Search Results (9,608)

We review the progress in multiplexed single-photon sources, including overviews on heralded single-photon sources and photon-number-resolving detectors, the various approaches to multiplexing, and their successful experimental realizations. We also summarize the recent results on the theoretical description and optimization of multiplexed single-photon sources, focusing on the procedures and methods that enable the improvement of the performance of these sources. Full article

Offshore mariculture is critical for global food security and economic development. Advances in deep learning and data-driven approaches, enable the rapid and effective monitoring of offshore mariculture distribution and changes. However, detector performance depends heavily on training data quality. The lack of standardized classifications and public datasets for offshore mariculture facilities currently hampers effective monitoring. Here, we propose to categorize offshore mariculture facilities into six types: TCC, DWCC, FRC, LC, RC, and BC. Based on these categories, we introduce a benchmark dataset called OMAD-6. This dataset includes over 130,000 instances and more than 16,000 high-resolution remote sensing images. The images with a spatial resolution of 0.6 m were sourced from key regions in China, Chile, Norway, and Egypt, from the Google Earth platform. All instances in OMAD-6 were meticulously annotated manually with horizontal bounding boxes and polygons. Compared to existing remote sensing datasets, OMAD-6 has three notable characteristics: (1) it is comparable to large, published datasets in instances per category, image quantity, and sample coverage; (2) it exhibits high inter-class similarity; (3) it shows significant intra-class diversity in facility sizes and arrangements. Based on the OMAD-6 dataset, we evaluated eight state-of-the-art methods to establish baselines for future research. The experimental results demonstrate that the OMAD-6 dataset effectively represents various real-world scenarios, which have posed considerable challenges for current instance segmentation algorithms. Our evaluation confirms that the OMAD-6 dataset has the potential to improve offshore mariculture identification. Notably, the QueryInst and PointRend algorithms have distinguished themselves as top performers on the OMAD-6 dataset, robustly identifying offshore mariculture facilities even with complex environmental backgrounds. Its ongoing development and application will play a pivotal role in future offshore mariculture identification and management. Full article

This research presents a retrospective analysis of zero-shot object detectors in automating image labeling for eyeglasses detection. The increasing demand for high-quality annotations in object detection is being met by AI foundation models with open-vocabulary capabilities, reducing the need for labor-intensive manual labeling. There is a notable gap in systematic analyses of foundation models for specialized detection tasks, particularly within the domain of facial accessories. Six state-of-the-art models—Grounding DINO, Detic, OWLViT, OWLv2, YOLO World, and Florence-2—were evaluated across three datasets (FFHQ with custom annotations, CelebAMask-HQ, and Face Synthetics) to assess their effectiveness in zero-shot detection and labeling. Performance metrics, including Average Precision (AP), Average Recall (AR), and Intersection over Union (IoU), were used to benchmark foundation models. The results show that Detic achieved the highest performance scores (AP of 0.97 and AR of 0.98 on FFHQ, with IoU values reaching 0.97), making it highly suitable for automated annotation workflows. Grounding DINO and OWLv2 also showed potential, especially in high-recall scenarios. The results emphasize the importance of prompt engineering. Practical recommendations for using foundation models in specialized dataset annotation are provided. Full article

This paper presents a pH sensor with a Mach–Zehnder Interferometer (MZI) that operates in solutions of 4.0, 7.0, and 10.0. The sensor device consists of two tapered sections with dimensions of 1 mm/1 mm/1 mm for down-taper, waist-length, and up-taper, respectively, with a separation of 10 mm. The diameter of the waist is 40 $\mathsf{\mu }$m. This work includes the experimental evaluation of an MZI fiber optic pH sensor at 1559 nm, where 1559 nm represents a specific wavelength chosen for its optimal sensitivity in evaluating the sensor pH detection performance. It is not the central wavelength of the optical fiber, but one of the minimal values selected to enhance the interaction between the evanescent field and the sample, ensuring the reliable detection of pH variations. These sensor dimensions and the functionalized solution of multi-walled carbon nanotubes (MWCNTs) increase the detection of pH in dyes used in the textile industry. Alizarin is a strong anionic red dye that is part of the anthraquinone dye group. The experimental results demonstrated effective detection of pH levels in water contamination involving dye. This development could resolve the problem with Alizarin. The simple fabrication, low cost, and stability of the optical response make this sensor relevant for pH measurements in water contamination. Full article

