Search Results (19,362)

With smart buildings being widely adopted in urban digital transformation, interactive semantic question answering (QA) systems serve as a crucial bridge between user intent and environmental response. However, they still face substantial challenges in semantic understanding and dynamic reasoning. Most existing systems rely on static frameworks built upon Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), which suffer from rigid prompt design, breakdowns in multi-step reasoning, and inaccurate generation. To tackle these issues, we propose LoopRAG, a multi-agent RAG architecture that incorporates a Plan–Do–Check–Act (PDCA) closed-loop optimization mechanism. The architecture formulates a dynamic QA pipeline across four stages: task parsing, knowledge extraction, quality evaluation, and policy feedback, and further introduces a semantics-driven prompt reconfiguration algorithm and a heterogeneous knowledge fusion module. These components strengthen multi-source information handling and adaptive reasoning. Experiments on HotpotQA, MultiHop-RAG, and an in-house building QA dataset demonstrate that LoopRAG significantly outperforms conventional RAG systems in key metrics, including context recall of 90%, response relevance of 72%, and answer accuracy of 88%. The results indicate strong robustness and cross-task generalization. This work offers both theoretical foundations and an engineering pathway for constructing trustworthy and scalable semantic QA interaction systems in smart building settings. Full article

The separate-layer gas injection technology is a key means to improve the effect of refined gas injection development. Currently, the measurement and adjustment of separate injection wells primarily rely on manual experience and automatic measurement via instrument traversal, resulting in a long duration, low efficiency, and low qualification rate for injection allocation across multi-layer intervals. Given the different CO₂-containing natural gas injection rates across different intervals, this paper establishes a coupled flow model of a separate-layer gas injection wellbore–gas distributor–formation based on the energy and mass conservation equations for wellbore pipe flow, and develops a solution method for determining gas nozzle sizes across multi-layer intervals. Based on the maximum allowable gas nozzle size, an optimization method for multi-layer collaborative allocation of separate injection wells is established, with minimum wellhead injection pressure and layered injection allocation as the optimization objectives, and the opening of gas distributors for each layer as the optimization variable. Taking Well XXX as an example, the optimization process of allocation schemes under different gas allocation requirements is simulated. The research shows that the model and method proposed in this paper have high calculation accuracy, and the formulated allocation schemes have strong adaptability and minor injection allocation errors, providing a scientific decision-making method for formulating refined allocation schemes for separate-layer gas injection wells, with significant theoretical and practical value for promoting the refined development of oilfields. Full article

Precise prediction of structural settlement in subway tunnels is crucial for ensuring safety during both construction and operational phases; however, the non-linear characteristics of monitoring data pose a significant challenge to achieving this goal. To address this issue, this study proposes a hybrid predictive model, termed HO-GPR. This model integrates the Hippopotamus Optimization (HO) algorithm—a novel bio-inspired meta-heuristic—with Gaussian Process Regression (GPR), a non-parametric probabilistic machine learning method. Specifically, HO is utilized to globally optimize the hyperparameters of GPR to enhance its adaptability to complex deformation patterns. The model was validated using 52 months of field settlement monitoring data collected from the Urumqi Metro Line 1 tunnel. Through a series of comparative and generalization experiments, the accuracy and adaptability of the model were systematically evaluated. The results demonstrate that the HO-GPR model is superior to five benchmark models—namely Gated Recurrent Unit (GRU), Support Vector Regression (SVR), HO-optimized Back Propagation Neural Network (HO-BP), standard GPR, and ARIMA—in terms of accuracy and stability. It achieved a Coefficient of Determination (R²) of 0.979, while the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) were as low as 0.318 mm, 0.240 mm, and 1.83%, respectively, proving its capability for effective prediction with non-linear data. The findings of this research can provide valuable technical support for the structural safety management of subway tunnels. Full article

