Search Results (486)

In Japan, the shortage of airport ground-handling personnel has become a serious concern with the growing demand for aviation, necessitating improvements in operational efficiency. Accordingly, the expectations for automating aircraft ground support equipment (GSE) vehicles are growing to achieve labor savings. This study evaluated the changes in GSE traffic flow performance (travel speed, travel time, and number of stops) through traffic simulations under various scenarios of automated-driving GSEs penetrating the entire airport restricted areas. We simulated the traffic flow at Tokyo International Airport using the observation data of each GSE driving through the airport. Simulation results indicated that GSEs experience a reduced travel speed in some vehicle corridors when automated-driving GSEs, considering the safety risks associated with existing automated technology, run at lower speeds to ensure reliable driving performance. Consequently, the total travel time of the GSEs for the entire airport increases. These results confirm that the penetration of automated-driving GSEs can be facilitated by implementing measures, such as developing technology for reliable driving performance or operational rules at intersections to enable these vehicles to run at a speed equivalent to that of manned GSEs and to prevent speed reduction and travel time increase in airport vehicle corridors. Full article

Solar-electric propulsion offers a practical way to lengthen the endurance of small fixed-wing unmanned aerial vehicles while removing the noise, emissions, and upkeep that come with combustion engines. This work describes and tests a lightweight platform that couples a flexible thin-film photovoltaic array, a high-efficiency power-tracking controller, and a lithium–polymer battery to an electric brushless drivetrain. A ground-based flight emulator reproducing steady cruise allows continuous logging of the electrical flows between panel, battery, and motor. The results show that the solar subsystem can sustain most of the cruise demand, so the battery is called on only sparingly and is even able to recharge when sunlight is higher than a specific threshold. This balance translates into a clear endurance gain without upsetting the aircraft’s weight or handling. Full article

Background: In the dynamic world of commercial aviation, the efficient management of ground handling (GH) operations in aircraft turnarounds is an increasingly complex challenge, often perceived as operational chaos. Methods: This paper introduces the “Critical Minute Theorem” (CMT), a novel framework that integrates mathematical architecture principles into the optimization of GH processes. CMT identifies singular temporal thresholds, $t_k^{\mathrm{*}}$ at which small local disturbances generate nonlinear, system-wide disruptions. Results: By formulating the turnaround as a set of algebraic dependencies and nonlinear differential relations, the case studies demonstrate that delays are not random but structurally determined. The practical contribution of this study lies in showing that early recognition and intervention at these critical minutes significantly reduces propagated delays. Three case analyses are presented: (i) a fueling delay initially causing 9 min of disruption, reduced to 3.7 min after applying CMT-based reordering; (ii) baggage mismatch scenarios where CMT-guided list restructuring eliminates systemic deadlock; and (iii) PRM assistance delays mitigated by up to 12–15 min through anticipatory task reorganization. Conclusions: These results highlight that CMT enables predictive, non-technological control in turnaround operations, repositioning the human analyst as an architect of time capable of restoring structure where the system tends to collapse. Full article

Ground deformation monitoring in mining areas is essential for hazard prevention and environmental protection. Although interferometric synthetic aperture radar (InSAR) provides detailed phase information for accurate deformation measurement, its performance is often compromised in regions experiencing rapid subsidence and strong noise, where phase aliasing and coherence loss lead to significant inaccuracies. To overcome these limitations, this paper proposes JOTGLNet, a guided learning network with joint offset tracking, for multiscale deformation monitoring. This method integrates pixel offset tracking (OT), which robustly captures large-gradient displacements, with interferometric phase data that offers high sensitivity in coherent regions. A dual-path deep learning architecture was designed where the interferometric phase serves as the primary branch and OT features act as complementary information, enhancing the network’s ability to handle varying deformation rates and coherence conditions. Additionally, a novel shape perception loss combining morphological similarity measurement and error learning was introduced to improve geometric fidelity and reduce unbalanced errors across deformation regions. The model was trained on 4000 simulated samples reflecting diverse real-world scenarios and validated on 1100 test samples with a maximum deformation up to 12.6 m, achieving an average prediction error of less than 0.15 m—outperforming state-of-the-art methods whose errors exceeded 0.19 m. Additionally, experiments on five real monitoring datasets further confirmed the superiority and consistency of the proposed approach. Full article

