Search Results (5,874)

The storage of thermal energy has been hindered by the low heat-transfer rate of the solid phase of the phase-changing materiel. With water being the heat-transfer fluid as well as the liquid phase in the liquid–solid two-phase system, a novel type of fluidized bed is designed in this study. Numerous hollow spheres are fabricated with phase-changing materiel encapsulated. Adding the solid–liquid phase-change material capsules to the flowing fluid, the capsules are dispersed suspended in the carrier. The large spheres, 25 mm in present experiment, possess the merits of guaranteeing energy-storage density and tolerating internal interface chaotic motion. Both the fluidization status and phase-changing process are recorded by photography combined with image-processing technology. It is found that the large spheres, with density less than water, can be fluidized by the downward flowing fluid. As the flow rate increases, the expansion ratio of the solid phase increases and the regimes of incipient fluidization and bubbling fluidization can be observed. In comparison to the fixed bed, the oscillation of pressure drop across a fluidized bed is more severe, but the averaged value is less than the fixed bed. The melting and solidifying can be accelerated by 22.6% and 50%, respectively, thus proving the superiority of the fluidized bed in improving the heat-transfer rate while charging/discharging the thermal energy. Three types of basic movement of the spheres are shown to contribute to the enhanced phase-changing rate, which are shifting, colliding and rotating. Full article

This paper introduces WaterGPT, a language model designed for complex multimodal tasks in hydrology. WaterGPT is applied in three main areas: (1) processing and analyzing data such as images and text in water resources, (2) supporting intelligent decision-making for hydrological tasks, and (3) enabling interdisciplinary information integration and knowledge-based Q&A. The model has achieved promising results. One core aspect of WaterGPT involves the meticulous segmentation of training data for the supervised fine-tuning phase, sourced from real-world data and annotated with high quality using both manual methods and GPT-series model annotations. These data are carefully categorized into four types: knowledge-based, task-oriented, negative samples, and multi-turn dialogues. Additionally, another key component is the development of a multi-agent framework called Water_Agent, which enables WaterGPT to intelligently invoke various tools to solve complex tasks in the field of water resources. This framework handles multimodal data, including text and images, allowing for deep understanding and analysis of complex hydrological environments. Based on this framework, WaterGPT has achieved over a 90% success rate in tasks such as object detection and waterbody extraction. For the waterbody extraction task, using Dice and mIoU metrics, WaterGPT’s performance on high-resolution images from 2013 to 2022 has remained stable, with accuracy exceeding 90%. Moreover, we have constructed a high-quality water resources evaluation dataset, EvalWater, which covers 21 categories and approximately 10,000 questions. Using this dataset, WaterGPT achieved the highest accuracy to date in the field of water resources, reaching 83.09%, which is about 17.83 points higher than GPT-4. Full article

Background/Objectives: Adolescents with idiopathic scoliosis require emotional support to change their experience of their desired body shape and to feel optimistic about the cosmetic results of surgical treatment. Recently, the use of virtual reality in psychological assessment and treatment has given specialists a technology that appears particularly well-suited for addressing body image disorders. The study objectives were two-fold. Firstly, we aimed to evaluate changes within the body image of scoliosis patients pre- and postoperatively. Secondly, we aimed to investigate if differences in body image exist in scoliosis females after implementing cognitive-behavioral therapy. Methods: Thirty-six total scoliosis patients participated in the 1st and 2nd study phases. The psychotherapy took place before and after surgery and during the patient’s stay in the hospital. Body image was assessed using a virtual reality-based application, “Avatar Scoliosis 3D”. Results: Regarding body image dissatisfaction evaluated via virtual tasks, the difference between the desired by patients and actual (based on the radiographic parameters) body shape is significant preoperatively in both scoliosis samples: with and without therapy (p < 0.000001 and p < 0.000001, respectively). Conclusions: The results of the present study may have important implications for developing standards for body image disorder treatments in scoliosis patients. We revealed that irrespective of received therapeutic support, scoliosis patients accurately estimate their body shape pre- and postoperatively, and they feel dissatisfied with their body preoperatively but not postoperatively. Full article

