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A high-gain transmitarray antenna with low radar cross section (RCS) properties is presented in this paper. Compared with conventional high-profile multilayer designs, we introduce a transmit-reflect selective metasurface integrated with high-gain transmission and random scattering functions, achieving a reduced thickness of 0.13 λ 0 (where λ 0 is the wavelength at the center frequency). For the meta-atom design, we combine geometric rotation and dimension optimization to realize 1-bit independent transmit and reflection phase modulation, respectively. Moreover, in metasurface coding strategy, we employ an initial phase optimization method and a simulated annealing algorithm to determine the optimal coding matrices. The experimental results demonstrate high-performance radiation characterized by the peak gain of 23.6 dB, maximum aperture efficiency of 28.5%, and 3 dB gain bandwidth of 18.4%. For x -polarization, measured 10 dB RCS reduction bandwidth under transverse magnetic (TM) 0°-45° and transverse electric (TE) 0°-20° incidence are 9.02–11.36 GHz. For y -polarization, 10 dB RCS reduction bandwidth under TM 0–60° and TE 0–30° incidence is 8.82–10.96 GHz. [ABSTRACT FROM AUTHOR]

The present study aimed at investigating and modelling the release behaviour of encapsulated d‐limonene from electrosprayed Alyssum homolocarpum seed gum (AHSG) nanoparticles. For this purpose, release profiles of d‐limonene from 10% or 20% loaded nanocapsules were obtained in deionised water and simulated mouth conditions by a spectrophotometric method. The experimental results showed that complete release takes ranging between 7 and 15 min, depending on the d‐limonene loading and type of release medium. A gradual increase in the flavour release rate over time without initial burst revealed there are several phenomena involved in the release process due to the hydrophilic nature of AHSG nanocapsules. Based on these findings, a mechanistic approach modelled flavour diffusion as a transport process of combined matrix swelling and erosion mechanisms. The model parameters were obtained via best fitting procedure applying simulated annealing (SA) algorithm. The good correlation between the predicted and experimental results of d‐limonene release confirmed validation of the developed model. The simulation results showed that although matrix erosion contributes more than diffusion process in d‐limonene release from AHSG nanocapsules, the model describing the release mechanism as only governed by the erosion is not able to provide accurate predictions of flavour release as compared to the coupled model. [ABSTRACT FROM AUTHOR]

The limit equilibrium method has been widely used in the study of searching the slip surface of slopes. However, the method ignores the deformation characteristics of the rock mass and assumes that the shape of the slip surface is circular, which is quite different from the actual situation of the slope. For this reason, this paper proposes a fast search method for noncircular slip surface considering the deformation characteristics of the rock mass. The method is able to calculate the compression and shear deformation energies stored in the slip surface, as well as the virtual displacement generated by the slide mass when the slope is in a critical equilibrium state. The direction of motion of the slide mass is further calculated from the magnitude of the virtual displacement. In addition, this paper improves the generation of new solutions in the simulated annealing (SA) algorithm for the structural characteristics of the slip surface of the slope, thus achieving a fast search of the slip surface. Finally, the method of this paper is compared with the test question of ACADS and the simulation results of the finite difference method (FDM) to verify the effectiveness of the method of this paper. [ABSTRACT FROM AUTHOR]

In recent decades, the global imperative to address drinking water scarcity encourages initiatives that ensure a sustainable supply. In this context, this work presents a two-stage methodology designed to reduce background leakages in water distribution networks by controlling pressures during hours of lower water demand using pressure-reducing valves (PRVs). The first stage focuses on dividing the network into smaller structures, or modules, optimizing the topological modularity index. Here, conceptual cuts are determined at the boundaries between modules, identifying them as potential positions for the installation of PRVs. The second stage determines the quantity, optimal settings, and operational status of these valves. Focused on reducing elevated nighttime pressures, the strategy minimizes the network's nighttime resilience index using simulated annealing for optimization. The application of this methodology to two reference networks results in different levels of PRV activity, achieving a substantial decrease in pressure and nighttime background leakage volumes, without a negative impact on peak demand hours. Practical Applications: Water scarcity is a global challenge that requires innovative solutions to manage and conserve water resources. This study presents a two-stage method to reduce water leakages in distribution networks by managing pressure during off-peak hours, which are characterized by low demand and high system pressures. In the first stage, the network is divided into smaller sections using strategic cuts that identify optimal locations for interventions such as installing shut-off valves or pressure-reducing valves. In the second stage, the pressure-reducing valves are installed at these strategic points and initially set to be fully open. The optimization process, focused on nighttime hours, adjusts the settings to reduce excessive pressures, thus minimizing water leakages without affecting daytime water supply. Applying this methodology to reference networks has shown significant reductions in both pressure and nighttime water leaks. This approach provides practical guidelines for water utilities to improve the efficiency and sustainability of their distribution systems, addressing the broader goal of mitigating water scarcity. [ABSTRACT FROM AUTHOR]

