Search Results (313)

High resolution underwater mapping is fundamental to the sustainable development of the blue economy, supporting offshore energy expansion, marine habitat protection, and the monitoring of both living and non-living resources. This work presents a pose-graph SLAM and calibration framework specifically designed for 3D profiling sonars, such as the Coda Octopus Echoscope 3D. The system integrates a probabilistic scan matching method (3DupIC) for direct registration of 3D sonar scans, enabling accurate trajectory and map estimation even under degraded dead reckoning conditions. Unlike other bathymetric SLAM methods that rely on submaps and assume short-term localization accuracy, the proposed approach performs direct scan-to-scan registration, removing this dependency. The factor graph is extended to represent the sonar extrinsic parameters, allowing the sonar-to-body transformation to be refined jointly with trajectory optimization. Experimental validation on a challenging real world dataset demonstrates outstanding localization and mapping performance. The use of refined extrinsic parameters further improves both accuracy and map consistency, confirming the effectiveness of the proposed joint SLAM and calibration approach for robust and consistent underwater mapping. Full article

Precise sensor integration is crucial for autonomous vehicle (AV) navigation, yet traditional extrinsic calibration remains costly and labor-intensive. This study proposes an automated calibration approach that uses metaheuristic algorithms (Simulated Annealing (SA), Genetic Algorithms (GA), and Particle Swarm Optimization (PSO)) to independently optimize rotational and translational parameters, reducing cross-compensation errors. Bayesian optimization is used offline to define the search bounds (and tune hyperparameters), accelerating convergence, while computer vision techniques enhance automation by detecting geometric features using a checkerboard reference and a Huber estimator for noise handling. Experimental results demonstrate high accuracy with a single-pose acquisition, supporting multi-sensor configurations and reducing manual intervention, making the method practical for real-world AV applications. Full article

Background: Silybin, the primary active constituent of the milk thistle extract silymarin, has been historically recognized for its hepatoprotective properties. More recently, its potential effects on blood coagulation have garnered attention, suggesting a broader pharmacological profile. Methods: This study aimed to investigate silybin’s impact on hemostasis using rotational thromboelastometry (ROTEM) in normal human plasma. ROTEM enables the dynamic assessment of clot formation, providing a detailed analysis of coagulation processes in real-time. We specifically focused on the effects of silybin concentrations of 10 µM, 50 µM, and 100 µM on the ROTEM parameters compared to controls using normal human plasma with 0.1% dimethyl sulfoxide (DMSO). The parameters derived from the tests included clotting time (CT), α-angle (α), and amplitude at 10 and 20 min (A10 and A20) for each of the three channels: intrinsic pathway thromboelastometry (INTEM), extrinsic pathway thromboelastometry (EXTEM), and fibrinogen thromboelastometry (FIBTEM). Each measurement was performed four times. Results: Analysis of the INTEM assay results demonstrated that silybin at concentrations of 10 µM and 50 µM significantly reduced clotting time (CT) compared to the control. Additionally, all tested silybin concentrations significantly decreased the α-angle in the INTEM test. In the EXTEM assay, no significant effect on CT was observed at any silybin concentration. However, consistent with the INTEM findings, all silybin concentrations resulted in a significant reduction in the α-angle. In the FIBTEM assay, silybin at 10 µM and 50 µM significantly shortened CT. Furthermore, all tested concentrations led to a significant decrease in the α-angle and A20, while a reduction in A10 was observed only at the 50 µM concentration compared to the control. Conclusions: This study demonstrates that silybin modulates ROTEM parameters in a manner that tends to vary with concentration, with the strongest effects observed at lower concentrations (10–50 µM), notably reducing CT, α-angle, and clot firmness (A10, A20). These findings suggest a potential role of silybin in influencing coagulation dynamics. Full article

The detection of chiral properties is crucial for pharmacology and biochemistry, yet standard circular dichroism spectroscopy suffers from low sensitivity when probing minute sample volumes. While complex asymmetric chiral nanostructures can enhance these Circular Dichroic (CD) signals, their fabrication is intricate and costly. In this work, we analyzed an alternative based on extrinsic chirality in achiral square arrays of plasmonic circular NHAs realized via Displacement Talbot Lithography (DTL), thus exploring the chiroptical response arising from symmetry breaking induced by oblique illumination. Unlike isolated nanoparticles, nanohole arrays (NHAs) support propagating Surface Plasmon Polaritons (SPPs), allowing for unique light confinement capabilities essential for high-throughput sensing. A careful characterization in terms of Stokes parameters has been performed over a selected range of different optical angles of incidence and sample orientation to disentangle extrinsic chiral contribution from spurious effects related to sample imperfections. By optimizing such extrinsic chiral contributions, enhanced chiroptical response could be engineered by significantly amplifying the interaction between light and chiral biomolecules trapped within the holes. This methodology establishes DTL-fabricated achiral NHAs as an ultrasensitive, cost-effective platform for the detection and discrimination of enantiomers in biosensing applications. Full article

