Search Results (16,857)

Locating all roots of nonlinear equation of systems (NESs) in a single computational procedure remains a fundamental challenge in computational mathematics. The Dynamic Neighborhood Particle Swarm Optimization algorithm based on Euclidean Distance (EDPSO) is proposed to address this issue. First, a dynamic neighborhood strategy based on Euclidean distance is proposed, to facilitate particles within the population with forming appropriate neighborhoods. Secondly, the Levy flight strategy is integrated into the particle velocity-update mechanism to balance the global search capability and local search capability of particles. Furthermore, integrating a discrete crossover strategy into the PSO algorithm enhances its capability in solving high-dimensional nonlinear equations. Finally, to validate the effectiveness and feasibility of the proposed algorithms, the EDPSO algorithm, along with its comparative counterparts, is applied to solve 20 NESs problems and the forward kinematics equations of a 3-RPS parallel mechanism. Experimental results demonstrate that for the 20 NESs, the EDPSO algorithm achieved the highest success rate (SR = 0.992) and root rate (RR = 0.999) among all compared methods, followed by LSTP, NSDE, KSDE, NCDE, HNDE, and DR-JADE. In solving the forward kinematics of the 3-RPS parallel mechanism, the EDPSO algorithm achieved the highest SR of = 0.9975 and RR = 0.9800, followed by LSTP, KSDE, DR-JADE, NCDE, NSDE, and HNDE, based on these metrics. Full article

To enhance the spectral efficiency of hybrid beamforming in millimeter-wave massive MIMO systems, the problem is formulated as a high-dimensional non-convex optimization under constant modulus constraints. A novel algorithm based on fourth-order tensor Tucker decomposition is proposed. Specifically, the frequency-domain channel matrices are structured into a fourth-order tensor to explicitly capture the couplings across the spatial, frequency, and user domains. To tackle the non-convexity induced by constant modulus constraints, the analog precoder and combiner are derived by solving a truncated-rank Tucker decomposition problem through the Alternating Direction Method of Multipliers and Alternating Least Squares schemes. Subsequently, in the digital domain, the Regularized Block Diagonalization algorithm is integrated with the subcarrier and user factor matrices—obtained from the tensor decomposition—along with the water-filling strategy to design the digital precoder and combiner, thereby achieving a balance between multi-user interference suppression and noise enhancement. The proposed tensor-based algorithm is demonstrated through simulations to outperform existing state-of-the-art schemes. This work provides an efficient and mathematically sound solution for hybrid beamforming in dense multi-user scenarios envisioned for sixth-generation mobile communications. Full article

Building maintenance decisions are complex and often influenced by various factors. Multi-criteria decision-making (MCDM) methods have been widely applied to address this complexity, yet guidance on selecting the most appropriate method for specific problems remains limited. Considering these, the purpose of this study is to provide a guidance for the nexus of MCDM methods and facilities management (FM) and building maintenance with the aim of supporting the selection of the most appropriate MCDM method for a specific problem. To achieve this, the study first offers a comprehensive overview of MCDM applications in FM and building maintenance through a systematic literature review guided by the PRISMA framework combined with scientometric analysis. This approach identifies key trends, reviews the methods most frequently employed, and outlines future research directions. From an initial pool of 4291 records retrieved from Scopus and Web of Science between 2000 and 2024, 107 studies were further analyzed. Using VOSviewer and Bibliometrix, the review maps the application of MCDM methods in FM and building maintenance over this period. As a major outcome of the study, a contextual MCDM Method Selection Matrix is developed, linking specific FM and maintenance problems to the most suitable MCDM methods. The findings reveal growing adoption of hybrid MCDM methods and highlight persistent challenges, including subjectivity, uncertainty, expert qualifications, methodological gaps, and technology integration in the decision-making process. By providing structured guidance on method selection, the contextual MCDM Method Selection Matrix supports researchers and practitioners in achieving consistent, data-driven, and context-sensitive decision-making, ultimately enhancing the longevity, efficiency, and sustainability of the built environment. Full article