Cavitation is a phase-change phenomenon from the liquid to the gas phase due to an increased flow velocity. As it causes severe erosion and noise, it is harmful to hydraulic machinery such as pumps, valves, and screw propellers. However, it can be utilized for water treatment, in chemical reactors, and as a mechanical surface treatment, as radicals and impacts at the point of cavitation bubble collapse can be utilized. Mechanical surface treatment using cavitation impacts is called “cavitation peening”. Cavitation peening causes less pollution because it uses water to treat the mechanical surface. In addition, cavitation peening improves on traditional methods in terms of fatigue strength and the working life of parts in the automobile, aerospace, and medical fields. As cavitation bubbles are utilized in cavitation peening, the study of cavitation bubbles has significant value in improving this new technique. To achieve this, many numerical analyses combined with field experiments have been carried out to measure the stress caused by bubble collapse and rebound, especially when collapse occurs near a solid boundary. Understanding the mechanics of bubble collapse can help to avoid unnecessary surface damage, enabling more accurate surface preparation, and improving the stability of cavitation peening. The present study introduces three cavitation bubble types: single, cloud, and vortex cavitation bubbles. In addition, the critical parameters, governing equations, and high-speed camera images of these three cavitation bubble types are introduced to support a broader understanding of the collapse mechanism and characteristics of cavitation bubbles. Then, the results of the numerical and experimental analyses of non-spherical cavitation bubbles are summarized. Full article

This paper presents an advanced workflow for processing radar imagery stacks using Persistent Scatterer and Distributed Scatterer Interferometry (PSDS) to enhance spatial coherence and improve displacement detection accuracy. The workflow leverages Level 2 Coregistered Single Look Complex (L2-CSLC) images generated by the open-source COMPASS (Coregistered Multi-temporal Sar SLC) framework in combination with the Combined eigenvalue maximum likelihood Phase Linking (CPL) approach implemented in MiaplPy. Starting the analysis directly from Level 2 products offers a significant advantage to end-users, as they simplify processing by being pre-geocoded and ready for immediate analysis. Additionally, the open-source nature of the workflow and the use of L2-CSLC products simplify the processing pipeline, making it easier to distribute directly to users for practical applications in monitoring infrastructure stability in dynamic environments. The ISCE3-MiaplPy workflow is compared against ISCE2-MiaplPy and the European Ground Motion Service (EGMS) to assess its performance in detecting infrastructure deformations in dynamic environments, such as the Algeciras port. The results indicate that ISCE3-MiaplPy delivers denser measurements, albeit with increased noise, compared to its counterparts. This higher resolution enables a more detailed understanding of infrastructure stability and surface dynamics, which is critical for environments with ongoing human activity or natural forces. Full article

The waveguide chip with a phase measurement function has garnered significant attention in the field of imaging optics, emerging as a crucial component in optical interferometric imaging systems. Enhancing the working bandwidth of these waveguide chips is essential for improving the imaging quality of interferometric systems. However, most existing designs primarily focus on narrow bands, with no reported research on broadband designs. This paper introduces a novel broadband waveguide chip design that incorporates a phase measurement function. We explore the fundamental structure and working principle of this innovative design. Fabricated on a silicon substrate, the chip features a silicon dioxide cladding layer and a germanium-doped silicon dioxide core layer, strategically optimized for performance. Utilizing the Beam Propagation Method (BPM), we conduct detailed simulations to determine the optimal device parameters. The simulation results demonstrate the effectiveness of our design, showing a phase measurement deviation of approximately 5° at a center wavelength of 1550 nm across a 300 nm wavelength range. The loss of the device is approximately 0.8 dB. These findings provide a solid foundation for future experimental implementations and fabrications, offering both a theoretical framework and technical reference for advancing the practical use of broadband waveguide chips with phase measurement functions in optical interferometric imaging. Full article

As a technological advantage of ultrasonic non-destructive testing, fully focused imaging can accurately feedback the defective characteristics of the inspected object, greatly improving the detection efficiency. This article aims to address the challenges of outdated and low detection rates in the detection technology of branch pipe fillet welds. The full matrix acquisition (FMC) and total focus method (TFM) ultrasonic detection technology are used for detection and defect image feature analysis. Firstly, a multi-mode, fully focused real-time imaging software system was developed to address the specificity of the detection object; secondly, a phased array detection system based on 64 elements was constructed; finally, a region wavelet denoising method based on TFM images was proposed to solve the problem of artifacts caused by poor coupling; and based on the feature extraction method for a minimum rectangle, we analyzed the size, position, angle, and other information regarding defects. Through experiments, it has been found that this technology can effectively improve the detection efficiency of branch pipe weld defects, with a detection rate of 100%. Based on the partition fusion denoising method, the defect imaging quality can be further improved; at the same time, based on the feature extraction method, the error is 0.1 mm, the length range of various defects is 2.3 mm–6.3 mm, the width range is 0.6 mm–0.8 mm, and the angle range is 52°–75°, which can provide an application basis for the localization, classification, and risk assessment of corner weld defects in branch pipes. Full article

