Search Results (8,471)

Autonomous monitoring is essential for precision agriculture in greenhouses, yet deploying unmanned aerial vehicles (UAVs) in confined, GPS-denied environments remains limited by payload, power, and cost constraints. This study developed and validated an autonomous UAV system for reliable, low-cost operation in such conditions. The proposed system employs a dual-link edge-computing architecture: a lightweight onboard controller handles flight control and sensor acquisition, while visual simultaneous localization and mapping (V-SLAM) is offloaded to an edge computer via the FPV video link. Phenotyping (flower detection and tracking/counting) is performed offline from the side-view RGB stream and does not participate in the flight control loop. Using muskmelon (Cucumis melo L.) flower development as a case study, the UAV autonomously executed daily missions for 27 days in a commercial greenhouse, performing flower detection and tracking to monitor phenological dynamics. Localization and control accuracy were evaluated against a validated UWB reference system, achieving 5.4~8.0 cm 2D RMSE for trajectory tracking and 12.7 cm translation RMSE for greenhouse mapping. This work demonstrates a practical architecture for autonomous monitoring in GPS-denied agricultural environments, with operational boundaries characterized through the sustained field deployment. The system’s design principles may extend to other indoor or communication-limited scenarios requiring lightweight, intelligent robotic operation. Full article

Aircraft noise monitoring systems deployed at major airports typically rely on scalar energy-based indicators, which primarily describe integrated sound energy but provide limited representation of the spectral–temporal structure and perceptual attributes of aircraft noise. To address this limitation, this study proposes a sensor-based psychoacoustic feature extraction and spatiotemporal analysis framework for continuous aircraft noise monitoring under high-density operational conditions. An automatic noise monitoring system compliant with ISO 20906 was deployed to synchronously acquire acoustic waveforms and ADS-B trajectory data. A cascaded spatiotemporal fusion algorithm was developed to associate noise events with aircraft flight paths, followed by a model-stratified multidimensional IQR-based data cleaning strategy to suppress environmental interference and non-stationary outliers. Based on the cleaned dataset, a suite of psychoacoustic features—including loudness, sharpness, roughness, fluctuation strength, and tonality—was extracted to characterize the perceptual structure of aircraft noise beyond conventional energy metrics. Experimental results demonstrate that, under equivalent sound exposure levels, psychoacoustic features retain substantial discriminative information that is lost in scalar energy indicators. The coefficients of variation for fluctuation strength and tonality reach 43.2% and 22.1%, respectively, corresponding to 15–69 times higher sensitivity compared to traditional energy-based metrics. Furthermore, nonlinear manifold mapping using UMAP reveals clear topological separation between new-generation and legacy aircraft models in the psychoacoustic feature space, whereas severe overlap persists in energy-based representations. Correlation analysis further indicates decoupling between macro-level physical design parameters (e.g., bypass ratio, thrust) and perceptual feature dimensions, highlighting the limitations of energy-centric monitoring schemes. The proposed framework demonstrates the feasibility of integrating psychoacoustic feature extraction into continuous sensor-based aircraft noise monitoring systems. It provides a scalable signal processing pipeline for enhancing the resolution and interpretability of aircraft noise measurements in complex operational environments. Full article

Floods are among the most devastating natural hazards, intensified by climate change and rapid urbanization. This study introduces a novel deep learning framework, Transfer Learning-Enhanced FEDformer (TLE-FEDformer), designed for accurate and temporally consistent flood inundation mapping. The framework integrates pre-trained Xception backbones for robust multi-sensor feature extraction from Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery, a cross-modal fusion module to align heterogeneous modalities, and the Frequency Enhanced Decomposed Transformer (FEDformer) for efficient frequency-domain temporal modeling. This architecture effectively captures long-range dependencies and flood dynamics including onset, peak, duration, and recession, while addressing challenges such as cloud contamination, speckle noise, and limited labeled data. Comprehensive experiments demonstrate superior performance, achieving an overall accuracy of 98.12%, an F1-score of 98.55%, and an Intersection over Union (IoU) of 97.38%, outperforming baselines including Convolutional Neural Networks, Capsule Networks, and transfer learning alone. Ablation studies validate the contributions of each component, while sensitivity analyses confirm robustness across hyperparameters. Uncertainty quantification via Monte Carlo dropout highlights high confidence in core flooded regions. Preliminary generalization tests on independent events yield IoU > 94%, indicating strong transferability. TLE-FEDformer advances operational flood monitoring by providing reliable, scalable, and temporally consistent mapping from multi-sensor remote sensing data. This approach offers significant potential for real-time disaster response, early warning systems, and damage assessment in flood-prone regions worldwide. Full article