Alternative options have been studied to mitigate the negative impact of fossil fuel sources, mainly especially when it comes to alternative energy sources. In this work, cannabis residues have been considered as a potential biomass residues for energy recovery due to their energy content, and the increase in the cannabis market in Canada has created an opportunity niche for treating and valorizing these residues as energy. This study thus aims to investigate the potential of energy recovery from cannabis residue pellets via combustion and the impact of steam explosion on the pellets’ properties as well as combustion behavior. Two batches of pellets were produced namely with and without the steam explosion pretreatment. The properties of the pellets were then compared to those of the CANplus certification. Cannabis pellets were then combusted at 290 °C in a fixed-bed reactor using three different air coefficients (α) ranging from 1 to 1.3 (α = 1.0, α = 1.15, and α = 1.3). Flue gas quantification was performed using gas chromatography combined with a NO_x detector. Results showed that the properties of this biomass is comparable to other sources of lignocellulosic biofuels. The steam explosion pretreatment enhanced pellet properties, including higher heating value (HHV), ash content, durability, and fines allowing the product to reach the CANplus requirements. The air coefficients influenced the emission levels, with an optimal value at α = 1.15, that indicated an improved combustion quality. However, steam explosion negatively affected combustion efficiency, resulting in incomplete combustion. Overall, cannabis residues show a strong potential for energy recovery and could offer a sustainable option for bioenergy applications. Full article

This study introduces an innovative in situ lander/impact-penetrator design tailored for Discovery-class missions to Europa, specifically focused on conducting astrobiological analyses. The platform integrates a microfluidic capacitively coupled contactless conductivity detector (C4D), optimized for the detection of low-concentration salts potentially indicative of biological activity. Our microfluidic system allows for automated sample routing and precise conductivity-based detection, making it suitable for the harsh environmental and logistical demands of Europa’s icy surface. This technology provides a robust toolset for exploring extraterrestrial habitability by enabling in situ chemical analyses with minimal operational intervention, paving the way for advanced astrobiological investigations on Europa. Full article

Object detection often struggles with accurately identifying visually similar parts, a challenge commonly faced in industrial applications. To address this issue, we propose a clustering methodology based on the visual similarity of 3D object models. This approach is particularly effective when integrated with synthetic image generation, as both processes rely on 3D models. In this case study, we observed more than a 20% increase in classification performance on two different object detector architectures on a validation dataset when training an object detector on visually similar groups rather than on all classes, suggesting the potential of our method as a baseline for a multi-stage classification scheme. Full article

In the study of dual asteroid systems, a model that can rapidly compute the motion and orientation of these bodies is essential. Traditional modeling techniques, such as the double ellipsoid or polyhedron methods, fail to deliver sufficient accuracy in estimating the interactions between dual asteroids. This inadequacy primarily stems from the non-tidally locked nature of asteroid systems, which necessitates continual adjustments to account for changes in gravitational fields. This study adopts the finite element method to precisely model the dynamic interaction forces within irregular, time-varying dual asteroid systems and, thereby, enhance the planning of spacecraft trajectories. It is possible to derive the detailed characteristics of a spacecraft’s orbital patterns via the real-time monitoring of spacecraft orbits and the relative positions of dual asteroids. Furthermore, this study examines the orbital stability of a spacecraft under various trajectories, revealing that orbital stability is intrinsically linked to the geometric configuration of the orbits. And considering the influence of solar pressure on the orbit of asteroid detectors, a method was proposed to characterize the stability of detector orbits in the time-varying gravitational field of binary asteroids using cloud models. The insights gained from the analysis of orbital characteristics can inform the design of landing trajectories for binary asteroid systems and provide data for deep learning algorithms that are aimed at optimizing such orbits. By introducing the application of the finite element method, detailed analysis of spacecraft orbit characteristics, and a stability characterization method based on a cloud model, this paper systematically explores the logic and structure of spacecraft orbit planning in a dual asteroid system. Full article

