Assessment and improvement strategies for distribution grid capacity considering distributed energy resources and electric vehicles

High-performance chiral metasurface sensors optimized by a target-driven active learning framework

Optimal path reconstruction of plant chromosome evolution.

Hybrid Simulation–expertise approach to balancing cost and resilience in global herbal supplement supply chains

The surge in generative artificial intelligence (AI) may cause growing conflicts between securing electricity supplies and achieving sustainable development goals. Here, we propose off-grid hybrid wind-solar-storage (WSS) systems, which leverage the immense renewable resources in desert areas alongside relatively low-cost fiberoptic connectivity of data centers, to address this challenge. Using a global high-resolution techno-economic optimization model, we demonstrate that well-planned WSS systems can deliver cost-effective 24/7 uninterrupted power, primarily tailored for energy-intensive, latency-tolerant foundation model training. Furthermore, our analysis reveals that regions proximate to load centers can also support latency-sensitive inference tasks. This energy supply is capable of delivering 1 PWh globally in 2030 at levelized costs of around $39/MWh, meeting the forecasted electricity demand for AI by the International Energy Agency. Moreover, even if AI electricity demand increases 10-fold, reaching 10 PWh by 2030, the unit cost increment would be less than 20%. Further uncertainty analysis shows that under extreme investment (3.0×) and cooling (2.0×) cost assumptions for data centers operating with the desert WSS systems, this 10-PWh/yr demand could still be satisfied at competitive cost levels. The desert WSS systems could potentially align computational and clean energy infrastructure in the AI era, as well as simultaneously achieving decarbonization and ecological restoration. Broader context: The rapid expansion of artificial intelligence (AI) poses emerging challenges to electricity supply and climate goals. Addressing this critical energy-computing nexus, we investigate a potential solution: co-locating data centers with off-grid wind-solar-storage systems in desert regions. By utilizing efficient fiberoptic data transmission to circumvent the challenges of long-distance power transport, this approach offers a possible pathway to convert renewable resources into computational power. Our analysis suggests that this strategy could help meet growing AI energy demands in an economically viable and environmentally sustainable manner.

Large-size very-high-resolution (VHR) remote sensing imagery has emerged as a critical data source for high-precision vector mapping of multi-scale geographical elements such as building, water, road and etc. When dealing with the large-size image, due to the limited memory of GPU, the deep learning-based vector mapping methods often employ the sliding block strategy. This inevitably leads to the degenerated performance because of the stitching difficulty of the sliding blocks' vector mapping results. Therefore, it is necessary to conduct full-scope vector mapping via mining the consistent cue in large-size remote sensing imagery. To this end, this paper presents a novel global context-aware local point optimization method. To leverage the global context, this paper proposes a novel pyramid fusion network (PFNet) to conduct semantic segmentation of the large-size image in an end-to-end manner. Under the constraint of the global semantic segmentation result, a new inflection-point perception network (IPNet) is proposed to generate a set of stable points to depict the boundary of each element. Extensive experiments on building, water and road datasets, where each image has over 100 million pixels, show that our method obviously outperforms the existing methods.

Investigation of thermal management in a differentially heated square cavity through topology optimization

One of the pivotal strategies for achieving net-zero aviation emissions is the replacement of conventional jet fuel with Sustainable Aviation Fuels (SAF). The very limited availability of SAF necessitates strategic allocation to flight routes to optimize costs and emission reduction. Addressing this challenge, this paper introduces an innovative adaptive robust optimization framework for the distribution of SAF to flight routes in Canada based on a range of domestic production scenarios, fuel transportation costs, and jurisdictional carbon prices. The objective is to identify the optimal location of potential SAF distribution centers and allocate SAF to flight routes over a 25-year period. This complex problem incorporates flight data from the International Civil Aviation Organization (ICAO) and uncertain projections for SAF production. Leveraging a column and constraint generation algorithm, the paper achieves global optimality in solving the problem. The findings reveal that the proposed robust model results in emission cost savings ranging from 7.13% to 18.19% across various distribution center capacities, consistently outperforming the deterministic model. This underscores the effectiveness of the proposed approach in efficiently distributing available SAF under production uncertainties. • Developed a Robust model for SAF distribution to Canadian flight routes over 25 years. • Accounted for uncertainty in SAF production, transport costs, and carbon prices. • Identified ideal SAF distribution centers and allocation strategies using real flight data. • Achieved an 8.06% reduction in emission costs compared to traditional models. • Supports aviation’s transition to net-zero emissions with efficient SAF deployment strategies.

