Search Results (2,311)

Integrating bioluminescent organisms as passive lighting sources in the built environment is currently a hot topic. However, there are several limitations facing the implementation and up-scaling of these naturally bioluminescent organisms in the built environment on architectural and urban scales, such as the scale, sensitivity, enclosure, and difficulty of maintenance. Moreover, there are complex technicalities and operational aspects of conventional bioreactors that host these bioluminescent agents, especially in terms of managing their recharge and effluent, not to mention their high maintenance cost. The current work offers a sustainable, stand-alone, bioluminescent urban screen system employing Aliivibrio fischeri CECT 524 bioink on 3D-printed customized scaffolds as bioreceptive panel design based on a field-diffusion pattern to host the bioluminescent bacterial bioink. The field-diffusion pattern was employed thanks to its proven efficiency in entrapment of the various microbial cultures. Three different growth media were tested for culturing Aliivibrio fischeri CECT 524, including Luria Bertani Broth (LB), the Tryptone Soy Broth (TSB), and the standard Marine Broth (MB). The results revealed that the Marine Broth (MB) media achieved the highest bioluminescent intensity and duration. The maximum light emission typically in range of ~490 nm of blue–green light captured by a conventional reflex camera (human eye vision) was observed for 10 consecutive days in complete darkness after 3–10 s, at a room temperature of 25 °C. This was visible mainly at the thin curvilinear peaks of the 3D-printed field pattern. P1 achieved the highest performance in terms of visible blue–green light, and a duration of 10 days of active bioluminescence was achieved without the need for refilling, thanks to the high number of peaks and narrow wells at <0.5 cm of its field-diffusion pattern. This study proves the efficiency of this biomimetic pattern in terms of the bioreceptivity of the bioluminescent bacterial bioink. Furthermore, the proposed 3D-printed urban screens proved their economic sustainability in terms of affordability and their minimized production processes, in addition to their easy maintenance and recharge. These results qualify these 3D-printed bioluminescent urban screens for easy and decentralized adoption and application on an architectural and urban scale. Full article

Autonomous landing capability is crucial for fully autonomous UAV flight. Currently, most solutions use either color imaging from a camera pointed down, lidar sensors, dedicated landing spots, beacons, or a combination of these approaches. Classical strategies can be limited by either no color data when lidar is used, limited obstacle perception when only color imaging is used, a low field of view from a single RGB-D sensor, or the requirement for the landing spot to be prepared in advance. In this paper, a new approach is proposed where an RGB-D camera mounted on a gimbal is used. The gimbal is actively actuated to counteract the limited field of view while color images and depth information are provided by the RGB-D camera. Furthermore, a combined UAV-and-gimbal-motion strategy is proposed to counteract the low maximum range of depth perception to provide static obstacle detection and avoidance, while preserving safe operating conditions for low-altitude flight, near potential obstacles. The system is developed using a PX4 flight stack, CubeOrange flight controller, and Jetson nano onboard computer. The system was flight-tested in simulation conditions and statically tested on a real vehicle. Results show the correctness of the system architecture and possibility of deployment in real conditions. Full article

This work aimed to quantify axial deformations of a human premolar during occlusion with its antagonist and to compare them with the same premolar restored with a ceramic crown. The deformations were put under stress using a mechanical press with a force ranging from 1 to 100 Newtons. These deformations were quantified using the optical interferometry technique with a laser source (633 nm, 0.95 mW). Using a CMOS camera, interference fringes were obtained, stored, and subsequently processed. The premolars were restored with Cerasmart GC ceramic, using the CAD-CAM system. The average deformations of healthy premolars were found to be in a range of 0.69 to 1.74 µm, while the restored ones were deformed in a range of 0.53 to 1.10 µm. The results of this work showed that the Cerasmart ceramic material had similar properties to those of the natural tooth for small forces. However, for higher forces, the ceramics increased the coronal stiffness of the tooth. This modified the optimal combination of stiffness, strength, and resilience between the enamel and dentin, causing a decrease in the tooth’s ability to dissipate energy; therefore, the tooth could receive more stress. The observed mechanical properties lead to the conclusion that the Cerasmart material can be indicated for the restoration of anterior and premolar teeth in most cases where a fixed prosthesis is required. Full article

