Search Results (497)

Leaf pigment composition and concentration are crucial indicators of plant physiological status, photosynthetic capacity, and overall ecosystem health. While spectroscopy techniques show promise for monitoring vegetation growth, phenology, and stress, accurately estimating leaf pigments remains challenging due to the complex reflectance properties across diverse tree species. This study introduces a novel approach using a two-dimensional convolutional neural network (2D-CNN) coupled with a genetic algorithm (GA) to predict leaf pigment content, including chlorophyll a and b content (C_ab), carotenoid content (C_ar), and anthocyanin content (C_anth). Leaf reflectance and biochemical content measurements taken from 28 tree species were used in this study. The reflectance spectra ranging from 400 nm to 800 nm were encoded as 2D matrices with different sizes to train the 2D-CNN and compared with the one-dimensional convolutional neural network (1D-CNN). The results show that the 2D-CNN model (nRMSE = 11.71–31.58%) achieved higher accuracy than the 1D-CNN model (nRMSE = 12.79–55.34%) in predicting leaf pigment contents. For the 2D-CNN models, C_ab achieved the best estimation accuracy with an nRMSE value of 11.71% (R² = 0.92, RMSE = 6.10 µg/cm²), followed by C_ar (R² = 0.84, RMSE = 1.03 µg/cm², nRMSE = 12.29%) and C_anth (R² = 0.89, RMSE = 0.35 µg/cm², nRMSE = 31.58%). Both 1D-CNN and 2D-CNN models coupled with GA using a subset of the spectrum produced higher prediction accuracy in all pigments than those using the full spectrum. Additionally, the generalization of 2D-CNN is higher than that of 1D-CNN. This study highlights the potential of 2D-CNN approaches for accurate prediction of leaf pigment content from spectral reflectance data, offering a promising tool for advanced vegetation monitoring. Full article

Rapid access to defibrillators, blood products, and time-critical medicines can improve survival, yet urban congestion and fragmented infrastructure delay deliveries. We present and evaluate an end-to-end framework for beyond-visual-line-of-sight (BVLOS) UAV logistics in Essex (UK), integrating (I) strategic depot placement, (II) a hybrid obstacle-aware route planner, and (III) a time-window-aware (TWA) Mixed-Integer Linear Programming (MILP) scheduler coupled to a battery/temperature feasibility model. Four global planners—Ant Colony Optimisation (ACO), Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Rapidly Exploring Random Tree* (RRT*)—are paired with lightweight local refiners, Simulated Annealing (SA) and Adaptive Large-Neighbourhood Search (ALNS). Benchmarks over 12 destinations used real Civil Aviation Authority no-fly zones and energy constraints. RRT*-based hybrids delivered the shortest mean paths: RRT* + SA and RRT* + ALNS tied for the best average length, while RRT* + SA also achieved the co-lowest runtime at $v=60\mathrm{km}{\mathrm{h}}^{-1}$. The TWA-MILP reached proven optimality in 0.11 s, showing that a minimum of seven UAVs are required to satisfy all 20–30 min delivery windows in a single wave; a rolling demand of one request every 15 min can be sustained with three UAVs if each sortie (including service/recharge) completes within 45 min. To validate against a state-of-the-art operations-research baseline, we also implemented a Vehicle Routing Problem with Time Windows (VRPTW) in Google OR-Tools, confirming that our hybrid planners generate competitive or shorter NFZ-aware routes in complex corridors. Digital-twin validation in AirborneSIM confirmed CAP 722-compliant, flyable trajectories under wind and sensor noise. By hybridising a fast, probabilistically complete sampler (RRT*) with a sub-second refiner (SA/ALNS) and embedding energy-aware scheduling, the framework offers an actionable blueprint for emergency medical UAV networks. Full article