This study presents a physics-guided multi-resolution optimization approach using a digital twin framework for the systematic design of high-efficiency AlGaAs/GaAs single-junction solar cells as photovoltaic power sources. To investigate the strongly coupled design space governing device performance, a global coarse-grid dataset of 10,000 device configurations is generated using an automated PC1D-5 simulation across fabrication-realistic ranges of structural and doping parameters. Global coarse-grid exploration enables identification of high-performance regions, while physics-constrained feasibility screening ensures that only physically meaningful and operationally realistic configurations are retained. Within this feasible design space, global optimization identifies a coarse-grid candidate optimum with a conversion efficiency of 30.708%. Because coarse sampling may fail to resolve narrow performance maxima, localized high-resolution refinement is performed around this candidate, identifying an improved optimal configuration achieving 31.898% efficiency. This corresponds to an additional 1.19 percentage-point increase (approximately 3.9% relative improvement) over the coarse-grid optimum. These results demonstrate that coarse-grid exploration alone is insufficient to capture the true physical optimum in III–V heterojunction solar cells, whereas a coarse-to-fine optimization strategy enables accurate identification of physically meaningful optima with manageable computational cost. The proposed approach provides a practical and scalable simulation-driven methodology for optimizing photovoltaic power-source performance. • Physics-guided multi-resolution optimization of GaAs cells. • Timestep convergence ensures consistent terminal I–V metrics. • Coarse grid (30.7%) refined to 31.9% efficiency. • 3.9% relative gain via localized high-resolution search. • Design-stage GaAs performance under controlled assumptions.

This paper presents Monkey Jumping Optimization (MJO), a novel nature-inspired metaheuristic algorithm that emulates the arboreal locomotion behavior of monkeys to address complex global optimization problems. The MJO algorithm integrates three biologically inspired mechanisms: energy-aware leap dynamics, probabilistic branch selection, and canopy memory preservation to balance exploration and exploitation within multimodal search spaces. By representing candidate solutions as monkeys navigating a virtual tree structure, MJO employs a population-based framework that combines stochastic and deterministic strategies for efficient traversal of the solution landscape. Extensive benchmarking against eight state-of-the-art algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO), demonstrates competitive performance under standard benchmarking assumptions. Specifically, MJO achieves up to 28.7% faster convergence under standard experimental settings compared to PSO on challenging deceptive functions and attains 15-22% higher success rates in identifying global optima across the Congress on Evolutionary Computation (CEC 2024) benchmark suite. Beyond empirical evaluation, theoretical convergence properties are analyzed using a Markov chain framework. The algorithm's biologically inspired design, combined with computational efficiency, makes it suitable for engineering applications such as unmanned aerial vehicle (UAV) path planning and neural architecture search. Despite its strengths, MJO may exhibit relatively slower convergence on well-conditioned unimodal problems and shows sensitivity to parameter settings, highlighting opportunities for further refinement. The proposed MJO algorithm, accompanied by an open-source implementation, provides a flexible and extensible framework for solving complex optimization problems across diverse domains.

Cancer microarray datasets often contain many irrelevant, duplicate, and even noisy features, which are likely to reduce the accuracy of classification algorithms. As a branch of feature engineering, the feature selection process aims to improve the classification performance of the desired microarray analysis by restricting the number of features to only those that are specified and valuable. Feature selection is an NP-hard problem, and agents searching for solutions often fall into local optima, requiring increasing time and effort to compute. This implies that a well-designed global search strategy is of utmost importance. Lion optimization (LO) is a recently proposed metaheuristic for global optimization. Due to its biologically inspired pride-based social structure, LO is capable of strong exploration through a nomadic search while ensuring exploitation through cooperative hunting mechanisms. It is well-balanced for optimal feature subset selection in high-dimensional datasets. However, the LO methodology seems to be constructed for continuous optimization tasks. To address this limitation, a variant algorithm, binary LO (BLO), was developed using an S-shaped Transfer Function to address wrapping-based feature selection in microarray cancer datasets. The proposed method was tested on 11 benchmark datasets on cancer microarrays that represent a variety of tumors and high-dimensional feature spaces. In this study, mRMR (Minimum Redundancy Maximum Relevance) is used as a filter method to reduce dimensionality before the wrapper-based BLO optimization method. The efficacy of the mRMR-BLO approach was evaluated across several prominent cancer datasets. It was also compared to four newer binary optimization techniques to test its effectiveness. Various performance metrics, such as Accuracy, MCR, Precision, Recall, Specificity, FNR, FPR, and MCC, are used to evaluate the model. Non parametric Wilcoxon Paired Signed Ranks test is performed to evaluate mRMR-BLO. The results showed that, with smaller feature sets, the proposed mRMR-BLO algorithm achieved the highest prediction accuracy among the compared existing optimization techniques.