This paper pioneers the integration of hyperspectral imaging and robotics for the automated analysis of cultural heritage, representing a measurable advancement over existing manually operated systems. For the first time in the cultural heritage domain, a compact push-broom hyperspectral camera working in the VNIR range has been successfully mounted on a robotic arm, enabling precise and repeatable acquisition trajectories without the need for manual intervention. Unlike traditional approaches that rely on fixed paths or manual repositioning, the proposed approach allows dynamic and programmable imaging of both planar and volumetric objects, greatly improving adaptability to complex geometries. The integrated system achieves spectral reliability comparable to established manual methods, while offering superior flexibility and scalability. Current limitations, particularly regarding the illumination setup, are discussed alongside planned optimisation strategies. Full article

Fibre-reinforced composites are facing new challenges in the context particular in sustainability and recyclability. Vitrimers could be useful as new matrices to support the increase in sustainability. Due to their high strength, which is comparable to that of thermosets often used in composites, and their covalent adaptive networks, which make them reshapeable for scaled-up manufacturing and recycling purposes, they are very useful. Orthogonal cutting is used for precise reshaping and functional integration into carbon fibre reinforced plastics. Vitrimers could improve processing results at the cutting edge as well as surface quality thanks to their self-healing properties compared to brittle matrices, as well as enabling the recycling of formed chips and scrap. This study showcases the manufacturing of a carbon fibre-reinforced vitrimer using 4-aminophenyl disulfide as a hardener, with vacuum-assisted resin infusion. The temperature of chip formation and the cutting parameters are then shown for different fibre orientations, cutting widths and speeds. The observed cutting forces are lower (less than 140 N) and more irregular for fibre orientations 45°/135°, increasing with cutting depth, and fluctuating periodically during machining. Despite varying cutting speeds, the forces remain relatively constant in range between 85 N and 175 N for 0°/90° fibre orientation and 50 N and 120 N for 45°/135° fibre orientation, with no significant tool wear observed and lower-damage depth and overhanging fibres observed for 0°/90° fibre orientation. Damage observation of the cutting tool shows promising results, with lower abrasion observed compared to thermoset matrices. Microscopic images of the broached surface also show good quality, which could be improved by self-healing of the matrix at higher temperatures. Temperature measurements of chip formation using a high-speed camera show a high temperature gradient as cutting speeds increase, but the temperature only ever exceeds 180 °C at cutting speeds of 150 m/min, ensuring reprocessability since this is below the degradation temperature. Therefore, orthogonal cutting of vitrimers can impact sustainable composite processing. Full article

Multi-modal sensor fusion has become a cornerstone of robust autonomous driving systems, enabling perception models to integrate complementary cues from cameras, LiDARs, radars, and other modalities. This survey provides a structured overview of recent advances in deep learning-based fusion methods, categorizing them by architectural paradigms (e.g., BEV-centric fusion and cross-modal attention), learning strategies, and task adaptations. We highlight two dominant architectural trends: unified BEV representation and token-level cross-modal alignment, analyzing their design trade-offs and integration challenges. Furthermore, we review a wide range of applications, from object detection and semantic segmentation to behavior prediction and planning. Despite considerable progress, real-world deployment is hindered by issues such as spatio-temporal misalignment, domain shifts, and limited interpretability. We discuss how recent developments, such as diffusion models for generative fusion, Mamba-style recurrent architectures, and large vision–language models, may unlock future directions for scalable and trustworthy perception systems. Extensive comparisons, benchmark analyses, and design insights are provided to guide future research in this rapidly evolving field. Full article

Accurate evaluation of subgrade behaviour under dynamic loading is essential for the long-term performance of transport infrastructure. While the Light Weight Deflectometer (LWD) is commonly used to assess subgrade stiffness, it provides only a single stiffness value and may not fully capture the time-dependent response of soil. This study presents an image-based vision system developed to monitor soil surface displacements during loading, enabling more detailed analysis of dynamic behaviour. The system incorporates high-speed cameras and MATLAB-based computer vision algorithms to track vertical movement of the plate during impact. Laboratory and field experiments were conducted to evaluate the system’s performance, with results compared directly to those from the LWD. A strong correlation was observed (R² = 0.9901), with differences between the two methods ranging from 0.8% to 13%, confirming the accuracy of the vision-based measurements despite the limited dataset. The findings highlight the system’s potential as a practical and cost-effective tool for enhancing subgrade assessment, particularly in applications requiring improved understanding of ground response under repeated or transient loading. Full article

