Search Results (3,043)

Addressing the key pain points in detecting typical apparent diseases of concrete structures, where standalone object detection fails to achieve pixel-level quantification and standalone semantic segmentation, is inefficient. Therefore, a deep learning image-based fusion approach is proposed to identify the typical visible diseases in concrete structures, namely crack, spalling, water leakage, and seam deformation. To implement the approach, a deep learning fusion network is developed with the YOLO and UNet models to identify multiple apparent diseases rapidly. In the fusion network, the YOLO model is used to filter the images containing the visible diseases from all the images in the first stage. Then, the UNet model is used to extract the pixels containing diseases from the selected images. Lastly, analysis methods are proposed to quantify the diseases based on the segmented pixels, such as length, width, and area. In this paper, a dataset of 1488 images with the above diseases from a field inspection was used to train the deep learning fusion network. The training results demonstrated the robustness of the fusion network in identifying and segmenting diseases with a mean average precision of 0.72 and a Dice score of 0.82. Experiments were finally conducted on concrete slabs with simulated diseases for additional validation. The results indicated that the proposed fusion network could identify the diseases approximately 50% faster than the UNet model only. The quantification precision was found to be satisfactory, with relative errors below 11.07% for the area of water leakage, below 5% for the length and area of cracks, and below 6% for the width of seams. Full article

Undesirable water-in-crude oil emulsions in the oil and gas industry can lead to several issues, including equipment corrosion, high-pressure drops in pipelines, high pumping costs, and increased total production costs. These emulsions are commonly treated with surface-active chemicals called demulsifiers, which can break an oil–water interface and enhance phase separation. This study introduces a novel approach based on neural networks to estimate demulsification efficiency and to aid in the selection of demulsifiers under field conditions. The influence of various types of demulsifiers, demulsifier concentration, time required for demulsification, temperature and asphaltene content on the demulsification efficiency is analyzed. To improve model accuracy, a modified full-scale factorial design of experiments and the comparison of response surface method with multilayer perception neural networks were conducted. The results demonstrated the advantages of using neural networks over the response surface methodology such as a reduced settling time in separators, an improved crude oil dehydration and processing capacity, and a lower consumption of energy and utilities. The findings may enhance processing conditions and identify regions of higher demulsification efficiency. The neural network approach provided a more accurate prediction of maximum of demulsification efficiency compared to the response surface methodology. The automated multilayer perceptron neural network, with an architecture consisting of 3 input layers, 14 hidden layers, and 1 output layer, demonstrated the highest validation performance R² of 0.991932 by utilizing a logistic output activation function and a hyperbolic tangent activation function for the hidden layers. The identification of shifted optimal values of time required from demulsification, demulsifier concentration, and asphaltene content along with sensitivity analysis confirmed advantages of automated neural networks over conventional methods. Full article

Animal hoarding is a complex and multifactorial phenomenon that poses serious risks to animal welfare, public health, and environmental balance. Despite increasing attention, current interventions often remain fragmented and lack integration across medical, psychological, and social domains. Based on a critical review of existing tools and field experience in the Italian context, this study proposes a structured operational protocol to support multidisciplinary teams in the assessment and management of animal hoarding cases. The protocol integrates three complementary tools: a preliminary observational form, a clinical-relational interview, and a veterinary health form. Designed to be modular, replicable, and shareable among professionals from diverse backgrounds, the protocol aims to promote a One Welfare approach, recognizing the systemic interconnection between animal suffering, human psychological distress, and environmental degradation. While further experimental validation is required, this conceptual model provides a concrete operational basis for structured interventions and consistent data collection in support of research and public health. Full article

Unmanned Aerial Vehicles (UAVs) have become essential tools in inspection and emergency response operations due to their high maneuverability and ability to access hard-to-reach areas. However, their limited battery life significantly restricts their use in long-duration missions. This paper presents a tethered marsupial robotic system composed of a UAV and an Unmanned Ground Vehicle (UGV), specifically designed for autonomous, long-duration inspection tasks in Global Navigation Satellite System (GNSS)-denied environments. The system extends the UAV’s operational time by supplying power through a tether connected to high-capacity battery packs carried by the UGV. Our work details the hardware architecture based on off-the-shelf components to ensure replicability and describes our full-stack software framework used by the system, which is composed of open-source components and built upon the Robot Operating System (ROS). The proposed software architecture enables precise localization using a Direct LiDAR Localization (DLL) method and ensures safe path planning and coordinated trajectory tracking for the integrated UGV–tether–UAV system. We validate the system through three sets of field experiments involving (i) three manual flight endurance tests to estimate the operational duration, (ii) three experiments for validating the localization and the trajectory tracking systems, and (iii) three executions of an inspection mission to demonstrate autonomous inspection capabilities. The results of the experiments confirm the robustness and autonomy of the system in GNSS-denied environments. Finally, all experimental data have been made publicly available to support reproducibility and to serve as a common open dataset for benchmarking. Full article