Traditional Rime Optimization Algorithm (RIME) often encounters performance degradation when dealing with complex optimization landscapes, such as high dimensionality, strong multimodality, and application-driven image segmentation tasks. To overcome these limitations, this study develops a multi-strategy self-adaptive Rime Optimization approach, termed MSRIME, which enhances both search diversity and convergence stability through adaptive and collaborative mechanisms. Specifically, MSRIME introduces a dynamically adjusted differential mutation factor to regulate exploration and exploitation along the optimization process. In addition, a heterogeneous strategy pool composed of multiple update operators is constructed, enabling complementary search behaviors at different optimization stages. A probability-driven strategy selection scheme based on performance feedback is further employed to adaptively allocate search resources among strategies. The proposed algorithm is evaluated on the CEC2017 and CEC2022 benchmark suites and compared with several state-of-the-art metaheuristic algorithms. Experimental results show that MSRIME consistently achieves superior performance in terms of optimization accuracy and robustness. In the Friedman statistical test, MSRIME obtains the best mean rankings of 1.93 and 1.77 on CEC2017 (30D and 100D), and 1.67 and 2.17 on CEC2022 (10D and 20D), respectively, outperforming all competing algorithms. Furthermore, MSRIME is applied to multilevel threshold image segmentation using Otsu's criterion. The results demonstrate that the proposed algorithm achieves higher PSNR, SSIM, and FSIM values across different images and threshold levels, indicating its effectiveness in practical applications. Overall, the proposed MSRIME provides an effective and robust optimization framework, achieving significant performance improvements without increasing computational complexity.

Modern power systems face severe dynamic stability and operational scheduling challenges due to the rapid penetration of renewable energy sources (RES). Aiming at the problems of insufficient dynamic modeling accuracy, low efficiency of stability analysis, and the difficulty in balancing operational safety and economy under high RES integration, this study proposes an integrated theoretical and methodological framework for large-scale power system dynamic analysis and optimal scheduling. First, a sixth-order nonlinear differential equation model is established by integrating the electromechanical transients of synchronous generators, excitation regulation dynamics, load characteristics, and network power flow balance, which makes up for the deficiency of the traditional second-order swing equation in ignoring multi-subsystem coupling. Based on Taylor expansion at the equilibrium point, a linearized state-space representation is derived, and the analytical expressions of three key transient stability indices (the maximum rate of change of power angle, steady-state power angle deviation, and power angle oscillation amplitude) are obtained, which quantitatively reveal the influence of key parameters such as excitation gain and synchronous torque coefficient on system stability. Second, an improved ADMM-based distributed optimization algorithm with variable splitting and asynchronous iteration mechanisms is developed, which embeds dynamic security constraints into a multi-timescale scheduling framework. A two-layer control structure combining ADMM distributed global optimization and MPC centralized local control is constructed to solve the inefficiency of traditional centralized algorithms in large-scale system scheduling. Finally, the effectiveness of the proposed model and algorithm is verified on the IEEE 10-machine 39-bus system and extended to the 100-machine 300-bus system for scalability analysis.

This study proposes a hybrid variant of Elephant Herding Optimization that combines an adaptive clan-updating schedule, Lévy-flight exploration from Cuckoo Search, and elite opposition-based sampling. The design targets three known limitations-premature convergence, imbalance between exploration and exploitation, and slow late-stage progress-by (i) nonlinearly annealing clan influence across iterations, (ii) applying long-range moves exclusively to clan leaders, and (iii) injecting diversity around elite candidates. On ten standard benchmarks, the proposed method achieves faster convergence and lower error than EHO, PSO, SCA, and EHO-based hybrids.For example, it reaches the global optimum on F6 and attains a mean error on F1 that is 12.8× lower than that of PSO under identical evaluation budgets. A signal-processing case study (Wiener spline filter design) further demonstrates transferability to an industry-relevant task. We analyze parameter sensitivity, provide a time-complexity breakdown, and discuss limitations. The approach offers a systematic and generalizable hybrid framework that balances broad exploration early with precise exploitation late.