• A new sensor calibration method that requires no training data is established. • The framework effectively repairs high-dimensional sensor data. • The framework addresses the growing need for precise sensor calibration. • Simulated annealing outperformed five algorithms for robust calibration. • The method has been validated in a central cooling system in Hong Kong. Accurate calibration of sensors is critical for ensuring energy efficient operation of Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings. Due to the high dimensionality of sensor data and the complexity of multiple-fault scenarios, calibrating sensors in large and complex HVAC systems presents significant challenges. To address this issue, this study introduces a novel sensor calibration framework that integrates thermodynamic laws for high-dimensional sensor calibration in complex HVAC systems. The traditional calibration method heavily relies on accurate data, making it difficult to apply in practical engineering projects. The innovative aspect of our method lies in its integration of thermodynamic laws, such as mass balance and energy conservation, with sensor calibration framework. This approach enables the framework to handle high-dimensional sensor measurements effectively without any training data. We compared five optimization algorithms and applied them to a central cooling system in Hong Kong. The results demonstrated that the simulated annealing (SA) is the most robust for solving the calibration problem, even in scenarios with up to 21 faulty sensors, with the calibrated sensor accuracy meeting the standards for conventional chiller plant operations. This novel framework provides a robust and reliable solution for high-dimensional sensor calibration in large and complex HVAC systems, addressing the growing need for precise sensor calibration as the number of installed sensors increases. [ABSTRACT FROM AUTHOR]

Intensity-modulated radiotherapy (IMRT) is one of the main treatments for patients with cancer, and its treatment planning holds significant importance. Compared to manual treatment planning, automatic treatment planning (ATP) is expected to improve plan quality and efficiency, which is of great clinical importance. However, treatment planning is an intractable "black art" that calls for intuition and heuristics from medical dosimetrists. There are two main issues with the current ATP methods: (i) they rely strongly on prior clinical experience and are not sufficiently scalable, and (ii) they use a single-functional optimization concept that ignores delivery accuracy information, which lowers the delivery accuracy of the generated ATP plans. To address these issues, this paper presents a novel multi-functional simultaneous optimized automatic treatment planning (MFSO-ATP) method. First, to address the issues of high reliance and insufficient scalability, an evolutionary strategy (ES) and simulated annealing (SA)-based hybrid meta -heuristic trial-and-error (ESSA-HMTE) framework is designed to automatically adjust optimization parameters without human intervention. Second, to address the issue of delivery accuracy deterioration, an ensemble deep learning-based gamma passing rate (GPR) prediction (EnDL-GPR) framework was constructed and trained to predict the plan delivery accuracy a priori. The ESSA-HMTE framework effectively fuses dosimetric quality information and delivery accuracy a priori information to simultaneously optimize the two, ensuring delivery accuracy while achieving automatic planning. We tested the MFSO-ATP method using multiple diseases with high inter-tumor heterogeneity. The results showed that the proposed method can improve the quality and delivery accuracy of IMRT plans, reduce the percentage of quality assurance failures, is highly scalable, and has good clinical application prospects. [ABSTRACT FROM AUTHOR]

