Search Results (12,474)

Modeling thermal environments across scales is crucial for climate-adaptive design and energy management. Traditional numerical methods (e.g., CFD) offer high accuracy and physical consistency, but they are computationally expensive. In contrast, purely data-driven models, though efficient, lack physical consistency and generalization capability. This review systematically examines Physics-Informed Neural Networks (PINNs), a hybrid paradigm in which physical prior knowledge is embedded directly into the neural network training process. A structured keyword search of the Web of Science Core Collection was performed, and 94 peer-reviewed journal articles were analyzed. The evolution from numerical simulations and data-driven surrogate models to PINNs is outlined. PINN methods are classified according to the stage at which physical prior information is integrated (i.e., dataset development, model construction, or loss function formulation). Current research remains heavily focused on loss function constraints, whereas systematic integration into data augmentation and model construction remains limited. Application domains span indoor environments, outdoor environments, and building systems, with each domain exhibiting unique prior integration strategies tailored to specific problems. Future PINN modeling should evolve toward multi-physics coupling, adaptive loss balancing, cross-scenario transfer learning, and unified evaluation benchmarks. PINNs in this field are promising but remain at an early stage, especially for complex urban-scale deployment. This review synthesizes existing research around the three stages of dataset development, model construction, and loss function formulation, summarizes the prior integration strategies adopted in the domain of building thermal environments, and provides a practical workflow for embedding physical prior knowledge at different stages of model development. Full article

Microseismic monitoring is essential for the early warning of mine dynamic disasters; however, weak signal characteristics and strong environmental noise often lead to missed detections and false alarms. To address these challenges, this study proposes an optimized U-Net model for multi-class microseismic signal recognition under low-signal-to-noise-ratio conditions. The method combines Short-Time Fourier Transform, a U-Net encoder–decoder architecture, residual learning, and squeeze-and-excitation attention modules to enhance weak feature extraction and noise suppression. A multi-source dataset containing microseismic, knocking, blasting, noise, and earthquake signals was constructed using both field-measured data and public seismic datasets. Experimental results show that the proposed model achieved an overall validation accuracy of 99.25% and excellent recall performance for microseismic events. Under extreme noise conditions with a signal-to-noise ratio of −5 dB, the model still maintained a microseismic recognition accuracy of 98.25%. Comparative experiments further demonstrate that the integration of Short-Time Fourier Transform and residual attention modules significantly improves robustness and weak-signal discrimination capability. The proposed method provides an effective approach for intelligent microseismic monitoring and mine dynamic disaster early warning. Full article

This study explains how reinforcement learning (RL) contributes to congestion mitigation in inland freight terminal operations by testing a serial process model in which RL strengthens operational learning stability (OLStab), OLStab improves logistics efficiency, and logistics efficiency lowers congestion. Rather than presenting RL as a stand-alone congestion-reduction instrument, the paper examines a distinct inland-terminal application in which congestion emerges from interacting gate, yard, transfer, and dispatch frictions. Using a simulation-based explanatory design calibrated to a realistic macro-logistics context, and interpreting the results as simulation-informed evidence rather than direct field proof, the study analyzes 500 episode-level observations representing complete terminal runs under varying control conditions. The results show that RL positively affects OLStab, OLStab positively affects logistics efficiency, and logistics efficiency negatively affects congestion. The serial indirect pathway from RL through OLStab and logistics efficiency to congestion is statistically significant, whereas the direct effect of RL on congestion becomes non-significant once the mediators are introduced. Decision latency sensitivity does not significantly moderate the RL-to-OLStab relationship, suggesting that latency-related boundary conditions are more context-specific than the main capability pathway. The article contributes by offering a cautious simulation-based and mechanism-centered explanation of RL-enabled congestion mitigation in inland terminals, by treating OLStab as a simulation-grounded intermediate operational stability index, and by showing that the empirical pattern is better explained by theory-ordered simulator-level mechanism than by a residual direct RL effect. Full article

This study proposes an incremental neural network adaptive control algorithm based on composite learning for a two-degree-of-freedom (2-DOF) helicopter system characterised by dynamic event triggering and input saturation. Firstly, by integrating a composite learning strategy within the incremental neural network control framework, the study aims to overcome the challenges posed by system dynamic uncertainties. The proposed novel update algorithm effectively incorporates estimation error terms into the weight adaptation process, thereby improving the approximation capability for system dynamics while alleviating the dependence on the classical persistent excitation condition. In addition, to reduce the communication load between the controller and the actuator, we introduce an adaptive dynamic event-triggered mechanism. Furthermore, a saturation-resistant auxiliary system is constructed to address the input saturation phenomenon present in the system. Subsequently, the system is proven to be semi-globally consistent and bounded stable via Lyapunov functions. Finally, the effectiveness of the control strategy proposed in this study is verified through simulation. Full article