The rapid growth of electric vehicles (EVs) is reshaping transport systems and accelerating the sustainable digital transformation of smart mobility. EV battery-swapping, delivered through platform-based, data-driven service networks, offers a low-carbon alternative to conventional refueling and plug-in charging by shortening replenishment time and enabling centralized battery management. However, the behavioral mechanisms driving user adoption of this digitally enabled infrastructure remain insufficiently understood. This study develops a socio-technical system (STS) model in which social and technical drivers influence users’ intention to adopt EV battery-swapping services via the dual mediation of perceived trust and perceived risk. Using a three-stage mixed-methods design that combines a PRISMA-based literature review, expert interviews with user-journey mapping, and a large-scale user survey, the study identifies six social and technical antecedents of EV battery-swapping adoption. Based on 565 valid responses from EV users in the Beijing–Tianjin–Hebei region, partial least squares structural equation modeling and multi-group analysis are employed to test the proposed framework. The results show that all six antecedents significantly affect perceived trust and perceived risk, which in turn mediate their impacts on adoption intention, with notable heterogeneity across income and usage-frequency groups. The findings provide a mechanism-based extension of STS theory for digitally mediated battery-swapping infrastructure by showing how socio-technical conditions shape adoption via trust and risk, and they offer actionable implications for operators and policymakers to build secure, user-centered swapping services within intelligent transport systems. Full article

This study evaluates the effectiveness of laser cleaning techniques for the non-contact removal of unwanted deposits from the surface of contemporary urban mural paintings. Two sets of mock-up samples, painted with popular graffiti spray paints on lime-based plaster, and artificially contaminated, were subjected to various cleaning procedures using Nd:YAG lasers operated in Q-switched (QS), long Q-switched (LQS) or short free-running mode (SFR). A multi-analytical approach—including X-ray fluorescence spectroscopy (XRF), Fourier-transform infrared spectroscopy (FTIR), colorimetry, and hyperspectral imaging (HSI)—was used to identify pigments and binders, and to evaluate cleaning efficiency and pigment preservation. XRF and FTIR were useful in understanding the composition of the sprays, while colorimetric ΔE values quantified cleaning efficiency and potential damage, and hyperspectral reflectance and LSU (linear spectral unmixing) abundance maps provided spatial distribution insights into contaminant removal and pigment preservation. The results demonstrate that laser cleaning effectiveness and selectivity are strongly dependent on the operational regime and fluence. In particular, long Q-switched laser irradiation at moderate fluence levels achieved effective contaminant removal with minimal chromatic and chemical alteration of the original paint layers. These findings support the development of tailored, sustainable, and non-contact laser cleaning protocols for the conservation of contemporary urban murals and contribute to the establishment of objective, multi-parameter criteria for evaluating cleaning outcomes in street art conservation. Full article

This study investigates mechanism-based modeling and simulation of a single-shaft heavy-duty industrial gas turbine. Taking the PG9171E gas turbine as the case study, component-level steady-state and dynamic models are developed. The steady-state model is established using the constant mass flow (CMF) method. For dynamic modeling, both the CMF approach and the inter-component volume (ICV) approach are implemented to enable a comparative assessment of the two methods. On the basis of the steady-state model, an improved Dung Beetle Optimization (DBO) algorithm is proposed to perform model correction using measured operational data from the gas turbine. After model correction, the maximum relative error between the simulated results and the measured operating data is reduced to 1.01 × 10⁻⁵%. Following high-accuracy model correction, sensitivity analysis and a comparative dynamic study are conducted for the two dynamic modeling approaches. The results indicate that the most influential sensitivity parameter is the rotor rotational inertia, followed by the virtual volume of the combustor. Moreover, the primary discrepancy between the ICV and CMF approaches arises from differences in the operating trajectories on component characteristic maps. The ICV-based model exhibits a pronounced response lag; however, it requires less computational time than the CMF-based model, making it more suitable for rapid engineering simulation and practical applications. Full article