This study focused on comparing broiler meat quality across different production systems and seasons. Chicken carcasses from intensive, free-range, and dual-purpose poultry systems were analyzed for intrinsic and extrinsic quality traits. The results revealed significant effects of the production system and season. Carcasses from dual-purpose and intensive systems were heavier. Greater carcass weight was recorded in autumn and winter. The mean post-mortem pH of breast and thigh was lower in extensive and dual-purpose systems and significantly lower in winter and spring. Colorimetric parameters varied by system, as higher means of redness (intensive), yellowness (free-range), and lightness (dual-purpose) were observed. Meat from intensive systems was less firm, showed higher levels of unsaturated fatty acids and better oxidation stability. Dual-purpose displayed higher levels of polyunsaturated fatty acids. The interaction effect was significant for most quality parameters. Full article

Optical fiber sensors (OFSs) have emerged as essential tools in the monitoring of physical, chemical, and bio-medical parameters in harsh situations due to their high sensitivity, electromagnetic interference (EMI) immunity, and long-term stability. However, the current literature contains scattered information in most reviews regarding individual sensing technologies or domains. This study provides a structured exploratory review in a novel inter-family analysis of both intrinsic and extrinsic configurations by analyzing more than 23,000 publications between 2019 and 2025 in five key domains: industry, medicine and biomedicine, environmental chemistry, civil/structural engineering, and aerospace. The analysis aims to critically discuss how functional principles/parameters and methods of interrogation affect the applicability of different OFS categories. The results reveal leading trends in the use of techniques like the use of fiber Bragg gratings (FBG) and distributed sensing in high-accuracy conditions or the rising role of extrinsic sensors in selective chemical situations and point out new approaches in areas like Artificial Intelligence (AI)- or Internet of Things (IoT)-integrated sensors. Further, this synthesis not only connects pieces of knowledge but also defines the technological barriers in terms of calibration cost and standardization: this provides strategic insight regarding future research and the scalability of industry deployment. Full article

Although RGB images contain rich details, they lack 3D depth information. Depth data, while providing spatial positioning, is often affected by noise and suffers from sparsity or missing data at key feature points, leading to low accuracy and high computational complexity in traditional visual localization. To address this, this paper proposes a high-precision, sub-pixel-level localization method for workpiece feature points based on RGB-D data fusion. The method specifically targets two types of localization objects: planar corner keypoints and sharp-corner keypoints. It employs the YOLOv10 model combined with a Background Misdetection Filtering Module (BMFM) to classify and identify feature points in RGB images. An improved Prewitt operator (using 5 × 5 convolution kernels in 8 directions) and sub-pixel refinement techniques are utilized to enhance 2D localization accuracy. The 2D feature boundaries are then mapped into 3D point cloud space based on camera extrinsic parameters. After coarse error detection in the point cloud and local quadric surface fitting, 3D localization is achieved by intersecting spatial rays with the fitted surfaces. Experimental results demonstrate that the proposed method achieves a mean absolute error (MAE) of 0.17 mm for localizing flat, free-form, and grooved components, with a maximum error of less than 0.22 mm, meeting the requirements of high-precision industrial applications such as precision manufacturing and quality inspection. Full article

Background/Objectives: The onset of infection in patients in contact with a COVID-19-positive index case in healthcare settings depends on intrinsic factors such as demographic factors, immune status, severity of underlying diseases, and comorbidities. Critical extrinsic factors for transmission, especially in hospitals, are length of exposure and distance. This study aimed to determine the risk factors of COVID-19 infections in contacts of COVID-19 index cases by conducting a prospective cohort study. Methods: The prospective cohort study included 186 index patients with confirmed COVID-19 and their 416 close hospital contacts. All contacts were followed for five days and tested using antigen or RT-PCR assays, with additional follow-up through national registries if discharged earlier. Results: The risk of infection was significantly higher in contacts older than 60 years (p = 0.009), in those hospitalised within orthopaedics and haematology departments (p < 0.001), and in patients whose bed was located within 1.5 m of the index case (p < 0.001). Laboratory findings showed significant associations with lower lymphocytes, glucose and higher potassium and creatinine levels, while other haematological and biochemical parameters did not differ. Hyperkalaemia (RR = 6.2 95%CI = 1.2–32.1 p = 0.30) and bed distance ≥ 1.5 m (RR = 0.3 95%CI = 0.2–0.6 p < 0.001) demonstrated an independent association with COVID-19 infection among contact patients. Conclusions: To reduce nosocomial transmission from unrecognised COVID-19 reservoirs, patients with electrolyte imbalance and lower levels of blood elements should be placed at a greater distance of 1.5 m from others, especially patients in haematology departments. Full article