Accurate identification of drilling operation states is essential for improving drilling efficiency and operational safety. However, existing methods often suffer from limited temporal feature extraction capability, weak cross-well generalization, and insufficient model interpretability. To address these issues, this study proposes a drilling-state recognition framework based on MultiHead-BiLSTM and low-rank adaptation (LoRA) transfer learning. The MultiHead-BiLSTM model combines multi-head attention with bidirectional long short-term memory to capture both critical temporal segments and global sequential dependencies in drilling time series data. To improve cross-well adaptability while reducing training computational cost, a parameter-efficient LoRA fine-tuning strategy is introduced within the transfer learning framework. In addition, SHAP-based feature attribution and attention visualization are employed to enhance model interpretability. Experimental results show that the proposed method achieves an accuracy of 95.11% and an F1-score of 94.00%, outperforming LSTM, GRU, BiLSTM, and Transformer baselines. The LoRA-based transfer strategy reduces the cross-well error rate to 1.91%, compared with 8.79% for direct transfer and 4.48–5.39% for partial-layer freezing methods. Interpretability analysis qualitatively suggests that bit depth, weight on bit, and block position contribute strongly to drilling-state discrimination, while attention visualization qualitatively suggests that the model tends to focus on operational transition periods. The proposed framework provides an effective and computationally efficient solution for intelligent drilling-state recognition and cross-well deployment. Full article

With the rapid growth of Chinese social media data, many language-driven analytical tasks, such as sentiment analysis and malicious account detection, are increasingly formulated as computationally expensive optimization problems, particularly in the context of hyperparameter tuning for deep learning models. Due to the intrinsic characteristics of Chinese text, including implicit word boundaries, strong context dependency, and high linguistic variability, the resulting feature representations are often high-dimensional, sparse, and heterogeneously distributed. From an optimization perspective, these properties induce highly irregular, non-smooth, and multimodal objective landscapes, posing significant challenges to conventional surrogate-assisted data-driven evolutionary algorithms (DDEAs). To address this problem, this paper proposes a Normal Selection-based data-driven evolutionary algorithm (NSEA) for improving surrogate-assisted optimization under complex conditions. Specifically, a Normal distribution-based selection strategy (NSS) is developed to enable probabilistic selection of surrogate models, balancing exploitation of high-performing models and exploration of alternative candidates, thereby alleviating premature convergence in multimodal search spaces. In addition, an exponential weighting ensemble (EWE) method is introduced to aggregate surrogate models based on their relative ranking performance, which enhances the stability and generalization capability of fitness approximation across different regions of the search space. Extensive experiments on benchmark functions demonstrate that the proposed NSEA consistently outperforms several state-of-the-art DDEAs in terms of optimization accuracy and robustness. Furthermore, a real-world application of cheating official account (COA) detection on Chinese social media is conducted, in which the hyperparameter optimization of a heterogeneous graph transformer (HGT) model is formulated as an EOP. The results further prove the effectiveness and practical applicability of the NSEA in complex data-driven scenarios. Overall, this study provides an effective optimization framework for handling EOPs with complex and multimodal characteristics and offers a feasible computational approach for tasks associated with large-scale Chinese textual data. Full article

Accurate prediction of pavement performance is essential for sustainable pavement management, especially in flood-prone regions where environmental stressors accelerate deterioration. This study develops a machine learning-based comparative framework to evaluate the contributions of baseline pavement condition, maintenance and rehabilitation (M&R) activities, and environmental exposure to predicting changes in Pavement Condition Index (ΔPCI) across 11,214 matched pavement segments in Southeast Texas from 2019 to 2023. Three nested modeling scenarios were evaluated using Linear Regression, Random Forest, and XGBoost, with performance evaluated using R², MAE, and RMSE. Baseline variables alone showed limited predictive capability, whereas adding M&R history produced the largest improvement. Environmental and flood-related variables provided further gains, particularly for nonlinear ensemble models. XGBoost achieved the highest predictive performance in the fully integrated scenario (R² = 0.65, MAE = 10.63, RMSE = 14.02). SHAP analysis identified SDI2019 and PCI2019 as the strongest predictors, while selected M&R and environmental variables also contributed meaningfully. The findings demonstrate that integrating treatment history and environmental exposure substantially improves pavement performance prediction and supports more sustainable, climate-resilient pavement management and helps agencies prioritize maintenance and allocate resources more effectively. Full article

