Search Results (3,009)

The Backtracking Search Algorithm (BSA) has emerged as a promising stochastic optimization method. This paper introduces a novel hybrid evolutionary algorithm, termed LOBSA, integrating the strengths of BSA and Lemurs Optimizer (LO). The hybrid approach significantly improves global exploration and convergence speed, validated through rigorous tests on 23 benchmark functions from the CEC 2013 suite, encompassing unimodal, multimodal, and fixed dimension multimodal functions. Compared with state-of-the-art algorithms, LOBSA presents a relative improvement, achieving superior results and outperforming traditional BSA by up to 35% of global performance gain in terms of solution accuracy. Moreover, the applicability and robustness of LOBSA were demonstrated in practical constrained optimization and a fluid–structure interaction problem involving the dynamic analysis and optimization of a submerged boat propeller, demonstrating both computational efficiency and real-world applicability. Full article

As supply chains rapidly digitize, platform-driven models have become central to global commerce, requiring sophisticated mathematical modeling for optimization. This systematic review comprehensively analyzes research across six critical technological domains in platform supply chains (PSCs): blockchain integration, Internet of Things applications, Industry 4.0 systems, cloud computing, live streaming commerce, and generative artificial intelligence. Our analysis finds that operational coordination and strategic decision-making under information asymmetry represent primary research focuses, with pricing strategies receiving predominant attention. Methodologically, game theory, particularly Stackelberg models, emerges as the dominant optimization framework across all domains. However, significant gaps remain in dynamic modeling capabilities, empirical validation of theoretical frameworks, and cross-technology integration. This review provides foundational insights into mathematical optimization techniques and highlights the critical need for incorporating stochastic approaches and real-world data to advance PSC management in the digital era. Full article

Keratinocytes are the primary cells of the epidermis layer in our skin. They play a crucial role in maintaining skin health, responding to injuries, and counteracting disease progression. Understanding their behaviour is essential for advancing wound healing therapies, improving outcomes in regenerative medicine, and developing numerical models that accurately mimic skin deformation. To create physically representative models, it is essential to evaluate the nuanced ways in which keratinocytes deform, interact, and respond to mechanical and biochemical signals. This has prompted researchers to investigate various computational methods that capture these dynamics effectively. This review summarises the main mathematical and biomechanical modelling techniques (with particular focus on the literature published since 2010). It includes reaction–diffusion frameworks, finite element analysis, viscoelastic models, stochastic simulations, and agent-based approaches. We also highlight how machine learning is being integrated to accelerate model calibration, improve image-based analyses, and enhance predictive simulations. While these models have significantly improved our understanding of keratinocyte function, many approaches rely on idealised assumptions. These may be two-dimensional unicellular analysis, simplistic material properties, or uncoupled analyses between mechanical and biochemical factors. We discuss the need for multiscale, integrative modelling frameworks that bridge these computational and experimental approaches. A more holistic representation of keratinocyte behaviour could enhance the development of personalised therapies, improve disease modelling, and refine bioengineered skin substitutes for clinical applications. 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

Emerging power electronic devices like soft open points (SOPs) and electric springs (ESs) play a vital role in enhancing active distribution network (ADN) efficiency. SOPs enable flexible active/reactive power control, while ESs improve demand-side management and voltage regulation. This paper proposes a two-stage stochastic programming model to optimize ADN’s operation by coordinating these fast-response devices with legacy mechanical equipment. The first stage determines hourly setpoints for conventional devices, while the second stage adjusts SOPs and ESs for intra-hour control. To handle ES nonlinearities, a hybrid data–knowledge approach combines knowledge-based linear constraints with a data-driven multi-layer perceptron, later linearized for computational efficiency. The resulting mixed-integer second-order cone program is solved using commercial solvers. Simulation results show the proposed strategy effectively reduces power loss by 42.5%, avoids voltage unsafety with 22 time slots, and enhances 4.3% PV harvesting. The coordinated use of SOP and ESs significantly improves system efficiency, while the proposed solution methodology ensures both accuracy and over 60% computation time reduction. Full article

Amid the escalating climate crisis, integrating variable renewables into power systems demands innovative carbon-conscious grid management. This research presents a Graph Convolutional-Optimization Framework that synergizes Graph Convolutional Networks (GCNs) with hybrid optimization Interior-Point Method, Genetic Algorithms, and Particle Swarm Optimization to minimize emissions while ensuring grid stability under uncertainty. GCNs capture spatial–temporal grid dynamics, providing robust initial solutions that enhance convergence. Chance constraints, scenario reduction via k-medoids, and slack variables address stochasticity and stringent emission caps, overcoming infeasibility challenges. Validated on a 24-bus microgrid, the framework achieves superior performance, with PSO yielding minimal emissions (1.59 kg CO₂) and efficient computation. This scalable, topology-aware approach redefines sustainable grid operations, bridging machine learning and optimization for resilient, low-carbon energy systems aligned with global decarbonization goals. Full article

