Search Results (706)

Industrial energy systems are increasingly required to reduce operating costs and CO₂ emissions while integrating variable renewable energy sources. Managing these objectives under uncertainty requires advanced optimization strategies capable of delivering reliable and real-time decisions. To address these challenges, this study focuses on the short-term operational planning of an industrial energy supply system using the rolling horizon approach (RHA). The RHA offers an effective framework to handle uncertainties by repeatedly updating forecasts and re-optimizing over a moving time window, thereby enabling adaptive and responsive energy management. To solve the resulting nonlinear and constrained optimization problem at each RHA iteration, we propose a novel hybrid algorithm that combines Bayesian optimization (BO) with the Interior Point OPTimizer (IPOPT). While global deterministic and stochastic optimization methods are frequently used in practice, they often suffer from high computational costs and slow convergence, particularly when applied to large-scale, nonlinear problems with complex constraints. To overcome these limitations, we employ the BO–IPOPT, integrating the global search capabilities of BO with the efficient local convergence and constraint fulfillment of the IPOPT. Applied to a large-scale real-world case study of a food and cosmetic industry in Germany, the proposed BO–IPOPT method outperformed state-of-the-art solvers in both solution quality and robustness, achieving up to 97.25%-better objective function values at the same CPU time. Additionally, the influence of key parameters, such as forecast uncertainty, optimization horizon length, and computational effort per RHA iteration, was analyzed to assess their impact on system performance and decision quality. Full article

Renewable Energy Communities (RECs) are voluntary coalitions of citizens, small and medium-sized enterprises and local authorities, which cooperate to share locally produced renewable energy, providing environmental, economic, and social benefits rather than profits. Despite a favorable European and Italian regulatory framework, their development is still limited in the Member States. To this end, this paper proposes a methodology to identify optimal spatial configurations of RECs, based on proximity criteria and maximization of energy self-sufficiency. This result is achieved through the mapping of the demand, expressive of the energy consumption of residential buildings; the suitable areas for installing photovoltaic panels on the roofs of existing buildings; the supply; the supply–demand balance, from which it is possible to identify Positive Energy Districts (PEDs) and Negative Energy Districts (NEDs). Through an iterative process, the optimal configuration is then sought, aggregating only PEDs and NEDs that meet the chosen criteria. This method is applied to the case study of the Avellino Province in the Campania Region (Italy). The maps obtained allow local authorities to inform citizens about the areas where it is convenient to aggregate with their neighbors in a REC to have benefits in terms of energy self-sufficiency, savings on bills or incentives at the local level, including those deriving from urban plans. The latter can encourage private initiative in order to speed up the RECs’ deployment. The presented model is being implemented in the framework of an ongoing research and development project, titled Renewable Energy Communities Monitoring, Optimization, and Planning (RECMOP). Full article

Aiming to enable intelligent vehicles to achieve autonomous charging under low-battery conditions, this paper presents a navigation system for autonomous charging that integrates an improved bidirectional A* algorithm for path planning and an optimized YOLOv11n model for visual recognition. The system utilizes the improved bidirectional A* algorithm to generate collision-free paths from the starting point to the charging area, dynamically adjusting the heuristic function by combining node–target distance and search iterations to optimize bidirectional search weights, pruning expanded nodes via a greedy strategy and smoothing paths into cubic Bézier curves for practical vehicle motion. For precise localization of charging areas and piles, the YOLOv11n model is enhanced with a CAFMFusion mechanism to bridge semantic gaps between shallow and deep features, enabling effective local–global feature fusion and improving detection accuracy. Experimental evaluations in long corridors and complex indoor environments showed that the improved bidirectional A* algorithm outperforms the traditional improved A* algorithm in all metrics, particularly in that it reduces computation time significantly while maintaining robustness in symmetric/non-symmetric and dynamic/non-dynamic scenarios. The optimized YOLOv11n model achieves state-of-the-art precision (P) and mAP@0.5 compared to YOLOv5, YOLOv8n, and the baseline model, with a minor 0.9% recall (R) deficit compared to YOLOv5 but more balanced overall performance and superior capability for small-object detection. By fusing the two improved modules, the proposed system successfully realizes autonomous charging navigation, providing an efficient solution for energy management in intelligent vehicles in real-world environments. Full article

