Dynamic regulation-based stabilizing mutations are highly effective for designing RSV pre-fusion F mRNA vaccines.

ABSTRACT The Probability Integral Method (PIM) is a widely used technique for predicting surface displacements caused by underground mineral extraction. Precisely inverting the physical parameters of the PIM is essential to ensure the accuracy of displacement prediction. In recent years, it has become a promising way to invert the PIM parameters using interferometric synthetic aperture radar (InSAR) observations. However, nearly all existing studies invert the PIM parameters using deterministic nonlinear optimization methods, resulting in local optimal solutions and difficulty in quantitative uncertainty assessment. This letter proposes a new probabilistic method for inverting the PIM parameters from InSAR observations. A Bayesian inference was adopted to reduce the probability of obtaining the local optimal solutions. By incorporating prior knowledge, the Bayesian inversion framework with a multi-chain Markov chain Monte Carlo algorithm was then used to invert the PIM parameters and quantitatively assess the uncertainties of the parameter estimates. Simulations and real data tests indicate that the accuracy of the PIM parameters inverted by the proposed method is on average 83% and 29% higher than the parameters estimated by a conventional genetic algorithm and a single-chain Bayesian inversion algorithm.

• Digital Twin integrates IoT quantum sensors for adaptive greenhouse lighting. • Real-time DT interactions enables energy-efficient and responsive light control. • RL optimizes controller gains under multi-zone variable PPFD and light conditions. • Energy use and electricity costs reduced versus baseline on/off strategy. • GA and RL compared over 12 adaptive control strategies in real greenhouse data. Greenhouse lighting is vital for plant growth and contributes to nearly 30% of operational costs. However, managing lighting in response to dynamic sunlight conditions and varying photosynthetic photon flux density (PPFD) requirements across crop types remains a major challenge, which results in excessive energy use. This paper presents a digital twin (DT) adaptive control framework for greenhouse lighting, leveraging quantum sensors and reinforcement learning (RL) to enable energy-efficient, multi-zone operation. The proposed system dynamically adjusts multi light-emitting diode (LED) intensities in the extended Photosynthetically Active Radiation spectrum to satisfy uniform PPFD thresholds (single-crop scenario) or differentiated PPFD thresholds (multi-crop scenario). A set of 12 adaptive control strategies was evaluated, employing Genetic Algorithm (GA) and RL optimizers to configure proportional–integral-derivative (PID) controller parameters as well as their PI and P subsets. Real world validation demonstrates that the RL-based PI control with shared coefficients (RL-PI (Eq)) delivers the most robust performance across scenarios, achieving an average mean error of 1.248 μ mol s − 1 m − 2 with a standard deviation of 10.661 μ mol s − 1 m − 2 . Compared to a baseline on–off controller, the proposed strategy reduces electrical energy consumption by 23.6% and energy-related costs by 23.2%, while maintaining precise PPFD regulation. These findings highlight the potential of DT adaptive control systems to advance sustainable, cost-effective, and scalable multi crop greenhouse lighting management.

The EU-funded MARES project aims to develop a novel design of superconducting generator integrated into a wave energy converter (WEC). The superconducting generator consists of two parts, one assembled using REBCO tapes and the other using magnesium diboride (MgB₂) wires, with the latter offering a more cost-effective alternative. Assessing the optimal design of MgB₂ wires for this application is not straightforward, since AC losses represent a major technical issue of this device. In this work, a genetic algorithm is applied to determine the optimal design of the MgB₂ wires. The algorithm is coupled with a finite element method (FEM) electrodynamic model that computes the AC losses in the MgB₂ wires. The goal of the algorithm is to identify the key parameters of the wire configuration (filaments number and diameter, fill-factor, wire diameter, twist pitch) minimizing the AC losses in the device operating conditions.