The article presents an intelligent temperature transducer (ITT), which can work with a two-wire or a three-wire platinum resistance temperature detector (RTD). The ITT design allowed for compensation of the RTD’s lead wire resistance. The ITT used the author’s auto-calibration procedure, which minimized linearity errors of the ITT and RTD processing characteristics, ITT offset and gain errors, and errors resulting from changes in the ITT operating conditions concerning the nominal conditions. The presented results of a simulation and experimental studies confirmed the high effectiveness of this procedure. The determined uncertainty of temperature measurement using the Monte Carlo method and the obtained experimental results confirmed the possibility of measuring temperatures in the range of 0–200 °C with an expanded uncertainty of 0.02 °C at a 99% confidence level. Full article

Since Einstein’s prediction regarding the existence of gravitational waves was directly verified by the ground-based detector Advanced LIGO, research on gravitational wave detection has garnered increasing attention. To overcome limitations imposed by ground vibrations and interference at arm’s length, a space-based gravitational wave detection initiative was proposed, which focuses on analyzing a large number of waves within the frequency range below 1 Hz. Due to the weak signal intensity, the TMs must move along their geodesic orbit with a residual acceleration less than 10⁻¹⁵ m/s²/Hz^1/2. Consequently, the core payload-inertial sensor was designed to shield against stray force noise while maintaining the high-precision motion of the test mass through a drag-free control system, providing an ultra-stable inertial reference for laser interferometry. To meet these requirements, the inertial sensor integrates a series of unit settings and innovative designs, involving numerous subsystems and technologies. This article provides a comprehensive overview of these critical technologies used in the development of inertial sensors for space gravitational wave detection and discusses future trends and potential applications for these sensors. Full article

We propose a self-training scheme, SURABHI, that trains deep-learning keypoint detection models on machine-annotated instances, together with the methodology to generate those instances. SURABHI aims to improve the keypoint detection accuracy not by altering the structure of a deep-learning-based keypoint detector model but by generating highly effective training instances. The machine-annotated instances used in SURABHI are hard instances—instances that require a rectifier to correct the keypoints misplaced by the keypoint detection model. We engineer this scheme for the task of predicting keypoints of cattle from the top, in conjunction with our Eidetic Cattle Recognition System, which is dependent on accurate prediction of keypoints for predicting the correct cow ID. We show that the final cow ID prediction accuracy on previously unseen cows also improves significantly after applying SURABHI to a deep-learning detection model with high capacity, especially when available training data are minimal. SURABHI helps us achieve a top-6 cow recognition accuracy of 91.89% on a dataset of cow videos. Using SURABHI on this dataset also improves the number of cow instances with correct identification by 22% over the baseline result from fully supervised training. Full article

The accuracy assessment of cropland products is a critical prerequisite for agricultural planning and food security evaluations. Current accuracy assessments of remote sensing-based cropland products focused on the consistency of spatial patterns for specific years, yet the reliability of these cropland products in time-series analysis remains unclear. Using cropland area data from the second and third national land surveys of China (referred to as NLSCD) as a benchmark, we evaluate the area-based and spatial-based consistency of cropland changes in five 30 m time-series land cover products covering 2010 and 2020, including the annual cropland dataset of China (CACD), the annual China Land Cover Dataset (CLCD), China’s Land-use/cover dataset (CLUD), the Global Land-Cover product with Fine Classification System (GLC_FCS30), and GlobeLand30. We also employed the GeoDetector model to explore the relationships between the consistency in cropland change and the environmental factors (e.g., cropland fragmentation, topographic features, frequency of cloud cover, and management practices). The area-based consistency analysis showed that all five cropland products indicate a declining trend in cropland areas in China over the past decade, while the amount of cropland loss ranges from 5.59% to 57.85% of that reported by the NLSCD. At the prefecture-level city scale, the correlation coefficients between the cropland area changes detected by five cropland products and the NLSCD are low, with GlobeLand30 having the highest coefficient at 0.67. The proportion of prefecture-level cities where the change direction of cropland area in each cropland product is inconsistent with the NLSCD ranges from 13.27% to 39.23%, with CLCD showing the highest proportion and CLUD the lowest. At the pixel scale, the spatial-based consistency analysis reveals that 79.51% of cropland expansion pixels and 77.79% of cropland loss pixels are completely inconsistent across five cropland products, with the southern part of China exhibiting greater inconsistency compared to Northwest China. Besides, the frequency of cloud cover and management practices (e.g., irrigation) are the primary environmental factors influencing consistency in cropland expansion and loss, respectively. These results suggest low consistency in cropland change across five cropland products, emphasizing the need to address these inconsistencies when generating time-series cropland datasets via remote sensing. Full article

