Search Results (742)

The growing penetration of electric vehicles (EVs) and distributed photovoltaic (PV) generation is increasing operational uncertainty in distribution networks and intensifying long-standing challenges such as higher power losses, rising peak demand, and voltage instability. To address these issues, this paper proposes a multi-objective optimization framework for the strategic placement of smart meters equipped with demand response (DR) capability in radial distribution systems. Unlike conventional placement approaches that mainly focus on monitoring or reducing non-technical losses, the proposed method integrates active load control into the planning stage and explicitly considers the stochastic behavior of loads, PV generation, and electric vehicle charging stations (EVCSs). The problem is formulated with four objectives: minimizing total power losses, substation peak demand, voltage deviation penalty, and installation cost. A scenario-based stochastic model is employed to represent operational variability across the network. The resulting nonlinear mixed discrete optimization problem is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), an evolutionary multi-objective optimization technique that generates a set of Pareto-optimal solutions representing trade-offs among conflicting objectives. Smart meters are allowed to curtail a portion of controllable demand during critical loading conditions, which helps reduce feeder loading and improve voltage profiles. The proposed approach is evaluated on the IEEE 33-bus and IEEE 69-bus distribution systems. Simulation results demonstrate significant reductions in power losses and peak demand, with the IEEE 33-bus system achieving up to a 26.2% reduction in power losses and 52.5% reduction in substation peak demand compared with existing metaheuristic approaches. The results also indicate improved voltage stability and effective performance in the IEEE 69-bus system, confirming the importance of topology-aware DR-enabled planning. Overall, the findings show that embedding demand response capability within smart meter allocation can significantly enhance the resilience and operational efficiency of modern distribution networks with high EV and PV penetration. Full article

This study develops a game-theoretic framework to analyze collaborative advertising decisions between manufacturers and retailers in seasonal product supply chains characterized by competitive–cooperative channel relationships. We formulate a mathematical programming model to jointly optimize advertising efforts, the manufacturer’s advertising cost-sharing rate, order quantities, and inventory decisions across distinct channel configurations—including a single manufacturer–retailer dyad and a competitive multi-channel market. Numerical experiments and sensitivity analyses are conducted to investigate how key structural parameters—particularly demand elasticity and channel power asymmetry—influence overall system performance and equilibrium decision outcomes. Results indicate that well-designed collaborative advertising mechanisms enhance total channel profitability and, under specific conditions, yield Pareto-improving outcomes for both parties. This study makes three primary contributions: (i) it integrates inter-firm competition with intra-channel cooperation within a unified strategic framework; (ii) it jointly coordinates advertising and inventory decisions—two critical operational levers—rather than treating them in isolation; and (iii) it embeds financial arrangements (e.g., cost sharing) endogenously into the analytical model, thereby offering a novel, theoretically grounded, and practically implementable decision-support framework for distribution systems operating in complex, dynamic market environments. Full article

Spaceborne precipitation products such as NASA IMERG V07 provide sub-hourly data required for hydrological modelling, but systematic biases in wet-event frequency and extreme-intensity representation limit their reliability for applications sensitive to precipitation extremes. This study develops a three-stage calibration pipeline combining probability-of-precipitation frequency adaptation, empirical quantile mapping of the distribution body, and Generalised Pareto Distribution tail modelling with constrained blending. The approach is calibrated against 202 Roshydromet stations using 3-hourly observations and evaluated on 15 spatially independent stations over a 9-year validation period. At the station-optimal blending weight, the proposed pipeline reduces median absolute percentage bias at the P99 quantile from 43.9% to 10.2%, while maintaining comparable volume balance (|PBIAS| 6.5%). To suppress a disaggregation artefact arising from amplification of multi-hour accumulations, the operational gridded R-factor product instead adopts a more conservative blend (|PBIAS@P99| = 24.9%) together with an empirically constrained accumulation cap, although the absence of sub-hourly calibration data remains the principal limitation. The calibrated dataset is applied to derive a 24-year (2001–2024) rainfall erosivity climatology for the Volga region, yielding a domain-mean R-factor of 254 ± 55 MJ mm ha⁻¹ h⁻¹ yr⁻¹ with no detectable monotonic trend. The proposed framework improves the representation of precipitation extremes and provides a transferable preprocessing approach for hydrological modelling applications. Full article