Urban water distribution systems, as integral parts of underground pipeline networks, face challenges from aging infrastructure, operational demands, and transient pressure surges that can compromise structural integrity and service reliability. This work introduces a cost-oriented multi-objective design framework that explicitly accounts for both the likelihood of fire flow failure and the risks posed by transient pressures. The approach links a probabilistic reliability model with a transient pressure evaluation module, and couples both within a non-dominated sorting genetic algorithm to generate Pareto-optimal design solutions. Design solutions are constrained to maintain transient pressures within permissible limits, ensuring enhanced pipeline safety while optimizing capital costs. Case studies show that adopting a minimum 150 mm distribution main improves fire flow capacity and reduces transient-induced failure risks. The proposed method provides a predictive, computational tool that can be integrated into digital twin environments, supporting sustainable infrastructure planning, long-term monitoring, and proactive maintenance for resilient urban water supply systems. Full article

The joint is the weak point of HV (high voltage) cable insulation systems; creep discharge between insulation layers of the cable joint, due to moisture intrusion, is one of the main defects leading to single-phase grounding. Carbonization on the insulation interface after creep discharge would lead to a short-circuit defect in the sheath loops and result in abnormal sheath current. In this study, a novel diagnostic criterion using the phasor difference of sheath currents at both ends of the same circuit is proposed. The coupling effect between the sheath and the conductor under defect conditions is considered, and the original lumped parameter model of the cable circuit is optimized. The cable parameters are further corrected using a genetic algorithm. The diagnostic criterion comprehensively accounts for the adverse effects of unequal cable segment lengths, load current fluctuations, grounding impedance, and phase voltage variations. When the phase angle fluctuation of the phasor difference is within 10° and the defect impedance is below 100 Ω, the defective joint can be accurately diagnosed by this method. The conclusion has been validated through PSCAD simulations, with a diagnostic accuracy above 97%. Even under 20 dB noise interference, the error increase remains within 2%. Full article

With the development of active magnetic bearings (AMBs) toward higher speeds, understanding high-speed rotor dynamics has become a crucial focus in AMB research. Traditional finite element modeling (FEM) methods, however, are unable to rapidly and comprehensively uncover the complex interplay between controller parameters and dynamic behavior. To address this limitation, a surrogate modeling approach based on a hybrid gated recurrent unit–Kolmogorov–Arnold network (GRU-KAN) is introduced to mathematically capture the effects of coupled control gains on rotor dynamics. To enhance model generalization, a genetic algorithm-driven uniform design sampling strategy is also implemented. Comparative studies against support vector regression and Kriging surrogates indicate a higher coefficient of determination ($R^2=0.9887$) and lower residuals for the proposed approach. Experimental validation across multiple controller parameter combinations shows that the resulting machine learning surrogate predicts the critical speed with a mean absolute error of only 38.51 rpm and a mean absolute percentage error of $1.56\times {10}^{-1}\%$, while requiring merely $1.14\times {10}^{-4}$ s per evaluation—compared to 201 s for traditional FEM. These findings demonstrate the surrogate’s efficiency, accuracy, and comprehensive predictive capabilities, offering an effective method for rapid critical speed estimation in AMB–rotor systems. Full article

The rapid shift toward electric vehicles (EVs) has underscored the critical importance of battery pack crashworthiness, creating a demand for lightweight, energy-absorbing protective systems. This review systematically explores bio-inspired cellular structures as promising solutions for improving the impact resistance of EV battery packs. Inspired by natural geometries, these designs exhibit superior energy absorption, controlled deformation behavior, and high structural efficiency compared to conventional configurations. A comprehensive analysis of experimental, numerical, and theoretical studies published up to mid-2025 was conducted, with emphasis on design strategies, optimization techniques, and performance under diverse loading conditions. Findings show that auxetic, honeycomb, and hierarchical multi-cell architectures can markedly enhance specific energy absorption and deformation control, with improvements often exceeding 100% over traditional structures. Finite element analyses highlight their ability to achieve controlled deformation and efficient energy dissipation, while optimization strategies, including machine learning, genetic algorithms, and multi-objective approaches, enable effective trade-offs between energy absorption, weight reduction, and manufacturability. Persistent challenges remain in structural optimization, overreliance on numerical simulations with limited experimental validation, and narrow focus on a few bio-inspired geometries and thermo-electro-mechanical coupling, for which engineering solutions are proposed. The review concludes with future research directions focused on geometric optimization, multi-physics modeling, and industrial integration strategies. Collectively, this work provides a comprehensive framework for advancing next-generation crashworthy battery pack designs that integrate safety, performance, and sustainability in electric mobility. Full article

