Search Results (253)

Traditional cognitive practices in the Engineering Management Major (EMM) are often constrained by safety risks, high costs, and geographical limitations. This study proposes a novel Virtual Cognitive Practice (VCP) mode that integrates ubiquitous learning (U-learning) with web-based panoramic maps to overcome these challenges. We developed a VCP system leveraging panoramic data of roads, bridges, and tunnels from commercial web mapping platforms to provide high-fidelity, interactive observation environments. To evaluate its effectiveness, 147 undergraduate students participated in a virtual practice course and subsequently completed a structured questionnaire. The results demonstrate that the accuracy on objective knowledge tests exceeded 80%, alongside a high mean score of 4.27/5 for visualization satisfaction. Statistical analysis using Chi-square tests indicates that students with prior on-site experience are significantly more confident in the VCP mode’s potential as a pedagogical alternative. This research bridges the technical gap in EMM practical education by providing a flexible, ubiquitous learning ecosystem. Full article

Inclusive education requires not only classroom-level adaptations but also coordinated interdisciplinary practices that strengthen the institutional conditions supporting diverse learners. However, counselor–teacher collaboration in many schools remains informal and episodic, limiting its potential contribution to an inclusive school climate. This study evaluated the effectiveness of a 12-week Structured Counselor–Teacher Collaboration (SCTC) program designed as a cyclical and replicable interdisciplinary model. A multi-site cluster quasi-experimental design with matched non-equivalent control groups was implemented in 12 public inclusive junior secondary schools in Yogyakarta, Indonesia (6 intervention; 6 control), involving 360 teachers (n = 180 per condition) and 24 school counselors as facilitators. Teachers completed the 35-item Inclusive School Climate Scale (ISCS) at pre-test and post-test. Data were analyzed using two-level linear mixed-effects modeling (teachers nested within schools) with pre-test scores as covariates. Results showed that the intervention significantly improved inclusive school climate compared with routine practice (B = 0.41, p < 0.001), yielding a moderate-to-large adjusted effect (Hedges’ g = 0.76). Dimension-level models indicated the largest gains in collaborative professional culture and perceived belonging. Implementation fidelity was high (82–91%). These findings suggest that institutionalizing structured counselor–teacher collaboration can serve as a promising approach for enhancing inclusive school climate in secondary school contexts. Full article

Oxide perovskites with simultaneously large band gaps and high-static dielectric constants are of considerable interest for advanced microelectronics, dielectric devices, and energy storage applications, yet their discovery remains challenging because electronic insulation, lattice polarizability, and thermodynamic accessibility are strongly coupled and often mutually competitive. Here, we develop a physics-guided multitask learning framework for the joint prediction of the band gap and static dielectric response in chemically constrained single-perovskite oxide ABO₃ compounds. To ensure data fidelity and physical comparability, the learning space is strictly restricted to simple oxide ABO₃ perovskites from the Materials Project, while mixed-fidelity band gaps, heterogeneous dielectric definitions, and chemically inconsistent samples are excluded. The model integrates role-aware A-/B-site descriptors, perovskite-specific geometric and structural features, multitask prediction of Eg, εtotal, εelectronic, and εionic, explicit physical consistency constraints, auxiliary candidate classification, ranking learning, and reliability-aware screening with uncertainty and out-of-distribution control. Under B-site-grouped cross-validation, the framework achieves 97.4% accuracy, Recall of 96.5%, and an F1 score of 96.1%, while maintaining robust transferability on the independent JARVIS validation set. The results show that high-gap/high-k candidates occupy a chemically non-random subspace governed by B-site-centered electronic–lattice coupling, and that physically consistent multitask learning substantially improves both predictive coherence and candidate enrichment. More broadly, this study establishes a data-consistent, physics-constrained, and transferable paradigm for the intelligent discovery of functional oxide dielectrics. Full article