Winter storms cause severe damage in German forests. Different modelling approaches have already been used to try and map endangered areas to minimize the risk of wind damage by stand adaption. Prevalent models for Germany include empirical-statistical and hybrid-mechanistic models, such as ForestGALES (FG). As of yet, FG is not extensively used in Germany as its parametrization requires extensive experimental efforts to derive regionally sensitive species-specific parameters. Here, we implement a statistical calibration approach for German forest conditions with observed damage from single tree data, soil types, topography (topex) and gust speed data. We use simulated annealing to generate new species-specific values for the tree species, Norway spruce, European beech, and Douglas fir from within the range of all coniferous (deciduous) species for Norway spruce and Douglas fir (European beech) and an additional 10 % buffer around the default species-specific values for each species. We compare two optimization approaches: First, we aim to maximize the Matthew's correlation coefficient (MCC), which is calculated from the confusion matrix, applying a fixed classification threshold of 0.5. In comparison to the optimization at a fixed threshold, we optimized the species-specific parameters by maximizing the area-under-curve (AUC) value directly generated from the receiver-operator characteristic (ROC) analysis. We compare our statistical parametrizations for the considered species to those currently implemented in FG and validate the resulting damage probabilities based on confusion matrices and related performance measures. We created separate parametrizations for a single-tree and stand-wide analysis of storm damage risk, which we validated with gust speed data for Germany. Our results show, that for the single-tree method, MCC improved for all species: By 0.26 (0.22) for the calibration (validation) subset for Douglas fir, by 0.22 (0.18) for Norway spruce and by 0.08 (0.05) for European beech. The optimization for the stand-method shows an increase in MCC as well, with results not being considered due to low numbers of observation data. We show that for German forests, FG's predictive capability can be improved by statistical optimization when no tree-pulling data is available, which could be valuable for creating further regionalizations of FG. • Modelling storm damage risk for German forests using ForestGALES. • Regional parametrization using statistical optimization. • Adjusted model has higher predictive capacity in storm damage risk estimations. [ABSTRACT FROM AUTHOR]

Due to numerous uncertainties in the environment, unmanned underwater vehicle (UUV) sometimes deviate from their originally planned paths. To address this issue, a path replanning algorithm based on threat assessment using a dynamic Bayesian network is proposed. This ensures that UUV can adjust their paths to avoid danger when facing uncertain events. Initially, the UUV plans a path using the PSO-SMPC (Particle Swarm Optimization-Stochastic Model Predictive Control) algorithm, utilizing environmental data. Subsequently, a dynamic Bayesian network evaluates the likelihood of uncertain events occurring based on environmental and UUV state information. The algorithm then determines the level of threat posed by these events and decides whether to activate the PSO-SMPC algorithm for path replanning accordingly. Simulation results demonstrate the effectiveness of this approach in enhancing UUV operational safety and improving mission completion rates across various uncertain event scenarios. Furthermore, compared to alternative methods such as simulated annealing and traditional genetic algorithms, the proposed algorithm exhibits superior path planning capabilities. • A path planning algorithm that integrates PSO and SMPC, leveraging historical data to enhance path planning efficiency, optimization speed, and control accuracy. • Assessment of threats posed by uncertain events using a dynamic Bayesian network to improve decision-making capabilities for UUV. • Introduction of a path re-planning strategy, thereby increasing mission completion rates and ensuring UUV safety. [ABSTRACT FROM AUTHOR]

As global demand for renewable energy surges, offshore wind power has become an indispensable component in the transition towards a more sustainable energy landscape. Selecting appropriate wind turbines for offshore wind power facilities is a critical process influenced by various factors, including turbine parameters, environmental conditions, and economic indicators. Traditional decision-making methods often fall short in addressing the complexity and dynamic nature of real-world scenarios, relying heavily on expert experience. This study proposes a novel and scalable method for offshore wind turbine selection by leveraging a fuzzy decision-making network and a conditional probability table optimized through a simulated annealing algorithm. The method is structured into three key modules. It allows for learning from existing similar cases and transferring knowledge to the current selection task in the absence of expert opinion. It has proven its worth in a case study at the Rudong Wind Farm in Jiangsu Province, China. The results show that this method can account for the complexities inherent in turbine selection and serve as a more comprehensive and adaptable tool for decision-makers. This study contributes to the renewable energy sector by providing a novel, data-driven, and practical method for offshore wind turbine selection and other real-world related applications. • A novel integrated approach for offshore wind turbine selection is proposed. • The simulated annealing algorithm is used to achieve parametric optimization. • The new approach adeptly handles uncertain data without expert experience. • The efficacy of the new methodology is demonstrated in a real-world project. • Future directions like factor recalibration and cross-scenario validation are discussed. [ABSTRACT FROM AUTHOR]