Difficult Optimization problems can be efficiently solved through nature-inspired optimization algorithms, which remain problem-independent and are computational models at a conceptual level. In this work, a Jaya algorithm will be offered for tackling the Optimal Power Flow (OPF) problem. The basic idea of the Jaya algorithm for optimization problems is that the candidate solutions should move closer to the global optimum solution without converging at suboptimal solutions. Like other nature-inspired optimization techniques, the Jaya method is parameter-free and does not require any algorithm-specific control factors, such as learning or mutant parameters. The parameter freedom of an optimization algorithm not only improves its simplicity, but it also overcomes the challenge of adjusting optimization algorithm parameters, which affects performance as indicated in the literature, and can be expensive for some computations. The OPF problem aims to optimize the different objective functions using the control variables of the power systems. These include improving the voltage stability, decreasing the cost and emissions, and reducing power loss. For comparison purposes, the Jaya optimization technique for the OPF problem has been tested on the IEEE 30-Bus, 57-Bus and IEEE 118 Bus systems. The results were compared to previously reported outcomes of optimization approaches. The result of the computational study reveals the effectiveness of the Jaya optimization technique when compared with the other reported techniques. For instance, when the Jaya algorithm was applied to the IEEE 30-bus system, the fuel cost was reduced by approximately 11.31% compared to the initial operating condition.

In regions such as Japan, where natural disasters frequently occur, it is crucial to evacuate swiftly in the event of a disaster. However, evacuees tend to behave selfishly, that is, they typically head to the nearest shelter along the shortest path. This tendency can lead to severe traffic congestion, thereby exacerbating the damage. To address this issue, we formulate an evacuation route optimization problem that enhances evacuation efficiency as a binary quadratic programming (BQP) problem. The proposed formulation simultaneously minimizes the distance each vehicle must travel to reach a safe location and the penalty associated with overlapping routes among vehicles. In this way, we implicitly aim to reduce the overall completion time of evacuation for all vehicles. For an operational deployment of the proposed method during a disaster, the computation of optimal routes must be completed within a short time; otherwise, it is not practically useful. We therefore investigate the feasibility of employing the quantum annealing machine developed by D-Wave Systems Inc., which has been attracting attention as a promising high-speed solver. Since the current D-Wave machine cannot directly handle large-scale problems that cover an entire city, we design a decomposition method that exploits the intrinsic sampling diversity of the D-Wave machine. We examine the trade-off between solution quality and computation time. Numerical experiments using a traffic simulator demonstrate that the solution of the proposed BQP formulation can shorten the evacuation completion time by up to 33.6% in a specific region of Japan, compared with a locally optimal approach in which all vehicles select the shortest route to the nearest shelter. Although the solution obtained by the proposed decomposition method does not reach the global optimum, it achieves significantly shorter evacuation times than the locally optimal approach, while reducing the computation time drastically. These results are obtained under the assumption that all vehicles strictly follow the computed routes. We further perform simulations under a more realistic assumption in which a fraction of cars choose routes different from those prescribed by the optimization. The results reveal that even if only 1% of vehicles deviate from the optimized routes, the evacuation efficiency deteriorates sharply. Nevertheless, the proposed method still yields a shorter evacuation completion time than the locally optimal approach. These findings suggest that, in time-critical disaster situations, our method provides practical insights for evacuation planning that prioritize rapid and effective action under uncertain conditions, rather than insisting on strict optimality.

We compare two systematic approaches for constructing the kinetic transition network associated with a catalytic reaction, namely reactive global optimization (RGO) and discrete path sampling (DPS). We test convergence of pathways for selected steps of the dealloying processes occurring in Pt2Mn slab models under oxidative conditions for a DFT-parametrized machine learning interaction potential (MLIP). We find close agreement between the approaches. In particular, both schemes resolve multistep transformations that appeared as single steps in a previous meta-dynamics (m-Dyn) treatment. RGO and DPS are therefore proposed as effective tools for the systematic exploration of reaction paths in catalysis.