The move toward environmentally friendly methods in the global manufacturing sector has led to the use of minimum quantity lubrication (MQL) as an eco-friendly alternative to traditional flood cooling. However, the natural limits of MQL in high-performance settings have led to the use of nanotechnology, which has resulted in the creation of nanofluids, engineered colloidal suspensions that significantly improve the thermophysical and tribological properties of base fluids. This paper gives a complete overview of the latest developments in nanofluid technology for use in machining. It starts with the basics of MQL and the rules for making, describing, and keeping nanofluids stable. The review examines the application and effectiveness of single and hybrid nanofluids in various machining processes. It goes into detail about how they improve tool life, surface integrity, and overall efficiency. It also examines the benefits of integrating nanofluid-assisted MQL (NMQL) with more advanced and hybrid systems, including cryogenic cooling (cryo-NMQL), ultrasonic atomization, electrostatic–magnetic assistance, and multi-nozzle delivery systems. The paper also gives a critical look at the main problems that these technologies face, such as the long-term stability of nanoparticle suspensions, their environmental and economic viability as measured by life cycle assessment (LCA), and the important issues of safety, toxicology, and disposal. This review gives a full picture of the current state and future potential of nanofluid-assisted sustainable manufacturing by pointing out important research gaps, like the need for real-time LCA data, cost-effective scalability, and the use of artificial intelligence (AI) to improve processes, and by outlining future research directions. Full article

This study presents the Fourier–Gegenbauer integral Galerkin (FGIG) method, a new numerical framework that uniquely integrates Fourier series and Gegenbauer polynomials to solve the one-dimensional advection–diffusion (AD) equation with spatially symmetric periodic boundary conditions, achieving exponential convergence and reduced computational cost compared to traditional methods. The FGIG method uniquely combines Fourier series for spatial periodicity and Gegenbauer polynomials for temporal integration within a Galerkin framework, resulting in highly accurate numerical and semi-analytical solutions. Unlike traditional approaches, this method eliminates the need for time-stepping procedures by reformulating the problem as a system of integral equations, reducing error accumulation over long-time simulations and improving computational efficiency. Key contributions include exponential convergence rates for smooth solutions, robustness under oscillatory conditions, and an inherently parallelizable structure, enabling scalable computation for large-scale problems. Additionally, the method introduces a barycentric formulation of the shifted Gegenbauer–Gauss (SGG) quadrature to ensure high accuracy and stability for relatively low Péclet numbers. This approach simplifies calculations of integrals, making the method faster and more reliable for diverse problems. Numerical experiments presented validate the method’s superior performance over traditional techniques, such as finite difference, finite element, and spline-based methods, achieving near-machine precision with significantly fewer mesh points. These results demonstrate its potential for extending to higher-dimensional problems and diverse applications in computational mathematics and engineering. The method’s fusion of spectral precision and integral reformulation marks a significant advancement in numerical PDE solvers, offering a scalable, high-fidelity alternative to conventional time-stepping techniques. Full article

Integrated Pest Management (IPM) is a system that combines ready-made plant protection methods. IPM guidelines apply to all users of plant protection products and require the prioritization of preventative methods. Adherence to IPM principles contributes to the production of healthy and safe food. In Poland, the implementation of IPM into agricultural practice remains a solution to the problem. Furthermore, it is necessary to ensure education and implementation of IPM at the basic or implementation level. The IPM element, particularly emphasized in the 2009/128/EC Directive, is the use of so-called warning systems, tools that address the issue of plant protection application. In this regard, it is necessary to use decision support systems (DSSs). DSSs are digital solutions that integrate meteorological, global, and field data. They include the risk of disease and pest occurrence and the timing of the application. DSSs are not part of the farmer’s experience or presentation but support them in making sound decisions. DSS reduces costs, the side effects of plant protection, and energy consumption. Examples of such solutions in Poland include the eDWIN platform and OPWS, classified, among others, in cereal protection against fungi. The aim of this article is to present the role, capabilities, and limitations of decision support systems in modern agricultural production and their importance in the context of the Green Deal and digital agriculture. Full article

Soil saline–alkali stress is a major problem faced by global agriculture, and there is an urgent need to develop efficient amelioration strategies. While both probiotics and plant stress-resistant molecules play critical roles in the alleviation of crop stress, their efficient retention in crop rhizosphere regions remains a great challenge. In this study, the nanocarrier ZIF-8@SPBP@betaine (ZSBet) was constructed by introduction of the synthesized polysaccharide-binding protein (SPBP) and the stress-resistant molecule betaine to the metal–organic framework ZIF-8. During co-incubation, the probiotic Novosphingobium capsulatum and ZSBet efficiently bound together to form ZSBet + Novo co-assemblies, i.e., the integrated protein-ZIF-8-probiotic complexes mediated by polysaccharide-receptor recognition, which exhibited strong root-binding abilities. Microbiome analysis revealed that ZSBet + Novo reduced the α-diversity of rhizosphere bacteria and increased the absolute abundance of biofilm formation-related bacteria, e.g., Novosphingobium, Sphingobium, and Lactococcus. During wheat cultivation in saline–alkali soil, ZSBet + Novo reduced soil pH by 0.63 units, decreased soil salt content by 0.11 g/kg, and increased soil nutrient levels. Furthermore, the co-assembly enhanced the wheat grain number by 145.05% and reduced root malondialdehyde and proline contents by 42.00% and 39.13%, respectively. This study provides a new strategy for improving crop resistance under saline–alkali stress in combination with nanotechnology and synthetic biology. Full article