We report on an electro-optically (EO) spectrum tunable, multiline optical parametric oscillator (OPO) based on a nonperiodically poled lithium niobate (NPPLN). Besides being an efficient optical parametric down converter (OPDC) for multi-wavelength signal generation, the NPPLN can function as an EO modulator for fast spectral tuning based on a unique asymmetric-duty-cycle (ADC) equivalent domain structure. We have developed a genetic algorithm to construct and optimize such an ADC equivalent domain configuration in NPPLN to maximize its EO spectral tuning rate with the least compromise of its nonlinear gain for conducting the multiple down-conversions. When the novel EO NPPLN device with an equivalent ADC of 29%/71% works in an OPO intracavity pumped by a diode-pumped, 1064-nm Nd:YVO 4 laser, we measured EO tuning rates of ∼0.5 and ∼0.53 nm/(kV/mm) for the down-converted dual signals at 1540 and 1550 nm, respectively. The tuning rates have been three orders of magnitude higher than a conventional one without an engineered domain asymmetry and 12% greater than another domain asymmetric LN device designed by a simulated annealing optimization method. The EO tuning technology developed in this study can be potentially implemented in micro/nano-photonic LN waveguide circuits. [ABSTRACT FROM AUTHOR]

The bio-polarized compass is an autonomous navigation technology with long-term endurance that has recently attracted the attention of numerous researchers. However, current algorithms for polarimetric compasses based on a single scattering model demonstrate poor adaptability to environmental perturbations. Numerous academic studies have conclusively demonstrated that the multi-scattering model provides a more accurate approximation of the actual scenario. Inspired by insects, we find that multi-scattering models have better environmental adaptability. However, the mathematical formalism of multi-scattering models is generally complex, making it difficult to obtain the solar vector directly from the polarization pattern. Therefore, we propose an inverse algorithm that combines the simulated annealing algorithm and a multi-scattering model, the equivalent incident light model(EIL model), to derive the solar vector from the polarized pattern with strong environmental adaptability. Five experimental sets were conducted across diverse environments, revealing that the errors associated with the bionic polarized compass are consistently below 0.4°, representing a substantial improvement compared to existing compass technology. • Analyzed the polarization pattern of multi-scattering light and mimicked the navigation mechanism of insects. • An environmental adaptive algorithm for polarized compass that combines the simulated annealing algorithm and the equivalent incident light model. • The errors of the bionic polarized compass are all less than 0.4 degrees. [ABSTRACT FROM AUTHOR]

Spherical holography technology, a 3D display technology with the advantage of an infinite viewing zone and 360°observation, has received widespread attention. However, the quality of existing computer-generated spherical holograms is limited. To address this issue, we propose a Pre-compensated annealing gradient descent method (PAGD) to quickly obtain spherical holography. Compared to traditional SGD algorithms, the PAGD algorithm significantly improves convergence speed. The annealed gradient descent helps avoid local optima during the optimization process, while pre-compensation enhances the optimizer's rate. Numerical simulations have confirmed the effectiveness of this method. The optimization rate of PAGD is 16.81 times faster than that of traditional SGD. It is noteworthy that this method has broad application prospects in spherical holographic display and three-dimensional display. • Greatly improving the optimization speed of spherical computer holography. • Pre-compensation balances the numerical range of diffraction results enhancing the efficiency of the optimizer. • Easier to overcome local minima during the optimization process. • Applying simulated annealing gradient descent to the optimization of holograms. [ABSTRACT FROM AUTHOR]

• Two–phase modeling of PEM–FC, taking into account various phenomena: contact and mass exchange effects between non–isothermal gas and liquid phases, and pressure effects (non-isobaric on the cathode and anode sides);. • Calculating the optimal values of the main geometrical parameters using different optimization methods;. • Providing a comprehensive model to investigate PEM–FC performance to reduce computational costs;. • Decreasing energy generation costs by the PEM–FC using the parameter optimization method. This study models a non-isobaric, non-isothermal two-phase flow in a Polymer Electrolyte Membrane Fuel Cell (PEM-FC), focusing on conservation equations for mass, energy, and momentum across its components. Verification involves comparing PEM-FC performance and temperature distribution with experimental and literature data, showing consistent agreements. Results indicate that increasing cathode channel pressure enhances membrane moisture and reduces power loss. Higher oxygen partial pressure improves PEM-FC performance, whereas increased anode channel pressure heightens ohmic losses and lowers output voltage. Temperature distribution reveals highest temperatures near the cathode catalyst layer due to electrochemical reactions. Adjusting pressures in cathode and anode channels affects these temperatures accordingly. PEM-FC power density is optimized using various algorithms, with simulated annealing proving most effective. Optimal values for gas diffusion layer thickness, electrode porosity, and inlet humidity are determined. Under constant current density, power density increases by 6 % compared to baseline conditions, demonstrating effective parameter optimization for enhancing PEM-FC performance. [ABSTRACT FROM AUTHOR]