Implantable artificial kidneys represent a promising alternative for patients with end-stage renal disease (ESRD), aiming to overcome the limitations of conventional dialysis through the integration of microfluidic and electrokinetic technologies. In this study, we present a sawtooth electrode microfluidic chamber that achieves blood cell separation via negative dielectrophoresis at a record-low operating voltage of 1.4 V, representing a fivefold reduction compared with rectangular electrode designs and supporting potential integration into implantable artificial kidney systems. A microfluidic chip incorporating an asymmetric sawtooth electrode geometry was developed to enhance local electric field gradients while reducing power consumption. Device performance was investigated using COMSOL Multiphysics simulations. Response Surface Methodology (RSM) based on a Box–Behnken design was employed to optimize the number of teeth per unit length (N), sawtooth height (H), and applied voltage (V), while excitation frequency was fixed at 1 MHz and flow velocity was maintained constant at 0.1 µL·min⁻¹. Statistical analysis was conducted using analysis of variance (ANOVA) in Minitab (Version 27; Minitab, LLC, State College, PA, USA, 2024) . The optimization model showed strong predictive capability (R² = 95.8%) and identified applied voltage (59.45% contribution) and sawtooth height (33%) as the dominant factors affecting separation efficiency, with a significant H × V interaction (p = 0.023). Comprehensive voltage-response mapping over the range of 0.8–4.0 V revealed four operational regimes, including a previously unreported high-voltage failure zone above 2.8 V, where electrothermal flow and electroporation degrade performance. Under physiological conductivity conditions, the optimized design maintained a separation efficiency of 78.3% at 1.4 V with a tip temperature rise of only 1.2 °C, while full recovery of performance was achieved at 2.2 V. Cell-specific separation efficiencies reached 97.3% for white blood cells, 95.8% for red blood cells, and 84.7% for platelets, reducing the downstream cellular load by 92.6%. These findings demonstrate that the proposed low-voltage, high-efficiency separation platform has strong potential as a cellular pre-filtration module in implantable artificial kidney systems and other lab-on-chip biomedical devices. Full article

As traffic demand increases and intelligent transportation systems continue to develop, traffic signal control must operate reliably in complex and heterogeneous network environments, especially under communication instability. Traditional approaches often lack sufficient resilience when facing packet loss, delay, and other communication disturbances. This study proposes a resilient collaborative control (RCC) method for transportation hubs that explicitly considers communication reliability. A multi-layer computational framework is developed to support real-time mapping and interaction between physical and virtual networks. A fuzzy-logic-based communication state perception model is introduced to guide adaptive control-mode switching. To improve network-level performance, a recovery-oriented optimization algorithm is applied for dynamic load balancing across the hub area. Co-simulation results show that, compared with traditional adaptive control, the proposed method reduces average vehicle delay by 42.3%, increases network speed by 52.3%, shortens recovery time by 63%, and improves the resilience index to 0.87. These results support the effectiveness of the proposed framework within the evaluated co-simulation setting. Full article

In recent years, both the number and the size of ships have increased considerably, whereas port areas have expanded much more slowly. As a result, port manoeuvres are increasingly performed in restricted waters, which increases navigational risk during ship operations. For this reason, ship-handlers must know the instantaneous position of the pivot point (PP) at any time. The aim of this paper is to propose novel mathematical models to determine the PP’s position and to identify the most relevant variables influencing it. For this purpose, a full-mission bridge simulator was used to generate a dataset based on multiple simulations performed under different combinations of rudder angles and engine telegraph orders. First, a new trigonometric formulation is proposed to determine the instantaneous position of the PP using only directly measurable variables, namely the speed through water and the transverse velocities at the bow and stern. Subsequently, additional predictive models were developed using Design of Experiments (DOE) and response surface techniques. These models achieve high predictive accuracy while remaining simple enough to be applied in practical ship-handling scenarios. The resulting models can assist ship-handlers in anticipating PP behaviour and improving manoeuvring safety, particularly in restricted waters. Original 3D charts showing the combination of several input variables are included to identify the map of the whole process. Full article