A novel unified progressive hybrid censoring is introduced to combine both progressive and hybrid censoring plans to allow flexible test termination either after a prespecified number of failures or at a fixed time. This work develops both frequentist and Bayesian inferential procedures for estimating the parameters, reliability, and hazard rates of the inverted Nadarajah–Haghighi lifespan model when a sample is produced from such a censoring plan. Maximum likelihood estimators are obtained through the Newton–Raphson iterative technique. The delta method, based on the Fisher information matrix, is utilized to build the asymptotic confidence intervals for each unknown quantity. In the Bayesian methodology, Markov chain Monte Carlo techniques with independent gamma priors are implemented to generate posterior summaries and credible intervals, addressing computational intractability through the Metropolis—Hastings algorithm. Extensive Monte Carlo simulations compare the efficiency and utility of frequentist and Bayesian estimates across multiple censoring designs, highlighting the superiority of Bayesian inference using informative prior information. Two real-world applications utilizing rare minerals from gold and diamond durability studies are examined to demonstrate the adaptability of the proposed estimators to the analysis of rare events in precious materials science. By applying four different optimality criteria to multiple competing plans, an analysis of various progressive censoring strategies that yield the best performance is conducted. The proposed censoring framework is effectively applied to real-world datasets involving diamonds and gold, demonstrating its practical utility in modeling the reliability and failure behavior of rare and high-value minerals. Full article

Stope dilution is a major hidden cost driver for the underground operation, especially in terms of reducing ore quality, increasing the amount of processing feed, and effects on operational cost. Accurate calculation and consideration of planned and unplanned dilution and mining loss amounts are essential during mine planning. The user interface named D–Loss has been developed with MATLAB R2023b, which provides a multiparadigm numerical computing environment for faster and more practical calculation of these dilution amounts to address these challenges by quantifying dilution and linking them directly to economic and CO₂ emissions indicators. By determination and analysis of the stope overall dilution amounts, it helps us understand greenhouse gas emissions and ensures the efficient use of underground equipment. Calculation of stope dilution in a practical and rapid manner allows for stope design and operational improvements, which can help reduce dilution in underground operations. This progress is tracked through the D–Loss interface within the short- and long-term production planning. Moreover, by quantifying dilution impacts on comminution and haulage costs, D–Loss becomes a critical software for tracking economic losses and optimizing financial outcomes in the mining industry. D–Loss helps users iteratively assess the efficiency of updates and provides support in mine design, scheduling, and environmental impact control by comparing planning and operational improvements before and after. Full article

This work presents the development of a functional real-time SLAM system designed to enhance the perception capabilities of an Automated Guided Vehicle (AGV) using only a 2D LiDAR sensor. The proposal aims to address recurring gaps in the literature, such as the need for low-complexity solutions that are independent of auxiliary sensors and capable of operating on embedded platforms with limited computational resources. The system integrates scan alignment techniques based on the Iterative Closest Point (ICP) algorithm. Experimental validation in a controlled environment indicated better performance using Gauss–Newton optimization and the point-to-plane metric, achieving pose estimation accuracy of 99.42%, 99.6%, and 99.99% in the position ($x$, $y$) and orientation ($\theta$) components, respectively. Subsequently, the system was adapted for operation with data from the onboard sensor, integrating a lightweight graphical interface for real-time visualization of scans, estimated pose, and the evolving map. Despite the moderate update rate, the system proved effective for robotic applications, enabling coherent localization and progressive environment mapping. The modular architecture developed allows for future extensions such as trajectory planning and control. The proposed solution provides a robust and adaptable foundation for mobile platforms, with potential applications in industrial automation, academic research, and education in mobile robotics. Full article