Background: Knowledge-based (KB) planning is a promising approach to model prior planning experience and optimize radiotherapy. To enable the sharing of models across institutions, their transferability must be evaluated. This study aimed to validate KB prediction models developed by a national consortium using data from another multi-institutional consortium in a different country. Methods: Ten right whole breast tangential field (RWB-TF) models were built within the national consortium. A cohort of 20 patients from the external consortium was used for testing. Transferability was defined when the ipsilateral (IPSI) lung first principal component (PC1) was within the 10th–90th percentile of the training set. Predicted dose–volume parameters were compared with clinical dose–volume histograms (cDVHs). Results: Planning target volume (PTV) coverage strategies were comparable between the two consortia, even though significant volume differences were observed for the PTV and contralateral breast (p = 0.002 and p = 0.02, respectively). For the IPSI lung, the standard deviation of predicted mean dose/V20 Gy was 1.13 Gy/2.9% in the external consortium versus 0.55 Gy/1.6% in the training consortium. Differences between the cDVH and the predicted IPSI lung mean dose and the volume receiving more than 20 Gy (V20 Gy) were <2 Gy and <5% in 88.7% and 92.3% of cases, respectively. PC1 values fell within the 10th–90th percentile for ≥90% of patients in 6/10 models and 65–85% for the remaining 4. Conclusions: This study demonstrates the feasibility of applying RWB-TF KB models beyond the consortium in which they were developed, supporting broader clinical implementation. This retrospective study was supported by AIRC (Associazione Italiana per la Ricerca sul Cancro) and registered on ClinicalTrials.gov (NCT06317948, 12 March 2024). Full article

The high cost of traditional structural health monitoring systems limits their application to only a few major bridges, leaving most structures unmonitored between manual inspections. To address this issue, this study proposes a UAV mobile detection device (UMD) system that integrates a Raspberry Pi, data acquisition module, and accelerometer for rapid, contact-based vibration measurement. A vibration transmission model between the UMD and the bridge deck is developed to guide hardware design and quantify the influence of isolator stiffness and damping. The UMD’s performance is validated through both laboratory floor tests and field bridge experiments, demonstrating reliable identification of modal frequencies in the range of 0.00–51.95 Hz with a maximum acceleration error below 0.01 g and a relative modal frequency deviation within 3.4%. The analysis further determines that an accelerometer resolution of $0.02\times {10}^{-1}$ g is required for accurate frequency domain measurement. These findings establish the UMD as a fast, low-cost, and accurate tool for rapid bridge vibration assessment and lay the groundwork for future multi-UAV synchronized monitoring. Full article

The proton-exchange membrane fuel cell (PEMFC) generates a significant reaction and ohmic heat during operation, imposing stringent cooling requirements. This study employs a three-dimensional, non-isothermal, steady multiphase multiphysics model to investigate heat generation and transport in a three-cell PEMFC stack using deionized water, CuO, and Al₂O₃ nanofluids (1 vol%) as coolants. The base (no-coolant) configuration was validated against a published polarization curve for a nine-cell stack. Introducing coolant channels increased the area-averaged current density from 2426 A m⁻² (no coolant) to 2613 A m⁻² (water), 2678 A m⁻² (CuO), and 2702 A m⁻² (Al₂O₃), representing up to an 11.4% performance improvement while reducing the peak cell temperature by approximately 7–8 °C. Among the examined coolants, Al₂O₃ nanofluid achieved the lowest maximum temperature and a favorable pressure drop, whereas water maintained the most uniform temperature field. A price-performance factor (PPF) was introduced to evaluate the techno-economic trade-off between cost and cooling benefit. This study highlights that, despite scale-related limitations between three-cell simulations and nine-cell experiments, nanofluid coolants offer a practical route toward thermally stable and high-performance PEMFC operation. Full article