Navigating ships through one-way channels introduces significant uncertainties due to their unique navigational constraints, yet a comprehensive and tailored risk evaluation system for such channels remains notably underdeveloped. Recognizing its critical role as a global maritime artery, this study selects the Suez Canal as the case study to address this gap. The study begins by analyzing the navigational characteristics of one-way channels, systematically identifying key risk factors such as channel width, traffic density, and environmental conditions. Building on this, a novel risk evaluation model is developed, integrating the entropy weight method to assign objective weights, fuzzy logic to handle uncertainty, and Evidential Reasoning (ER) to aggregate multi-criteria assessments. The Suez Canal is then utilized as a case study to demonstrate the model’s effectiveness and practical applicability. The results reveal that Channel C exhibits the highest risk utility value, consistent with its history of the most grounding incidents, including the notable “Ever Given” event during 2021–2023. These findings not only provide valuable insights for enhancing Suez Canal management strategies but also contribute to filling the existing void in risk evaluation frameworks for one-way channels, paving the way for future research into dynamic risk assessment methodologies. Full article

Background: Prostate cancer is the most commonly occurring cancer amongst men. The detection and treatment of this cancer is therefore of great importance. The severity level of this cancer, which is established as a score in the Gleason Grading Group (GGC), guides the treatment of the cancer. Methods: In this paper, traditional machine learning (ML) classification methods such as Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB), which have recently been shown to accurately identifying biomarkers for computational biology, are leveraged to find potential biomarkers for the different GGC scores. A ML framework that maps the Gleason Grading Group (GGG) into five severity levels—low, intermediate-low, intermediate, intermediate-high, and high—has been developed using the above methods. The microarray data for this ML method have been derived from immunohistochemical tests. The study includes severity level-wise biomarker identification, incorporating missing value imputation, class imbalance handling using the SMOTE-Tomek link method, and stratified k-fold validation to ensure robust biomarker selection. Results: The framework is evaluated on prostate cancer tissue microarray gene expression data from 1119 samples. A combination of high-aggressive and low-aggressive signatures are used in four experimental setups. The results demonstrate the effectiveness of the approach in distinguishing between critical biomarkers with highly accurate models, obtaining 96.85% accuracy using the XGBoost method. Conclusions: Leveraging ML gives a potential ground to involve the domain experts and the satisfactory results have approved that. For the future physician-in-the-loop approach can be tested to ensure further diagnosis impact. Full article

To address low threat avoidance efficiency and poor global path adaptability in UAV path planning under threatening environments, this paper proposes a hybrid A*-Artificial Potential Field (APF) path planning method based on spatio-temporal grid optimization. First, a new global fine-grained spatio-temporal grid system is developed by integrating advantages of GeoSOT binary encoding and BeiDou grid location code subdivision rules, enabling unified modeling of complex spatio-temporal environments. Ground threat and maze scenarios are constructed for verification. Second, traditional A* and APF algorithms are improved: the A* algorithm is enhanced with threat costs, dynamic neighborhood search, and local backtrack mechanisms to address low efficiency and incompatibility with threat avoidance; the APF algorithm is optimized with a dual gravitational field collaboration mechanism and distance-parameter-based repulsive field model to overcome local minima and unreachable goals. Finally, a sliding window-driven path association model achieves seamless collaboration between global and local planning. Experimental results show the proposed method outperforms traditional algorithms in comprehensive performance, with the improved A* algorithm excelling in path length, computation time, threat value, and search nodes, and the improved APF algorithm achieving complete safe obstacle avoidance in dynamic environments. The collaborative mechanism effectively handles complex scenarios. Full article

Maintaining physiologically controlled conditions during the transport of biological experiments remains a long-standing but under-addressed challenge in spaceflight operations. Pre-launch thermal or mechanical stress induce artefacts that compromise the interpretation of biological responses to space conditions. Existing transport systems are limited to basic heating of small sample containers and lack the capability to power and protect full experimental hardware during mission-critical phases. A modular transport incubator was developed and validated that combines active thermal regulation, battery-buffered power management, and mechanical protection in a compact, field-deployable platform. It enables autonomous environmental conditioning of complex biological payloads and continuous operation of integrated scientific instruments during ground-based transport and recovery. Validation included controlled experiments under sub-zero ambient temperatures, demonstrating rapid warm-up, stable thermal regulation, and uninterrupted autonomous performance. A steady-state finite difference thermal model was experimentally validated across 21 boundary conditions, enabling predictive power requirement estimation for mission planning. Field deployments during multiple MAPHEUS^® sounding rocket campaigns confirmed functional robustness under wind, snow, and airborne recovery scenarios. The system closes a critical infrastructure gap in spaceflight logistics. Its validated performance, modular architecture, and proven operational readiness establish it as an enabling platform for standardized, reproducible ground handling of biological payloads and experiment hardware. Full article