Electric Vehicle (EV)-based parcel delivery is getting much more popular these days due to EVs being eco-friendly and sustainable. One well-known problem in the literature to study last-mile delivery via EVs is the Electric Vehicle Routing Problem (EVRP). One limitation of EVRP is that it assumes all customers must be visited directly by EVs. This forces companies to purchase or utilize additional EVs, increasing operational costs for EV utilization and routing. To overcome this limitation, the present work introduces the Prize-collecting EVRP with Time Windows (PC-EVRP-TW), utilizing multiple EVs for the home-delivery service of high-priority customers (customers with high loyalty or premium memberships), leaving the rest for the next day or outsourcing to a third-party shipper. It also ensures a minimum number of served demands to maintain service quality. PC-EVRP-TW is modeled by mixed-integer linear programming and solved by a problem-specific hybrid metaheuristic aiming to minimize EVs' route and usage cost and maximize collected profits (prizes) while satisfying EVs' load and battery capacity, task completion, and time window constraints. Analyses were performed on the optimal solution of PC-EVRP-TW. The results show that the company could achieve significant route cost savings, up to 22% compared to EVRP-TW. Additionally, remarkable reductions in EV usage costs, 6%, 16%, and 18%, were observed for various-size instances of PC-EVRP-TW. These findings highlight the potential for practitioners and managers to realize substantial cost savings and optimize EV usage by selectively serving a prioritized subset of customers daily, particularly when facing high utilization costs and the inability to serve all customers directly. • Prize-collecting Electric Vehicle Routing Problem with Time Windows is studied. • The aim is to minimize EVs' route and usage costs while maximizing collected prizes. • PC-EVRP-TW is mathematically modeled using Mixed-integer Linear Programming (MILP). • A hybrid Variable Neighborhood Search and Simulated Annealing Algorithm is proposed. • The findings demonstrate substantial cost savings by implementing the PC-EVRP-TW. [ABSTRACT FROM AUTHOR]

The mixture of Gaussian processes is a powerful statistical learning model that can be effectively applied to curve clustering and prediction. However, the corresponding model selection problem, that is, selecting an appropriate number of components in the mixture, is rather difficult to solve. In our previous work, we established the split-and-merge automatic model selection algorithm for mixtures of Gaussian processes along the output space under the framework of Reversible Jump Markov Chain Monte Carlo (RJMCMC), which can not only determine the number of actual Gaussian processes but also dynamically adjust the Gaussian process components to avoid dependence on parameter initialization and initial partitioning of the dataset during the parameter learning on a given dataset. In this study, we propose two algorithms: Penalized Likelihood RJMCMC and Penalized Prior RJMCMC. The former integrates a penalized term into the likelihood, while the latter incorporates a penalized term into the prior and operates within the full Bayesian inference framework, both aiming to focus more sharply on determining the number of components in the convergence process. Furthermore, we prove the geometric ergodicity of the RJMCMC algorithm for the mixture of Gaussian processes model, ensuring convergence of the posterior distribution with sufficient iterations. The experimental results further demonstrate the robustness of our PP-RJMCMC algorithm in model selection, showing superior performance compared to traditional approaches in curve classification and clustering. Additionally, the prediction performance is comparable to the EM algorithm. Although not directly explored in this study, the RJMCMC results can be used to initialize the EM algorithm, which could potentially improve prediction accuracy and accelerate computation. • Penalized likelihood and simulated annealing are used in RJMCMC for mix-GP to focus on a component. • Penalty term is integrated into the prior for full Bayesian inference in RJMCMC. • NUTS sampler replaces HMC for adaptive tuning, boosting RJMCMC efficiency. • Ergodicity of RJMCMC for mix-GP has been proved, implying convergence. [ABSTRACT FROM AUTHOR]

Ribonucleic acid (RNA) design is the inverse of RNA folding. RNA folding aims to identify the most likely secondary structure into which a given strand of nucleotides will fold. RNA design algorithms, on the other hand, attempt to design a strand of nucleotides that will fold into a specified secondary structure. Despite the apparent NP-hard nature of RNA design, promising results can be achieved when formulated as a combinatorial optimization problem and approached with simple heuristics. The main focus of this paper is to describe an RNA design algorithm based on simulated annealing. Additionally, noteworthy features and results will be presented herein., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)