This paper presents a decision-support framework for analysing the trade-off between total operational cost and customer service level in multi echelon inventory systems. The model integrates fixed-order-quantity replenishment policies, lead-time dynamics and multi objective optimisation to generate a detailed Pareto frontier of efficient solutions. A real multi echelon distribution network is used to demonstrate the model’s applicability and managerial relevance. The results indicate that raising the service level from 46% to the sector standard of 96% increases total cost by approximately 19%, while achieving full demand satisfaction requires an additional 5% cost increase for only marginal service improvement. This pattern reveals a clear cost–service turning point around the 96% service level, beyond which additional gains exhibit sharply diminishing returns. The framework, therefore, provides a transparent and analytical mechanism for identifying replenishment strategies that balance cost efficiency with service performance. By decomposing total cost into ordering, holding, transport and lost-sales components, the model enhances managerial visibility and supports targeted policy adjustments. The paper also discusses limitations of the current formulation and outlines avenues for future research, including alternative replenishment policies, multi-product extensions and richer uncertainty modelling. Full article

To address the challenges of combinatorial explosion and expensive evaluations in truck–drone (truck–UAV) collaborative delivery under complex geographical constraints, this paper proposes a Surrogate-assisted Rezone-Enhanced Multi-objective Adaptive Evolutionary Algorithm (SRE-MAEA). As a knowledge-driven decomposition-based surrogate-assisted framework, the proposed algorithm aims to synergistically optimize a four-dimensional conflicting objective space consisting of economic cost, social satisfaction, environmental emissions, and battery resilience. To overcome the curse of dimensionality in high-dimensional and strongly constrained environments, SRE-MAEA constructs an adaptive Rezone Search architecture. By dynamically deconstructing the decision space, it transforms global search pressure into refined knowledge mining within high-potential local regions. The core mechanism incorporates an intelligent sampling strategy based on the Multi-Armed Bandit (MAB). By utilizing real-time evolutionary feedback to dynamically prioritize the Pareto contribution of each rezone, the MAB achieves pruning-level scheduling of expensive evaluation resources. Simulation results on 15 benchmark instances with clustered, random, and mixed spatial distributions demonstrate that SRE-MAEA exhibits superior convergence boundaries and distribution uniformity in terms of IGD and HV metrics, significantly outperforming state-of-the-art regression-based strategies. Furthermore, computational efficiency analysis confirms that by precisely identifying invalid search paths via the MAB mechanism, SRE-MAEA maintains a high-precision Pareto front while reducing the average CPU time by approximately 35.2–48.5%. This effectively resolves the computational bottleneck caused by complex battery resilience integral models. This research provides an efficient algorithmic paradigm for resilient logistics scheduling in extreme environments and holds significant academic value and engineering application prospects. Full article

Modern machine learning systems can achieve remarkable predictive performance. Nevertheless, in several fields, this is not enough to produce acceptable solutions as we need formal guarantees of robustness, fairness, and interpretability. Most existing approaches treat these properties separately or introduce them through external constraints, which makes their interaction difficult to analyze. In this work, we develop a unified variational perspective that incorporates these requirements directly into the learning objective. Concretely, we model learning as the minimization of a composite functional that combines predictive risk, regularization, and additional terms that capture robustness, fairness, and interpretability. This viewpoint allows us to study these properties within a single mathematical framework. Under standard assumptions, we prove the existence of minimizers and show that the resulting solutions are Pareto-optimal for the associated multi-objective problem. We illustrate the framework using examples based on adversarial and distributional robustness, statistical fairness criteria, and a notion of interpretability. The analysis points out the trade-offs that inevitably arise. We also examine statistical aspects of the proposed objective and show that classical generalization guarantees can still be obtained under appropriate conditions. The resulting framework provides a flexible basis for designing reliable learning systems. Full article