Tool structure design methodologies predominantly rely on trial-and-error approaches or single-objective optimization but fail to achieve coordinated enhancement of multiple performance metrics while lacking thorough investigation into complex cutting coupling mechanisms. This study proposes a multi-objective optimization framework integrating joint simulation approaches. First, a finite element model for orthogonal turning was developed, incorporating the hyperbolic tangent (TANH) constitutive model and variable coefficient friction model. The cutting performance of four micro-groove configurations is comparatively analyzed. Subsequently, parametric modeling coupled with simulation–data interaction enables multi-objective optimization targeting minimized cutting force, reduced cutting temperature, and decreased wear rate. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) explores Pareto-optimized solutions for arc micro-groove geometric parameters. Finally, optimized tools manufactured via powder metallurgy undergo experimental validation. The results demonstrate that the optimized tool achieves significant improvements: a 19.3% reduction in cutting force, a 14.2% decrease in cutting temperature, and tool life extended by 33.3% compared to baseline tools. Enhanced chip control is evidenced by an 11.4% reduction in chip curl radius, accompanied by diminished oxidation/adhesive wear and superior surface finish. This multi-objective optimization methodology effectively overcomes the constraints of conventional single-parameter optimization, substantially improving comprehensive tool performance while establishing a reference paradigm for cutting tool design under complex operational conditions. Full article

Effective containment of contaminant plumes in heterogeneous aquifers is critically challenged by the inherent uncertainty in hydraulic conductivity (K). Conventional, deterministic optimization approaches for pump-and-treat (P&T) system design often fail when confronted with real-world geological variability. This study proposes a novel robust simulation-optimization framework to design reliable hydraulic containment systems that explicitly account for this subsurface uncertainty. The framework integrates the Karhunen–Loève Expansion (KLE) for efficient stochastic representation of heterogeneous K-fields with a Genetic Algorithm (GA) implemented via the pymoo library, coupled with the MODFLOW groundwater flow model for physics-based performance evaluation. The core innovation lies in a multi-scenario assessment process, where candidate well configurations (locations and pumping rates) are evaluated against an ensemble of K-field realizations generated by KLE. This approach shifts the design objective from optimality under a single scenario to robustness across a spectrum of plausible subsurface conditions. A structured three-step filtering method—based on mean performance, consistency (pass rate), and stability (low variability)—is employed to identify the most reliable solutions. The framework’s effectiveness is demonstrated through a numerical case study. Results confirm that deterministic designs are highly sensitive to the specific K-field realization. In contrast, the robust framework successfully identifies well configurations that maintain a high and stable containment performance across diverse K-field scenarios, effectively mitigating the risk of failure associated with single-scenario designs. Furthermore, the analysis reveals how varying degrees of aquifer heterogeneity influence both the required operational cost and the attainable level of robustness. This systematic approach provides decision-makers with a practical and reliable strategy for designing cost-effective P&T systems that are resilient to geological uncertainty, offering significant advantages over traditional methods for contaminated site remediation. Full article

This study, inspired by the impact resistance of the pistol shrimp’s predatory claw, investigates the design and optimization of bionic energy absorption structures. Four types of bionic hierarchical porous metamaterial lattice structures with a negative Poisson’s ratio were developed based on the microstructure of the pistol shrimp’s fixed claw. These structures were validated through finite element models and quasi-static compression tests. Results showed that each structure exhibited distinct advantages and shortcomings in specific evaluation indices. To address these limitations, four new bionic structures were designed by coupling the characteristics of the original structures. The coupled structures demonstrated a superior balance across various performance indicators, with the EOS (Eight pillars Orthogonal with Side connectors on square frame) structure showing the most promising results. To further enhance the EOS structure, a parametric study was conducted on the distance d from the edge line to the curve vertex and the length-to-width ratio y of the negative Poisson’s ratio structure beam. A fifth-order polynomial surrogate model was constructed to predict the Specific Energy Absorption (SEA), Crush Force Efficiency (CFE), and Undulation of Load-Carrying fluctuation (ULC) of the EOS structure. A multi-objective genetic algorithm was employed to optimize these three key performance indicators, achieving improvements of 1.98% in SEA, 2.42% in CFE, and 2.05% in ULC. This study provides a theoretical basis for the development of high-performance biomimetic energy absorption structures and demonstrates the effectiveness of coupling design with optimization algorithms to enhance structural performance. Full article