Existing studies conduct general cost analyses for prefabricated components, yet structural heterogeneity results in distinct cost drivers. Most studies concentrate on the technical performance of prefabricated staircases, with insufficient investigation into dedicated cost-estimation methods. This study establishes a hybrid prediction framework integrating GAN-based data augmentation and SHAP-empowered Multilayer Perceptron (SHAP-MLP) modeling, using prefabricated straight staircases as empirical objects for multidimensional analysis. Total cost is classified into production, transportation, and on-site installation phases, followed by systematic screening of 33 influencing factors for predictive modeling. The Analytic Hierarchy Process (AHP), with a 1–9 scale, is adopted to quantify indicator weights and prioritize features. Triple verification (multi-expert consistency test, group opinion coordination test, and sensitivity analysis) removes five weakly correlated parameters to form a preliminary indicator system. Based on 240 original engineering data samples, the GAN generates 60 high-fidelity synthetic samples. Distribution consistency between synthetic and original data is validated via the Kolmogorov–Smirnov (KS) test, p-value verification, and kernel density estimation (KDE). SHAP interpretability analysis identifies four core determinants: prefabrication rate, total staircase area, standardization level, and number of floors. Eight low-impact parameters are excluded to optimize model input, leaving 20 validated indicators. The GAN-SHAP-MLP model maintains superior performance in testing, with a test-set RMSE of 49.538, representing improvements of 41.3%, 22.5%, and 25.7% over LSTM (89.33), CNN (67.59), and standard MLP (70.56), respectively. The difference between its test-set and overall R² is only 0.69%, significantly lower than 2.06% for LSTM and 5.47% for MLP. Empirical validation with real engineering cases from four different regions further confirms the model’s high prediction accuracy, with a minimum error of only 1.49%. The integration of data augmentation and interpretable deep learning provides a high-precision, interpretable cost prediction tool for prefabricated straight staircases, promoting methodological progress in construction economics. Full article

We present the performance of a middle atmosphere Rayleigh lidar system that employs three receiver channels. We characterize the biases in the density and temperature profiles retrieved from each of the receiver channels as well as the combined receiver signal. We associate these biases with pulse pile-up, gain switching, and variations in the detector gain due to signal amplitude. We use a top-down temperature convergence methodology to determine the upper altitude up to which the signals should be compensated for the variations in detector gain. We find that the channels have warm biases in their temperatures of 2–8 K at 40 km. These biases decrease to between 1 K and 3 K at 60 km. Uncertainty estimates derived from the photon-counting statistics indicate temperature uncertainties on the order of 2–5 K in the 40–70 km region, which are consistent with the observed level of inter-channel variability after correction. A comparison with MERRA-2 reanalysis indicates an overall agreement in temperatures and differences that are consistent with the comparisons between the Rayleigh lidars and MERRA-02 at other sites. These results demonstrate that the proposed approach proves reliable for processing the multi-channel Rayleigh lidar data, particularly for systems employing more than two detection channels, and improves the fidelity and accuracy of the temperature retrievals. Full article

Open-pit mining operations rely heavily on visual inspection to identify indicators of slope instability such as surface cracks. Early identification of these geotechnical hazards enables timely safety interventions to protect both workers and assets in the event of slope failures or landslides. While computer vision (CV) approaches offer a promising avenue for autonomous crack detection, their effectiveness remains constrained by the scarcity of labelled geotechnical datasets. Deep learning (DL)-based models, in particular, require large amounts of representative training data to generalize to unseen conditions; however, collecting such data from operational mine sites is limited by safety, cost, and data confidentiality constraints. To address this challenge, this study proposes a novel hybrid game engine–generative artificial intelligence (AI) framework for large-scale dataset generation without requiring real-world training data. Leveraging a parameterized virtual environment developed in Unreal Engine 5 (UE5), the framework generates realistic images of open-pit surface cracks and enhances their fidelity and diversity using StyleGAN2-ADA. The synthesized datasets were used to train the YOLOv11 real-time object detection model and evaluated on a held-out real-world dataset of open-pit slope imagery to assess the effectiveness of the proposed framework in improving model generalizability under extreme data scarcity. Experimental results demonstrated that models trained using the proposed framework consistently outperformed the UE5 baseline, with average precision (AP) at intersection over union (IoU) thresholds of 0.5 and [0.5:0.95] increasing from 0.792 to 0.922 (+16.4%) and 0.536 to 0.722 (+34.7%), respectively, across the best-performing configurations. These findings demonstrate the effectiveness of hybrid generative AI frameworks in mitigating data scarcity in CV applications and supporting the development of scalable automated slope monitoring systems for improved worker safety and operational efficiency in open-pit mining. Full article