The operation of microgravity research missions, such as sounding rockets, CubeSats, and small landers, typically relies on proprietary mission control infrastructures, which limit reproducibility, portability, and interdisciplinary use. In this work, we present an open-source blueprint for a distributed ground-segment architecture designed to support telemetry, telecommand, and mission operations across institutional and geographic boundaries. The system integrates containerized services, broker bridging for publish–subscribe communication, CCSDS-compliant telemetry and telecommand handling, and secure virtual private networks with two-factor authentication. A modular mission control system based on Yamcs was extended with custom plug-ins for CRC verification, packet reassembly, and command sequencing. The platform was validated during the MAPHEUS-10 sounding rocket mission, where it enabled uninterrupted remote commanding between Sweden and Germany and achieved end-to-end command–response latencies of ~550 ms under flight conditions. To the best of our knowledge, this represents the first open-source ground-segment framework deployed in a space mission. By combining elements from computer science, aerospace engineering, and systems engineering, this work demonstrates how interdisciplinary integration enables resilient, reproducible, and portable mission operations. The blueprint offers a practical foundation for future interdisciplinary research missions, extending beyond sounding rockets to CubeSats, ISS experiments, and planetary landers. This study is part two of a three-part series describing the apex Mk.2/Mk.3 experiments, open-source ground segment, and service module simulator. Full article

Tunnel lining voids, a common latent defect induced by the coupling effects of complex geological, environmental, and load factors, pose severe threats to operational and personnel safety. Traditional detection methods relying on Ground-Penetrating Radar (GPR) combined with manual interpretation suffer from high subjectivity, low efficiency, frequent missed or false detections, and an inability to achieve real-time monitoring. Thus, this paper proposes an intelligent identification methodology for tunnel lining voids based on an improved version of YOLOv8. Key enhancements include integrating the RepVGGBlock module, dynamic upsampling, and a spatial context-aware module to address challenges from diverse void geometries—resulting from interactions between the environment, geology, and load—and complex GPR signals caused by heterogeneous underground media and the varying electromagnetic properties of materials, which obscure void–background boundaries, as well as interference signals from detection processes. Additionally, the C2f-Faster module reduces the computational complexity (GFLOPs), parameter count, and model size, facilitating edge deployment at detection sites to achieve real-time GPR signal interpretation for tunnel linings. Experimental results on a heavy-haul railway tunnel’s lining defect dataset show 11.57% lower GFLOPs, 14.55% fewer parameters, and 13.85% smaller weight files, with average accuracies of 94.1% and 94.4% in defect recognition and segmentation, respectively, meeting requirements for the real-time online detection of tunnel linings. Notably, the proposed model is specifically tailored for void identification and cannot handle other prevalent tunnel lining defects, which restricts its application in comprehensive tunnel health monitoring scenarios where multiple defects often coexist to threaten structural safety. Full article

In recent years, spacecraft have been developed to support higher data-rate communication systems and accommodate a wider range of payloads. These advancements have led to the generation of large volumes of data and increased system complexity. In particular, during the ground-testing phase, the need for an effective test data management strategy has become increasingly important to improve test efficiency and reduce costs, as sorting, distributing, and analyzing extensive test data is both time consuming and resource intensive. To address these challenges, this study introduces a practical and implementation-oriented autonomous system for centralized test data handling, which has been successfully applied and verified during actual spacecraft development and ground testing operations. The system enables the rapid transfer of test data to centralized storage without waiting for test completion or requiring human intervention by utilizing an event-triggered architecture. In addition, it automatically provides the transferred test data in multiple formats tailored to each engineering team, facilitating effective data comparison and analysis. It also performs automated test data validation without manual input. The performance of the enhanced test data management was evaluated through big-data analysis of logs generated during automated test data transfer and post-processing in actual spacecraft ground tests. Full article

