Search Results (85)

The digital preservation of built cultural heritage requires precise documentation techniques capable of capturing complex architectural geometries often affected by occlusions and data voids. This study presents a robust multi-sensor fusion workflow integrating Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) photogrammetry for the 3D reconstruction of the Hasaköy (Sasima) Church in Niğde, Türkiye. To address the limitations of traditional registration methods, specifically the susceptibility of the Iterative Closest Point (ICP) algorithm to local minima in datasets with partial overlaps, this study proposes a fine-tuning approach based on the Multi-population Based Differential Evolution (MDE) algorithm. The methodology employs a coarse-to-fine strategy, initiating with Fast Point Feature Histogram (FPFH) extraction and RANSAC (Random Sample Consensus) for global alignment, followed by TR-ICP, MDE, PSO, and Aquila Optimizer (AO) evaluation, computational-time analysis, FPFH-radius sensitivity testing, and 6-DoF transformation decomposition to characterize both accuracy and operational cost. In the 30-run fine-tuning evaluation, MDE reduced the mean bidirectional trimmed RMSE from 0.4152 m for TR-ICP to 0.3726 m. With a population parameter of 10, MDE retained a low median RMSE of 0.3718 m, while PSO exhibited a wider stochastic tail under the same bounded 6-DoF search budget. AO produced a higher mean bidirectional trimmed RMSE of 0.5233 m. The decimeter-scale bidirectional RMSE should be interpreted as a cross-source, partial-overlap distance metric rather than sensor precision; the overlapping facade objective was approximately 2.4–2.8 cm, and the UAV block was independently controlled with a 1.34 cm GCP RMSE. This study establishes a transparent and reproducible framework for heritage documentation, supporting the faithful digital preservation of endangered monuments with complex typologies. Full article

In transient stability preventive control of power systems, time-domain simulation is computationally intensive. In addition, the initial operating feature data often contain abundant redundant and irrelevant information. These factors may adversely affect the assessment performance of machine learning models. To address these issues, a transient stability preventive control method based on the sample convolution and interaction network (SCINet) is proposed. First, a feature selection algorithm based on the orthogonal maximal information coefficient and information gain (OMICIG) is developed to extract the key operating features of the system. Second, the SCINet model is employed to learn the nonlinear mapping relationship between the selected key operating features and the transient stability index (TSI). Then, the trained SCINet model is embedded into the transient stability constrained optimal power flow (TSCOPF) model as a surrogate transient stability constraint. In this way, the complicated computation associated with nonlinear differential-algebraic equations (DAE) in the conventional TSCOPF model is avoided. Furthermore, an improved dung beetle optimizer (IDBO) algorithm is used to iteratively solve the resulting model, thereby deriving a preventive control strategy that ensures transient stability while maintaining system operating economy. Finally, simulation studies on the New England 10-machine 39-bus and the IEEE 118-bus system demonstrate the effectiveness of the proposed method. Full article

Evaluating the productivity contribution of individual fracturing stages in deep coalbed methane (CBM) horizontal wells remains a critical challenge, hindering the optimization of stimulation designs. This study systematically integrates dual-phase (aqueous and gaseous) fracturing tracer monitoring with machine learning algorithms to address this issue. Based on large-scale field applications across ten deep CBM horizontal wells in the Changqing mining area of the Ordos Basin, comprising 132 monitored stages, quantitative production profile data were interpreted. Three distinct gas production archetypes—Homogeneous, Heel-Dominated, and Heterogeneous—were identified, each governed by specific geomechanical and stratigraphic controls. Pearson correlation analysis and Random Forest feature importance ranking were employed to decouple the hierarchical influence of geological parameters (Class I coal intersection length, trajectory position, coal thickness) and engineering parameters (proppant volume, pumping rate, fluid volume). A power-law correlation between Class I coal length and initial gas productivity was quantified (R² = 0.71). For the first time, an economically viable “differentiated fracturing scale window” tailored to coal petrophysical classes and wellbore trajectory positions was defined. Subsequently, a machine learning-assisted geology-engineering closed-loop optimization methodology was established, using tracer data as a dynamic feedback bridge to iteratively refine fracturing designs. This research provides a reliable technical approach and practical template for enhancing single-well productivity and recovery efficiency in deep unconventional gas reservoirs. Full article