We investigate wheat lodging segmentation from UAV RGB imagery acquired over real production fields rather than controlled experimental sites. Besides pixel-level accuracy, our evaluation also emphasizes robustness under heterogeneous farmland conditions and deployment-oriented efficiency. We propose CASGNet, an edge-oriented segmentation network with a content-aware spatial gating mechanism that reweights intermediate features according to local structural variation. Instead of uniformly aggregating features, the module suppresses responses in homogeneous regions while preserving activation in structurally complex areas. In practice, this improves the continuity of irregular lodging shapes and reduces spurious responses in relatively homogeneous backgrounds. The dataset spans 46 farms across Jiaozuo, Jiyuan, and Luoyang, covering progressively fragmented farmland. Under a stricter mission-level data-isolation protocol, CASGNet achieves 94.4% mIoU and 90.38% IoU for the lodging class on the combined dataset. Under sequential regional adaptation, performance remains relatively stable in continuous parcels, and degradation is less severe than most compact baselines in highly fragmented landscapes. On Jetson Nano, CASGNet achieves 1.94 FPS embedded inference under the 5 W mode. Smaller networks achieve higher speed but show reduced structural continuity in complex scenes. The results indicate that CASGNet provides a favorable balance between structural fidelity and computational cost, while its robustness remains constrained by scene complexity. Full article

While Digital Twins offer transformative potential, their efficacy for real-time control is constrained by the slow data acquisition and the high computational intensity required to process raw datasets like point clouds. This paper identifies these critical bottlenecks—specifically the latency between data capture and actionable insight—and proposes a refined theoretical framework for near-real-time automated progress monitoring and quality reporting. Building on the findings of the NORMENG project and informing the subsequent AutoGreenTraC project, this research synthesizes state-of-the-art advancements in reality capture, including LIDAR, SfM-MVS, and 360-degree vision. The study highlights a fundamental divergence in stakeholder requirements: the need for millimeter-level precision in quality control versus the demand for high-velocity documentation for progress monitoring. A key innovation presented is the shift toward neural rendering techniques to bypass the computational delays of traditional photogrammetry and enable immediate on-site visualization. By structuring a tiered processing hierarchy that combines lightweight edge analysis for immediate safety and progress monitoring with asynchronous high-fidelity Digital Twin updates, the framework aims to establish a single source of truth. Full article

The operational reliability of Emergency Diesel Generators (EDGs) is paramount for the safety of nuclear power plants. This study investigates the fretting wear mechanism on the non-working back-face of connecting rod big-end bearings—a critical failure mode that can lead to catastrophic engine damage. A synergistic approach was employed, integrating theoretical pressure calculations, on-site strain measurement experiments, and high-fidelity non-linear finite element analysis (FEA). The results demonstrate that while the theoretical design back-face pressure ranges from 8.1 to 10.1 MPa, the actual pressure is highly sensitive to bolt preload. A 16.2% attenuation in preload (from 550 kN to 461 kN), common during maintenance cycles, causes the interfacial pressure to drop to 6.9 MPa, falling below the recommended safety threshold of 7 MPa required to inhibit fretting. Furthermore, comparative experiments reveal that used bearings exhibit significantly lower and less uniform radial pressure retention compared to new bearings, even when physical dimensions appear compliant. Dynamic FEA indicates that peak inertial loads induce an out-of-roundness (DOR) of 0.295 mm, triggering a transition from a “partial slip” to a “macro-slip” regime at the interface. The findings confirm that the coupling of preload attenuation and loss of bearing elasticity drives the fretting process, providing a theoretical basis for optimized maintenance and selective assembly strategies. Full article