For unmanned ground vehicles in squad mission support systems (SMSS-UGVs), tracking the entire squad as a group, rather than focusing on individual members, can effectively mitigate issues such as target loss caused by occlusion and environmental interference. However, most existing group target tracking methods are designed for extended targets, which typically assume a rigid and unchanging shape. In contrast, pedestrian groups in SMSS-UGV scenarios exhibit inconsistent motions among members, resulting in continuous changes in the overall group shape. To address this challenge, this paper proposes a group target tracking method specifically tailored for SMSS-UGVs in pedestrian tracking scenarios. We introduce a tracking framework that incorporates a data selection mechanism based solely on positional information, enabling robust handling of dynamic group composition through adaptive shape modeling. Furthermore, a novel group target tracking method based on multi-ellipse shape modeling (ME-CGT-UGV) is presented, which effectively captures complex and evolving group formations. The experimental results show that the proposed method reduces orientation error by 86.13% compared to single-target tracking and by 54.79% compared to shapeless modeling methods. It also maintains strong performance under challenging conditions, including occlusions, environmental disturbances, sharp turns, and formation changes. These findings indicate that the proposed approach significantly enhances the effectiveness and operational reliability of SMSS-UGVs in real-world applications. Full article

The growth of e-government has rendered automated element extraction from official documents a critical bottleneck for administrative efficiency. The core challenge lies in unifying deep semantic understanding with the structured domain knowledge required to interpret complex formats and specialized terminology. To address the limitations of existing methods, we propose a hybrid framework. Our approach leverages a BERT-CRF model for robust sequence labeling, a knowledge graph (KG)-driven retrieval system to ground the model in verifiable facts, and a large language model (LLM) as a reasoning engine to resolve ambiguities and identify complex relationships. Validated on the DovDoc-CN dataset, our framework achieves a macro-average F1 score of 0.850, outperforming the BiLSTM-CRF baseline by 2.41 percentage points, and demonstrates high consistency, with a weighted F1 score of 0.984. The low standard deviation in the validation set further indicates the model’s stable performance across different subsets. These results confirm that our integrated approach provides an efficient and reliable solution for intelligent document processing, effectively handling the format diversity and specialized knowledge characteristic of government documents. Full article

Agricultural tractors possess front loaders that are employed for the handling and transportation of materials, but are exposed to mechanical vibrations and shocks from ground undulations and sudden variations in the load. These vibrations are harmful to the durability of the parts, the comfort of the driver, and the longevity of the machine. In this current study, the performance of the hydraulic accumulator to mitigate such vibrations for a Foton 904 wheeled tractor equipped with a TZ10C-824 front loader is studied. Vibration measurements were taken by an experimental Brüel & Kjær 3050-A040 analyzer under various loading configurations (no loading, 180 kg, and 312 kg), with or without a 1.4 L, 50-bar nitrogen gas-charged Fox Opera Mi Italy hydraulic accumulator. Results reveal that maximum accelerations were as much as 6.24 m·s⁻² without an accumulator during testing of a 312 kg load, whereas they were extremely low at 2.66 m·s⁻² when the accumulator was activated. Frequency-domain analysis verified that the main vibrations were within the range of 3–4 Hz, with FFT peak amplitudes dropping from 5.6 m·s⁻² to 2.4 m·s⁻² upon the accumulator’s operation. The observations verify the effectiveness of the accumulator in vibration intensity reduction, absence of high-frequency shock loads, and ride comfort, along with structural safety improvement. The study provides a solid platform for further enhancement in vibration control techniques for agricultural machines and loader system design. Full article

The COVID-19 pandemic unprecedentedly challenged families worldwide, yet little is known about how parents from diverse cultural contexts retrospectively interpret their parenting roles and coping strategies. This study explores parenting adjustments two years after the pandemic’s onset among five cultural groups: Bulgarian and Spanish (Eastern and Western Europe), Israeli Arabs and Jews (Middle East), and U.S. families. Fifty parents, primarily mothers of children aged 2–8, were recruited through snowball sampling. Semi-structured interviews were conducted using the Parenting Pentagon Model (PPM), which includes five constructs: Partnership, Parental Leadership, Love, Encouraging Independence, and Adherence to Rules. Data were analyzed using grounded theory and directed content analysis. Across cultures, Love and Parental Leadership were central to maintaining emotional stability and family cohesion. Partnership showed cultural variation: Bulgarian and Spanish parents often shared responsibilities, while U.S. mothers reported handling childcare alone, heightening work–life tension. Israeli-Arab fathers became more involved in caregiving, while Israeli-Jewish mothers described both strengthened and strained partnerships. Coping strategies were shaped by cultural values and family demographics (e.g., family size). The findings emphasize parents’ vital role in fostering family resilience during crises and stress the importance of culturally sensitive support to enhance families’ adaptive capacity for future challenges. Full article