As a core component of automated logistics systems, delivery robots hold significant application value in the field of unmanned delivery. This research addresses the robot path planning problem, aiming to enhance delivery efficiency and reduce operational costs through systematic improvements to the RIME optimization algorithm. Through in-depth analysis, we identified several major drawbacks in the standard RIME algorithm for path planning: insufficient global exploration capability in the initial stages, a lack of diversity in the hard RIME search mechanism, and oscillatory phenomena in soft RIME step size adjustment. These issues often lead to undesirable phenomena in path planning, such as local optima traps, path redundancy, or unsmooth trajectories. To address these limitations, this study proposes the Multi-Strategy Controlled Rime Algorithm (MSRIME), whose innovation primarily manifests in three aspects: first, it constructs a multi-strategy collaborative optimization framework, utilizing an infinite folding Fuch chaotic map for intelligent population initialization to significantly enhance the diversity of solutions; second, it designs a cooperative mechanism between a controlled elite strategy and an adaptive search strategy that, through a dynamic control factor, autonomously adjusts the strategy activation probability and adaptation rate, expanding the search space while ensuring algorithmic convergence efficiency; and finally, it introduces a cosine annealing strategy to improve the step size adjustment mechanism, reducing parameter sensitivity and effectively preventing path distortions caused by abrupt step size changes. During the algorithm validation phase, comparative tests were conducted between two groups of algorithms, demonstrating their significant advantages in optimization capability, convergence speed, and stability. Further experimental analysis confirmed that the algorithm’s multi-strategy framework effectively suppresses the impact of coordinate and dimensional differences on path quality during iteration, making it more suitable for delivery robot path planning scenarios. Ultimately, path planning experimental results across various Building Coverage Rate (BCR) maps and diverse application scenarios show that MSRIME exhibits superior performance in key indicators such as path length, running time, and smoothness, providing novel technical insights and practical solutions for the interdisciplinary research between intelligent logistics and computer science. Full article

Buildings are a significant component of urban space and are essential to smart cities, catastrophe monitoring, and land use planning. However, precisely extracting building polygons from remote sensing images remains difficult because of the variety of building designs and intricate backgrounds. This paper proposes an end-to-end polygon dynamic adjustment algorithm (PDAA) to improve the accuracy and geometric consistency of building contour extraction by dynamically generating and optimizing polygon vertices. The method first locates building instances through the region of interest (RoI) to generate initial polygons, and then uses four core modules for collaborative optimization: (1) the feature enhancement module captures local detail features to improve the robustness of vertex positioning; (2) the contour vertex tuning module fine-tunes vertex coordinates through displacement prediction to enhance geometric accuracy; (3) the learnable redundant vertex removal module screens key vertices based on a classification mechanism to eliminate redundancy; and (4) the missing vertex completion module iteratively restores missed vertices to ensure the integrity of complex contours. PDAA dynamically adjusts the number of vertices to adapt to the geometric characteristics of different buildings, while simplifying the prediction process and reducing computational complexity. Experiments on public datasets such as WHU, Vaihingen, and Inria show that PDAA significantly outperforms existing methods in terms of average precision (AP) and polygon similarity (PolySim). It is at least 2% higher than existing methods in terms of average precision (AP), and the generated polygonal contours are closer to the real building geometry. Values of 75.4% AP and 84.9% PolySim were achieved on the WHU dataset, effectively solving the problems of redundant vertices and contour smoothing, and providing high-precision building vector data support for scenarios such as smart cities and emergency response. Full article