Compared with the amplitude modulation of conventional interrupted sampling repeater jamming (ISRJ), the image feature control method based on phase modulation exhibits greater energy efficiency and, therefore, has received wide attention recently. In this paper, an Inverse Synthetic Aperture Radar (ISAR) image feature suppression method based on arbitrary phase encoding (APE) is proposed. The parameter design criterion is further analyzed. Through the nonperiodic segmented coding and modulation of the imaging signal in fast and slow time domains, the modulated signal produces a two-dimensional suppression region with uniform energy distribution in the ISAR image. Simulations via the measured Yak-42 aircraft data have verified the effectiveness of the proposed method for target feature control. Compared to binary phase modulation jamming, the APE method with a phase modulation accuracy of 1 degree can achieve the same jamming effect while reducing the jamming power requirement by 3 dB. By optimizing with the proposed method, the image entropy of the interfered image increases by 1.1 to 1.5 compared to the original image. Full article

Single-resolution homography estimation of infrared and visible images is a significant and challenging research area within the field of computing, which has attracted a great deal of attention. However, due to the large modal differences between infrared and visible images, existing methods are difficult to stably and accurately extract and match features between the two image types at a single resolution, which results in poor performance on the homography estimation task. To address this issue, this paper proposes an end-to-end unsupervised single-resolution infrared and visible image homography estimation method based on graph neural network (GNN), homoViG. Firstly, the method employs a triple attention shallow feature extractor to capture cross-dimensional feature dependencies and enhance feature representation effectively. Secondly, Vision GNN (ViG) is utilized as the backbone network to transform the feature point matching problem into a graph node matching problem. Finally, this paper proposes a new homography estimator, residual fusion vision graph neural network (RFViG), to reduce the feature redundancy caused by the frequent residual operations of ViG. Meanwhile, RFViG replaces the residual connections with an attention feature fusion module, highlighting the important features in the low-level feature graph. Furthermore, this model introduces detail feature loss and feature identity loss in the optimization phase, facilitating network optimization. Through extensive experimentation, we demonstrate the efficacy of all proposed components. The experimental results demonstrate that homoViG outperforms existing methods on synthetic benchmark datasets in both qualitative and quantitative comparisons. Full article

This paper deals with the imaging problem from data collected by means of a microwave photonics-based distributed radar network. The radar network is leveraged on a centralized architecture, which is composed of one central unit (CU) and two transmitting and receiving dual-band remote radar peripherals (RPs), it is capable of collecting monostatic and multistatic phase-coherent data. The imaging is herein formulated as a linear inverse scattering problem and solved in a regularized way through the truncated singular value decomposition inversion scheme. Specifically, two different imaging schemes based on an incoherent fusion of the tomographic images or a fully coherent data processing are herein developed and compared. Experimental tests carried out in a port scenario for imaging both a stationary and a moving target are reported to validate the imaging approach. Full article

Background: Lung cancer, also known as lung carcinoma, has a high mortality rate; however, an early prediction helps to reduce the risk. In the current literature, various approaches have been developed for the prediction of lung carcinoma (at an early stage), but these still have various issues, such as low accuracy, high noise, low contrast, poor recognition rates, and a high false-positive rate, etc. Thus, in this research effort, we have proposed an advanced algorithm and combined two different types of deep neural networks to make it easier to spot lung melanoma in the early phases. Methods: We have used WDSI (weakly supervised dense instance-level lung segmentation) for laborious pixel-level annotations. In addition, we suggested an SS-CL (deep continuous learning-based deep neural network) that can be applied to the labeled and unlabeled data to improve efficiency. This work intends to evaluate potential lightweight, low-memory deep neural net (DNN) designs for image processing. Results: Our experimental results show that, by combining WDSI and LSO segmentation, we can achieve super-sensitive, specific, and accurate early detection of lung cancer. For experiments, we used the lung nodule (LUNA16) dataset, which consists of the patients’ 3D CT scan images. We confirmed that our proposed model is lightweight because it uses less memory. We have compared them with state-of-the-art models named PSNR and SSIM. The efficiency is 32.8% and 0.97, respectively. The proposed lightweight deep neural network (DNN) model archives a high accuracy of 98.2% and also removes noise more effectively. Conclusions: Our proposed approach has a lot of potential to help medical image analysis to help improve the accuracy of test results, and it may also prove helpful in saving patients’ lives. Full article