The cross-scale fusion and consistent representation of cross-source heterogeneous vector polygon data are fundamental tasks in the field of GIS, and they play an important role in areas such as the refined management of natural resources, territorial spatial planning, and the urban emergency response. However, the existing methods suffer from two key limitations: the insufficient utilization of semantic information, especially non-standardized attributes, and the lack of differentiated modeling for 1:1, 1:M, and M:N matching relationships. To address these issues, this study proposes a geometric–attribute collaborative matching method for multi-scale polygonal entities. First, matching relationships are classified into 1:1, 1:M, and M:N based on the intersection of polygons. Second, geometric similarities including spatial overlap, size, shape, and orientation are computed for each relationship type. Third, semantic similarity is enhanced by fine-tuning the pre-trained Sentence-BERT model, which effectively captures the complex semantic information from non-standardized descriptions. Finally, a three-branch attention network is constructed to specifically handle the three matching relationships, with adaptive feature weighting via attention mechanisms. The experimental results on datasets from Tunxi District, Huangshan City, China show that the proposed method outperforms the existing approaches including geometry–attribute fusion and BPNNs in precision, recall, and F1-score, with improvements of 3.38%, 1.32%, and 2.41% compared to the geometry–attribute method, and 2.91%, 0.27%, and 1.66% compared to BPNNs, respectively. A generalization experiment on Hefei City data further validates its robustness. This method effectively enhances the accuracy and adaptability of multi-scale polygonal entity matching, providing a valuable tool for multi-source GIS database integration. Full article

This study addresses the challenges of uneven recovery in strongly water-sensitive reservoirs through the development of a time-dependent two-phase flow model capturing the dynamic evolution of permeability and relative permeability. Based on laboratory core flooding experiments and integrated into a black-oil simulator, the model accurately reproduces reservoir behavior under long-term water flooding, significantly improving the history matching of water cut and pressure—especially in high water-cut stages. Results demonstrate that water sensitivity causes staged damage: initial reduction in heterogeneity is followed by intensified interlayer conflict and earlier water breakthrough. The relative permeability curve shifts rightward, accompanied by reduced residual oil saturation. These findings overcome the limitations of conventional static-property models and provide a reliable basis for optimizing enhanced oil recovery strategies. Further validation with field data will enhance the model’s applicability under diverse geological conditions. Full article

Unmanned Aerial Vehicle (UAV) detection often faces challenges such as small target size, loss of textural details, and interference from complex backgrounds. To address these issues, this paper proposes a novel object detection algorithm named Spatial-Frequency Aware DETR (SFA-DETR), which integrates both spatial- and frequency-domain perception. For spatial-domain modeling, a backbone network named IncepMix is designed to dynamically fuse multi-scale receptive field information, enhancing the model’s ability to capture contextual information while reducing computational cost. For frequency-domain modeling, a Frequency-Guided Attention Block (FGA Block) is introduced to improve perception of target boundaries through frequency-aware guidance, thereby increasing localization accuracy. Furthermore, an adaptive sparse attention mechanism is incorporated into AIFI to emphasize semantically critical information and suppress redundant features. Experiments conducted on the DUT Anti-UAV dataset demonstrate that SFA-DETR improves mAP50 and mAP50:95 by 1.2% and 1.7%, respectively, while reducing parameter count and computational cost by 14.44% and 3.34%. The results indicate that the proposed method achieves a balance between detection accuracy and computational efficiency, validating its effectiveness in UAV detection tasks. Full article

Temperature excursions during refrigerated transport strongly affect the quality and shelf life of perishable food, yet reproducing realistic, time-varying cold-chain temperature histories in the laboratory remains challenging. In this study, we present a compact, portable climate chamber driven by Peltier modules and an identification-guided control architecture designed to reproduce real refrigerated-truck temperature histories with high fidelity. Control is implemented as a cascaded regulator: an outer two-degree-of-freedom PID for air-temperature tracking and faster inner PID loops for module-face regulation, enhanced with derivative filtering, anti-windup back-calculation, a Smith predictor, and hysteresis-based bumpless switching to manage dead time and polarity reversals. The system integrates distributed temperature and humidity sensors to provide real-time feedback for precise thermal control, enabling accurate reproduction of cold-chain conditions. Validation comprised two independent 36-day reproductions of field traces and a focused 24-h comparison against traditional control baselines. Over the long trials, the chamber achieved very low long-run errors ($\mathrm{MAE}\cong 0.19 °\mathrm{C}$, $\mathrm{MedAE}\cong 0.10 °\mathrm{C}$, $\mathrm{RMSE}\cong 0.33 °\mathrm{C}$, $R^2=0.9985$). The 24-h test demonstrated that our optimized controller tracked the reference, improving both transient and steady-state behaviour. The system tolerated realistic humidity transients without loss of closed-loop performance. This portable platform functions as a reproducible physical twin for cold-chain experiments and a reliable data source for training predictive shelf-life and digital-twin models to reduce food waste. Full article