Fracture analysis and lightweight optimization of an ultra-large scale anvil

Optimizing facility location and inventory management in food supply chains is essential for reducing costs, prevent spoilage of perishable products, and ensuring equitable food distribution across rural and urban districts. To deal with this issue, this paper proposes a comprehensive model for the integrated optimization of facility location and inventory management within a three-tier hierarchical hub network architecture. The network topology is a complete-star-star structure, with fully interconnected central hub nodes at the highest level. The intermediate and lowest tiers consist of star-shaped subnetworks, where end nodes, including manufacturers, connect to non-central hubs. Given the NP-complete nature of the problem, we propose a hybrid algorithm combining an exact solution with a meta -heuristic genetic algorithm. These algorithms are implemented in GAMS and MATLAB software. Sensitivity analysis is conducted on model’s parameters. The results show that decreasing the costs of establishing the hub by more than 75% increases the number of median hubs. Production quantity and inventory levels remain steady with cost variations up to −50%, but decrease with production cost increases up to 50%, where inventory levels drop to zero

A MILP and a genetic algorithm for the flying sidekick TSP with variable drone speeds

We have studied the optimal shape design of compact REBCO magnets using a distributed genetic algorithm (DGA). The proposed optimization method is very useful for shape design of compact MRI or NMR magnets. In REBCO magnets, a screening-current-induced magnetic field (SCMF) occurs due to the shape of the tape, and the negative effects of this SCMF become much more pronounced as the coil size becomes smaller. In general, the SCMF of REBCO magnets can be accurately calculated using a 2D or 3D finite element method (FEM). However, it is difficult to combine the FEM calculation with the DGA method. Therefore, it is necessary to propose a method for calculating the SCMF of REBCO magnets applicable to the DGA method. In this study, we propose a simplified calculation method for the SCMF of REBCO magnets based on an electric circuit model that can be applied to the DGA method. In the proposed simplified calculation method, the pancake-shaped REBCO coil is converted into a circular sheet with each turn, and the screening current induced in the coil is calculated by considering the self-inductance and mutual inductance of each sheet. In the width of the REBCO wire is also divided into multiple equivalent electrical circuits. Although the SCMF values ​​obtained by the proposed simplified calculation method did not completely match the results calculated by FEM, the calculation accuracy was sufficient to design the basic shape of a compact REBCO magnet taking into account the effects of the magnetic field due to the screening current.

Design and experimental evaluation of an omnidirectional leveling system for crawler orchard working platforms

This study presents an optimization approach for interventional micro-axial blood pumps, aimed at addressing the rising burden of cardiovascular diseases and the limited availability of heart transplant donors in China. The methodology integrates design of experiments, computational fluid dynamics (CFD) analysis, and response surface methodology (RSM), with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) multi-objective genetic algorithm employed to optimize impeller geometry. The primary objectives were to enhance hydraulic efficiency and improve hemocompatibility. The optimized blood pump model demonstrated notable performance improvements across various flow conditions: at a low flow rate of 1 L/min, the pump head increased by 2.97% and the hemolysis index decreased by 12.04%; at the design point, head improved by 5.22% while hemolysis was reduced by 11.71%; under high-flow conditions (4 L/min), head increased by 8.5% and hemolysis decreased by 12.57%. These enhancements contribute to higher energy efficiency and lower hemolytic risk, thereby improving the safety and reliability of the device in clinical applications. The findings provide a robust foundation for future advancements in blood pump design and optimization.

Adaptive active decoding and novel disjunctive graph-based improved genetic algorithm for multi-type machine robot cell scheduling in mass customization

Multi-dimensional characterization and ensemble genetic algorithm–monte carlo simulation assisted multi-objective optimization of surface integrity in intelligent machining

Dynamic airspace configuration through genetic algorithm optimization using air traffic complexity

Hybrid genetic algorithm and deep reinforcement learning for autonomous multi-mode satellite task scheduling

Geomechanics-motivated discovery of three-dimensional coupled governing equations via physics-informed symbolic genetic algorithms

A multiscale modeling approach to study the role of mechanics and inflammation in the pathophysiology of articular cartilage.