Background: Upgrading existing housing, particularly affordable housing in China’s high-altitude cold regions, to near-zero energy standards requires balancing three key considerations: carbon reduction, life-cycle cost, and residents’ affordability. Methods: We developed a simulation-based multi-objective optimization framework to evaluate repair-and-retrofit packages involving the building envelope, ventilation, heating electrification, on-site renewables, and control strategies, subject to social feasibility and affordability constraints. Results: The Pareto-optimal solutions revealed a clear knee region in which substantial operational carbon reductions and acceptable thermal safety could be achieved at moderate investment levels. Further decarbonization was enabled by strong system-level synergies among heat recovery ventilation, heat pumps, and photovoltaic systems. Affordability-constrained optimization shifted the feasible solution space toward options associated with lower household energy burdens and more favorable distributional outcomes. Conclusions: Policy scenario analysis indicates that grid decarbonization and targeted financial support can expand the feasible space for low-carbon pathways and improve equity, thereby enabling scalable near-zero energy upgrading strategies. Full article

The discrete version of the Pareto distribution, popularly known as the Zipf distribution, and its truncated version are not new in the literature but several structural properties have not yet been discussed, and nor has its application to a wide range of observed phenomena. Here, we refer to the truncated Zipf distribution meaning the Zipf distribution when the support set is finite. Several interesting and useful structural properties of the truncated Zipf distribution, such as the modal dominance bounds, recurrence relation among successive raw moments, and stochastic ordering are thoroughly discussed. To exhibit the flexibility of the assumed probability model, COVID-19 datasets from three different geographical regions have been re-analyzed and are compared with several rival univariate discrete probability models which establishes the superiority of the truncated Zipf distribution. Full article

The global energy system is undergoing a significant transformation driven by rapid electrification, urbanization, and the emergence of new categories of electricity consumers. In particular, the increasing load density in low-voltage distribution networks within urban areas requires a reconsideration of conventional methodologies for the placement of transformer substations. Traditional planning approaches are often based on empirical service radii or static demand factors and therefore fail to adequately reflect the complexity of modern urban power systems. This study proposes a multi-objective optimization model for the optimal placement of transformer substations in 10/0.4 kV urban distribution networks. The proposed model simultaneously considers power losses, economic costs, and system reliability. In addition, the design load model is extended through the introduction of a comfort coefficient that captures additional electricity consumers typical of modern urban infrastructure, including HVAC systems, elevators, pumping systems, and electric vehicle charging stations. In contrast to traditional empirical approaches, the transformer service radius is modeled as a physical parameter determined by voltage drop limits, cable thermal constraints, and failure intensity. The optimization problem is solved using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Each candidate solution generated by the algorithm is validated through AC load-flow simulations performed in the DIgSILENT PowerFactory environment. The proposed methodology is evaluated using real data from a 0.48 km² urban area in the city of Tashkent. The results indicate that increasing the transformer service radius reduces capital investment costs but leads to higher power losses and longer interruption durations. According to the Pareto analysis, a service radius of approximately 300 m represents the optimal compromise between technical, economic, and reliability criteria for the studied area. The proposed methodology can serve as an effective tool for the scientifically grounded planning of urban power supply systems and for improving energy efficiency in modern distribution networks. Full article

The escalating frequency and intensity of extreme rainfall events driven by climate change threaten infrastructure resilience and societal safety, underscoring the urgent need for robust models to predict these events. Previous studies on the integration of Extreme Value Theory (EVT) and machine learning in modelling extreme rainfall events have not explored the use of a time-varying threshold. This study introduces a novel time-varying threshold Generalised Pareto (GP) regression tree for modelling extreme rainfall in Durban, South Africa. The proposed hybrid model combines EVT with covariate-driven regression tree partitioning, allowing the threshold to evolve dynamically with meteorological conditions. Using daily rainfall and meteorological covariate data from 1981 to 2025, the model was developed, pruned, and benchmarked against a static-threshold GP regression tree and a time-varying threshold Generalised Pareto Distribution (GPD). Evaluation based on the Bayesian Information Criterion (BIC) and log-likelihood demonstrated the superior performance of the proposed model in capturing covariate-driven heterogeneity and temporal variability of rainfall extremes. Four distinct climatic regimes with different tail behaviours and return levels were identified. This study provides the first meteorological application of a time-varying threshold GP regression tree and offers practical insights into flood risk assessment and climate resilience planning in the city of Durban. Full article