While the Triton compiler has revolutionized GPU kernel development, its deployment on general-purpose CPUs struggles to fully utilize the underlying hardware capabilities. This is primarily due to the semantic gap between Triton’s SPMD execution model and CPU vector architectures, which leads to suboptimal utilization of vector units during complex memory accesses. In this paper, we present a comprehensive compilation and runtime co-optimization framework for Triton-CPU, specifically targeting Vector Length Agnostic architectures (VLA) like ARM SVE. At the compiler level, we propose a novel semantic reconstruction and explicit base-offset decoupling strategy, enabling native VLA gather/scatter generation and eliminating scalar loop overheads. At the runtime level, we introduce a Machine Learning-driven thread scheduling model to optimally orchestrate the synergy between Thread-Level Parallelism and Vector-Level Parallelism. Extensive evaluations on an ARM-based multi-core processor demonstrate that our framework achieves up to a 2.0× throughput improvement for compute-bound GEMM operators (peaking at 346 GFLOPS), notably outperforming the hand-optimized OpenBLAS library by up to 1.54× at small-to-medium scales. Additionally, it delivers a 1.7× speedup for element-wise workloads. Furthermore, our optimizations saturate memory bandwidth (up to 55 GB/s) for memory-bound operators with zero compilation bloat, establishing a robust, high-performance foundation for deploying deep learning models on general-purpose CPUs. Full article

While existing curricula often focus on theoretical aspects of sustainability, they frequently fail to equip students with practical design skills required by the green industry. To address this disconnect, this study seeks to answer: How can a structured pedagogical framework effectively enhance students’ ability to translate abstract sustainability principles into concrete technical solutions? This study introduces a comprehensive CDIO-based framework reform for Embedded Intelligent Systems education, weaving sustainability throughout every phase. We put forward a “Sustainable CDIO Capability Model” that charts a progressive pathway—starting from basic resource awareness and advancing through to sophisticated sustainable system innovation. Our four-dimensional teaching strategy brings this model to life: first, project-based learning driven by real sustainability challenges; second, a hybrid ecosystem blending online resources, hands-on practice, and immersion in green industry contexts; third, hierarchical team-based pedagogy backed by personalized support mechanisms; and fourth, a multi-dimensional assessment system that weights energy efficiency, resource stewardship, and social value creation alongside conventional metrics. We implemented this approach with Intelligent Science and Technology majors at Wuhan Institute of Technology. The results show the model effectively bridges the persistent gap between dry technical content and the practical demands of green industry. Students made substantial gains not merely in core engineering capabilities—system architecture, hardware-software co-development—but crucially in sustainable design awareness and their capacity to untangle complex sustainability challenges. This work offers a readily transferable framework for embedding Education for Sustainable Development (ESD) into engineering curricula worldwide. It provides practitioners with a concrete, tested model for cultivating the next generation of engineers who naturally think and act with sustainability in mind. Full article

Metallic particle contaminants have been shown to have a detrimental effect on the reliability, performance and capacity of lithium-ion battery cells. In addition, they pose a significant safety risk. Typical contaminants, such as iron (Fe), copper (Cu) and aluminium (Al), often enter the cell via mechanical abrasion from production equipment, as burrs during electrode cutting, or through environmental exposure during handling. In such instances, the degradation mechanisms are known to accelerate, dendrite formation is increased, and, in the most unfavourable circumstances, thermal runaway is the likely outcome. Contaminants that do not affect cell behavior during formation and the initial cycles, yet only compromise safety at a subsequent stage, are of particular concern. Affected cells are known to pass end-of-line testing and make their way into the market as latent safety risks. Consequently, there is an urgent requirement for non-destructive diagnostic methods that are capable of identifying latent defects. The issue under discussion is approached in the present paper through the utilization of an innovative methodology that integrates the distribution of relaxation time (DRT) analysis of electrochemical impedance spectroscopy (EIS) data with machine learning techniques. The objective of this integrated approach is to facilitate the detection of critically contaminated pouch cells with a high degree of sensitivity. Full article

In this paper, we propose a novel deep learning-based framework for the joint design and decoding of linear block codes, the end-to-end deep coding cross-attention message-passing Transformer (E2E DC-CrossMPT). To improve linear block code design and decoding, we redesign the conventional error correction code (ECC) decoder, CrossMPT, to fit within an end-to-end framework. The redesigned decoder separately utilizes magnitude and syndrome vectors obtained from the received signals as inputs. It further employs one-hot encoding based syndrome embedding and incorporates a parity-check matrix into the output layers. Experimental results demonstrate that, across various code lengths and code rates, E2E DC-CrossMPT consistently outperforms both traditional decoding algorithms and a conventional end-to-end deep coding model in terms of decoding performance. Moreover, the codes designed by E2E DC-CrossMPT achieve superior error-correction capability compared with both traditional linear block codes and those designed by the conventional end-to-end deep coding model, while requiring lower computational complexity. Full article