Obesity-associated carcinogenesis offers a model to explore the transition from metabolic dysregulation to genomic instability and carcinogenesis. Adenosine 5′-monophosphate-activated protein kinase (AMPK), the principal cellular energy sensor, coordinates adenosine triphosphate (ATP) production with metabolic demand; however, in obesity, AMPK activity is impaired, resulting in reduced ATP, elevated Adenosine Monophosphate (AMP), and cellular energy stress. Deoxyribonucleic Acid (DNA) polymerases ε (Pol ε) and δ (Pol δ) maintain replication fidelity via a 3′→5′ exonuclease proofreading activity that removes misincorporated nucleotides. Elevated AMP directly binds and selectively inhibits the exonucleases, conserving energy at the expense of genomic accuracy. As a result, replication errors escape correction and accumulate, some conferring a selective advantage and driving carcinogenic evolution. Therapeutic and lifestyle interventions that activate AMPK—including weight loss, exercise, metformin, and aspirin—restore ATP production, lower AMP, and relieve inhibition of exonuclease proofreading, thereby preserving genomic integrity and slowing mutation-driven carcinogenesis. This framework reveals two core biological principles: 1. Energy metabolism and DNAreplication fidelity are mechanistically coupled at the DNA polymerase active site. 2. The mutation rate is an adaptive metabolic phenotype, modulated by AMP levels. These concepts redefine the metabolic–genetic interface in carcinogenesis and highlight AMPK activation as a rational target for obesity-associated cancer prevention. Full article

Digital Twins for cultural heritage buildings commonly depend on high-fidelity 3D scanning, detailed onsite surveys, and unrestricted data acquisition. In many countries, however, legal, regulatory, and conservation constraints render such methods inaccessible or explicitly prohibited, significantly limiting the deployment of digital-heritage technologies in real settings. This paper introduces HeritageTwin Lite, a regulation-compliant workflow for constructing low-detail yet operational Digital Twins of protected cultural heritage buildings using only publicly permissible data sources. The proposed approach relies on a GIS-based 2D application through which users select a site and manually delineate building footprints and structural outlines. These 2D sketches are combined with satellite imagery, publicly available photographs, archival records, and open datasets to generate a massing-level 3D model. Building height and volumetric characteristics are estimated using contextual cues such as surrounding structures, known architectural typologies, and scale references derived from people or urban elements. The resulting Digital Twin prioritizes geometric plausibility over fine architectural detail, enabling simulation, analysis, and decision-support tasks, such as environmental modeling, airflow and CFD approximation, and high-level Heritage BIM integration, while fully respecting cultural heritage restrictions. Three case studies illustrate the proposed workflow and systematically identify which components of conventional smart-building and Digital Twin pipelines remain feasible and which become infeasible under heritage regulations. The results demonstrate a practical and scalable path toward compliant Digital Twins for protected buildings, positioning low-detail modeling not as a limitation but as a regulatory necessity. Full article

Serial flow cytometry was recently introduced as a method that can estimate measurement uncertainty (i.e., imprecision, the coefficient of variation of repeated measurements of individual particles) independent from population characteristics. Replication of light sources and detectors at multiple sites along a flow cytometer’s microchannel requires more equipment and can complicate detector synchronization. Here, we introduce amplitude modulation to encode each region of a serial cytometer with a unique carrier frequency, which enables demultiplexing of the combined signal incident on a single photodetector by fast Fourier transform (FFT) peak magnitude. To facilitate validation of detection, matching, and uncertainty quantification of fluorescence signals, we designed a microfluidic amplitude modulation (AM) serial flow cytometer that has ground truth detectors on individual regions (serial cytometry) in parallel with the combined channel detection for AM demultiplexing. With this report, we present metrics for event detection and dynamic range, prevalence and processing of overlapping detections, region-decoding accuracy, process yield, and uncertainty quantification on a brightness ladder of calibration microspheres. Despite being operated with reduced light intensities, the AM cytometer was capable of high-fidelity performance in comparison to conventional serial cytometry. For events above the detection limit, over 97% were analyzed. Both conventional and AM serial cytometers achieved median imprecisions in the range of 0.53% to 2.1% after outlier removal, which was well below the inherent intensity distribution of any of the microsphere subpopulations. Overall, AM cytometry supports uncertainty quantification and temporal analyses of serial cytometry data with a reduced number of photodetectors, which offers simplification of chip design with multiple measurement regions and wide-field detectors. Full article

