of grain boundary and solid solution strengthening. The model demonstrates that increasing Cr content significantly enhances grain boundary strengthening through elevated shear modulus and Peierls stress, whereas Ni has a minimal effect. Solid solution strengthening, determined by the minimum resistance among Co–Cr, Co–Ni, and Cr–Ni atomic pairs, peaks at 1726.21 MPa for the composition Co₁₇Cr₆₄Ni₁₉. For equiatomic CoCrNi, theoretical yield strengths range from 1287.8 to 1575.4 MPa across grain sizes of 0.5–50 µm, showing excellent agreement with experimental results. This work provides a reliable, composition-dependent predictive framework that surpasses traditional trial-and-error methods, enabling efficient design of high-strength MEAs through targeted control of lattice distortion and elemental interactions. Full article

Hydrophilic interaction liquid chromatography (HILIC) is widely used for the analysis of glycans and oligosaccharides, yet the molecular basis of retention remains incompletely understood. In this study, we investigated dextran ladders labelled with 2-aminobenzamide (2-AB) and Rapifluor-MS™ (Waters, Milford, MA, USA) across a wide range of degrees of polymerization (DP 2–15), temperature conditions (10 °C to 70 °C), and gradient programs using a Acquity™ Premier Glycan BEH Amide column (Bridged Ethylene Hybrid, Waters, Milford, MA, USA). Van’t Hoff analysis revealed distinct enthalpic and entropic contributions to retention, allowing identification of a mechanistic transition from enthalpy-dominated docking interactions at low DP to entropy-driven dynamic adsorption at higher DP. This transition occurred reproducibly between DP 4–6, depending on the fluorescent label, while gradient steepness primarily influenced the location of the minimum enthalpy. Molecular dynamics simulations provided additional evidence, showing increased conformational flexibility and end-to-end distance variability for longer oligomers. This finding is consistent with entropy-dominated adsorption accompanied by displacement of structured interfacial water. Together, these results establish a molecular-level framework linking retention thermodynamics, conformational behavior, and solvation effects, thereby advancing our mechanistic understanding of glycan separation in HILIC. Full article

Background: In lung cancer, the occurrence of brain metastasis (BM) is closely associated with the heterogeneity of the primary lung tumor. This study aimed to develop a habitat-based radiomics model using enhanced computed tomography (CT) lung imaging to predict the risk of BM in patients with non-small cell lung cancer (NSCLC). Methods: A retrospective cohort of 475 patients with NSCLC who underwent enhanced CT of the lungs prior to anti-tumor treatment was analyzed. Volumetric CT images were segmented into tumor subregions via k-means clustering based on voxel intensity and entropy values. Radiomics features were extracted from these subregions, and predictive features were selected using minimum redundancy maximum relevance and least absolute shrinkage and selection operator regression. Two logistic regression models were constructed: a whole-tumor radiomics model and a habitat-based model integrating subregional heterogeneity. Model performance was evaluated via receiver operating characteristic analysis and compared via DeLong’s test. Results: A total of 195 eligible patients with NSCLC were included. The volume of interest of the whole tumor was clustered into three subregions based on voxel intensity and entropy values. In the training cohort (n = 138), the areas under the curve of the clinical model, the whole-tumor model and the habitat-based model were 0.639 (95% confidence interval [CI]: 0.543–0.731), the whole-tumor model and the habitat-based model were 0.728 (95% confidence interval [CI]: 0.645–0.812) and 0.819 (95% CI: 0.744–0.894), respectively. The habitat-based model demonstrated superior predictive performance compared with the whole-tumor model (p = 0.022). Conclusions: The habitat-based radiomics model outperformed the whole-tumor model in terms of predicting BM, highlighting the importance of subregional tumor heterogeneity analysis. Full article

