Search Results (16,072)

This study introduces a comprehensive framework combining building information modeling (BIM), project management body of knowledge (PMBOK), and machine learning (ML) to optimize energy efficiency and reduce environmental impacts in Riyadh’s construction sector. The suggested methodology utilizes BIM for dynamic energy simulations and design visualization, PMBOK for integrating sustainability into project-management processes, and ML for predictive modeling and real-time energy optimization. Implementing an integrated model that incorporates building-management strategies and machine learning for both commercial and residential structures can offer stakeholders a thorough solution for forecasting energy performance and environmental impact. This is particularly essential in arid climates owing to specific conditions and environmental limitations. Using a simulation-based methodology, the framework was evaluated based on two representative case studies: (i) a commercial complex and (ii) a residential building. The neural network (NN), reinforcement learning (RL), and decision tree (DT) were implemented to assess performance in energy prediction and optimization. Results demonstrated notable seasonal energy savings, particularly in spring (15% reduction for commercial buildings) and fall (13% reduction for residential buildings), driven by optimized heating, ventilation, and air conditioning (HVAC) systems, insulation strategies, and window configurations. ML models successfully predicted energy consumption and greenhouse gas (GHG) emissions, enabling targeted mitigation strategies. GHG emissions were reduced by up to 25% in commercial and 20% in residential settings. Among the models, NN achieved the highest predictive accuracy (R² = 0.95), while RL proved effective in adaptive operational control. This study highlights the synergistic potential of BIM, PMBOK, and ML in advancing green project management and sustainable construction. Full article

Traditional Case-Based Reasoning (CBR) methods face significant methodological challenges, including limited information resources in case databases, methodologically inadequate similarity calculation approaches, and a lack of standardized case revision mechanisms. These limitations lead to suboptimal case matching and insufficient solution adaptation, highlighting critical gaps in the development of CBR methodologies. This paper proposes a novel CBR framework enhanced by generative AI, aiming to improve and innovate existing methods in three key stages of traditional CBR, thereby enhancing the accuracy of retrieval and the scientific nature of corrections. First, we develop an ontology model for comprehensive case representation, systematically capturing scenario characteristics, risk typologies, and strategy frameworks through structured knowledge representation. Second, we introduce an advanced similarity calculation method grounded in triangle theory, incorporating three computational dimensions: attribute similarity measurement, requirement similarity assessment, and capability similarity evaluation. This multi-dimensional approach provides more accurate and robust similarity quantification compared to existing methods. Third, we design a generative AI-based case revision mechanism that systematically adjusts solution strategies based on case differences, considering interdependence relationships and mutual influence patterns among risk factors to generate optimized solutions. The methodological framework addresses fundamental limitations in existing CBR approaches through systematic improvements in case representation, similarity computation, and solution adaptation processes. Experimental validation using actual case data demonstrates the effectiveness and scientific validity of the proposed methodological framework, with applications in risk assessment and emergency response scenarios. The results show significant improvements in case-matching accuracy and solution quality compared to traditional CBR approaches. This method provides a robust methodological foundation for CBR-based decision-making systems and offers practical value for risk management applications. Full article

The Centralized Teacher with Decentralized Student (CTDS) framework is a multi-agent reinforcement learning (MARL) approach that utilizes knowledge distillation within the Centralized Training with Decentralized Execution (CTDE) paradigm. In this framework, a teacher module learns optimal Q-values using global observations and distills this knowledge to a student module that operates with only local information. However, CTDS has limitations including inefficient knowledge distillation processes and performance gaps between teacher and student modules. This paper proposes the evolutionary sampling method that employs genetic algorithms to optimize selective knowledge distillation in CTDS frameworks. Our approach utilizes a selective sampling strategy that focuses on samples with large Q-value differences between teacher and student models. The genetic algorithm optimizes adaptive sampling ratios through evolutionary processes, where the chromosome represent sampling ratio sequences. This evolutionary optimization discovers optimal adaptive sampling sequences that minimize teacher–student performance gaps. Experimental validation in the $StarCraft$ Multi-Agent Challenge (SMAC) environment confirms that our method achieved superior performance compared to the existing CTDS methods. This approach addresses the inefficiency in knowledge distillation and performance gap issues while improving overall performance through the genetic algorithm. Full article

