Natural hazards such as earthquakes, floods, and tropical cyclones pose significant threats to the operation of critical infrastructure systems (CISs) in urban environments. Rapid recovery of post-disaster CISs is essential not only for mitigating immediate socio-economic impacts but also for strengthening urban resilience against future shocks. A key challenge in this recovery process is the efficient scheduling of resources to repair damaged infrastructure, a task complicated by the dynamic and uncertain post-disaster environment, the interdependencies within infrastructure networks, and the diverse priorities and demands of various stakeholders. Given the multifaceted nature of these challenges, numerous repair resource scheduling models have been developed, each incorporating distinct algorithmic strategies tailored to different disaster types and infrastructure systems. Despite a growing body of literature on optimization problems in disaster recovery, a comprehensive understanding of the variations in these models and methods remains lacking. This review aims to systematically explore and synthesize the landscape of repair resource scheduling models, highlighting model variants and their solution algorithms. In particular, it addresses the emerging challenges in post-disaster recovery, exacerbated by the coupled effects of climate change and rapid urbanization. By categorizing the variants and extensions of existing models, this study seeks to refine current frameworks and inspire the development of more comprehensive models, ultimately contributing to more informed restoration decisions and enhanced resilience of urban infrastructure systems.

Hierarchical maximum likelihood estimation for time-resolved NMR data.

Solutions reproducibility in multiresponse optimization problems: A new desirability-based objective function

Optimization of drug diffusion in drug-eluting stents for coronary artery based on deep reinforcement learning.

Hardware image signal processing (ISP) transforms RAW inputs into high-quality RGB images through a series of processing modules, each with numerous tunable parameters. Traditionally, these parameters are manually tuned by imaging experts, a time-consuming and subjective process. Recent deep learning approaches predict ISP parameters, but often treat the process as a black box and overlook the intrinsic relationships among ISP modules. To address these fundamental issues, we introduce a novel ISP parameter optimization model based on single-agent reinforcement learning (RL) (i.e., SARL-ISP), formulating the hardware ISP parameter tuning as a sequential optimization problem. During the optimization process, the agent updates ISP parameter tuning strategies for different tasks through interaction with the environment. In order to explore the influence of the sequential structure of hardware ISP modules and the coupling relationships among ISP parameters on the tuning process, we further propose a sequential ISP framework based on collaborative multi-agent RL (i.e., MARL-ISP). Specifically, the serialized parameter tuning module (SPTM) realistically simulates the process of manual prediction and module pipeline. Additionally, the feature selection module (FSM) facilitates the transmission and fusion of agent features, thereby selecting more appropriate feature inputs for downstream tasks. Extensive experiments across various tasks (e.g., object detection, instance segmentation) validate the effectiveness and efficiency of our models. Even with minimal training data, our models also outperform current state-of-the-art methods in both quantitative metrics and qualitative evaluations.

A coupled spiking neural P system integrated with two-level neighborhood search for solving flexible job shop scheduling problems.

Gilbert’s Potoroo Optimization: A Novel Nature-inspired Metaheuristic Algorithm for Solving Optimization Problems

Red-crested Turaco Optimization (RCTO): A Novel Nature-inspired Metaheuristic for Solving Optimization Problems

Pink Fairy Armadillo Optimization (PFAO): A Novel Bio-inspired Metaheuristic for Solving Optimization Problems

Florist Optimization Algorithm: A Novel Human-inspired Metaheuristic for Solving Optimization Problems

Non-Cartesian k-space sampling in MRI is widely used, yet images reconstructed on scanners with preliminary corrections (e.g. off-resonance) often exhibit residual artifacts (e.g. ringing and streaking) that can compromise interpretation. We propose a zero-shot residual artifact suppression method that operates directly on scanner-reconstructed images without requiring labeled data, pre-training, or an explicit degradation model. The method builds on a decoder-style generative prior and incorporates a fixed blur-kernel operator that reshapes the network's inductive bias without introducing additional learnable parameters. We formulate the procedure as an optimization problem by minimizing a data-fidelity objective between the network output and the corrupted input image. We evaluate the method on simulated data and demonstrate improved image quality over conventional baselines, while remaining competitive with supervised comparisons under acceleration factors up to R = 4. Across these settings, relative to the artifact-corrupted input, SSIM improves by up to 38% and PSNR increases by up to 10.64 dB. In in vivo experiments, the proposed method consistently attenuates residual aliasing-like artifacts, indicating reproducible performance across acquisitions. Overall, the proposed framework offers a practical and general-purpose post-processing strategy for artifact suppression in non-Cartesian MRI, with applicability across diverse sampling patterns and imaging settings.