This study assesses the extent to which an AI-driven circular waste management tool, previously developed by the same authors as a decision-support system for the circular management of healthcare waste in compliance with international guidelines, reflects the operational needs and perceived priorities of healthcare professionals and environmental managers. Within a context characterised by high regulatory complexity and increasing pressure toward more sustainable management models, the research adopts a qualitative approach based on the thematic analysis of 11 semi-structured interviews, followed by a systematic mapping of the emergent themes onto the tool’s thematic areas, indicators, and operational actions. The results demonstrate a high degree of alignment between the tool and operational practice, with 93% of the tool’s actions supported by empirical evidence and the emergence of a shared core cluster focused on hard-to-manage waste streams, mandatory training, and day-to-day operational challenges. The alignment between the priorities expressed by interviewees and the importance scores generated by the computational model is high for actions of greater relevance, while it decreases for less frequent actions that are more context-dependent. Circular economy practices are recognised as relevant but remain predominantly positioned at a strategic rather than an operational level. Overall, the study confirms the conceptual robustness of the tool and identifies its main limitations and the conditions required for its integration into hospital workflows. Full article

Urban heat islands (UHIs) pose escalating threats to public health and thermal comfort in dense urban environments. However, physics-based evaluations of material-specific cooling interventions and their integration into operational digital twin platforms remain limited. This study develops an integrated framework connecting computational fluid dynamics (CFD) modeling with digital twin visualization to evaluate UHI mitigation strategies. The objectives are to quantify the thermal mitigation effects of surface emissivity optimization on land surface temperature (LST) and pedestrian-level air temperature ($T_{air}$) to establish a data preprocessing pipeline converting CFD outputs into platform-independent visualization datasets, and to comparatively evaluate 2D GIS-based and 3D voxelization visualization approaches. Four emissivity scenarios were simulated using STAR-CCM+ for a 4 km² residential area in Gwacheon City, Republic of Korea. Comprehensive optimization (Case D) reduced the mean LST from 46.6 °C to 42.0 °C and T_air from 35.7 °C to 35.3 °C. Concrete-only optimization achieved 90.5% of the total thermal reduction while decreasing spatial variability (σ) from 7.1 to 5.8 during peak hours. The voxel-based 3D visualization provided a superior representation of vertical thermal stratification compared to 2D mapping. These findings establish a scalable foundation for climate-responsive urban management. Full article

Modern enterprise information systems must simultaneously support complex organizational structures, ensure robust security, and remain scalable and maintainable over time. Traditional Role-Based Access Control (RBAC) models, while effective for permission management, operate primarily as post-design security layers and do not provide a unified methodology for structuring system architecture. This paper introduces the Zoned Role-Based (ZRB) model, a mathematically formalized and comprehensive framework that integrates organizational modeling, system design, implementation, access control, and long-term maintenance. ZRB models an organization as a hierarchy of zones, each containing its own roles, applications, operations, and users, forming a recursive Zone Tree that directly mirrors real organizational semantics. Through formally defined role hierarchies, zone-scoped permission sets, and inter-zone inheritance mappings, ZRB provides a context-aware permission calculus that unifies authentication and authorization across all zones. The paper presents the theoretical foundations of ZRB, a multi-phase engineering methodology for constructing integrated enterprise systems, and a complete implementation architecture with permission inference, navigation design, administrative subsystems, and deployment models. Primary validation and evaluations across several developed systems demonstrate significant improvements in permission accuracy, administrative efficiency, scalability, and maintainability. ZRB thus offers a rigorously defined and practically validated framework for building secure, scalable, and organizationally aligned enterprise information systems. Full article