This cross-sectional study analyzed the lower limb (LL) behavior in terms of gait asymmetry and joints’ kinematic parameters, comparing people with multiple sclerosis (pwMS) and unaffected individuals. Methods: Data from 15 patients, EDSS ≤ 4.5, and 15 healthy control volunteers were gathered. The VICON Motion Capture System (14 infrared cameras), NEXUS software, Plug-in–Gait skeleton model and reflective markers were used to collect data for each subject during five gait cycles on a plane surface. Biomechanical analysis included evaluation of LL joints’ range of motion (ROM) bilaterally, as well as movement symmetry. Results: Comparative biomechanical analysis revealed a hierarchy of vulnerability between the groups: the ankle is the most affected joint in pwMS (p = 0.008–0.014), the knee is moderately affected (p = 0.015 in swing phase), and the hip is the least affected (p > 0.05 in all phases). The swing phase showed the most significant left–right asymmetry impairment, as reflected by root mean square error (RMSE) values: swing-phase RMSE = 9.306 ± 4.635 (higher and more variable) versus stance-phase RMSE = 6.363 ± 2.306 (lower and more consistent). Conclusions: MS does not affect the joints structurally; rather, it eliminates the ability to differentiate the fine-tuning control between them. The absence of significant left–right joint asymmetry differences during complete gait cycle indicates dysfunction in the global motor control. Full article

This paper proposes a transfer learning-based approach to enhance video-driven safety risk detection in industrial environments, addressing the critical challenge of limited generalization across diverse operational scenarios. Conventional deep learning models trained on specific operational contexts often fail when applied to new environments with different lighting, camera angles, or machinery configurations, exhibiting a significant drop in performance (e.g., F1-score declining below 0.85). To overcome this issue, an incremental feature transfer learning strategy is introduced, enabling efficient adaptation of risk detection models using only small amounts of data from new scenarios. This approach leverages prior knowledge from pre-trained models to reduce the reliance on large-labeled datasets, particularly valuable in industrial settings where rare but critical safety risk events are difficult to capture. Additionally, training efficiency is improved compared with a classic approach, supporting deployment on resource-constrained edge devices. The strategy involves incremental retraining using video segments with average durations ranging from 2.5 to 25 min (corresponding to 5–50% of new scenario data), approximately, enabling scalable generalization across multiple forklift-related risk activities. Interpretability is enhanced through SHAP-based analysis, which reveals a redistribution of feature relevance toward critical components, thereby improving model transparency and reducing annotation demands. Experimental results confirm that the transfer learning strategy significantly improves detection accuracy, robustness, and adaptability, making it a practical and scalable solution for safety monitoring in dynamic industrial environments. Full article

Situation awareness is essential for ensuring safety in hazardous environments, where timely and accurate information is critical for decision-making. Unmanned Aerial Vehicles (UAVs) have emerged as valuable tools in enhancing situation awareness by providing real-time data and monitoring capabilities in high-risk areas. This study explores the integration of advanced technologies, focusing on imaging and sensor technologies such as thermal, spectral, and multispectral cameras, deployed in critical zones. By merging these technologies into UAV platforms, responders gain access to essential real-time information while reducing human exposure to hazardous conditions. This study presents case studies and practical applications, highlighting the effectiveness of these technologies in a range of hazardous situations. Full article

The study presents a spectral assessment and analysis of various data and methods for snow cover analysis in different ranges of the electromagnetic spectrum through a differentiated approach applied to the territory of Bansko, Pirin Mountain. The aim of the presented research is to assess the effectiveness and accuracy of satellite observations together with field (in situ) measurements and to create a model of an integrated methodology. To achieve this goal, several indices, such as land surface temperature (LST), optical indices, Tasseled Cap Transformation (TCT) with wetness component (TCW), High-Resolution (HR) imagery, and Synthetic Aperture Radar (SAR) measurements, were analyzed. The results of the analysis proved that combining satellite and field data through a mobile thermal camera provides an accurate and comprehensive picture of snow conditions in high mountain regions for powder, hard-packed and wet snow. As the most important, there is the verification and validation of the results through the so-called regression analysis of the different data types, through which multiple correlations (over 10) were established, both in data from Sentinel 1SAR, Sentinel 2MSI, Sentinel 3 SLSTR, and PlanetScope. The results showed the effectiveness of optical indices for hard and fresh snow and radar and LST data for wet snow. The results can be used to improve snow surveys, event prediction (e.g., avalanches), and the interpretation of spectral analysis of snow. The study does not aim to perform a temporal analysis; all satellite data is from the temporal period 30 December 2024–5 January 2025. Full article