To address the limitations of the hippopotamus optimization (HO) algorithm, primarily its insufficient global exploration capability and susceptibility to premature convergence to local optima, this paper proposes a hybrid-strategy-improved HO (HSIHO) algorithm for deep learning denoising of controlled source electromagnetic method (CSEM) data. Initially, various strategies are evaluated, and the most effective strategy incorporating lens opposite-based learning (LOBL) and adaptive t-distribution perturbation (ATP) is selected to enhance the hippopotamus optimization algorithm. Subsequently, the HSIHO algorithm is employed to optimize key hyperparameters of the deep learning model, including the learning rate, number of neurons, and number of iterations. Finally, the optimized deep learning model is applied to CSEM data denoising, and its performance is compared with that of the unoptimized deep learning model. Experimental results demonstrate that the proposed HSIHO algorithm outperforms other intelligent optimization algorithms in terms of convergence speed, solution accuracy, flexibility, and scalability in benchmark functions tests. In the application of CSEM data denoising, the optimized bidirectional long short-term memory (BiLSTM) network significantly surpasses the probabilistic neural network (PNN), convolutional neural network (CNN), long short-term memory network (LSTM) and unoptimized BiLSTM methods in noise identification and denoising accuracy. The quality of the processed CSEM data is notably enhanced, with a more stable electric field curve profile. The satisfactory performance in the results verifies the effectiveness of the design and optimization method. Full article

Artificial Intelligence (AI) is emerging as a transformative enabler in aviation, with applications spanning Guidance, Navigation and Control (GNC), Air Traffic Management (ATM), and predictive maintenance. However, the adoption of AI in safety-critical domains remains constrained by the absence of established certification guidance. Traditional standards such as Aerospace Recommended Practice (ARP), ARP4754B, ARP4761A, DO-178C, and DO-254 assume deterministic behaviour and verifiable logic, whereas AI exhibits adaptive and non-deterministic characteristics. Regulatory initiatives, including the European Union Artificial Intelligence Act, the European Union Aviation Safety Agency (EASA) AI Roadmap 2.0, the Federal Aviation Administration (FAA) AI Safety Assurance Roadmap, and ISO/IEC Technical Report (TR) 5469:2024, signal progress but remain fragmented, exploratory, and often limited to low-level autonomous use cases. This study adopts a qualitative approach combining literature and standards analysis with expert interviews to identify gaps in post-deployment assurance, data governance, explainability, and accountability. A conceptual life cycle-oriented framework is proposed that embeds AI-specific assurance activities such as dataset validation, iterative verification, drift detection, and retraining oversight into established certification processes. The framework extends classical and emerging verification and validation models into operational service, linking machine learning constituents to system-level safety arguments and regulatory expectations to support the development of trustworthy and certifiable AI-enabled aviation systems. Full article

Effective monitoring of surface damage in mining areas is vital for ecological restoration. Unmanned aerial vehicles (UAVs) have been widely used to obtain ground subsidence data owing to their low cost and ease of operation. The images captured by UAVs can generate dense image matching (DIM) point clouds, which, after screening, can be used to create a digital elevation model (DEM) required for deformation analysis. Existing filtering algorithms mainly rely on the spatial geometric features of point clouds and rarely utilize color information, which limits their accuracy in areas with vegetation coverage. To address this issue, this study proposes a H-PTD method that combines visible light with progressive triangulated irregular network densification (PTD). First, initial ground seeds are selected based on the H value in the HSV space. Subsequently, a triangulated irregular network (TIN) is constructed, and iterative densification is performed by evaluating the relationship between the target point and adjacent triangular faces, thereby achieving an accurate distinction between ground and non-ground. Evaluated on three terrain datasets and against five classical methods, the results indicate that the Total error in the H-PTD cross-matrix is controlled between 2.9% and 7.8%, and remains below 8% overall. The standard deviation of the DEM difference is around 0.02 m. Compared to other methods, H-PTD shows higher filtering accuracy and better terrain adaptability, making it more promising for monitoring mining areas and providing a more reliable tool for subsidence detection based on UAVs. Full article