In order to determine thermally safe driving parameters of ignition coils for hydrogen internal combustion engines (ICE), a reliable estimation of internal power losses is essential. These losses include resistive winding losses, magnetic core losses due to hysteresis and eddy currents, dielectric losses in the insulation, and electronic switching losses. Direct experimental assessment is difficult because the components are inaccessible, while conventional computer-aided engineering (CAE) approaches face challenges such as the need for accurate input data, the need for detailed 3D models, long computation times, and uncertainties in loss prediction for complex structures. To address these limitations, we propose an artificial intelligence (AI)-based framework for estimating internal losses from external temperature measurements. The method relies on an artificial neural network (ANN), trained to capture the relationship between external coil temperatures and internal power losses. The trained model is then employed within an optimization process to identify losses corresponding to experimental temperature values. Validation is performed by introducing the identified power losses into a CAE thermal model to compare predicted and experimental temperatures. The results show excellent agreement, with errors below 3% across the −30°C to 125°C range. This demonstrates that the proposed hybrid ANN–CAE approach achieves high accuracy while reducing experimental effort and computational demand. Furthermore, the methodology allows for a straightforward determination of the coil safe operating area (SOA). Starting from estimates derived from fitted linear trends, the SOA limits can be efficiently refined through iterative verification with the CAE model. Overall, the ANN–CAE framework provides a robust and practical tool to accelerate thermal analysis and support coil development for hydrogen ICE applications. Full article

Predictive maintenance (PdM) is a cornerstone of smart manufacturing, enabling the early detection of equipment degradation and reducing unplanned downtimes. This study proposes an advanced machine learning framework that integrates the Extreme Learning Machine (ELM) with a novel hybrid metaheuristic optimization algorithm, the Polar Lights Salp Cooperative Optimizer (PLSCO), to enhance predictive modeling in manufacturing processes. PLSCO combines the strengths of the Polar Light Optimizer (PLO), Competitive Swarm Optimization (CSO), and Salp Swarm Algorithm (SSA), utilizing a cooperative strategy that adaptively balances exploration and exploitation. In this mechanism, particles engage in a competitive division process, where winners intensify search via PLO and losers diversify using SSA, effectively avoiding local optima and premature convergence. The performance of PLSCO was validated on CEC2015 and CEC2020 benchmark functions, demonstrating superior convergence behavior and global search capabilities. When applied to a real-world predictive maintenance dataset, the ELM-PLSCO model achieved a high prediction accuracy of 95.4%, outperforming baseline and other optimization-assisted models. Feature importance analysis revealed that torque and tool wear are dominant indicators of machine failure, offering interpretable insights for condition monitoring. The proposed approach presents a robust, interpretable, and computationally efficient solution for predictive maintenance in intelligent manufacturing environments. Full article

In this paper, the natural frequencies of pure bending, axial–bending, and torsional-bending coupled modes of CFRP Anisogrid cylindrical shell-beams supporting non-structural masses and inertias at the boundaries are firstly analytically investigated and, secondly, verified by FEM. Indeed, the design of shell-beam elements in various engineering applications is driven by the minimum frequency value that is necessary to achieve in order not to compromise the proper functionality of the assembly for which these elements are designed. In turn, this minimum frequency depends on the geometry, mass, and dynamics of the main components of the assembly. A typical point in space applications is to control the lowest frequency of the spacecraft body, commonly supported by a shell structure, in order to avoid the occurrence of resonance issues that may be induced by dynamic loads during the launch phase. As a rule, to keep the lowest frequency sufficiently high, in conjunction with non-structural masses, means to increase the stiffness and the mass of the load-carrying structure and, ideally, to identify the most efficient solution. In order to effectively address this topic, the analytical models of the natural frequencies of Anisogrid cylindrical shell-beams are finally introduced into an optimization routine as constraints on the fundamental frequency. This approach allows us to readily explore the various Anisogrid configurations and find the best candidate solutions in the framework of preliminary design. Full article

Assessment of cultural ecosystem services (CESs) is a key component in advancing the sustainable development of urban ecosystems. Mapping the spatial distribution of CESs provides spatially explicit insights for urban landscape planning. However, most assessments lack regional adaptability, particularly in cities with pronounced environmental and cultural heterogeneity. To address this gap, this study focused on the central urban area of Lhasa, using communities as units to develop a tailored CES assessment framework. The framework integrated the MaxEnt model with multi-source indicators to analyze the spatial distribution of five CES categories and their relationships with environmental variables. Spatial statistics and classification at community level informed the CES spatial optimization strategies. Results indicated that high-value CES areas were predominantly concentrated in the old city cluster, typified by Barkhor and Jibenggang subdistricts, following an east–west spatial pattern along the Lhasa River. Distance to tourist spot contributed 78.3% to cultural heritage, 86.1% to spirit and religion, and 42.2% to ecotourism and aesthetic services, making it the most influential environmental variable. At the community level, CESs exhibited a distinct spatial gradient, with higher values in the central area and lower values in the eastern and western peripheries. For the ecotourism and aesthetic category, 61.47% of the community area was classified as low service, whereas only 1.48% and 7.33% were identified as excellent and high. Moreover, communities within subdistricts such as Barkhor and Zhaxi demonstrated excellent service across four CES categories, with notably lower performance in the health category. This study presents a quantitative and adaptable framework and planning guidance to support the sustainable development of CESs in cities with similar characteristics. Full article