Phase carries crucial information about the light propagation process, and the visualization and quantitative measurement of phase have important applications, ranging from ultra-precision metrology to biomedical imaging. Traditional phase measurement techniques typically require large and complex optical systems, limiting their applicability in various scenarios. Optical metasurfaces, as flat optical elements, offer a novel approach to phase measurement by manipulating light at the nanoscale through light-matter interactions. Metasurfaces are advantageous due to their lightweight, multifunctional, and easy-to-integrate nature, providing new possibilities for simplifying traditional phase measurement methods. This review categorizes phase measurement techniques into quantitative and non-quantitative methods and reviews the advancements in metasurface-based phase measurement technologies. Detailed discussions are provided on several methods, including vortex phase contrast, holographic interferometry, shearing interferometry, the Transport of Intensity Equation (TIE), and wavefront sensing. The advantages and limitations of metasurfaces in phase measurement are highlighted, and future research directions are explored. Full article

During the last decades photovoltaic solar energy has continuously increased its share in the electricity mix and has already surpassed 5% globally. Even though photovoltaic (PV) installations are considered to require very little maintenance, their efficient exploitation relies on accounting for certain environmental factors that affect energy generation. One of these factors is the soiling of the PV surface, which could be observed in different forms, such as dust and bird droppings. In this study, visible spectrum data and machine learning algorithms were used for the identification of soiling. A methodology for preprocessing the images is proposed, which puts focus on any soiling of the PV surface. The performance of six classification machine learning algorithms is evaluated and compared—convolutional neural network (CNN), support vector machine (SVM), random forest (RF), k-nearest neighbor (kNN), naïve-Bayes, and decision tree. During the training and validation phase, RF proved to be the best-performing model with an F1 score of 0.935, closely followed by SVM, CNN, and kNN. However, during the testing phase, the trained CNN achieved the highest performance, reaching F1 = 0.913. SVM closely followed it with a score of 0.895, while the other two models returned worse results. Some results from the application of the optimal model after specific weather events are also presented in this study. They confirmed once again that the trained convolutional neural network can be successfully used to evaluate the soiling state of photovoltaic surfaces. Full article

As information systems become more widespread, data security becomes increasingly important. While traditional encryption methods provide effective protection against unauthorized access, they often struggle with multimedia data like images and videos. This necessitates specialized image encryption approaches. With the rise of mobile and Internet of Things (IoT) devices, lightweight image encryption algorithms are crucial for resource-constrained environments. These algorithms have applications in various domains, including medical imaging and surveillance systems. However, the biggest challenge of lightweight algorithms is balancing strong security with limited hardware resources. This work introduces a novel nonlinear matrix permutation approach applicable to both confusion and diffusion phases in lightweight image encryption. The proposed method utilizes three different chaotic maps in harmony, namely a 2D Zaslavsky map, 1D Chebyshev map, and 1D logistic map, to generate number sequences for permutation and diffusion. Evaluation using various metrics confirms the method’s efficiency and its potential as a robust encryption framework. The proposed scheme was tested with 14 color images in the SIPI dataset. This approach achieves high performance by processing each image in just one iteration. The developed scheme offers a significant advantage over its alternatives, with an average NPCR of 99.6122, UACI of 33.4690, and information entropy of 7.9993 for 14 test images, with an average correlation value as low as 0.0006 and a vast key space of $2^{800}$. The evaluation results demonstrated that the proposed approach is a viable and effective alternative for lightweight image encryption. Full article

Early biofilm formation could be inhibited by applying a thin biocompatible copper coating to reduce periprosthetic infections. In this study, we deposited crystalline Cu-doped TiO₂ films using one-step DC magnetron sputtering in an oxygen atmosphere on a biased Ti6Al4V alloy without external heating. The bias voltage varied from −25 V to −100 V, and the resultant substrate temperature was measured. The deposited coatings were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), microhardness, scratch and hydrophilicity tests, potentiodynamic polarization measurements, and antibacterial assays against S. aureus and E. coli. The findings demonstrated that when a higher negative bias is applied, the substrate temperature drops, and the anatase to rutile transformation is initiated without indicating obvious Cu-containing phases. The SEM images of the films showed spherical agglomerates with homogeneously distributed Cu with decreasing Cu content as the bias value increased. Higher bias results in the grain refinement of the thinning coatings with more lattice microstrain and more defects, together with an increase in water contact angles and hardness values. Samples biased at −75 V exhibited the highest adhesive strength between coatings and substrate, whereas the specimen biased at −50 V demonstrated higher corrosion resistance. Cu-containing TiO₂ coatings with pure anatase phase composition and Cu concentrations of 2.62 wt.% demonstrated excellent bactericidal activity against both S. aureus and E. coli. The layers containing 2.34 wt.% Cu exhibited very good antibacterial properties against S. aureus, only. According to these findings, the produced copper-doped TiO₂ coatings have high bactericidal qualities in vitro and may be used to prepare orthopaedic and dental implants in the future. Full article