To address the stochastic, nonlinear, and strongly coupled characteristics of multivariate long-term structural response in bridge health monitoring, this study proposes the Informer-SEnet prediction model. The model integrates a Squeeze-and-Excitation (SE) channel attention mechanism into the Informer framework, enabling adaptive recalibration of channel importance to suppress redundant information and enhance key structural response features. A sliding-window strategy is used to construct the datasets, and extensive comparative experiments and ablation studies are conducted on one public bridge-monitoring dataset and two long-term monitoring datasets from real bridges. In the best case, the proposed model achieves improvements of up to 54.67% in MAE, 52.39% in RMSE, and 7.73% in R². Ablation analysis confirms that the SE module substantially strengthens channel-wise feature representation, while the sparse attention and distillation mechanisms are essential for capturing long-range dependencies and improving computational efficiency. Their combined effect yields the optimal predictive performance. Five-fold cross-validation further evaluates the model’s generalization capability. The results show that Informer-SEnet exhibits smaller fluctuations across folds compared with baseline models, demonstrating higher stability and robustness and confirming the reliability of the proposed approach. The improvement in prediction accuracy enables more precise characterization of the structural response evolution under environmental and operational loads, thereby providing a more reliable basis for anomaly detection and early damage warning, and reducing the risk of false alarms and missed detections. The findings offer an efficient and robust deep learning solution to support bridge structural safety assessment and intelligent maintenance decision-making. Full article

Dryland grasslands face elevated risks of rapid threshold crossing under a regime of warming, precipitation redistribution, and intensified interannual hydrothermal variability. Using the Ebinur Lake Basin (ELB) as a case, we developed an integrated structure × function assessment—linking land-use/cover change (LUCC) transitions with functional indicators of net primary productivity (NPP), net ecosystem production (NEP), soil conservation (SC), and grass supply (GS)—and coupled it with Bayesian-optimized XGBoost, SHAP, and partial dependence plots (PDPs) at a 30 m pixel scale to identify dominant drivers and ecological thresholds, subsequently translating them into governance zones. From 2003 to 2023, overall grassland status was dominated by degradation (20,160.62 km²; 69.42%), with restoration at 8878.85 km² (30.57%) and stability at 2.79 km² (0.01%). NPP/NEP followed a rise–decline–recovery trajectory, while SC exhibited marked bipolarity. Precipitation and temperature emerged as primary drivers (interaction X3 × X4 = 0.0621), whose effects, together with topography and accessibility, shaped a spatial paradigm of piedmont sensitive–oasis sluggish–lakeshore vulnerable. Key thresholds included an annual precipitation recovery threshold of ~200 mm and an optimal window of 272–429 mm; a road-density divide near ~0.06 km km⁻²; and sustainable grazing windows of ~2.2–4.2 and ~4.65–5.61 livestock units (LU) km⁻². These thresholds underpinned four management units—Priority Control (52.53%), Monitoring and Alert (21.53%), Natural Recovery (20.40%), and Optimized Maintenance (5.55%)—organized within a “two belts–four zones–one axis” spatial framework, closing the loop from threshold detection to adaptive governance. The approach provides a replicable paradigm for climate-adaptive management and ecological risk mitigation of dryland grasslands under warming. Full article

Dynamic task planning for heterogeneous platforms across land, sea, air, and space is essential for achieving integrated situational awareness, yet current systems suffer from limited spatiotemporal coverage and inefficient resource scheduling. To address these challenges, we propose a novel mission planning method that integrates spatiotemporal segmentation with Deep Reinforcement Learning (DRL). The approach establishes a multidimensional spatiotemporal decomposition model to break down complex observation scenarios into manageable subtasks, while incorporating a unified accessibility–visibility computation framework that accounts for Earth curvature, platform dynamics, and sensor constraints. Using a Spatio-Temporal Adaptive Scheduling Network (STAS-Net) algorithm optimized with a multi-objective reward function covering mission completion rate, temporal coordination, and residual detection capacity, the method enables intelligent coordination of heterogeneous platforms. Experimental results across small-, medium-, and large-scale scenarios demonstrate that the proposed framework consistently achieves high target coverage (up to 98.4% in small-scale and 89.7% in large-scale tasks), with a reduction in coverage loss that is only about half of that exhibited by greedy and genetic algorithms as task scale expands. Moreover, STAS-Net maintains low planning time (as low as 9.5 s in small-scale and only 18.3 s in large-scale scenarios) and high resource utilization (reaching 86.8% under large-scale settings), substantially outperforming both baseline methods in scalability and scheduling efficiency. The framework not only establishes a solid theoretical foundation but also provides a practical and feasible solution for enhancing the overall performance of multi-platform cooperative observation systems. Full article