Given the critical importance of timely forest fire detection to mitigate environmental and socio-economic consequences, this research aims to achieve high detection accuracy while maintaining real-time operational efficiency, with a particular focus on minimizing computational complexity. We propose a novel framework that systematically integrates normalization, feature selection, adaptive oversampling, and classifier optimization to enhance detection performance while minimizing computational overhead. The evaluation is conducted using three distinct Canadian forest fire datasets: Alberta Forest Fire (AFF), British Columbia Forest Fire (BCFF), and Saskatchewan Forest Fire (SFF). Initial classifier benchmarking identified the best-performing tree-based model, followed by normalization and feature selection optimization. Next, four oversampling methods were evaluated to address class imbalance. An ablation study quantified the contribution of each module to overall performance. Our targeted, stepwise strategy eliminated the need for exhaustive model searches, reducing computational cost by 97.75% without compromising accuracy. Experimental results demonstrate substantial improvements in F1-score, AFF (from 69.12% to 82.75%), BCFF (61.95% to 77.91%), and SFF (90.03% to 96.18%) alongside notable reductions in False Negative Rates compared to baseline models. Full article

This study presents the development of a noise-monitoring system for the storage ring at the Siam Photon Source, designed to detect and classify noise patterns in real time using beam position monitor (BPM) data. Noise patterns were categorized into four classes: broad peak, multipeak, normal peak, and no beam. Two BPMs located at the multipole wiggler section, BPM-MPW1 and BPM-MPW2, were selected for detailed monitoring based on consistent noise trends observed across the ring. The dataset was organized in two complementary formats: two-dimensional (2D) images used for training and validating the models and one-dimensional (1D) CSV files containing the corresponding raw numerical signal data. Pre-trained deep learning and 1D convolutional neural network (CNN) models were employed to classify these patterns, achieving an overall classification accuracy of up to 99.83%. The system integrates with the EPICS control framework and archiver log data, enabling continuous data acquisition and long-term analyses. Visualization and monitoring features were developed using CS-Studio/Phoebus, providing both operators and beamline scientists with intuitive tools to track beam quality and investigate noise-related anomalies. This approach highlights the potential of combining beam diagnostics with machine learning to enhance operational stability and optimize the synchrotron radiation performance for user experiments. Full article

Real-time coordination of heterogeneous multi-agent systems in dynamic and partially observable environments poses significant challenges. To address this, we propose a framework that integrates fuzzy inference systems with real-valued genetic algorithms to optimize decision-making under strict time constraints and sensory uncertainty. We evaluate the proposed method in the RoboCup Soccer Simulation 2D League, where 22 autonomous agents coordinate through a fuzzy-evaluated action sequence search. Spatial heuristics are encoded as fuzzy rules, and optimization based on genetic algorithms refines evaluation function parameters according to performance metrics such as number of shots, goal area entries, and scoring rates. The resulting control strategy remains interpretable; spatial heat maps reveal emergent behaviors such as coordinated positioning and ridgeline passing patterns near the penalty area. The experiments against established RoboCup teams, serving as benchmarks, demonstrate the competitive performance of our trained agents while enabling analyses of evolving decision structures and agent behaviors. Our method provides a transparent and adaptable framework for controlling heterogeneous agents in uncertain real-time environments, with broad applicability to robotics, autonomous systems, and distributed control systems. Full article

Cutting operational costs is a critical component for transportation agencies. To reduce these costs, agencies must optimize their scheduling. Typically, the total operating costs of transport include vehicle expenses and driver wages. Solving such tasks is complex, and optimal planning is usually broken down into multiple stages. These stages can include vehicle scheduling, driver shift planning, and driver assignment. This paper focuses specifically on developing a near-optimal driver schedule for a specified set of vehicle schedules. It shows how to efficiently assign drivers to predetermined optimal vehicle routes while ensuring compliance with regulatory constraints on driving hours. We address this challenge using a mathematical model based on the set covering problem, building on a framework established perviously. The set covering problem is typically formulated as an integer programming problem, solvable through column generation techniques. Our algorithm combines this method with heuristics, taking into account the practical aspects of the problem. The article also presents a computational analysis of the method using benchmark and real data. Full article