Climate change, rapid urbanization, land-use changes, and the creation of a multi-layered risk environment by multiple disaster hazards have made interdisciplinary educational models—capable of integrating resilience knowledge into planning and design education—all the more essential. Nevertheless, the systematic and competency-based integration of scientific knowledge generated in the fields of ecological planning, nature-based solutions, multi-hazard analysis, and digital planning tools into higher education curricula remains limited. This study aims to develop a competency-based curriculum model for ecological planning and disaster-resilient cities by adapting the resilience literature into a modular educational model. Literature mapping, thematic clustering, gap identification, competence framework building, and curricular architecture development are the steps of the study’s design-based analytical framework. Studies published between 2015 and 2025 were examined in terms of disaster types, analytical tools, and planning approaches; they were then reorganized based on three competency areas: green skills, digital skills, and resilience skills. The findings have resulted in a modular curriculum comprising 35 modules and 105 topics, structured within a three-tiered framework consisting of conceptual content, practical application, and case-based learning. The original contribution of this study is its proposal of a structured educational model that bridges the gap between the production of scientific knowledge and curriculum design. The proposed framework provides a scalable and adaptable foundation for undergraduate, graduate, and professional education contexts; it also establishes a foundation for AI-supported personalized learning pathways in ecological planning and disaster resilience education. Full article

This discussion paper examines the evolution of freshwater distribution networks from smart to intelligent and ultimately meta-intelligent or wise systems, highlighting the transition from human-supervised operation to autonomous adaptive management. Smart systems integrate monitoring, automation and remote control through information technologies. Intelligent systems extend these capabilities by adding predictive analytics, demand forecasting and automated operational optimization. Wise systems further evolve through adaptive learning mechanisms that allow continuous self-improvement while minimizing dependence on operators. Evidence from Greek water utilities demonstrates practical applications and operational outcomes. The analysis discusses implementation challenges including investment costs, system complexity, data governance and resilience. Finally, the paper proposes design principles for scalable adaptive water networks applicable to utilities with different sizes, resources and levels of technological maturity. Full article

The rapid evolution of data-driven enterprises demands scalable and intelligent systems capable of managing substantial volumes of heterogeneous data in real time. However, traditional systems lack a holistic approach to managing distributed data engineering, real-time analytics, and intelligent decision-making. To address these limitations, this paper proposes a Cognitive Lakehouse Framework that integrates distributed data processing, transformer-based deep learning, real-time analytics, and autonomous decision intelligence. Data are gathered from high-velocity, heterogeneous streams using Apache Kafka. Subsequently, data are processed using the hybrid batch/streaming paradigm, implemented via Apache Spark and Apache Flink, providing low latency and scalability. For data storage, a unified lakehouse layer is created using Delta Lake and Apache Iceberg, both of which support ACID transactions and schema evolution. In addition, transformer-based Deep Learning (DL) algorithms are utilized to capture temporal dependencies for predictive analytics, anomaly detection, and adaptive learning. Model lifecycle management is handled by MLflow, while ClickHouse and Apache Druid are used for real-time analytics. The architecture uses microservices and an event-driven approach on Kubernetes, and the workflow is automated with Apache Airflow. The performance assessment is conducted using TPC-H, TPC-DS, and real-time stream data to measure latency, throughput, and accuracy. Data quality, security, and compliance are provided by governance layers consisting of Apache Ranger and Apache Atlas. Experimental results show that significant gains can be made in terms of performance, with an accuracy of 98.5%, a query response time of 120 ms, a peak throughput of 85,000 records/s, and an end-to-end latency of 95 ms. Full article

Predicting the evolution of microstructure and field quantities under varying processing and loading conditions is a central challenge in computational materials science and metal additive manufacturing (AM). While deep learning (DL) methods offer ultra-fast prediction capabilities post-training, existing models often struggle with poor spatial and temporal extrapolation, high parameter burdens, and an inability to effectively integrate diverse conditioning parameters alongside high-dimensional input fields. To address these bottlenecks, we propose a novel conditional generative adversarial network (CPGAN), which is designed to seamlessly ingest both initial fields and governing condition parameters. The CPGAN framework offers three distinct advantages: (1) it accurately maps the combined effects of initial states and process conditions onto evolved fields; (2) it demonstrates robust extrapolation capabilities across diverse spatial and temporal scales, including the unique ability to natively generate high-resolution rectangular domains; and (3) it achieves superior predictive accuracy and training stability compared to standard convolutional baselines by effectively suppressing spurious artifacts. We validate CPGAN’s performance against rigorous physics-based ground truths across three representative engineering applications: porosity evolution in selective laser sintering (SLS), spatial distribution of 2D von Mises stress fields in solid structures, and the spatiotemporal evolution of grain growth. The results confirm that CPGAN is a highly adaptable and efficient surrogate model, capable of simulating continuous structural and morphological evolutions even when driven by highly non-uniform spatial or temporal kinetics. Full article