In this paper, we have constructed a closed-form optimal strategy within a social network using stochastic McKean–Vlasov dynamics. Each agent independently minimizes their dynamic cost functional, driven by stochastic differential opinion dynamics. These dynamics reflect agents’ opinion differences from others and their past opinions, with random influences and stubbornness adding to the volatility. To gain an analytic insight into the optimal feedback opinion, we employed a Feynman-type path integral approach with an appropriate integrating factor, marking a novel methodology in this field. Additionally, we utilized a variant of the Friedkin–Johnsen-type opinion dynamics to derive a closed-form optimal strategy for an agent and conducted a comparative analysis. Full article

Public healthcare infrastructure is among the most energy-intensive of public facilities; therefore, it needs to become more environmentally and economically sustainable by increasing energy efficiency and improving service reliability. Achieving these goals requires modernizing hospital energy systems with renewable energy sources (RESs). This process often involves Energy Service Companies (ESCOs), which propose integrated RES technologies with tailored contractual schemes. However, comparing ESCO offers is challenging due to their heterogeneous technologies, contractual structures, and long-term performance commitments, which make simple cost-based assessments inadequate. This study develops a structured Multi-Criteria Decision-Making (MCDM) methodology to evaluate energy projects in public healthcare facilities. The framework, based on the Analytic Hierarchy Process (AHP), combines both quantitative (net present value, stochastic simulations of energy cost savings, and CO₂ emission reductions) with qualitative assessments (redundancy, flexibility, elasticity, and stakeholder image). It addresses the lack of standardized tools for ranking real-world ESCO proposals in public procurement. The approach, applied to a case study, involves three ESCO proposals for a large hospital in Southern Italy. The results show that integrating photovoltaic generation with trigeneration achieves the highest overall score. The proposed framework provides a transparent, replicable tool to support evidence-based energy investment decisions, extendable to other public-sector infrastructures. Full article

This study develops an optimization-based scheduling framework for coordinating the energy-intensive operations of a steel enterprise with estimated solar power availability. Unlike prior approaches that focus primarily on process efficiency or carbon reduction in isolation, the proposed model integrates demand response with linear programming to improve solar utilization while respecting load priorities. The solar generation profile is derived from typical meteorological year (TMY) irradiance data, adjusted for panel efficiency and system parameters, thereby serving as an estimated input rather than measured data. Simulation results over a 31-day horizon show that coordinated scheduling can reduce grid dependence and increase solar energy utilization by up to 99% under the simulated conditions. While the findings demonstrate the potential of load scheduling for industrial decarbonization, they are based on estimated solar data and a simplified system representation. Future work should incorporate real-world solar measurements and stochastic models to address uncertainty and further validate industrial applicability. Full article

Electric vehicles (EVs) and thermostatically controlled loads (TCLs) are key demand-side resources for load regulation in modern power systems. However, effective load regulation faces significant challenges due to the stochastic nature of EV travel times and environmental uncertainties, such as temperature and solar irradiation fluctuations affecting TCL performance. Additionally, load rebound effects, caused by TCLs increasing power consumption to restore preset indoor temperatures after regulation, may induce secondary demand peaks, thereby offsetting regulation benefits. To address these challenges, this study aims to meet regulation requirements under such uncertainties while mitigating rebound-induced peaks. A rolling-horizon co-optimization method for EV and TCL clusters is proposed, which explicitly considers both uncertainties, load rebound effects and economic losses. First, to address the limited regulation capacity of individual EVs and TCLs, a user clustering mechanism is developed based on willingness to participate in demand response across multiple time intervals. A load rebound evaluation model for TCL clusters is developed to characterize post-regulation load variations and assess the rebound intensity. Subsequently, a load rebound-aware co-optimization model is proposed and solved within a rolling-horizon optimization approach, which performs rolling optimization within each prediction horizon to determine the participating clusters and their regulation capacities for each execution time slot under uncertainties. Simulation results demonstrate that the proposed method, compared with conventional day-ahead and robust optimization, not only meets load regulation requirements under uncertainty, but also effectively mitigates rebound-induced secondary peaks while achieving economic benefits. Full article

When complex computations cannot be performed on board a mobile robot, sensory data must be transmitted to a remote station to be processed, and the resulting actions must be sent back to the robot to execute, forming a repeating cycle. This involves stochastic round-trip times in the case of non-deterministic network communications and/or non-hard real-time software. Since robots need to react within strict time constraints, modeling these round-trip times becomes essential for many tasks. Modern approaches for modeling sequences of data are mostly based on time-series forecasting techniques, which impose a computational cost that may be prohibitive for real-time operation, do not consider all the delay sources existing in the sw/hw system, or do not work fully online, i.e., within the time of the current round-trip. Marginal probabilistic models, on the other hand, often have a lower cost, since they discard temporal dependencies between successive measurements of round-trip times, a suitable approximation when regime changes are properly handled given the typically stationary nature of these round-trip times. In this paper we focus on the hypothesis tests needed for marginal modeling of the round-trip times in remotely operated robotic systems with the presence of abrupt changes in regimes. We analyze in depth three common models, namely Log-logistic, Log-normal, and Exponential, and propose some modifications of parameter estimators for them and new thresholds for well-known goodness-of-fit tests, which are aimed at the particularities of our setting. We then evaluate our proposal on a dataset gathered from a variety of networked robot scenarios, both real and simulated; through >2100 h of high-performance computer processing, we assess the statistical robustness and practical suitability of these methods for these kinds of robotic applications. Full article