The Sumaco volcano in Ecuador, which has a distinct geological origin from the Andes and is located in the Amazon basin, offers an opportunity to study untouched microbiomes. We explored comparative patterns of abundance from predicted functional profiling in soil samples collected along the elevation and sulfur gradients on its slopes. Using 16S rRNA gene metabarcoding, we inferred metagenome functional profiles, contrasting sample groups by altitude or soil sulfur concentration. We inferred that high-altitude communities may have higher predicted abundance for anaerobic metabolism (crotonate fermentation), coenzyme B12 synthesis, and degradation of diverse carbon sources (sugars and octane). High-sulfur soils were associated with an inferred enrichment of pathways for degrading complex organic compounds and nitrogen metabolism, reflecting adaptation to unique geochemical conditions. In contrast, low-sulfur soils are consistent with a higher predicted abundance of glycerol degradation. Within the limitation imposed by the potential weak associations of the applied predicted functional profiling to actual gene content, we propose that the inferred metabolic changes represent different ecological strategies for resource acquisition, energy generation, and stress tolerance, and they are optimized for varying conditions in this unique volcanic ecosystem. Our findings highlight how environmental gradients shape soil microbiome functional diversity and offer insights into microbial adaptation in Sumaco’s exceptional geochemistry within the Amazon. Further efforts linking functional predictions back to specific taxa will offer a complete ecological perspective of the microbiome exploration in the Sumaco volcano. Full article

Urban heat islands (UHIs) have intensified in rapidly urbanizing regions like New York, exacerbating thermal discomfort, public health risks, and energy consumption. While previous research has highlighted various environmental and socioeconomic contributors, most existing studies lack interpretable, fine-scale models capable of quantifying the effects of specific drivers—limiting their utility for targeted planning. To address this challenge, we develop an interpretable machine learning framework using Random Forest and XGBOOST to predict land surface temperature across 1800+ census tracts in the New York metropolitan area, incorporating vegetation indices, water proximity, urban morphology, and socioeconomic factors. Both models performed strongly (mean R² ≈ 0.90), with vegetation coverage and water proximity emerging as the most influential cooling factors, while built form features played supporting roles. Socioeconomic vulnerability indicators showed weak correlations with temperature, suggesting a relatively equitable thermal landscape. Optimization simulations further revealed that increasing vegetation to a threshold level could lower average surface temperatures by up to 6.38 °C, with additional but smaller gains achievable through adjustments to water access and urban form. These findings provide evidence-based guidance for climate-adaptive urban design and green infrastructure planning. More broadly, the study illustrates the potential of explainable machine learning to support data-driven environmental interventions in complex urban systems. Full article

The study investigates the combined effects of material selection, manufacturing constraints, and a dynamic stress constraint function on the resulting material distribution achieved through a structural optimization process, while ensuring full compliance with the relevant European assessment standards for railway bogie. A high-fidelity finite element model of the complete bogie system was developed to accurately reproduce the operational loads and the structural interactions between the motor support and its surrounding components. The proposed methodology integrates topology optimization within a manufacturability-oriented framework, enabling a systematic evaluation of the influence of material properties, draw direction, and minimum feature size on the optimized configuration. In this context, an adaptive stress coefficient, derived from the performance of the original component, was introduced and proved effective in improving both the material distribution and the resulting stress levels of the optimized design. The results demonstrate that the combined consideration of material selection, manufacturing constraints, and adaptive stress control leads to a structurally efficient and production-feasible design. Three different materials were tested, showing consistent stress distributions and mass savings across all cases. The innovative optimized configuration achieved over 16% mass reduction while maintaining admissible stress levels. The proposed approach provides a generalizable and standard-compliant framework for future applications of topology optimization in railway engineering. Full article