Azo dyes demonstrate dose-dependent carcinogenic and mutagenic effects in exposed cells. Among remediation approaches, microbial adsorption is the most sustainable and environmentally friendly method for eliminating azo dyes. A novel Aspergillus terreus silica composite was developed as a sustainable adsorbent for crystal violet dye (CVD) removal. The fungal strain was isolated from dye wastewater and was genetically identified by 18S rRNA gene sequencing. Dried mycelia of A. terreus (PX920301) were combined with SiO₂ (1:1 w/w) through iterative hydration-drying cycles, yielding a composite characterized by FTIR analyses. Removal CVD %, adsorption capacity, and CVD residual were calculated, and the adsorption process was optimized using Box–Behnken design (four factors, 25 runs). The biosafety of the composite was assessed for phytotoxicity and microbial toxicity. The composite was also applied to real dyes wastewater collected from the bacteriological laboratory. Aspergillus terreus-silica composite showed the highest CVD removal percentage by 85.4%, adsorption capacity (qe) 121.1 mg/L, and lowest CVD residual by 7.26 mg/L, followed by the dried active mycelia (DA-mycelia) with CVD removal 40.23%, adsorption capacity (qe) 57.05 mg/L, and CVD residual by 29.73 mg/L. Optimization data cleared that the maximum experimental values of CVD removal (%) was 99.59% (predicted value 100%) obtained in run number (4) using initial CVD concentration (200 mg/L), pH (8), adsorbent composite weight (0.1 g), and contact time (48 h). Biosafety evaluation demonstrated negligible phytotoxicity against Triticum aestivum seedlings post-treatment, with restored germination and growth comparable to controls. Microbial toxicity assays via well-diffusion to seven microbial isolates confirmed no toxic activities against the tested bacteria, yeast, and fungi, underscoring the composite’s environmental safety. The composite could decolorize the real dye wastewater of laboratories by 95.37%. In conclusion, A. terreus mycelial-silica composite offers a cost-effective, sustainable, and eco-friendly alternative solution for dye bioremediation. Full article

An online off-policy asynchronous real-time model reference tracking control (OOART-MRTC) algorithm is proposed and validated for unmanned aerial vehicles (UAVs) characterized by faulty actuation and parametric uncertainty. The optimal control problem is posed based on approximate dynamic programming (ADP) and reinforcement learning (RL) theory, using a virtual state-space representation constructed exclusively on input–output true system data, which exploits the observability theory. OOART-MRTC learns control by interacting with the system, starting from an initial stabilizing controller derived from an approximate uncertain model. Learning convergence and stability under the proposed adaptive behavior are analyzed. Since the learning iterations cannot update within a sampling period, an asynchronous mechanism is proposed for updating the controller parameters, leveraging real-time control and multi-tasking. The complexity associated with the resulting high-dimensional system is solved by efficient linear parameterization and validated on a realistic case study where three coupled double integrators describe the UAV attitude control. Full article