ABSTRACT This paper outlines a hybrid experimental and computational model to assess and optimize the mechanical behavior of sustainable concrete with silica fume (SF), waste glass powder (WGP), and crumb rubber (CR). Compared to current research, which mainly concentrates on individual or binary systems, this paper explores a ternary blended system and characterizes the intricate interactions among mix parameters with advanced machine learning methods. To establish compressive, flexural and tensile strengths of 7, 28 and 56 days, experimental investigations were carried out. The findings suggest that SF and WGP increase strength because of pozzolanic reactivity and refinement of microstructure, and CR decreases strength because of the weaker interfacial bonding. Several machine learning models, such as Random Forest, XGBoost, Support Vector Machine, Decision Tree, and Ensemble, were built on the basis of strength prediction with the highest level of accuracy being XGBoost and Ensemble (R2 > 0.88). Moreover, optimization with the help of Genetic Algorithms was used to determine the optimal mix proportions leading to the increase in compressive, flexural, and tensile strengths by 34%, 37%, and 46%, respectively. The proposed framework offers a sound and data-driven methodology of designing high-performance and environmentally friendly concrete mixtures.

ABSTRACT Rapid urban development and climate change reveal limits of traditional construction, highlighting a need to integrate adaptive and innovative technologies in architecture. A systematic review of sustainable projects and technologies connects architectural strategies with remote sensing data. Case studies, including Malakoff, Tianbao, Roskilde, and Macau, assess spatial, structural, and functional flexibility to evaluate adaptive, energy-efficient solutions and long-term environmental performance. Key principles of adaptive design are highlighted, encompassing genetic algorithms, parametric methods, machine learning, intelligent and bio-inspired systems, AI-based technologies such as micro-GAs (exemplified by a building façade in Boston, U.S.A.), evolutionary computation, artificial neural networks, generative design, ML algorithms, digital twins for urban forecasting, a bio-inspired ventilation system at the Eastgate Centre in Harare, Zimbabwe, and energy-efficient façade and spatial systems at the Vancouver Convention Centre West in Canada, while factors limiting widespread application of these intelligent technologies are identified. Achieving sustainable architecture goals requires a comprehensive approach integrating intelligent and adaptive systems tailored to specific conditions, thereby creating harmonious buildings that meet societal demands and support environmental sustainability.

Beyond block time: a head-to-head comparison of reinforcement learning, genetic algorithms, and predict-then-optimize scheduling for operating room workflow using discrete-event simulation.

This study investigates the creation of eco-efficient gypsum plaster composites that integrate untreated textile waste (TW) with a hybrid experimental-computational methodology that combines machine learning (ML) prediction with multi-objective metaheuristic optimization. Prismatic specimens of 40 × 40 × 160 mm were fabricated with TW concentrations varying from 0% to 1% and water-to-plaster (W/P) ratios between 0.55 and 0.70. The composites were evaluated for rheological parameters (initial and final setting times, spreadability), durability (capillary absorption), mechanical performance (compressive strength [CS] and flexural strength [FS]), and thermal conductivity (TC). The results indicated that TW markedly affected plaster performance: a 0.75% TW addition produced the maximum compressive strength (11.67 MPa) and flexural strength (4.17 MPa), while thermal conductivity reduced from 0.20 to 0.15 W/m·K, hence improving thermal insulation. Nonetheless, workability was diminished—spreadability decreased from 210 mm to 130 mm, and initial setting time reduced from 7 to 3 min—underscoring a trade-off. A deep neural network enhanced by the Improved Grey Wolf Optimizer (DNN–IGWO) attained superior prediction accuracy (R² > 0.95), proficiently simulating nonlinear relationships between TW and W/P ratios. A genetic algorithm (GA) produced a Pareto front of 71 non-dominated solutions, optimizing strength, thermal performance, and workability. Optimal formulations were achieved at W/P ratios of 0.55–0.65 and TW levels of 0.25–1 wt%, enabling the development of high-performance, sustainable gypsum composites derived from industrial textile by-products. The findings support the incorporation of recycled textiles in construction and illustrate how data-driven optimization can inform the advancement of sustainable gypsum-based materials utilizing industrial by-products.