The post-combustion carbon capture process with monoethanolamine/piperazine (MEA/PZ) blends encounters notable modeling and optimization challenges. These arise from strong thermodynamic–kinetic nonlinear coupling, as well as limited availability of high-quality experimental data. To address this, we propose a machine learning and multi-objective optimization strategy driven by physicochemical features. By extracting explicit physical features and embedding physicochemical constraints into data-driven models, this study evaluated the predictive performance of three distinct algorithms based on wet-wall column experimental data. These algorithms included natural gradient boosting (NGBoost), sure independence screening and sparsifying operator (SISSO), and gaussian process regression (GPR). Subsequently, an optimization problem aimed at minimizing $P_{{\mathrm{CO}}_2}^{*}$ and maximizing $k_{\mathrm{g}}^{\prime }$ was formulated. The multi-objective beluga whale optimization (MOBWO) algorithm was then employed for global optimization and benchmarked against the traditional non-dominated sorting genetic algorithm II (NSGA-II). Results indicate that the Gaussian process regression (GPR) model performed best when it was enhanced by physicochemical features and optimized via Bayesian hyperparameter tuning. It achieved R² values of 0.989 and 0.953 for $P_{{\mathrm{CO}}_2}^{*}$ and $k_{\mathrm{g}}^{\prime }$, with average absolute relative deviations (AARDs) kept below 15.7% and 12.2% respectively. Feature importance analysis validated the underlying physical laws. Specifically, temperature dictates thermodynamic equilibrium, while CO₂ loading limits mass transfer kinetics. In the optimization phase, MOBWO outperformed NSGA-II by generating a more uniformly distributed Pareto front. Decision-making analysis further identified three typical operating regimes encompassing kinetics-dominant, thermodynamics-dominant, and comprehensive equilibrium conditions. This framework provides a robust paradigm for small-sample modeling and optimization in complex chemical processes. Full article

Two-dimensional transition metal carbides and nitrides, known collectively as MXenes, are attractive photocatalyst candidates because their surface chemistry and atomic composition can be tuned over a wide compositional window. A crucial design quantity is the electronic bandgap, which selects whether a given MXene couples with solar radiation and aligns with the redox levels of water splitting. High-fidelity bandgap calculations using the PBE0 hybrid functional are computationally expensive, which has motivated several machine learning surrogates. To the best of our knowledge, this is the first study to integrate a continuous-depth Neural Ordinary Differential Equation backbone with multi-fidelity $\Delta$ learning, distribution-free split-conformal calibration, and uncertainty-aware Pareto screening into a single mathematically grounded pipeline for MXene bandgap prediction. In this work, we develop a physics-constrained neural ordinary differential equation (PC-NODE) that predicts MXene bandgaps from a compact 34-dimensional descriptor set, without relying on the density of states. The model couples a classifier head for the metal/semiconductor decision with a regression head for the gap magnitude, and enforces three physically motivated properties: non-negativity of the predicted gap and monotonicity between the low-fidelity Perdew–Burke–Ernzerhof (PBE) and the high-fidelity PBE0 estimates are obtained exactly through a softplus-parameterised $\Delta$ learning construction, while a hurdle coupling that drives metal predictions towards zero is enforced via a quadratic penalty and verified empirically. In short, two of the three physical constraints are guaranteed by construction, and the third is approximately enforced and verified empirically; the same distinction is maintained consistently in the methodology, the constraint audit and the conclusion. Trained on the 4356-structure MXgap database, a ten-seed ensemble reaches a mean absolute error of $0.186$ eV (per-seed $0.206\pm 0.006$ eV) and a coefficient of determination $R^2=0.880$ on the semiconductor test subset, with a classifier accuracy of $0.856$ and a Receiver Operating Characteristic Area Under the Curve (ROC-AUC) of $0.925$. A split-conformal calibration step then delivers prediction intervals whose empirical coverage matches the 90% target within 0.5 percentage points. Finally, an uncertainty-aware Pareto screening step applies the trained surrogate to a held-out subset of 396 lanthanum-based MXenes and identifies 74 candidates inside the photocatalytic water splitting window [1.23, 3.10] eV. The framework offers a mathematically grounded, data-efficient alternative to feature-heavy pipelines and is reproducible from the open MXgap resource. Full article