Extensive studies about the human immunodeficiency virus type 1 (HIV-1) have allowed the generation of lentiviral vectors as gene delivery vehicles with enhanced safety and efficacy features. In this review, several strategies for controlling the molecular mechanisms occurring during the lentiviral vector manufacturing process are presented. Specifically, modifications focused on LVV manufacturing components, such as plasmids or the producer cell line, that enable increased safety, integrity, and potency of the produced LVV, as well as manufacturing efficiency. Considering the stochasticity of the LVV manufacturing process from plasmid transfection until the budding of the virus from the target cell, minimal modifications might have a huge impact on the final LVV yield. Indeed, the extent of a potential impact may vary depending on the specificities of each LVV regarding the particular genetic payload or the envelope protein. Thus, the feasibility of each of the optimizations described herein requires thorough evaluation. The second part of the review examines the potential multi-purpose nature of the LVV. Growing research in the field has enabled the development of new engineered modalities of LVV, expanding their application scope beyond the traditional ex vivo DNA delivery approach. LVVs are becoming a versatile tool for the packaging or delivery of cargo in the form of DNA, RNA, or protein, allowing their use for in vivo approaches, vaccinology, or gene editing, among others. Full article

Wind turbine power forecasting is crucial for optimising energy production, planning maintenance, and enhancing grid stability. This research focuses on predicting the output of a Senvion MM92 wind turbine at the Kelmarsh wind farm in the UK using SCADA data from 2020. Two approaches are explored: a hybrid model combining Stochastic Differential Equations (SDEs) with Neural Networks (NNs) and Deep Learning models, in particular, Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and the Combination of Convolutional Neural Networks (CNNs) and LSTM. Notably, while SDE-NN models are well suited for predictions in cases where data patterns are chaotic and lack consistent trends, incorporating stochastic processes increases the complexity of learning within SDE models. Moreover, it is worth mentioning that while SDE-NNs cannot be classified as purely “white box” models, they are also not entirely “black box” like traditional Neural Networks. Instead, they occupy a middle ground, offering improved interpretability over pure NNs while still leveraging the power of Deep Learning. This balance is precious in fields such as wind power prediction, where accuracy and understanding of the underlying physical processes are essential. The evaluation of the results demonstrates the effectiveness of the SDE-NNs compared to traditional Deep Learning models for wind power prediction. The SDE-NNs achieve slightly better accuracy than other Deep Learning models, highlighting their potential as a powerful alternative. Full article

In the domain of Shape Encoding, the approximation of the Medial Axis of a solid region in ${\mathbb{R}}^3$ with Boundary Representation M, is relevant because the Medial Axis is an efficient encoding for M in Design, Manufacturing, and Shape Learning. Existing Medial Axis approximations include (a) full Voronoi and (b) and partial Shape Diameter Function (SDF)-based ones. Methods (a) produce large high-frequency data, which must then be pruned. Methods (b) reduce computing expenses at the price of not handling some shapes (e.g., prismatic), and currently, they only synthesize 1D Medial Axes. To partially overcome these limitations, this investigation performs a direct synthesis of a 1D and 2D simplex-based Medial Axis approximation by a combination of stochastic geometric reasoning and graph operations on the SDF-originated point cloud. Our method covers one- and two-dimensional Simplicial Complex Medial Axes, thus improving on 1D Medial Axes approximation methods. Our approach avoids the expensive full computing plus pruning of Medial Axis based on Voronoi methods. Future work is needed in the synthesis of Medial Axis approximation for high-frequency neighborhoods of mesh M. Full article

Stochastic weather generators are commonly employed to create synthetic sequences of daily weather variables across diverse fields, including hydrological, ecological, and agricultural studies. Realistic precipitation sequences, in particular, serve as essential inputs in numerous modeling frameworks. Generalized linear models (GLMs) that incorporate covariates to capture seasonality and teleconnections represent one effective approach for stochastic weather generation. However, these models often underestimate the interannual variability of seasonally aggregated variables, notably precipitation intensity during wet seasons. Recent methods developed to mitigate the issue of overdispersion have nevertheless struggled to adequately replicate observed precipitation intensities in wet seasons. To overcome this limitation, we propose integrating a modified hybrid gamma and generalized Pareto distribution into the GLM-based weather generator. This enhanced method was evaluated using daily precipitation data from Seoul, Korea, and successfully reproduced realistic precipitation intensities while effectively addressing the overdispersion issue. Full article