BackgroundOptimizing cancer screening and surveillance frequency requires accurate information on parameters such as sojourn time and cancer risk from premalignant lesions. These parameters can be estimated using multistate cancer models applied to screening or surveillance data. However, the performance of these models has not been thoroughly investigated in settings in which cancer precursors are treated upon detection, preventing progression to cancer. Our main goal is understanding the performance of available multistate methods in this challenging censoring setting.MethodsWe assumed progression hazards between consecutive health states in a 3-state model (healthy [HE], cancer precursor, and cancer) to be either time independent or dependent on time since state entry and compared 6 methods implemented in R software packages with varying assumptions: time-independent hazards (msm), hazards dependent on time since state entry (msm with a phase-type model, cthmm, smms, BayesTSM), and hazards dependent on time since the start of the process (hmm). Risk estimates from each method were compared in simulations and illustrated using colorectal cancer surveillance data from 734 individuals, classified into 3 health states: HE, non-advanced adenoma (nAA), and advanced neoplasia (AN).ResultsAll methods performed well with time-independent hazards in the simulation study. With hazards dependent on time since state entry, only smms and BayesTSM provided unbiased risk estimates. In the application, only msm,hmm, and BayesTSM yielded converged solutions. The nAA risk estimates were similar between hmm and BayesTSM but differed for msm, while AN risk estimates varied across methods.ConclusionsMethods for multistate cancer models, specifically with unobservable precursor-to-cancer transition, are strongly affected by the time dependency of the hazard. With time-dependent hazards since state entry, BayesTSM provided robust estimates, in both the simulation and application.HighlightsThis study presents the first comprehensive comparison of available multistate modeling options for screening and surveillance data, focusing on the specific setting of a 3-state progressive model (healthy, cancer precursor, cancer) in which cancer precursors are treated upon detection so that the transition to cancer is prevented (censoring after intervention). Sample R code and simulated data demonstrating the compared methods, along with documentation (including installation instructions, manual, and/or worked examples) for the corresponding R software packages, are available at https://github.com/EddymurphyAkwiwu/MultiStateMethods.All methods provide unbiased risk estimates for transition times when the true progression hazards are time independent. With more realistic models in which progression hazards are dependent on time since state entry, only BayesTSM and smms yield unbiased risk estimates for transition times.In situations with weakly identifiable likelihoods, the smms package may suffer from numerical and optimization problems. The BayesTSM package overcomes these problems by applying regularized parameter estimation using weakly informative priors.Methods for multistate cancer models, more specifically with unobservable precursor-to-cancer transition, are strongly affected by the time dependency of the hazard. An inappropriate choice can lead to biased parameter estimates.

In online continual learning (CL), models trained on changing distributions easily forget previously learned knowledge and bias toward newly received tasks. To address this issue, we present Continual Bias Adaptor (CBA), a bi-level framework that augments the classification network to adapt to catastrophic distribution shifts during training, enabling the network to achieve a stable consolidation of all seen tasks. However, the CBA module adjusts distribution shifts in a class-specific manner, exacerbating the stability gap issue and, to some extent, fails to meet the need for continual testing in online CL. To mitigate this challenge, we further propose a novel class-agnostic CBA module that separately aggregates the posterior probabilities of classes from new and old tasks, and applies a stable adjustment to the resulting posterior probabilities. We combine the two kinds of CBA modules into a unified Dual-CBA module, which thus is capable of adapting to catastrophic distribution shifts and simultaneously meets the real-time testing requirements of online CL. Besides, we propose Incremental Batch Normalization (IBN), a tailored BN module to re-estimate its population statistics for alleviating the feature bias arising from the inner loop optimization problem of our bi-level framework. To validate the effectiveness of the proposed method, we theoretically provide some insights into how it mitigates catastrophic distribution shifts, and empirically demonstrate its superiority through extensive experiments based on four rehearsal-based baselines and three public continual learning benchmarks.