This study proposes an intelligent control algorithm for multiple-input multiple-output (MIMO) industrial processes. This algorithm is based on the integration of a digital twin (DT), model predictive control (MPC), a genetic algorithm (GA), and a neural network (NN). The developed architecture employs a hybrid MPC scheme incorporating an additional NN correction branch. The workflow includes input data pre-processing, operating point linearization and NN training, computation of the optimal control sequence over a receding horizon, closed-loop control and adaptation based on prediction error. This innovative hybrid control law uses a linear state-space model as the base predictor and a compact NN superstructure to compensate for unmodeled nonlinearities. The GA searches for the optimal sequence of control actions while respecting process constraints and ensuring stable use of the NN correction. The methodology was tested on a phosphoric acid purification process. Compared to baseline MPC, the proposed algorithm increased purification efficiency to $95.1\%$, reduced the integral tracking error by $11.4\%$, and decreased the control signal amplitude by 10–15%. Selecting the appropriate reagent supply and vacuum modes ensured stable operation despite fluctuations in the raw material. These results confirm the effectiveness of DT-based hybrid control in applications requiring precision, adaptability, and strict constraint compliance. The approach is scalable and can be applied to other continuous production systems within Industry 4.0 initiatives. Full article

Overburden layers, composed of unconsolidated sediments, are widely distributed in construction, transportation, and water conservancy projects, but their inherent defects (e.g., developed pores, low strength) easily induce engineering disasters. Grouting is a core reinforcement technology, yet traditional design relying on empirical formulas and on-site trials suffers from high costs and low prediction accuracy. Numerical simulation has become a key bridge connecting grouting theory and practice. This study systematically reviews the numerical simulation of overburden grouting based on 82 core articles screened via the PRISMA framework. First, the theoretical system is clarified: core governing equations for seepage, stress, grout diffusion, and chemical fields, as well as their coupling mechanisms (e.g., HM coupling via effective stress principle), are sorted out, and the advantages/disadvantages of different equations are quantified. The material parameter characterization focuses on grout rheological models (Newtonian, power-law, Bingham) and overburden heterogeneity modeling. Second, numerical methods and engineering applications are analyzed: discrete (DEM) and continuous (FEM/FDM) methods, as well as their coupling modes, are compared; the simulation advantages (visualization of diffusion mechanisms, parameter controllability, low-cost risk prediction) are verified by typical cases. Third, current challenges and trends are identified: bottlenecks include the poor adaptability of models in heterogeneous strata, unbalanced accuracy–efficiency, insufficient rheological models for complex grouts, and theoretical limitations of multi-field coupling. Future directions involve AI-driven parameter optimization, cross-scale simulation, HPC-enhanced computing efficiency, and targeted models for environmentally friendly grouts. The study concludes that overburden grouting simulation has formed a complete “theory–parameter–method–application” system, evolving from a “theoretical tool” to the “core of engineering decision-making”. The core contradiction lies in the conflict between refinement requirements and technical limitations, and breakthroughs rely on the interdisciplinary integration of AI, multi-scale simulation, and HPC. This review provides a clear technical context for researchers and practical reference for engineering technicians. Full article

Background/Objectives: Fever and pain are among the most common symptoms in pediatric infections and chronic diseases, causing significant discomfort for children and concern for caregivers. Effective management is essential to relieve distress while avoiding overtreatment or undertreatment. Paracetamol and nonsteroidal anti-inflammatory drugs (NSAIDs), particularly ibuprofen, are the primary antipyretic and analgesic agents in pediatric care, but their use in children with chronic conditions might be challenging. Methods: A narrative review and clinical expert judgment were used to synthesize current evidence on the use of paracetamol and NSAIDs (especially ibuprofen) in children with some common chronic diseases. Results: Paracetamol is often considered a first-line option in several chronic conditions. Caution is warranted in children with pre-existing malnutrition, obesity, and neuromuscular disorders as these factors might increase the risk of hepatotoxicity. NSAIDs provide additional anti-inflammatory effects and comparable analgesic efficacy but should be used cautiously in some high-risk populations due to potential gastrointestinal, renal, and bleeding complications. Their use is contraindicated in children with dehydration, renal impairment, nephrotic syndrome relapses, while careful risk-benefit assessment is required in small and vulnerable neonates. Some data also suggests NSAIDs may worsen outcomes in certain acute bacterial and viral infections. Data on chronic infections such as tuberculosis, HIV, and viral hepatitis are limited, highlighting the need for further research. Combination therapy with paracetamol and ibuprofen may enhance analgesia in postoperative settings without significantly increasing adverse events. Overall, available evidence is limited and largely observational. Conclusions: This narrative review synthesizes current evidence and clinical expertise to provide practical guidance on the rational use of paracetamol and NSAIDs in children, emphasizing individualized therapy according to comorbidities, risk factors, and clinical context, particularly in vulnerable populations. A risk-adapted, evidence-based approach ensures optimal symptom control while minimizing harm, supporting safer, more effective, and family-centered care for children with fever and pain. Full article