Royal jelly, a high-value natural product rich in bioactive compounds, is highly susceptible to quality deterioration during storage and processing. However, current quality standards rely predominantly on basic physicochemical parameters and measuring the content of 10-hydroxy-2-decenoic acid (10-HDA), which fail to capture the comprehensive and dynamic nature of its freshness. This significant knowledge gap hinders the accurate assessment, prediction, and control of royal jelly quality throughout its supply chain. To address this limitation, this review systematically elucidates the molecular mechanisms underlying the deterioration of royal jelly freshness, including key pathways such as protein denaturation, Maillard reactions, enzymatic inactivation, and lipid oxidation, and analyzes the combined effects of intrinsic and extrinsic factors on its quality stability. It highlights the potential applications of novel biochemical markers—including major royal jelly proteins (MRJPs), Maillard reaction products, enzymatic activity indicators, and energy metabolites—while comparing the advantages and limitations of traditional chromatographic techniques with modern rapid sensing and spectroscopic analysis methods. Regarding preservation, a critical yet inadequately summarized area, this review systematically evaluates the applicability and limitations of various approaches, including low-temperature storage, drying treatments, non-thermal sterilization, microencapsulation, and modified atmosphere packaging. Future directions for integrated quality control are outlined, providing a theoretical basis for holistic quality management of royal jelly. Full article

Accurate extrinsic calibration between radar and camera sensors is essential for reliable multi-modal perception in robotics and autonomous navigation. Traditional calibration methods often rely on artificial targets such as checkerboards or corner reflectors, which can be impractical in dynamic or large-scale environments. This study presents a fully targetless calibration framework that estimates the rigid spatial transformation between radar and camera coordinate frames by aligning their observed trajectories of a moving object. The proposed method integrates You Only Look Once version 5 (YOLOv5)-based 3D object localization for the camera stream with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Random Sample Consensus (RANSAC) filtering for sparse and noisy radar measurements. A passive temporal synchronization technique, based on Root Mean Square Error (RMSE) minimization, corrects timestamp offsets without requiring hardware triggers. Rigid transformation parameters are computed using Kabsch and Umeyama algorithms, ensuring robust alignment even under millimeter-wave (mmWave) radar sparsity and measurement bias. The framework is experimentally validated in an indoor OptiTrack-equipped laboratory using a Skydio 2 drone as the dynamic target. Results demonstrate sub-degree rotational accuracy and decimeter-level translational error (approximately 0.12–0.27 m depending on the metric), with successful generalization to unseen motion trajectories. The findings highlight the method’s applicability for real-world autonomous systems requiring practical, markerless multi-sensor calibration. Full article

In advanced manufacturing, accurate and reliable 3D geometry measurement is vital for the quality control of large-sized components with multiple small key local features. To obtain both the geometric form and spatial position of these local features, a hybrid robot-assisted laser scanning strategy is introduced, combining a laser tracker, a fringe-projection 3D scanner, and a mobile robotic unit that integrates an industrial robot with an Automated Guided Vehicle. As for improving the overall measurement accuracy, we propose an accuracy-enhanced calibration method that incorporates both error control and compensation strategies. Firstly, an accurate extrinsic parameter calibration method is proposed, which integrates robust target sphere center estimation with distance-constrained-based optimization of local common point coordinates. Subsequently, to construct a high-accuracy, large-scale spatial measurement field, an improved global calibration method is proposed, incorporating coordinate optimization and a hierarchical strategy for error control. Finally, a robot-assisted laser scanning hybrid measurement system is developed, followed by calibration and validation experiments to verify its performance. Experiments verify its high precision over 14 m (maximum error: 0.117 mm; mean: 0.112 mm) and its strong applicability in large-scale scanning of key geometric features, providing reliable data for quality manufacturing of large-scale components. Full article