Springback control in Multi-Point Stretch Forming (MPSF) is significantly hindered by the computational intensity of Finite Element Analysis (FEA) and the limited predictive robustness of traditional regression methods. This study develops a hybrid GWO-CatBoost model acting as a data-driven surrogate for MPSF simulations by integrating the Grey Wolf Optimizer (GWO) with the CatBoost algorithm for high-precision springback forecasting. An FEA model of the MPSF process was initially validated through experimental comparison under a representative working condition to assess modeling accuracy. A comprehensive dataset comprising 1200 scenarios was generated via a full factorial design, incorporating key variables: curvature radius, sheet thickness, cushion thickness, and pre-stretching rate. In this study, the GWO was employed to perform automated hyperparameter tuning for CatBoost by optimizing the learning rate, tree depth, and number of iterations, thereby enabling accurate modeling of the complex nonlinear relationship between process inputs and numerical springback values. Numerical evaluations demonstrate that the GWO-CatBoost model outperforms GWO-XGBoost and GWO-Random Forest benchmarks, achieving a Coefficient of Determination (R²) of 0.9293, a root mean square error (RMSE) of 0.0274 mm and mean absolute error (MAE) of 0.0189 mm. Sensitivity analysis identifies sheet thickness as the dominant factor (46% contribution), with cushion thickness as the secondary driver (23%). This predictive framework serves as a computationally efficient auxiliary surrogate, designed to assist iterative finite element analyses and support process optimization in the manufacture of complex-curved panels. Full article

The transition toward decentralized energy systems has amplified interest in peer-to-peer electricity trading. However, research on prosumer behavior in such markets remains fragmented, hindered by a lack of benchmarkable experimental infrastructure. Addressing this gap, the LabChain system was developed—a modular, interactive prototype designed to study human behavior in synthetic P2P electricity markets under controlled laboratory conditions. This system integrates real-world technologies, such as blockchain-based transaction backends, flexibility market interfaces, and asset control tools, allowing fine-grained observation of strategic and perceptual dimensions of prosumer activity. The research followed an iterative design approach to develop the infrastructure for experimental energy economics research, and to assess its effectiveness in aligning participant experience with design intentions. Based on the meta-requirements generality, affordance-centric design, and technological grounding, 13 detailed peer-to-peer market, software, and system requirements that allow for system evaluation were developed. As a proof of concept, seven participants simulated prosumer behavior over a week through interaction with the system. Their interaction with the system was analyzed through simulation data and focus group interviews, using a modified thematic content analysis with a hybrid inductive–deductive coding approach. The main achievements are (i) the design and implementation of the LabChain system as a modular infrastructure for P2P electricity market experiments, (ii) the development of an associated experimental workflow and research design, and (iii) its demonstration through an illustrative, proof-of-concept evaluation based on thematic content analysis of a single focus group session focusing on interaction and perceptions. The behavioral results from an initial session are limited, exploratory, and demonstrative in nature and should be interpreted as illustrative only. They nevertheless revealed tension between system flexibility and cognitive usability: while the system supports diverse strategies and market roles, limitations in interface clarity and information feedback constrain strategic engagement. Full article

The development of shale gas reservoirs relies on complex fracture networks created via multistage hydraulic fracturing, yet most existing models still use oversimplified fracture geometries and therefore cannot fully capture the coupled effects of multiscale fracture topology on flow and production. To address this gap, in this study, we combine fractal geometry with the Embedded Discrete Fracture Model (EDFM) to analyze the production dynamics of hydraulically fractured horizontal wells in shale gas reservoirs. A tree-like fractal fracture network is first generated using a stochastic fractal growth algorithm, where the iteration number, branching number, scale factor, and deviation angle control the self-similar hierarchical structure and spatial distribution of fractures. The resulting fracture network is then embedded into an EDFM-based, fully implicit finite-volume simulator with Non-Neighboring Connections (NNCs) to represent multiscale fracture–matrix flow. A synthetic shale gas reservoir model, constructed using representative geological and engineering parameters and calibrated against field production data, is used for all numerical experiments. The results show that increasing the initial water saturation from 0.20 to 0.35 leads to a 26.4% reduction in cumulative gas production due to enhanced water trapping. Optimizing hydraulic fracture spacing to 200 m increases cumulative production by 3.71% compared with a 100 m spacing, while longer fracture half-lengths significantly improve both early-time and stabilized gas rates. Increasing the fractal iteration number from 1 to 3 yields a 36.4% increase in cumulative production and markedly enlarges the pressure disturbance region. The proposed fractal–EDFM framework provides a synthetic yet field-calibrated tool for quantifying the impact of fracture complexity and design parameters on shale gas well productivity and for guiding fracture network optimization. Full article