Practical Bayes filters often assume the state distribution of each time step to be Gaussian for computational tractability, resulting in the so-called Gaussian filters. When facing nonlinear systems, Gaussian filters such as extended Kalman filter (EKF) or unscented Kalman filter (UKF) typically rely on certain linearization techniques, which can introduce large estimation errors. To address this issue, this paper reconstructs the prediction and update steps of Gaussian filtering as solutions to two distinct optimization problems, whose optimal conditions are found to have analytical forms from Stein's lemma. It is observed that the stationary point for the prediction step requires calculating the first two moments of the prior distribution, which is equivalent to that step in existing moment-matching filters. In the update step, instead of linearizing the model to approximate the stationary points, we propose an iterative approach to directly minimize the update step's objective to avoid linearization errors. For the purpose of performing the steepest descent on the Gaussian manifold, we derive its natural gradient that leverages Fisher information matrix to adjust the gradient direction, accounting for the curvature of the parameter space. Combining this update step with moment matching in the prediction step, we introduce a new iterative filter for nonlinear systems called Natural Gradient Gaussian Approximation filter, or NANOfilter for short. We prove that NANO filter locally converges to the optimal Gaussian approximation at each time step. Furthermore, the estimation error is proven exponentially bounded for nearly linear measurement equation and low noise levels through constructing a supermartingale-like property across consecutive time steps. Real-world experiments demonstrate that, compared to popular Gaussian filters such as EKF, UKF, iterated EKF, and posterior linearization filter, NANO filter reduces the average root mean square error by approximately 45% while maintaining a comparable computational burden.

ABSTRACT Production – logistics synchronization systems face increasing challenges from volatile market demand and stringent resource constraints in the era of personalized manufacturing. Conventional forecasting and static resource configuration methods often fail to deliver responsiveness, cost efficiency, and adaptability under such uncertainty. To address these limitations, this study develops an integrated Predictive Opti-State Control (POsC) framework for dynamic resource configuration. Guided by the POsC strategy, a hybrid Temporal Convolutional Network – Long Short-Term Memory (TCN – LSTM) model is employed to capture nonlinear demand fluctuations and long-term dependencies across multiple product categories. Based on forecasting outputs, a dynamic resource configuration model is formulated to minimize total system costs, including production, transition, storage, and rental costs, under multi-resource constraints. To efficiently solve the resulting combinatorial optimization problem, an Improved Simulated Annealing (ISA) algorithm is designed, which enhances global exploration and local exploitation capabilities. The proposed approach is validated through an industrial case study of a leading paint manufacturer in the Guangdong – Hong Kong – Macao Greater Bay Area, where idle equipment capacity and excessive rental costs frequently arise due to demand uncertainty. Experimental analysis shows that the integrated framework significantly outperforms conventional forecasting and heuristic configuration methods, achieving superior performance in demand prediction accuracy, resource utilization, cost reduction, and system flexibility.

ABSTRACT Optimizing feeder characteristics is crucial for improving public transit efficiency. This study focused on optimizing feeder vehicle headway and capacity for a fixed route, formulating a non-linear mixed-integer optimization problem, aiming to minimize the total cost, including operator and user cost. Three distinct headway strategies: optimal, capacity, and policy headways are considered. A heuristic optimization algorithm, combining an analytical gradient-based method with a brute-force search, is developed to obtain optimal solutions. The model is applied to two feeder routes serving the Ratmalana and Agulana railway stations, Sri Lanka, using observed on peak hour transfer demand. Experimental results indicate that smaller vehicles with shorter headways are favoured for low- to medium demand situations. The sensitivity analysis reveals that as demand increases, smaller vehicles become less suitable, necessitating a shift to higher-capacity vehicles. The corresponding vehicle sizes with headways are presented, demonstrating this transition across different demand levels.