This paper presents an optimal scheduling framework for a multi-energy hub (EH) that integrates electricity, natural gas, wind energy, energy storage systems, and demand response (DR) programs. The EH incorporates key system components including transformers, converters, boilers, combined heat and power (CHP) units, and both thermal and electrical energy storage. A novel aspect of this work is the joint coordination of multi-carrier energy flows with DR flexibility, enabling consumers to actively shift or reduce loads in response to pricing signals while leveraging storage and renewable resources. The optimisation problem is formulated as a mixed-integer linear programming (MILP) model and solved using the CPLEX solver in GAMS. To evaluate system performance, five case studies are investigated under varying natural gas price conditions and hub configurations, including scenarios with and without DR and CHP. Results demonstrate that DR participation significantly reduces total operating costs (up to 6%), enhances renewable utilisation, and decreases peak demand (by around 6%), leading to a flatter demand curve and improved system reliability. The findings highlight the potential of integrated EHs with DR as a cost-effective and flexible solution for future low-carbon energy systems. Furthermore, the study provides insights into practical deployment challenges, including storage efficiency, communication infrastructure, and real-time scheduling requirements, paving the way for hardware-in-the-loop and pilot-scale validations. Full article

The emergence of large language models (LLMs) has opened new avenues for integrating artificial intelligence into architectural design workflows. This paper explores the feasibility of applying generative AI to solve a classic combinatorial problem: generating valid building geometries of a modular family house structure. The problem involves identifying all valid placements of six spatial blocks under strict architectural constraints. The study contrasts the conventional algorithmic solution with generative approaches using ChatGPT-3.5, ChatGPT-4o, and a hybrid expert model. While early GPT models struggled with accuracy and solution completeness, the hybrid expert-guided approach demonstrated a successful synergy between LLM-driven code generation and domain-specific corrections. The findings suggest that, while LLMs alone are insufficient for precise combinatorial tasks, hybrid systems combining classical and AI techniques hold great promise for supporting architectural problem solving including building geometry generation. Full article

The integration of machine learning with metaheuristic optimization has emerged as one of the most promising frontiers in artificial intelligence and global search. Metaheuristics offer flexibility and effectiveness in solving complex optimization problems where gradients are unavailable or unreliable, but often struggle with premature convergence, parameter sensitivity, and poor scalability. ML techniques, especially supervised, unsupervised, reinforcement, and meta-learning, provide powerful tools to address these limitations through adaptive, data-driven, and intelligent search strategies. This review presents a comprehensive survey of ML-enhanced metaheuristics for global optimization. We introduce a functional taxonomy that categorizes integration strategies based on their role in the optimization process, from operator control and surrogate modeling to landscape learning and learned optimizers. We critically analyze representative techniques, identify emerging trends, and highlight key challenges and future directions. The paper aims to serve as a structured and accessible resource for advancing the design of intelligent, learning-enabled optimization algorithms. Full article

With the growing global demand for clean energy, fuel cells have been adopted as key components in renewable energy systems. Their high efficiency and environmentally friendly operation make them attractive. However, during maximum power point tracking (MPPT), traditional proportional–integral–derivative (PID) controllers often fail to maintain optimal power output. Dynamic load changes and complex operating conditions exacerbate this issue. As a result, system response is slowed, and tracking accuracy is reduced. To address these problems, an online identification method based on recursive least squares (RLS) is employed. A cubic power–current model is identified in real time. Polynomial fitting and the golden section search are then applied to estimate the current at the maximum power point. Following model-based estimation, adaptive particle swarm optimization (APSO) is utilized to tune the PID controller parameters. Precise regulation is thus achieved. The use of RLS enables real-time model identification. The golden section search improves the efficiency of current estimation. APSO enhances global optimization, while PID provides fast dynamic response. By integrating these methods, both tracking accuracy and system responsiveness are significantly improved in fuel cell MPPT applications. Simulation results demonstrate that the proposed strategy enhances maximum power output by up to 12.40% compared to conventional P&O, fuzzy logic control, GWO-PID, and PSO-PID methods, as well as maintaining a consistent improvement of 1.50% to 1.90% even when compared to other optimization algorithms. Full article