Since the hydrogen-production process is not yet fully efficient, this paper proposes a poly-generation system that is driven by a geothermal energy source and utilizes a combined Kalina/organic Rankine cycle coupled with an electrolyzer unit to produce, simultaneously, power and green hydrogen in an efficient way. A comprehensive thermodynamic analysis and an exergetic evaluation are carried out to assess the effect of key system parameters (geothermal temperature, high pressure, ammonia–water concentration ratio, and terminal thermal difference) on the performance of concurrent production of power and green hydrogen. Thereby, two configurations are investigated with/without the separation of turbines. The optimal ammonia mass fraction of the basic solution in KC is identified, which leads to an overall optimal system performance in terms of exergy efficiency and green hydrogen production rate. In both configurations, the optimal evaluation is made possible by conducting a genetic algorithm optimization. The simulation results without/with the separation of turbines demonstrate the potential of the suggested cycle combination and emphasize its effectiveness and efficiency. Exemplary, for the case without the separation of turbines, it turns out that the combination of ammonia–water and MD2M provides the best performance with net power of 1470 kW, energy efficiency of 0.1184, and exergy efficiency of 0.1258 while producing a significant green hydrogen amount of 620.17 kg/day. Finally, an economic study allows to determine the total investment and payback time of $3,342,000 and 5.37 years, respectively. The levelized cost of hydrogen (LCOH) for the proposed system is estimated at 3.007 USD/kg H₂, aligning well with values reported in the literature. Full article

A novel high-temperature-resistant W-B₄C-poly(4-methyl-1-pentene) (PMP) composite shielding material against neutron and gamma rays was developed and fabricated. Firstly, utilizing the ²³⁵U-induced fission spectrum as the source term, the compositional ratio of the W-B₄C-PMP ternary composite was optimized using the genetic algorithm-based GENOCOPIII program coupled with MCNP simulations. Then, the composite was fabricated through coupling agent modification, melt mixing, and hot pressing. Finally, the effects of coupling modification and tungsten content on the thermomechanical properties of the composite were investigated. Results demonstrated that functional groups from the silane coupling agent KH550 were successfully grafted onto the filler surfaces. For composites containing 30 wt% modified B₄C and 40 wt% modified W in the PMP matrix, the heat deflection temperature (HDT) increased by 18.5% and 19.1%, respectively, compared to their unmodified counterparts. The impact strength also improved by 31.6% and 5.0%, respectively. The variation trend of the composite’s modulus approximately followed the classical Einstein model, while its tensile strength and flexural strength conformed precisely to the model: ${\displaystyle \frac{{\mathsf{\sigma }}_{\mathrm{c}}}{{\mathsf{\sigma }}_{\mathrm{m}}}}=0.88{\mathrm{V}}_{\mathrm{f}}^{-0.02}$. Thermal analysis indicated that the composites possessed a melting point exceeding 230 °C, and their thermal stability improved with increasing filler content. Full article

Marine noise pollution is a significant threat to global marine ecosystems and human activities. Most underwater sound-absorbing materials operate in the mid-to high-frequency bands (typically 1–10 kHz for mid-frequency and above 10 kHz for high-frequency), and current underwater low-frequency sound absorption performance remains unsatisfactory, with large structural sizes. To address these issues, a novel metasurface composed of a hexagonal Helmholtz resonator structure made of rubber and metal, combined with an embedded rough neck, is proposed. By introducing roughness into the neck of the Helmholtz resonator, this structure effectively provides the necessary acoustic impedance for low-frequency sound absorption without changing the overall size, thus lowering the resonance frequency. The finite element method is used for simulation, and theoretical validation is performed. The results show that the Helmholtz resonator with the rough neck achieves near-perfect acoustic absorption at a deep subwavelength scale at 81 Hz. At the absorption peak, the wavelength of the sound wave is 370 times the thickness of the resonator. By coupling seven absorption units and optimizing the parameters using a genetic algorithm, the metasurface achieves an average absorption coefficient greater than 0.9 in the 60 Hz to 260 Hz range. The complementary sound absorption coefficients of the unit cells at different frequency bands effectively broaden the absorption bandwidth. Full article