Biochar-based fertilizers have attracted increasing attention as sustainable soil amendments due to their potential to enhance nitrogen (N) retention and mitigate N losses. However, their effects on N dynamics in tea orchard soils remain inadequately understood. This study investigated the impact of biochar-based fertilizer (BF) on N migration and transformation into acidic tea orchard soils through controlled laboratory experiments comprising nine treatments, including sole urea (U) applications and various combinations of BF and U. The results showed that ammonia (NH₃) volatilization peaked within seven days after application. Compared with urea-only treatments, the application of BF at 15 t·ha⁻¹ combined with a low U application rate (0.72 t·ha⁻¹) significantly reduced NH₃ and total dissolved nitrogen losses by up to 22.33% and 33.56%, respectively, while higher BF rates increased these losses. BF applications markedly improved soil N sequestration, as evidenced by increases in total nitrogen, ammonium nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃⁻-N), and the NH₄⁺-N/NO₃⁻-N ratio. Additionally, soil organic carbon, urease activity, and pH were significantly enhanced. Random forest analysis identified soil pH and organic carbon as the primary predictors of NH₃ volatilization and soil N retention. Partial least squares path modeling revealed that the BF-to-urea ratio governed N dynamics by directly influencing N transformation and indirectly modifying soil physicochemical properties. BF applied at ≤15 t·ha⁻¹ with low U inputs exhibited potential for improving N use efficiency and sustainability, pending further field validation. Full article

Studies examining emoji applications in digital marketing and advertising are characterized by considerable heterogeneity in their theoretical orientation, methodologies, and contextual factors. A domain-based systematic literature review with the Theory-Context-Characteristics-Methodology (T-C-C-M) framework following PRISMA guidelines was conducted to answer how emojis are researched in marketing, and a bibliometric review was constructed to shed light on important aspects. We found a field growing in volume yet immature, with a diversity of theories and methodologies used to explore the multiple roles of emojis. An analysis of explicit and implicit theories identified that almost a quarter of studies are atheoretical, and the mostly used theories are the Emotions as Social Information Theory (EASI) and the emotional contagion theory. Emojis are mainly researched in social media and in the travel and food industry. The most common methodological categories are experimental designs, with emojis used as independent variables in simple designs. Despite the focus on short-term outcomes (engagement, purchase intention), little attention was given to advertising and to field experiments, constraining ecological validity. Our study reveals the need for a robust theoretical framework that can explain the multiple functions of emojis, and EASI emerged as the leading theory to be tested more extensively. Full article

Autonomous berthing of unmanned surface vehicles (USVs) requires high-precision positioning and accurate detection of navigable region in complex port environments. This paper presents an integrated LiDAR-based approach to address these challenges. A high-precision 3D point cloud map of the berth is first constructed by fusing LiDAR data with real-time kinematic (RTK) measurements. USV pose is then estimated by matching real-time LiDAR scans to the prior map, achieving robust, RTK-independent localization. For safe navigation, a novel navigable region detection algorithm is proposed, which combines point cloud projection, inner-boundary extraction, and target clustering. This method accurately identifies quay walls and obstacles, generating reliable navigable areas and ensuring collision-free berthing. Field experiments conducted in Ling Shui Port, Dalian, China, validate the proposed approach. Results show that the map-based positioning reduces absolute trajectory error (ATE) by 55.29% and relative trajectory error (RTE) by 38.71% compared to scan matching, while the navigable region detection algorithm provides precise and stable navigable regions. These outcomes demonstrate the effectiveness and practical applicability of the proposed method for autonomous USV berthing. Full article

The rapid increase in production of small unmanned rotorcrafts (SURs) has made real-time drone surveillance critical for airspace security. Effective SUR detection is essential for maintaining aviation safety, protecting privacy, and ensuring public security. However, conventional radar systems struggle to detect hovering SURs due to their low velocity and small radar cross-section (RCS), which make them nearly indistinguishable from stationary clutter. To address this issue, this paper proposes a hovering SUR detection method through identifying the micro-Doppler signal (MDS). By applying the multiple reassignment squeeze processing and exhaustive Hough transform, the proposed approach effectively enhances the accumulation of micro-Doppler signal generated by the rotor blades, which enables the separation of hovering SUR signals from stationary clutter. Numerical simulations and field experiments validate the effectiveness of the proposed method, demonstrating its potential for micro-Doppler signal detection using a UHF-band horizontally co-polarized radar system. Full article