Coal-free zones, particularly scouring zones, reduce recoverable reserves and increase water inrush risk in coal mining. Existing sedimentological, geophysical, and geostatistical methods are often constrained by coring conditions, lithological interpretation accuracy, and geological complexity. Given that well log signals provide the most continuous carriers of geological information, this study integrates Singular Spectrum Analysis (SSA), Maximum Entropy Spectral Analysis (MESA), and Integrated Prediction Error Filter Analysis (INPEFA) to establish a multi-curve framework for analyzing the genesis and logging responses of coal-free zones. A two-stage SSA workflow was applied for noise reduction, and a Trend–Fluctuation Composite (TFC) curve was constructed to enhance depositional rhythm detection. The minimum singular value order (N), naturally derived from SSA-decomposed INPEFA curves, emerged as a quantitative indicator of mine water inrush risk. The results indicate that coal-free zones resulted from inhibited peat-swamp development followed by fluvial scouring and are characterized by dense inflection points and frequent cyclic fluctuations in TFC curves, together with the absence of low anomalies in natural gamma-ray logs. By integrating multi-curve logs, core data, and in-mine three-dimensional direct-current resistivity surveys, the genetic mechanisms and boundaries of coal-free zones were effectively delineated. The proposed framework enhances logging-based stratigraphic interpretation and provides practical support for working face layout and mine water hazard prevention. Full article

The traditional multi-attribute group decision-making (MAGDM) method easily ignores the interference effect among decision-makers (DMs), while quantum theory can effectively portray the uncertainty in the decision-making process and quantify the preference interference among DMs. The asymmetry of evaluation information in social networks can have a significant impact on decision-making. In this paper, a quantum MAGDM algorithm based on probabilistic linguistic term sets (PLTSs) and a quantum-like Bayesian network (QLBN) is proposed (PL-QLBN), utilizing quantum theory and social network concepts and introducing a novel method for calculating interference effects based on belief entropy. Firstly, a complete trust network is constructed based on the probabilistic linguistic trust transfer operator and the minimum path method. A trust aggregation method, considering interference effects, is proposed for the QLBN to determine the DM weights. Next, the attribute weights are calculated based on the entropy weight method. Then, a probabilistic linguistic MAGDM considering interference effects is proposed based on the QLBN. Finally, the feasibility and validity of the provided method are verified through Hefei City’s selection of wastewater reuse alternatives. Full article

Gas disasters in coal mines are the principal constraint on safe operations; accordingly, accurate gas time-series forecasting and real-time fluctuation monitoring are essential for prevention and early warning. A method termed Decomposition-Enhanced Cross-Graph Forecasting and Anomaly Finding is proposed. The Multi-Variate Variational Mode Decomposition (MVMD) algorithm is refined by integrating wavelet denoising with an Entropy Weight Method (EWM) multi-index scheme (seven indicators, including SNR and PSNR; weight-solver error ≤ 0.001, defined as the maximum absolute change between successive weight vectors in the entropy-weight iteration). Through this optimisation, the decomposition parameters are selected as K = 4 (modes) and $\alpha$ = 1000, yielding effective noise reduction on 83,970 multi-channel records from longwall faces; after joint denoising, SSIM reaches 0.9849, representing an improvement of 0.5%–18.7% over standalone wavelet denoising. An interpretable Cross Interaction Refinement Graph Neural Network (CrossGNN) is then constructed. Shapley analysis is employed to quantify feature contributions; the m1t2 gas component attains a SHAP value of 0.025, which is 5.8× that of the wind-speed sensor. For multi-timestep prediction (T0–T2), the model achieves MAE = 0.008705754 and MSE = 0.000242083, which are 8.7% and 12.7% lower, respectively, than those of STGNN and MTGNN. For fluctuation detection, Pruned Exact Linear Time (PELT) with minimum segment length L_min = 58 is combined with a circular block bootstrap test to identify sudden-growth and high-fluctuation segments while controlling FDR = 0.10. Hasse diagrams are further used to elucidate dominance relations among components (e.g., m3t3, the third decomposed component of the T2 gas sensor). Field data analyses substantiate the effectiveness of the approach and provide technical guidance for the intellectualisation of coal-mine safety management. Full article