This study aims to systematically reveal, from the perspective of organizational scale, the differences between large and small architectural design organizations in China in terms of characteristics of production activities, technological capabilities and innovation levels, resource integration capabilities, and client groups, and to quantify the priority order of clients’ attention to architectural design products, thereby providing a reference for industry structure optimization and strategic decision making. This research combines case analysis and comparative study to construct a four-dimensional comparative framework. The results show that large design organizations, leveraging their advantages in technological research and development as well as resource integration, focus on large-scale complex projects, technology-driven projects, cultural landmark projects, and multi-stakeholder collaborative projects, primarily serving government agencies and large enterprises. In contrast, small design organizations excel in flexibility, concentrating on small-scale simple projects, specialized niche projects, localized projects, and short-cycle, low-budget projects, serving individual owners and small businesses. Furthermore, this study adopts the Analytic Hierarchy Process (AHP) to establish an evaluation model. Twenty experts from architectural design organizations, construction organizations, and research institutions were invited to score the survey questionnaires, and quantitative weight analysis was performed. The research findings provide support for the optimization of the industry. Full article

As technologies become increasingly diverse and complex, Intelligent Massage Systems (IMS) are evolving from traditional mechanically executed modes toward personalized and predictive health interventions. However, the field still lacks a unified grading standard for intelligence, making it difficult to quantitatively assess a system’s overall intelligence level. To address this gap, this paper proposes a task-driven six-level (L0–L5) classification framework and constructs a Massage-Driven Task (MDT) model that decomposes the massage process into six subtasks (S1–S6). Building on this, we design a three-dimensional evaluation scheme comprising a Functional Delegation Structure (FDS), an Anomaly Perception Mechanism (APM), and a Human–Machine Interaction Boundary (HMIB), and we select eight key performance indicators to quantify IMS intelligence across the perception–decision–actuation–feedback closed loop. We then determine indicator weights via the Delphi method and the Analytic Hierarchy Process (AHP), and obtain dimension-level scores and a composite intelligence score S0 using normalization and weighted aggregation. Threshold intervals for L0–L5 are defined through equal-interval partitioning combined with expert calibration, and sensitivity is verified on representative samples using ±10% data perturbations. Results show that, within typical error ranges, the proposed grading framework yields stable classification decisions and exhibits strong robustness. The framework not only provides the first reusable quantitative basis for grading IMS intelligence but also supports product design optimization, regulatory certification, and user selection. Full article

Cyanobacterial blooms in urban rivers present critical ecological threats worldwide, yet their mechanisms in fluvial systems remain inadequately explored compared to lacustrine environments. This study addresses this gap by investigating bloom dynamics in the eutrophic Majiagou River (Harbin, China) through phytoplankton resource use efficiency (RUE), calculated as chlorophyll-a per unit TN/TP. Seasonal sampling (2022–2024) across 25 rural-to-urban sites revealed distinct spatiotemporal patterns: urban sections exhibited 1.9× higher cyanobacterial relative abundance (RAC, peaking at 40.65% in autumn) but 28–30% lower RUE than rural areas. Generalized additive models identified nonlinear RAC–RUE relationships with critical thresholds: in rural sections, RAC peaked at TN-RUE 40–45 and TP-RUE 25–30, whereas urban sections showed lower TN-RUE triggers (20–25) and suppressed dominance above TP-RUE 10. Seasonal extremes drove RUE maxima in summer and minima during freezing/thawing periods. These findings demonstrate that hydrological stagnation (e.g., river mouths) and pulsed nutrient inputs reduce nutrient conversion efficiency while lowering bloom-triggering thresholds under urban eutrophication. The study establishes RUE as a predictive indicator for bloom risk, advocating optimized N/P ratios coupled with flow restoration rather than mere nutrient reduction. This approach provides a science-based framework for sustainable management of urban river ecosystems facing climate and anthropogenic pressures. Full article

We propose a novel telescope concept based on Earth’s gravitational lensing effect, optimized for the detection of distant dark matter sources, particularly axion-like particles (ALPs). When a unidirectional flux of dark matter passes through Earth at sufficiently high velocity, gravitational lensing can concentrate the flux at a distant focal region in space. Our method combines this lensing effect with stimulated backward reflection (SBR), arising from ALP decays that are induced by directing a coherent electromagnetic beam toward the focal point. The aim of this work is to numerically analyze the structure of the focal region and to develop a framework for estimating the sensitivity to ALP–photon coupling via this mechanism. Numerical calculations show that, assuming an average ALP velocity of 520 km/s—as suggested by the observed stellar stream S1—the focal region extends from $9\times {10}^9$ m to $1.4\times {10}^{10}$ m, with peak density near $9.6\times {10}^9$ m. For a conservative point-like ALP source located approximately 8 kpc from the solar system, based on the S1 stream, the estimated sensitivity in the eV mass range reaches $g/M=\mathcal{O}\left({10}^{-22}\right){\mathrm{GeV}}^{-1}$. This concept thus opens a path toward a general-purpose, space-based ALP observatory that could, in principle, detect more distant sources—well beyond $\mathcal{O}\left(10\right)\mathrm{kpc}$—provided that ALP–photon coupling is sufficiently strong, that is, $M\ll M_{\mathrm{Planck}}$. Full article