Driven by the dual-carbon goals, the high-proportion integration of distributed renewable energy into distribution networks poses significant challenges to operational flexibility due to the inherent intermittency and uncertainty of renewable sources. While direct control of flexible resources is possible, it often entails high costs and lacks mechanisms to incentivize proactive participation. This paper investigates the flexible optimal operation of distribution networks with the active participation of aggregated user-side flexible resources. A two-layer day-ahead optimization framework is proposed. At the lower layer, user-side flexible resource participants employ a deep learning-based intelligent decision-making model to formulate their clearing strategies rapidly, eliminating the need for detailed physical models and iterative calculations. At the upper layer, the distribution network operator (DNO) establishes a multi-objective optimization model that simultaneously minimizes comprehensive operational costs and the net load fluctuation rate to enhance flexibility. The model coordinates distributed generation, energy storage, and user-side resources via a time-of-use pricing mechanism. The fast non-dominated sorting genetic algorithm (NSGA-II) is adopted to obtain the Pareto-optimal set, from which the optimal solution is selected using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Case studies on a modified IEEE 33-bus distribution system demonstrate that the proposed method effectively guides the demand response of user-side resources. The results confirm significant improvements in the economic operation of the distribution network, along with enhanced flexibility evidenced by increased net load adequacy and a reduced net load fluctuation rate, thereby improving the system’s accommodation capability for renewable energy. Full article

In both experimental and theoretical analyses of data, we often look to select a set of simple components that can be combined to create an appropriate model for the data. A convenient way to do this is to use quantile functions that can be added or transformed to obtain new distributions. In this work, we connect quantile statistics and mean absolute deviations (MADs) by deriving a general class of MAD-based shape metrics expressed as integrals of the quantile function, with a direct geometric interpretation. Our approach is applicable to distributions with finite mean that include many of the commonly used distributions, including those without a variance, such as the Pareto. When simple midpoint quadrature is used, the proposed metrics recover widely used quantile-shape metrics, including the interquartile range, Galton skewness, and Moore’s octile kurtosis as special cases. We further propose a C-Trapezoid quadrature approximation that combines cubic polynomial endpoint extrapolation with trapezoidal integration, achieving approximation errors that are significantly lower than those of the midpoint approximation for many common distributions. The proposed approximation provides simple-to-compute formulas for shape analysis and yields closed-form, non-iterative parameter-estimation formulas. These formulas are easy to compute and interpret, and they are applicable to a wide class of distributions, including those without an explicit cumulative distribution function or some with heavy tails. Unlike maximum likelihood estimation, the proposed method is more robust and has simple geometric interpretation. We illustrate the methodology with two detailed case studies. The proposed approach gives a simple way to quickly assess distributional shape without any specialized tools. Full article

Wireless power transfer (WPT), characterized by its excellent insulation properties and ease of maintenance, has recently emerged as a promising solution to the power supply challenges faced by online monitoring equipment on high-voltage transmission towers in complex environments. Existing research primarily relies on regular, closely wound solenoids to power these monitoring devices; however, this approach often makes it difficult to optimize the magnetic field distribution to maximize mutual inductance, thereby limiting transmission efficiency and power and hindering lightweight design. To address these issues, this paper proposes an optimized design scheme for variable-pitch (non-uniform) domino WPT coils based on insulator string structures. First, a parameter calculation model utilizing segmented current analysis is constructed to accurately determine the inductance of non-uniform solenoids, with simulations confirming an error rate below 5%. Subsequently, by integrating domino multi-coil theory into an elitist non-dominated sorting genetic algorithm (NSGA-II), dual-objective optimization is performed. Targeting maximum transmission efficiency and output power under spatial and insulation constraints, a set of Pareto optimal solutions is derived. Ultimately, a 113.7 W insulator domino coil WPT system prototype is constructed to validate the design’s stability. The proposed system achieves a maximum efficiency of 85.73%, with a single-stage load delivering up to 97.48 W. Full article