When planning distributed intelligent power distribution networks, it is necessary to take into account the interests of various distributed generation (DG) operators and power supply enterprises, thereby diversifying and complicating planning models. Additionally, the integration of a high proportion of distributed resources has triggered a transformation in the power flow pattern of active distribution networks, shifting from the traditional unidirectional flow mode to a bidirectional interactive mode. The intermittent and fluctuating operation modes of distributed photovoltaic and wind power generation have also increased the difficulty of distribution network planning. To address the aforementioned challenges, this paper proposes an active distribution network planning strategy that considers the allocation of diverse flexible resources, exploring scheduling flexibility from both the power supply side and the load side. Firstly, a bi-level optimization model integrating planning and operation is constructed, where the upper-level model determines the optimal capacity of investment and construction equipment, and the lower-level model formulates an economic dispatching scheme. Through iterative solving of the upper and lower levels, the final planning strategy is determined. Meanwhile, to reduce the complexity of problem-solving, this paper employs an improved PSO-CS hybrid algorithm for iterative optimization. Finally, the effectiveness and feasibility of the proposed algorithm are demonstrated through validation using an improved IEEE-33-bus test system. Compared with conventional algorithms, the convergence speed of the method proposed in this paper can be improved by up to 21.4%, and the total investment cost can be reduced by up to 3.26%. Full article

This study responds to the urgent need for education that fosters sustainable, self-directed development by introducing a five-phase narrative instruction pre-module grounded within the Life Design Thinking (LDT) framework. Integrating design thinking with narrative theory, LDT promotes learner agency, identity coherence, and adaptive future planning through structured autobiographical reflection. The intervention was conducted in a group setting with 14 adult learners (n = 14) from China. Although participant demographics were not the primary focus, the study emphasized theoretical and methodological development through iterative instructional design. A mixed-methods approach was employed, including rubric-based scoring of 101 written narratives and a thematic analysis of the reflective content. The results demonstrated significant improvements in narrative competence, especially in technical application (+80.91%), reflective depth (+70.0%), with thematic clarity (+58.11%), also showing notable gains related to meaning synthesis and narrative focus. Learners also exhibited enhanced thematic continuity and future-oriented integration. These outcomes highlight the value of narrative pedagogy as a transformative learning approach aligned with SDG 4.7. By cultivating metacognitive awareness and future-readiness, the module contributes to education for sustainable development and provides initial evidence for a potentially scalable and transferable model that supports learners in navigating complexity with meaning and purpose. Full article

A cooperative game negotiation strategy considering multiple constraints is proposed for distributed impulsive multi-spacecraft approach missions in the presence of defending spacecraft. It is a dual-stage decision-making method that includes offline trajectory planning and online distributed negotiation. In the trajectory planning stage, a relative orbital dynamics model is first established based on the Clohessy–Wiltshire (CW) equations, and the state transition equations for impulsive maneuvers are derived. Subsequently, a multi-objective optimization model is formulated based on the NSGA-II algorithm, utilizing a constraint dominance principle (CDP) to address various constraints and generate Pareto front solutions for each spacecraft. In the distributed negotiation stage, the negotiation strategy among spacecraft is modeled as a cooperative game. A potential function is constructed to further analyze the existence and global convergence of Nash equilibrium. Additionally, a simulated annealing negotiation strategy is developed to iteratively select the optimal comprehensive approach strategy from the Pareto fronts. Simulation results demonstrate that the proposed method effectively optimizes approach trajectories for multi-spacecraft under complex constraints. By leveraging inter-satellite iterative negotiation, the method converges to a Nash equilibrium. Additionally, the simulated annealing negotiation strategy enhances global search performance, avoiding entrapment in local optima. Finally, the effectiveness and robustness of the dual-stage decision-making method were further demonstrated through Monte Carlo simulations. Full article

This study presents a discrete event simulation model to minimize operating costs in quarry mining processes by determining the optimal allocation of backhoes and dump trucks, which are the primary mining equipment. The modeling focuses on four principal vehicle types (24-ton dump truck, 2.0 m³ backhoe, 41-ton dump truck, 4.64 m³ backhoe) commonly deployed in quarry mining. The simulation replicates the sequential mining stages involving soil removal, rock ripping (weathered rock or weathered soil), and blasting operations. This methodology is applied to a case study of mining process planning under resource constraints, incorporating real-world quarry conditions in South Korea. Results demonstrate that optimizing the number of equipment units reduces construction costs and shortens the construction period by decreasing dump truck waiting times. When the number of backhoes is limited to 10 during operations, findings indicate an increase in costs and a gradual decline in net profit. Additionally, the interaction between the 24-ton and 41-ton dump trucks is shown to influence the optimal allocation strategy. The simulation-based optimization executes iterative experiments for each scenario, yielding statistically robust results within a 95% confidence interval, thereby supporting informed decision-making for managers. Full article