Injection well placement and rate allocation are among the most decisive factors in determining the efficiency and bankability of CCS projects. However, optimizing these parameters is notoriously complex: even a small number of injection wells leads to a virtually infinite set of injection scenarios, while traditional optimization techniques typically require thousands of high-fidelity reservoir simulations. For project developers, this computational burden can stall critical Final Investment Decisions (FID). The approach proposed here addresses this bottleneck by using a Design of Experiments (DoE) framework combined with nonlinear surrogate modeling, which efficiently maps the relationship between injection rates and storage performance, to identify near-optimal solutions with a minimal number of simulations. We show that our method achieves up to 97% of the initially targeted CO₂ sequestration with as few as 15 simulations, demonstrating a step-change reduction in time and cost. From a business standpoint, CCS operators can de-risk projects earlier, accelerate FID timelines, and evaluate multiple site configurations in parallel while minimizing computational overhead. Rather than waiting weeks or months for exhaustive optimization, decision-makers can gain timely, reliable insights that directly support capacity commitments, regulatory submissions, and ultimately revenue realization. Full article

The geometric dimensional accuracy of large-scale prefabricated components is critical for the successful implementation of prefabricated construction. However, traditional manual contact-based inspection methods are inefficient and are often simplified or even neglected in practice due to operational difficulties. To address this challenge, this study proposes an automated non-contact dimensional inspection system based on UAV photogrammetry. The system consists of three core modules: First, the 3D Model Generation Module utilizes UAV-captured multi-view imagery to rapidly reconstruct high-fidelity 3D models of construction sites using improved 3D Gaussian Splatting technology, while recovering true physical scales by integrating GPS metadata. Second, the Segmentation Module extracts target components from complex backgrounds through flexible target selection and achieves automated planar segmentation using the Region Growing algorithm. Finally, the Dimensional Inspection Module accurately calculates geometric dimensions using a self-developed “Measurement Tree” algorithm. Engineering validation demonstrates that the system achieves an average relative error of only 0.35% in the inspection of prefabricated bent caps, exhibiting excellent measurement accuracy and robustness. This study provides an efficient, precise, and intelligent solution for the quality control of prefabricated components, effectively bridging the gaps inherent in traditional inspection methods. Full article

This study examines the impact of photovoltaic (PV) modeling fidelity utilizing single-diode (SDM), double-diode (DDM), and triple-diode (TDM) representations on the precision of hydrogen production (H₂P) estimates when integrated with various electrolyzer technologies, specifically proton exchange membrane (PEM), alkaline (AEL), and solid oxide electrolysis cells (SOECs). Precise evaluation of solar-powered green hydrogen (H₂) systems necessitated a dependable estimate of PV power under authentic working circumstances. Hourly site-specific irradiance and ambient temperature (T_a) data for Riyadh, Saudi Arabia, were used to calculate PV power outputs, which were then sent to physically based electrolyzer models regulated by electrochemical voltage relationships and Faraday’s law. The findings indicate that while all PV models display the same seasonal patterns, SDM somewhat overestimates yearly PV energy in comparison to DDM and TDM, with relative errors around 0.03%. These discrepancies somewhat affect H₂ yield estimations but do not change the relative ranking of electrolyzer technology. Among the assessed options, SOEC consistently produced the highest H₂ output, generating approximately 21.8% more H₂ than PEM and 9.1% more than AEL, with annual yields of 62.46–62.47 g for PEM, 69.70–69.71 g for AEL, and 76.04–76.05 g for SOEC across the SDM, DDM, and TDM frameworks under equivalent solar power inputs. The findings indicate that the selection of electrolyzer technology significantly impacts H₂P more than the choice of a PV model, while high-fidelity PV modeling is crucial for a physically realistic and precise system-level assessment of integrated PV-H₂ energy systems. Full article