L360QS pipeline steel, due to its high toughness, high strength, resistance to sulfide stress cracking, and resistance to hydrogen-induced cracking, is increasingly being used in pipeline network construction. Its fracture behavior is a critical factor for safe operation in mountainous steep-slope environments, but it has not yet been widely studied. Therefore, this paper conducts extensive experiments on the ductile fracture of L360QS pipeline steel. The tests employed standard tensile, notched tensile, shear, and compression specimens, covering a stress triaxiality range from approximately −0.33 to 0.92. The study combined Ling’s iterative method to establish an elastoplastic constitutive model considering post-necking behavior, and incorporated it into finite element models to extract the average stress triaxiality and equivalent plastic strain at the moment of fracture initiation for each type of specimen. Based on the extracted data, a piecewise ductile fracture model was established: a simplified Johnson–Cook criterion is used in the high triaxiality range, while an empirical function is used to describe fracture behavior in the medium, low, and negative triaxiality ranges. The model was validated using a train–test split approach, predicting fracture displacements for an independent test set of specimens. The results showed all prediction errors were within 5%, demonstrating the model’s high accuracy. Furthermore, a Spearman correlation analysis quantified the influence of geometric factors, revealing that notch curvature has the strongest monotonic relationship in controlling average stress triaxiality and fracture strain. The fracture model established in this paper can accurately predict the fracture behavior of L360QS pipeline steel and provides a reliable basis for failure prediction and safety assessment under complex service conditions (such as mountainous steep slopes). Full article

Introduction: Mass casualty incident (MCI) simulation scenarios are developed based on detailed review and planning by multidisciplinary trauma teams. This study aimed to assess the feasibility of using generative artificial intelligence (AI) in developing mass casualty trauma simulation scenarios. The study evaluated a range of mass casualty trauma simulation scenarios generated from a public generative artificial intelligence platform based on publicly available data with a validated objective simulation scoring tool. Methods: Using a large language model (LLM) platform (ChatGPT4, OpenAI, San Francisco, CA, USA), 10 complex MCI trauma simulation scenarios were generated based on publicly available US reported trauma data. Each scenario was evaluated by two Advanced Trauma Life Support (ATLS) certified raters based on the Simulation Scenario Evaluation Tool (SSET), a validated scoring tool out of 100 points. The tool scoring is based on learning objectives, tasks for performance, clinical progression, debriefing criteria, and resources. Two publicly available mass casualty trauma scenarios were similarly evaluated as controls. Revision and recommended feedback was provided for the scenarios, with review time recorded. Post-revision scenarios were evaluated. Interrater reliability was calculated based on Intraclass Correlation Coefficients (2, k) (ICCs). For the scenarios, scores and review times were reported as medians with interquartile range (IQR) as 25th and 75th percentiles. Results: Ten mass casualty trauma simulation scenarios were generated by an LLM, producing a total of 62 simulated patients. The initial LLM-generated scenarios demonstrated a median SSET score of 78.5 (IQR 74–82), substantially lower than the median score of 94 (IQR 93–95) observed in publicly available scenarios. The interrater reliability ICC for the LLM-generated scenarios was 0.965 and 1.00 for publicly available scenarios. Following secondary human revision and iterative refinement, the LLM-generated scenarios improved, achieving a median SSET score of 94 (IQR 93–96) with an interrater reliability ICC of 0.7425. Conclusions: The feasibility study suggests that a structured, collaborative workflow combining LLM-based generation with expert human review may enable a new approach to mass casualty trauma simulation scenario creation. LLMs hold promise as a scalable tool for the development of MCI training materials. However, consistent human oversight, quality assurance processes, and governance frameworks remain essential to ensure clinical accuracy, safety, and educational value. Full article