Model predictive control (MPC) has become an attractive solution for doubly fed induction motors (DFIMs) due to its fast dynamic response and multi-variable constraint handling capability. However, the performance of conventional MPC relies on the accuracy of the system model. To further enhance the control performance and adaptability, this paper proposes a fuzzy logic-based model predictive control (FL-MPC) strategy. The proposed method continuously monitors the current tracking errors and their rates of change, utilizing a fuzzy inference system to dynamically optimize the weight distribution within the predictive model. This enables the controller to autonomously adjust its behavior for optimal performance across a wide range of operating conditions. Both simulation and experimental results demonstrate that, compared to the conventional MPC, the proposed FL-MPC strategy achieves superior dynamic response. Full article

Helicopter vibration is an inherent characteristic of rotorcraft operations, arising from transmission dynamics and unsteady aerodynamic loading, posing challenges to flight control and longevity of structural components. Excessive vibration elevates pilot workload and accelerates fatigue damage in critical components. Leveraging advances in optimal control and microelectronics, the active vibration control methods offer superior adaptability compared to the passive techniques, which are limited by added weight and narrow bandwidth. In this study, a comprehensive vibration analysis and optimal control framework are developed for the Bo 105 helicopter rotor blade exhibiting flapping, lead-lag, and torsional (triply coupled) motions, where a Linear Quadratic Regulator (LQR) is employed to suppress vibratory responses. An analytical formulation is constructed to estimate the blade’s sectional properties, used to compute the coupled natural frequencies of vibration by the modified Galerkin method. An orthogonality condition for the coupled flap–lag–torsion dynamics is established to derive the corresponding state-space equations for both hovering and forward-flight conditions. The LQR controller is tuned through systematic variation of the weighting parameter Q, revealing an optimal range of 10²–10⁴ that balances vibration attenuation and control responsiveness. The predicted frequencies of the vibrating rotor blade are compared with the finite element modeling results and published experimental data. The proposed framework captures the triply coupled rotor blade dynamics with optimal control, achieves modal vibration reductions of approximately 60–90%, and provides a clear theoretical benchmark for future actuator-integrated computational and experimental studies. Full article

The increasing adoption of electric vehicles (EVs) for last-mile delivery requires adapting existing routes designed for internal combustion engine (ICE) vehicles. This study introduces an enhanced Ant Colony System (ACS) that optimizes EV routing by dynamically incorporating state of charge (SOC), charging station proximity, and time constraints. Unlike traditional methods, our approach adjusts pheromone deposition in real time, prioritizing charging stops only when necessary, significantly improving adherence to delivery times. Using real-world delivery data from Paris, our results show that routes under 90 km tend to remain energetically feasible, although intermediate time-window violations may occur due to cumulative charging delays. For longer routes, the need for additional charging stops introduces a risk of delays, but the system effectively manages these constraints to minimize disruption. These results provide fleet operators with a practical decision-support tool to identify which pre-optimized routes can be efficiently adapted to EVs, thus offering a pathway for the integration of electric vehicles into existing logistics without significant operational disruption. Future work will focus on enhancing the system by incorporating real-time traffic updates and charging station availability to further optimize the routing process. Full article