One-shot Federated Learning (OFL) has emerged as a promising paradigm, enabling global model training with minimal communication overhead. In OFL, the server model is usually distilled from an ensemble of pre-trained client models, while the ensemble also facilitates synthetic data generation for the knowledge distillation process. Prior works show that the performance of the final model is fundamentally tied to both the quality of the synthetic data and the ensemble. However, existing methods often optimize these two components separately, overlooking their interaction. To address this coupled optimization problem and provide a unified solution to the dual challenges of data and model heterogeneity inherent in OFL, we introduce Co-Boosting++, a novel OFL framework where synthetic data generation and ensemble construction mutually enhance each other in an iterative fashion. First, we fix the ensemble and generate hard samples in an adversarial manner. These samples are crucial for enhancing the robustness of knowledge transfer, as they challenge the model to generalize better, thereby improving quality of the synthetic data and subsequent distillation process. Second, leveraging these hard samples, we enhance the ensemble via a Mixture of Experts (MoE) mechanism. MoE allows dynamic adjustment of ensemble weights based on the generated hard samples, which enables the ensemble to better capture diverse and heterogeneous knowledge from client models. Furthermore, we extend Co-Boosting++ to support the simultaneous generation of multiple heterogeneous target models, enabling efficient adaptation to diverse device constraints. Extensive experiments on benchmark datasets demonstrate that Co-Boosting++ consistently outperforms state-of-the-art methods due to its coupled optimization of data and ensemble quality. Additionally, Co-Boosting++ is highly practical in real-world model market scenarios, requiring no local training modifications, additional transmissions, or restrictions on client model architectures. Our code is available at https://github.com/rong-dai/Co-Boosting-PP.

ABSTRACT Quantitative differential phase contrast (qDPC) microscopy enables high‐resolution, label‐free imaging of weakly absorbing samples by combining asymmetrical illumination with phase transfer function (PTF) deconvolution. However, conventional methods are limited by the ill‐posed nature of deconvolution and the band‐limited characteristics of the PTF, leading to poor robustness against noise and background fluctuations, particularly in thick or complex samples. To overcome these challenges, we propose a pupil‐driven differential phase contrast (PD‐DPC) framework that integrates system PTFs into both the data fidelity and regularization terms of the reconstruction model. The proposed model incorporates an edge‐sparsity‐promoting regularization to preserve structural detail and suppress noise, along with a Retinex‐inspired fidelity formulation to mitigate background fluctuations. The resulting non‐convex optimization problem is solved via an efficient Split Bregman algorithm with iterative reweighted soft‐thresholding. Simulations and experiments demonstrate that PD‐DPC outperforms L2‐DPC, Iso‐DPC, TV‐DPC, and Retinex‐DPC in terms of background suppression, phase fidelity, and edge preservation. The framework is compatible with diverse DPC modalities and enables automatic cell contour segmentation as well as high‐resolution imaging of absorbing tissues beyond the weak‐object approximation. By combining physics‐informed priors with a data‐adaptive reconstruction strategy, PD‐DPC offers a robust, broadly applicable solution that substantially enhances the accuracy and applicability of qDPC for biomedical imaging. The MATLAB code is available on GitHub .

In this article, we introduce the notion of approximate convexity for set-valued mappings, specifically in the forms of approximate pseudoconvexity and approximate quasiconvexity. These generalizations are motivated by the need to handle optimization problems involving multi-valued operators and vector-valued objective functions, where classical convexity assumptions are too restrictive. We demonstrate that the proposed framework preserves the essential structural features of convex analysis while broadening its applicability. After that, we investigate the introduced definitions through illustrative examples. Furthermore, we consider a set-valued optimization problem and rigorously investigate the relationships among its efficient solutions and the solutions of generalized Minty and Stampacchia variational inequality problems. The results provide a coherent theoretical bridge between optimality conditions and variational inequality formulations for set-valued mappings.

For the problem of fuzzy model predictive control (FMPC), this paper tackles two fundamental challenges: handling the nonconvexity inherent in the optimisation problem and designing reliable algorithms to enlarge the feasible region. To this end, nonlinear systems are first exactly represented by Takagi-Sugeno (T-S) fuzzy models. By exploiting the representational properties of fuzzy models and applying a convex envelope approach to the membership functions (MFs), the original nonlinear constraints are reformulated into linear ones. Subsequently, building on a dual-mode FMPC framework, two algorithms – iterative FMPC (IFMPC) and hierarchical FMPC (HFMPC) – are proposed. IFMPC decouples fuzzy subsystems by reformulating the optimisation as a quadratic programme, eliminating dependencies on linear mappings between premise variables and MFs. HFMPC employs a hierarchical structure: its upper layer coordinates submodels to approximate the original problem, while the lower layer solves submodel optimisations sequentially. Crucially, HFMPC incorporates free fuzzy control variables into online optimisation, enhancing feasible regions and robustness against parametric disturbances. Both algorithms are rigorously validated through numerical examples encompassing stabilisation, reference tracking, and partial tracking scenarios.