This paper investigates integrating a sensory data model for managing an existing 50-year-old building. A primary challenge in retrofitting older structures is the optimal deployment of high-quality sensors, systematic data acquisition, and subsequent data management. To address this, the study implemented a network of over 50 sensors connected via 270 m of wired infrastructure, deliberately avoiding wireless transmission to ensure data reliability. This configuration generates 5568 data points daily, which are archived on a dedicated server. The data is planned for integration into the Campus Geographical Information System (GIS), enabling private and public access. A methodology was employed, involving the strategic placement of sensors based on building use patterns, continuous data monitoring, and iterative sensor performance evaluation. The findings from the study indicate that integrating sensory data through this structured approach significantly enhances building management capabilities. Specifically, the results demonstrate improved energy efficiency and environmental performance, which is particularly relevant for public and educational facilities. The research highlights that a data-driven, monitoring-based management system can optimize operational functions and inform future retrofitting strategies for aging buildings. Full article

Many dogs suffer from pruritus, which is commonly caused by atopic dermatitis and associated with skin inflammation. The immune system and inflammatory response, and in particular the gut–skin axis, are central to the pathogenesis of atopic dermatitis. Managing atopic dermatitis involves complex, iterative treatment plans; early strategies supporting gut–skin health are needed to prevent elevated itching from progressing toward a disease condition requiring drug therapy. This double-blind, placebo-controlled, randomized trial evaluated the ability of a novel, indole-rich canine immune health postbiotic (CIHP) to reduce itching and promote a healthy gut microbiome in dogs with subclinical, but elevated itching behavior. Thirty dogs were stratified into two groups based on baseline scratching frequency, receiving either CIHP or placebo as a powder topper for 28 days. Canine itching was evaluated through accelerometer-based tracking and the Pruritus Visual Analog Scale (PVAS) score on Days 0, 7, 14, 21, and 28. Skin and coat health was assessed on Days 0, 14, and 28, and the gut microbiome was sequenced from fecal samples on Days 0 and 28. CIHP reduced scratching by 20% relative to the baseline (p = 0.032) and PVAS score by 27% compared to the placebo (p = 0.02). CIHP improved skin and coat quality compared to the placebo at Day 14 (p = 0.01) and increased Shannon diversity by 4.6% (p = 0.043), shifting gut microbiome composition. These findings validate this postbiotic’s ability to reduce itching in dogs with subclinical, but elevated itching behavior, provide evidence of promoting a healthy gut–skin axis, and suggest potential as an early intervention in the context of pruritic conditions, as well as for broader immune-related benefits. Full article

This study investigates a stochastic production planning problem with regime-switching parameters, inspired by economic cycles impacting production and inventory costs. The model considers types of goods and employs a Markov chain to capture probabilistic regime transitions, coupled with a multidimensional Brownian motion representing stochastic demand dynamics. The production and inventory cost optimization problem is formulated as a quadratic cost functional, with the solution characterized by a regime-dependent system of elliptic partial differential equations (PDEs). Numerical solutions to the PDE system are computed using a monotone iteration algorithm, enabling quantitative analysis. Sensitivity analysis and model risk evaluation illustrate the effects of regime-dependent volatility, holding costs, and discount factors, revealing the conservative bias of regime-switching models when compared to static alternatives. Practical implications include optimizing production strategies under fluctuating economic conditions and exploring future extensions such as correlated Brownian dynamics, non-quadratic cost functions, and geometric inventory frameworks. In contrast to earlier studies that imposed static or overly simplified regime-switching assumptions, our work presents a fully integrated framework—combining optimal control theory, a regime-dependent system of elliptic PDEs, and comprehensive numerical and sensitivity analyses—to more accurately capture the complex stochastic dynamics of production planning and thereby deliver enhanced, actionable insights for modern manufacturing environments. Full article