This study aimed to evaluate the antiproliferative, apoptotic, and oxidative stress-inducing effects of the combination of metformin and doxorubicin (adriamycin) in OVCAR3 and SKOV3 ovarian cancer cell lines and to investigate the potential synergistic interactions between the two agents. Cell viability was assessed using the MTT assay. Apoptosis was quantified via Annexin V/PI staining followed by flow cytometry. Caspase-8 and caspase-9 activities were measured using colorimetric assays. Oxidative stress parameters, including reactive oxygen species (ROS) and nitric oxide (NO), were determined using DCFH-DA fluorescence and the Griess assay, respectively. The mRNA expression levels of apoptosis-related genes (Bcl-2, Survivin, Bax, and Caspase-3) were analyzed by qRT-PCR. Drug interaction and synergy were evaluated using the Chou–Talalay combination index (CI) model and the highest single agent (HSA) model. Prognostic relevance of target genes and protein interaction networks was examined through TCGA and STRING databases. The metformin–doxorubicin combination demonstrated strong synergistic antiproliferative effects in both cell lines (CI < 0.7 in OVCAR3). The combination significantly increased apoptosis compared with single-agent treatments, yielding a total apoptotic rate of 62.5 ± 4.2% in OVCAR3. Caspase-8 and caspase-9 activities were elevated by 5.6 ± 0.7-fold and 7.3 ± 0.8-fold, respectively. Combination treatment also induced marked oxidative stress, increasing NO levels to 12.4 ± 1.1 µM and ROS levels to 412 ± 25% in OVCAR3 cells. qRT-PCR analyses revealed downregulation of anti-apoptotic Bcl-2 (0.28 ± 0.04-fold) and Survivin (0.25 ± 0.03-fold), along with upregulation of pro-apoptotic Bax (5.8 ± 0.6-fold) and Caspase-3 (6.5 ± 0.7-fold). Bioinformatic analyses indicated that high Bcl-2 and Survivin expression correlated with poorer overall survival in ovarian cancer patients. Metformin enhances the anticancer efficacy of doxorubicin through synergistic activation of intrinsic and extrinsic apoptotic pathways, induction of oxidative and nitrosative stress, and transcriptional regulation of key apoptotic markers. These findings support the potential use of metformin as an adjuvant agent to strengthen doxorubicin-based chemotherapy in ovarian cancer. Full article

Digital image correlation (DIC) is widely used for full-field deformation measurement, yet its spatial resolution is often underutilized when measuring slender components due to their high aspect ratio. To address this limitation, a novel virtual multi-camera DIC method that integrates a conventional dual-camera setup with two Michelson shear devices (MSDs) was proposed. Each MSD splits the image of the slender component along its longitudinal direction, projecting two segments side-by-side onto the same camera sensor. This configuration effectively enhances the resolution utilization of each camera, enabling high-resolution measurement of the entire slender surface without requiring additional cameras. The system is calibrated to establish extrinsic parameters between DIC subsystems, allowing stitching of 3D data from different regions. Experimental validation through translation and bending tests demonstrates that the proposed method achieves accurate full-field morphology and deformation measurements, with sub-pixel level agreement in overlapping regions. This approach offers a practical and cost-effective solution for enhancing DIC performance in constrained measurement environments. Full article

Traditional calibration methods rely on precise targets and frequent manual intervention, making them time-consuming and unsuitable for large-scale deployment. Existing learning-based approaches, while automating the process, are typically limited to single LiDAR–camera pairs, resulting in poor scalability and high computational overhead. To address these limitations, we propose a lightweight calibration network with flexibility in the number of sensor pairs, making it capable of jointly calibrating multiple cameras and LiDARs in a single forward pass. Our method employs a frozen pre-trained Swin Transformer as a shared backbone to extract unified features from both RGB images and corresponding depth maps. Additionally, we introduce a cross-modal channel-wise attention module to enhance key feature alignment and suppress irrelevant noise. Moreover, to handle variations in viewpoint, we design a modular calibration head that independently estimates the extrinsics for each LiDAR–camera pair. Through large-scale experiments on the nuScenes dataset, we show that our model, requiring merely 78.79 M parameters, attains a mean translation error of 2.651 cm and a rotation error of $0.{246}^{\circ }$, achieving comparable performance to existing methods while significantly reducing the computational cost. Full article

Background/Objectives: As of April 2025, La Réunion is facing a second major chikungunya virus (CHIKV) outbreak, following the 2005–2006 epidemic that infected nearly one-third of the population. IXCHIQ^®, a live-attenuated, single-dose vaccine, offers an opportunity for targeted immunization to complement vector control efforts. Using surveillance data up to 23 February 2025 (week 7), we estimated the potential scale of the 2024–2025 chikungunya outbreak in La Réunion and how much of the burden could have been averted by an emergency vaccination campaign at different detection thresholds. Methods: A stochastic SEIR–SEI host–vector model was calibrated to weekly case counts (weeks 46/2024–7/2025). We projected the epidemic under three vaccination-trigger scenarios (≥100, ≥3000, ≥40,000 detected cases) and two incremental vector-control assumptions (10% and 20% reductions in biting rate). Several mosquito-related parameters—extrinsic incubation period, offspring number, and mortality rate—were temperature-dependent, based on daily temperatures in La Réunion. Vaccination was applied homogeneously, using a 14.5% coverage to reflect the proportion of the population targeted in the initial public health recommendation. Results: Our findings indicate that without vaccination, up to 27.5% of the population could become infected. If vaccination would begin after 100 detected cases, 75% of infections could be prevented. Delaying until 3000 or 40,000 cases reduced effectiveness to 41% and 11%, respectively. Conclusions: Our results show that timely emergency vaccination can substantially reduce outbreak size. This underscores the importance of preparedness and rapid response by public health authorities in high-risk regions. Full article