The analysis of human movement data in sports science is often challenged by the inherent variability in movement speed and rhythm, which results in gait time-series data of inconsistent lengths (dynamic dimensionality). This poses a significant obstacle for traditional optimization algorithms in constructing accurate motion templates for performance analysis and rehabilitation. To address this, we propose a novel Dynamic Dimension Warping (DDW) algorithm specifically designed for efficient search in dynamic multidimensional spaces. DDW integrates a Cross-Dimensional Mapping (CDM) mechanism, fusing Dynamic Time Warping and Euclidean distance to enable comparison between variable-length sequences, and an Optimal Dimension Collection (ODC) method to break fixed-dimension constraints. When applied to the task of optimizing human gait templates from experimental data, DDW demonstrated superior performance against 31 benchmark algorithms, reducing average fitness to 9.16 (41% below mean) and achieving rapid convergence within 10 generations. The algorithm also attained global optima in 52.17% of classical function tests, confirming its robustness. This work establishes DDW as an effective optimization framework for complex, dynamic-dimensional problems, with direct methodological value for gait analysis and biomechanical motion assessment.

The predictive modeling of one-dimensional (1D) supramolecular assemblies depends on the identification of stable, low-energy configurations-a task frequently hindered by the vast configurational space and highly multimodal energy landscapes-inherent to non-covalently bonded systems. In this study, we introduce the -stack optimizer, a modular, open-source framework designed to generate energetically favorable 1D stacking motifs directly from a single monomeric building block with minimal computational overhead. The framework systematically explores high-dimensional space by globally sampling coupled rigid-body translational and rotational degrees of freedom, while optionally accounting for intramolecular torsional flexibility. Extensive validation across 14 chemically diverse supramolecular systems demonstrates that the framework reliably identifies stable low-energy configurations, including systems stabilized by directional intermolecular hydrogen-bonding networks. Comparative analyses indicate that, while algorithms differ in robustness and efficiency, they consistently converge to nearly identical low-energy minima. Coupled with automated hyperparameter optimization, the -stack optimizer serves as a scalable and practical tool for generating high-quality initial structures for advanced quantum-mechanical calculations and molecular simulations. The -stack optimizer utilizes global optimization algorithms within a multidimensional parameter space defined by rigid-body translations, rotations, and intramolecular degrees of freedom. By integrating the molecular symmetry constraints, the framework minimizes redundant exploration of equivalent configurations. Configurational sampling was performed using multiple metaheuristic algorithms, including Particle Swarm Optimization, Genetic Algorithms, Grey Wolf Optimizer, and a hybrid PSO-Nelder-Mead approach, with convergence governed by early-stopping criteria. Candidate stack geometries were evaluated using semi-empirical quantum-mechanical energy calculations, primarily employing the GFN2-xTB Hamiltonian. The objective function combines intermolecular binding energies with quadratic steric-penalty terms to bypass unphysical configurations and target chemically realistic minima. Developed in Python with a modular architecture, the framework features parallelized execution and automated hyperparameter optimization via Optuna, providing a flexible, open-source tool for efficient generation of supramolecular stacks with minimal user inputs.

Clinical translation of novel therapies can be hindered by heterogeneity-driven sample size inflation in late-stage trials. In acetaminophen-induced liver injury (APAP DILI), many patients recover spontaneously, diluting investigational drug efficacy signals. We developed a prognostic enrichment tool to identify patients with worsening injury trajectories for more efficient trial designs. Biomarker model discovery and evaluation used serum samples from three UK cohorts: the MAPP2 APAP DILI biobank (n = 147), an independent pre-intervention evaluation cohort from the ongoing MAIL trial (n = 34), and healthy controls (n = 13). We measured 63 biomarkers and evaluated 321,682 combinations using kernel naïve Bayes classification to predict liver injury trajectory (ALT rising vs. falling). Sensitivity analysis using patient-level grouped cross-validation showed combining multiple biomarkers while constraining collinearity was necessary to maximize performance. A four-biomarker model (MCSFR, WBC, Sodium, K18) achieved AUC 0.868 (derivation) and 0.854 (evaluation). When optimized for prognostic certainty, the model yielded a Positive Likelihood Ratio of 14.4, increasing the Positive Predictive Value for worsening injury from a baseline of 29.4% to 85.7%. Time-dependent cost-minimization modeling for a hypothetical phase 3 trial identified an application threshold (sensitivity 80.0%, specificity 91.7%, Number Needed to Screen 3.4) as the global economic optimum, resulting in an illustrative trial cost reduction from $39.0 M to $8.3 M. This proof-of-concept demonstrates multidimensional biomarker models can resolve signal dilution. Distinguishing patients destined for injury progression reduces sample size requirements, which could de-risk novel therapy development.