Detecting chemical residues on surfaces is critical in environmental monitoring, industrial hygiene, public health, and incident management after chemical releases. Compounds such as acrylonitrile (ACN) and tetraethylguanidine (TEG), widely used in chemical processes, can pose risks upon residual exposure. Hyperspectral imaging (HSI), a high-resolution, non-destructive method, offers a secure and effective solution to identify and spatially map chemical contaminants based on spectral signatures. In this study, we present an HSI-based framework to detect and differentiate ACN and TEG residues on textile surfaces. High-resolution spectral data were collected from three representative textiles using a hyperspectral camera operating in the short-wave infrared range. The spectral datasets were processed using standard normal variate transformation, Savitzky–Golay filtering, and principal component analysis to enhance contrast and identify material-specific features. The results demonstrate the effectiveness of this approach in resolving spectral differences corresponding to distinct chemical residues and concentrations but also provide a practical and scalable method for detecting chemical contaminants in consumer and industrial textile materials, supporting reliable residue assessment and holding promise for broader applications in safety-critical fields. Full article

Conventional projection systems typically require a fixed spatial configuration relative to the projection surface, with strict control over distance and angle. In contrast, UAV-mounted projectors overcome these constraints, enabling dynamic, large-scale projections onto non-planar and complex environments. However, such flexible scenarios introduce a key challenge: severe geometric distortions caused by intricate surface geometry and continuous camera–projector motion. To address this, we propose a novel image registration method based on global dense matching, which estimates the real-time optical flow field between the input projection image and the target surface. The estimated flow is used to pre-warp the image, ensuring that the projected content appears geometrically consistent across arbitrary, deformable surfaces. The core idea of our method lies in reformulating the geometric distortion correction task as a global feature matching problem, effectively reducing 3D spatial deformation into a 2D dense correspondence learning process. To support learning and evaluation, we construct a hybrid dataset that covers a wide range of projection scenarios, including diverse lighting conditions, object geometries, and projection contents. Extensive simulation and real-world experiments show that our method achieves superior accuracy and robustness in correcting geometric distortions in dynamic UAV projection, significantly enhancing visual fidelity in complex environments. This approach provides a practical solution for real-time, high-quality projection in UAV-based augmented reality, outdoor display, and aerial information delivery systems. Full article

The European wildcat exhibits considerable plasticity in its habitat requirements across its distribution, with differences increasing along a continental-scale latitudinal gradient. While wildcats often favor deciduous and mixed forests with dense cover and prey, studies show these preferences vary across their expansion. Range-wide conservation efforts will benefit from incorporating knowledge generated by robust regional ecological models. We used data from a large camera trap grid (n = 292 stations), spanning across eight wildcat-associated habitats, within its range in northern Greece, to understand the regional ecological parameters affecting the species’ habitat selection. We analyzed the data using single-season density-induced detection heterogeneity occupancy models (Royle–Nichols), considering 12 environmental and anthropogenic parameters. The global model’s GoF was high (p = 0.9). Elevation and percent forest cover were both significantly negatively related to wildcat occupancy (as derived from the modeled “relative abundance index” N). Likewise, there was a negative, but moderate, relation between distance to freshwater bodies and human settlements with wildcat occupancy. We used the model-average coefficients to generate a predictive map of wildcat relative abundance across northern Greece, which identified 47,930 km² of potential wildcat habitat. Assuming a range of densities between 0.05 and 0.3 ind/km² in areas with predicted low, medium, and high relative abundance, we speculate the putative wildcat population in northern Greece to be between 3535 and 7070 individuals. The findings, which vary from ecological models of the species in northern Europe, show the need for regional models and the importance of Greece, and the Balkan peninsula, for the species. Full article

Several approaches are currently being used by law enforcement to locate the remains of victims. Yet, traditional methods are invasive and time-consuming. Unmanned Aerial Vehicle (UAV)-based remote sensing has emerged as a potential tool to support the location of human remains and clandestine graves. While offering a non-invasive and low-cost alternative, UAV-based remote sensing needs to be tested and validated for forensic case work. To assess current knowledge, a systematic review of 19 peer-reviewed articles from four databases was conducted, focusing specifically on UAV-based remote sensing for human remains and clandestine grave location. The findings indicate that different sensors (colour, thermal, and multispectral cameras), were tested across a range of burial conditions and models (human and mammalian). While UAVs with imaging sensors can locate graves and decomposition-related anomalies, experimental designs from the reviewed studies lacked robustness in terms of replication and consistency across models. Trends also highlight the potential of automated detection of anomalies over manual inspection, potentially leading to improved predictive modelling. Overall, UAV-based remote sensing shows considerable promise for enhancing the efficiency of human remains and clandestine grave location, but methodological limitations must be addressed to ensure findings are relevant to real-world forensic cases. Full article