With the continuous elevation of aviation safety standards, accurate monitoring of ground support vehicles in aircraft stand areas has become a critical task for enhancing overall aircraft stand operational safety. Given the limitations of existing surface movement radar and multi-camera surveillance systems in terms of cost, deployment complexity, and coverage, this paper proposes a lightweight vision-based framework for vehicle state perception and spatiotemporal behavior analysis oriented toward aircraft stand safety. Leveraging existing fixed monocular monitoring resources in the stand area, the framework first establishes a precise mapping from image pixel coordinates to the physical plane through self-calibration and homography transformation utilizing scene line features, thereby achieving unified spatial measurement of vehicle targets. Subsequently, it integrates an improved lightweight YOLO detector (incorporating Ghost modules and CBAM for noise suppression) with the ByteTrack tracking algorithm to enable stable extraction of vehicle trajectories under complex occlusion conditions. Finally, by combining functional zone division within the stand, a semantic map is constructed, and a behavior analysis method based on a spatiotemporal finite state machine is proposed. This method performs joint reasoning by fusing multi-dimensional constraints including position, zone, and time, enabling automatic detection of abnormal behaviors such as “intrusion into restricted areas” and “abnormal stop.” Quantitative evaluations demonstrate the framework’s efficacy: it achieves an average physical localization error (RMSE) of 0.32 m, and the improved detection model reaches an accuracy (mAP@50) of 90.4% for ground support vehicles. In tests simulating typical violation scenarios, the system achieved high recall (96.0%) and precision (95.8%) rates in detecting ‘area intrusion’ and ‘abnormal stop’ violations, respectively. These results, achieved using only existing surveillance cameras, validate its potential as a cost-effective and easily deployable tool to augment existing safety monitoring systems for airport ground operations. Full article

This study proposes a real-time data-based Digital Value Stream Mapping (Digital VSM) framework that integrates Artificial Intelligence (AI) feature selection and discrete-event simulation validation to enhance production system performance. Unlike conventional VSM approaches that rely on static, manually aggregated data, the proposed framework uses real-time operational data to dynamically quantify Value Added (VA), Non-Value Added (NVA), and Necessary Non-Value Added (NNVA) activities. To improve decision accuracy, an Artificial Neural Network (ANN) combined with Genetic Algorithm (GA) feature selection is employed to identify dominant production variables influencing lead time and line imbalance. Furthermore, Ranked Positional Weight (RPW) optimization results are validated through Tecnomatix Plant Simulation to ensure robustness before physical implementation. The proposed framework was applied to a discrete manufacturing line, resulting in a reduction of total lead time from 8755 s to 6400 s and an increase in process ratio from 33.64% to 45.91%, with line efficiency reaching 91.7%. The findings demonstrate that integrating Digital VSM with AI-driven feature selection and simulation validation transforms Lean analysis from a descriptive tool into a predictive and validated decision-support system suitable for Industry 4.0 environments. Full article

Deep learning-based crop mapping from hyperspectral satellite data offers immense potential for capturing subtle phenological differences, yet leveraging sparse time series remains a major methodological challenge. This study evaluates the ability of the EnMAP sensor to identify nine major crop types in the intensive agricultural landscape of Southeastern Hungary. We utilized a limited time series (November, March, August) to benchmark two modeling strategies: a single-date dual-stream spatial–spectral 2D-CNN (DSS-2D) and a multi-temporal 3D-SE-ResNet. Model performance was assessed using parcel-level spatial cross-validation to ensure realistic accuracy estimates and reduce spatial autocorrelation bias. The results demonstrate that the DSS-2D model achieved superior single-date accuracy (OA > 97%), significantly outperforming pixel-based baselines. Furthermore, the multi-temporal 3D-SE-ResNet achieved a robust seasonal accuracy of 92.9%, effectively compensating for temporal sparsity by exploiting the deep spectral information of the SWIR domain. This study confirms that treating hyperspectral data as a 3D volume enables the extraction of phenological traits even from limited observations. These findings provide a strong proof-of-concept for the operational feasibility of future missions such as Copernicus CHIME for continental-scale food security monitoring. Full article