Background: Neoadjuvant chemotherapy (NAC) is the important and effective approach to treat locally advanced breast cancer (LABC). The prediction of response to NAC prior to start is an efficient approach to obtaining perspective about the effectiveness of treatment. The objective of this study is to design a machine learning pipeline to predict tumor response to NAC treatment for patients with LABC using the combination of clinical features and radiomics computed tomography (CT) features. Method: A total of 858 clinical and radiomics CT features were determined for 117 patients with LABC to predict the tumor response to NAC treatment. Since the number of features is greater than the number of samples, dimensionality reduction is an indispensable step. To this end, we proposed a novel hybrid feature selection to not only select top features but also optimize the classifier hyperparameters. This hybrid feature selection has two phases. In the first phase, we applied a filter-based strategy feature selection technique using matrix rank theorem to remove all dependent and redundant features. In the second phase, we applied a genetic algorithm which coupled with the SVM classifier. The genetic algorithm determined the optimum number of features and top features. Performance of the proposed technique was assessed by balanced accuracy, accuracy, area under curve (AUC), and F1-score. This is the binary classification task to predict response to NAC. We consider three models for this study including clinical features, radiomics CT features, and a combination of clinical and radiomics CT features. Results: A total of 117 patients with LABC with a mean age of 52 ± 11 were studied in this study. Of these, 82 patients with LABC were the responder group (response to NAC) and 35 were the non-response group to chemotherapy. The best performance was obtained by the combination of clinical and CT radiomics features with Accuracy = 0.88. Conclusion: The results indicate that the combination of clinical features and CT radiomic features is an effective approach to predict response to NAC treatment for patients with LABC. Full article

To address concrete cracking in submerged box-shaped hollow thin-walled piers under static ice and hydrostatic pressure, this study proposes a composite strengthening method employing externally bonded steel plates coupled with concrete infill blocks. Based on mechanical theoretical derivation, the strengthened structure is simplified as a cooperative system comprising compression–truss and suspended-cable mechanisms. Key design parameters—including steel plate span, thickness, infill block height, and plate corner configuration—are optimized using a genetic algorithm. The optimization objective minimizes strengthening cost, subject to constraints of concrete crack resistance, steel plate strength, and deformation control, ultimately determining the numerically optimal composite strengthening solution. Validation through planar finite element models demonstrates that: (1) the proposed system effectively suppresses cracking in the original structure; (2) peak stresses in the steel plates remain below the yield strength of Q345 steel; and (3) the theoretical design is reasonable and effective, which can solve the cracking problem of the wading-tank hollow thin-walled pier under the action of surrounding load. Full article

Winding hot-spot temperature (HST) is a key factor affecting the insulation life of transformers. This paper proposes an optimization method based on the Kriging response surface model, which minimizes HST by adjusting the key structural parameters of the number of winding zones, vertical oil channel width, and horizontal oil channel height. First, a two-dimensional axisymmetric temperature–fluid field coupling model is established, and the finite volume method is used to solve the HST under the actual structure, which is 92.59 °C. A total of 50 sample datasets are designed using Latin hypercube sampling, and the whale optimization algorithm (WOA) is used to determine the optimal kernel parameters of Kriging with the goal of minimizing the root mean square error (RMSE) under 5-fold cross-validation. Combined with the genetic algorithm (GA) global optimization of structural parameters, the Kriging model predicts that the optimized HST is 89.77 °C, which is verified by simulation to be 89.79 °C, achieving a temperature drop of 2.80 °C, proving the effectiveness of the structural optimization method. Full article