Aiming at the difficulty of feature extraction in complex environments during mulberry detection and the need for embedded devices to lighten the model, this study carries out lightweight improvements on the basis of the YOLOv8n model. First, the CSPPC module incorporates lightweight partial convolution (PConv) within its bottleneck structure, replacing the C2f module to enhance feature extraction efficiency. Secondly, the ADown module is used to replace the traditional downsampling module and the P-Head module is used to replace the traditional convolutional detector head with the partial convolutional detector head. Finally, a knowledge distillation technique is used to compensate for the loss of accuracy due to parameter reduction. Ablation experiments are conducted to evaluate the impact of each module on the model’s performance. The experimental results show that the improved YOLOv8 model has a precision of 88.9%, a recall of 78.1%, and an average precision of 86.8%. The number of parameters is 1.29 × 10⁶, the model size is 2.6 MB, the floating-point computation is 2.6 GFLOPs, and the frame rate reaches 19.84 FPS on the edge end. Therefore, this model provides theoretical and technical support for the deployment and application of mobile detection devices, such as automatic mulberry harvesting in practical scenarios. Full article

This review aimed to explore advances in radon detection methods, emphasizing cost-effectiveness and accessible techniques such as CDs, DVDs, and glass-based detectors. In this review, we compared traditional methods like alpha track detectors and continuous radon monitors with emerging innovations that leverage polycarbonate material and IoT-integrated systems. Our evaluation of the synthesis suggests that CDs and DVDs provide scalable solutions for long-term radon monitoring, while glass-based detectors like CR-39 offer high sensitivity for epidemiological studies. The integration of IoT and AI technologies further enhances real-time radon monitoring, paving the way for precise, scalable, and affordable radon mitigation strategies. This work highlights the importance of low-cost, innovative approaches in reducing radon-related lung cancer risks and informs future research on optimizing the technologies for diverse environments. Full article

Changing trends in the brewing market show that breweries want to attract consumers with new products. New flavours and aromas in beer can be achieved by using various additives. However, non-Saccharomyces yeast strains make it possible to produce beer with an original sensory profile but according to a traditional recipe (without additives). The aim of this study was to evaluate the influence of 10 different yeast strains, belonging to the species Lachancea thermotolerans and L. fermentati, on the creation of different physico-chemical profiles in beers. For this purpose, the same malt wort with a 12°P extract, hopped with Octawia hops (8.4% alpha acids), was inoculated with the aforementioned yeast strains. The fermentation kinetics, the yeast’s ability to ferment sugars, the production of organic acids and glycerol and the formation of volatile compounds in the beer were monitored. The beers obtained were classified as low-alcohol and regular. In addition, some beers were measured to have a low pH, qualifying them as “sour” beers, which are currently gaining in popularity. Most interesting, however, was the effect of the selected Lachancea yeast strains on the composition of the beer volatiles. In the second stage of this study, the beers obtained were again subjected to a chromatographic analysis, this time using an olfactometric detector (GC-O). This analysis was dictated by the need to verify the actual influence of the compounds determined (GC-MS) on the creation of the final aroma profile. This study showed that selected strains of Lachancea thermotolerans and L. fermentati have very high brewing potential to produce different original beers from the same hopped wort. Full article