Background/Objectives: Temperature control after cardiac arrest remains a recommended component of post-cardiac arrest care, yet substantial practice variability persists. Conflicting evidence regarding optimal temperature targets and mixed interpretations of recent trials, such as TTM2, may contribute to inconsistent bedside implementation. Understanding physician knowledge, attitudes, and practice patterns is essential for aligning post-cardiac arrest management with evolving evidence. This study aimed to characterize international physician perceptions of temperature control, patterns of use, understanding of neurologic injury, and the influence of emerging literature. Methods: A 39-item web-based survey was developed through iterative expert review and pilot testing and disseminated to members of critical care, neurology, and emergency medicine societies between September 2021 and January 2022. The instrument assessed demographics, temperature control practices, interpretation of new literature, and post-cardiac arrest management. Responses were analyzed using descriptive statistics in R Studio, with proportions reported for categorical variables and mode responses for ranked questions. Results: Among 501 respondents, 471 (94%) completed the survey. Most were attending-level physicians (73%), primarily practicing intensive care medicine (75%), and based in academic centers (60%). Targeted temperature management (TTM) was commonly initiated by the admitting intensivist (66%), most often because guidelines recommended it (67%). The most influential factors driving initiation were institutional protocols (21%), perceived neurologic prognosis (17%), and arrest etiology (14%). The most frequently selected temperature target was 36 °C (44%). Awareness of the TTM2 trial was high (70%), though only 31% reported altering their practice in response. Neurologists were more likely to individualize temperature targets and select lower temperatures, while physicians caring for higher cardiac arrest volumes also favored lower targets. Community clinicians more commonly selected lower temperature targets compared with those in academic settings. Conclusions: Substantial heterogeneity exists in the practice and rationale for temperature control after cardiac arrest. Physician specialty, cardiac arrest volume, and local practice environment influence the temperature target selection and attitudes toward emerging evidence. Despite awareness of new data, institutional protocols remain the dominant factor guiding implementation. Standardized, evidence-based institutional pathways may help reduce practice variability and promote consistent post-cardiac arrest care. Full article

The reliability of embedded processors in safety- and mission-critical domains is increasingly threatened by radiation-induced soft errors, particularly multiple-cell upsets (MCUs) that simultaneously corrupt adjacent cells in external SDRAM. While prior studies on the LEON3 processor have largely focused on single-event upsets (SEUs) in internal SRAM structures, they overlook MCU effects in off-chip SDRAM, a critical gap that limits fault coverage and compromises system-level reliability assessment in modern high-density embedded systems. This paper presents an SDRAM-based fault injection framework using FPGA emulation to evaluate the impact of MCUs on the LEON3 soft-core processor, with faults directly injected into the external memory subsystem where data corruptions can rapidly propagate into system-level failures. The methodology injects spatially correlated two-bit MCUs directly into SDRAM during realistic workload execution. Three architecturally diverse benchmarks were analyzed, each representing a distinct computational workload: a numerical (matrix multiplication), signal-processing (FFT), and a cryptographic (AES-128 encryption) application, chosen to capture arithmetic-intensive, iterative, and control-intensive execution profiles, respectively. The results reveal a distinct workload-dependent vulnerability profile. Matrix multiplication exhibited >99.99% fault activation, with outcomes overwhelmingly dominated by data store errors. FFT showed >97% activation in steady-state execution, following an initial phase sensitive to alignment and data access exceptions. AES displayed 88.12% non-propagating faults, primarily due to injections in inactive memory regions, but remained exposed to critical memory access violations and control-flow exceptions that enable fault-based cryptanalysis. These findings demonstrate that SEU-only models severely underestimate real-world MCU risks and underscore the necessity of selective, workload-aware fault-tolerance strategies: lightweight ECC for cryptographic data structures, alignment monitoring for signal processing, and algorithm-based fault tolerance (ABFT) for numerical kernels. This work provides actionable insights for hardening LEON3-based systems against emerging multi-bit threats in radiation-rich and adversarial environments. Full article