This study explores the integration of simulated environments into first aid training programmes within the field of Physical Activity and Sports Sciences. Grounded in the framework of student-centred teaching methodologies and competency-based education models, the research investigates the impact of simulated environments on students’ Emotional Intelligence (EI). The study hypothesizes that positive stress coping styles and good educational practices developed in simulated environments are correlated with higher levels of EI. Methodologically, a descriptive study was conducted, involving participants pursuing a Bachelor’s Degree in Physical Education and Sport Sciences. Measures included the Trait-Mood Scale 24 (TMMS-24) for EI assessment, the Stress Coping Questionnaire (SCQ) for stress evaluation, and the Educational Practices Questionnaire (EPQ) for assessing educational practices. Results revealed significant associations between active learning and higher levels of EI, problem-solving coping styles, and emotional clarity, as well as positive reappraisal coping styles and mood recovery. The study emphasizes the potential of integrating simulated environments into first aid training programmes, offering immersive learning experiences that enhance students’ practical skills and emotional development. Full article

With the aggravation of global climate change and the increasing frequency and intensity of extreme weather events, power systems with a high proportion of renewable energy are under threat. In response, in traditional wind–solar–storage–hydrogen multi-energy systems, it is difficult to balance power supply resilience, economy, and environmental protection, and such systems cannot meet actual demand due to the lack of a dynamic meteorological integration mechanism. Therefore, a dynamic collaborative optimization model of a multi-energy system driven by meteorological risk perception is proposed. The dynamic meteorological risk factor integrating various meteorological elements is introduced, and the risk response mechanism is established based on the system’s energy storage state to realize the adaptive adjustment of coupled weight parameters and achieve the goal of collaborative optimization of power supply resilience, economy, and environmental protection. The case analysis results show that, compared with other models, the proposed model can reduce the power supply shortage by 23.1% in extreme weather periods, and the system’s survival probability can reach 97.1% at most. The proposed model minimizes the assembly while ensuring that carbon emissions meet standards, and achieves the collaborative optimization of power supply toughness, economy, and environmental protection. It provides a theoretical tool for solving the collaborative optimization problem that energy systems with a high proportion of renewables face in coping with climate risks. Full article

In modern urban planning, the problem of bank location requires not only considering geographical factors but also integrating competitive elements to optimize resource allocation and enhance market competitiveness. This study addresses the multi-size bank location problem by incorporating competitive factors into the optimization process through a novel reinforcement learning-enhanced genetic algorithm (RL-GA) framework. Building upon an attraction-based model with competitive decay functions, we propose an innovative hybrid optimization approach that combines evolutionary computation with intelligent decision-making capabilities. The RL-GA framework employs Q-learning principles to adaptively select optimal genetic operators based on real-time population states and search progress, enabling meta-learning where the algorithm learns how to optimize rather than simply optimizing. Unlike traditional genetic algorithms with fixed operator probabilities, our approach dynamically adjusts its search strategy through an $\epsilon$-greedy exploration mechanism and multi-objective reward functions. Experimental results demonstrate that the RL-GA achieves improvements in early-stage convergence speed while maintaining solution quality comparable to traditional methods. The algorithm exhibits enhanced convergence characteristics in the initial optimization phases and demonstrates consistent performance across multiple optimization trials. These findings provide evidence for the potential of intelligence-guided evolutionary computation in facility location optimization, offering moderate computational efficiency gains and adaptive strategic guidance for banking facility deployment in competitive environments. Full article

With the growth of global energy demand, the application of smart grid technology has become widespread. Anomaly detection in power systems is crucial for ensuring the stability and economy of power supply. Deep learning technologies offer new opportunities in this field. This paper proposes a deep learning approach based on Convolutional Autoencoders (CAEs) and Gated Recurrent Units (GRUs) for anomaly detection in smart grid power data. This method integrates three types of feature data, namely user power consumption, line loss correlation, and meter error, and combines the moving window technology to construct a CAE-GRU network model. Experimental results show that, compared with traditional methods, this method has higher accuracy in anomaly detection, which can effectively identify potential problems in the power grid and provide strong support for the optimized operation of the smart grid. Full article