Explore the vast range of titles available.

High-pressure liquid CO[sub.2] jets possess the characteristics of low-temperature cooling and dry, residue-free impact, which makes this technology particularly suitable for removing hydroxyl-terminated polybutadiene (HTPB) propellant from decommissioned solid rocket motors. However, existing studies lack multi-objective optimization of impact efficiency and CO[sub.2] consumption, which limits their engineering applications and further promotion. In this study, a high-accuracy quadratic Response Surface Methodology (RSM) relating process parameters to damaged volume was established using a Box–Behnken design (BBD) combined with three-dimensional topography scanning. A theoretical model for CO[sub.2] consumption was developed based on the Homogeneous Equilibrium Model (HEM). On this basis, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used to obtain the Pareto-optimal set for maximizing propellant damaged volume and minimizing CO[sub.2] consumption. The results indicate that nozzle diameter has the most significant effect on damaged volume and exhibits a strong interaction with jet pressure. The knee-point solution gives a jet pressure of 15.35 MPa, a stand-off distance of 5 mm, and a nozzle diameter of 1.8 mm. Compared with the initial condition, this compromise condition increases the damaged volume by 72% while increasing CO[sub.2] consumption by only 4.9%. Furthermore, the temperature in the impact zone was reduced to a minimum of −92.4 °C, with no thermal accumulation observed. These findings reveal the influence of liquid CO[sub.2] jet process parameters on impact efficiency and CO[sub.2] consumption, providing a theoretical basis and parameter references for its engineering application in the safe removal of propellants from decommissioned solid rocket motors.

The Chinese H[alpha] Solar Explorer (CHASE), dubbed \"Xihe\"--Goddess of the Sun, was launched on October 14, 2021 as the first solar space mission of China National Space Administration (CNSA). The CHASE mission is designed to test a newly developed satellite platform and to acquire the spectroscopic observations in the H[alpha] waveband. The H[alpha] Imaging Spectrograph (HIS) is the scientific payload of the CHASE satellite. It consists of two observational modes: raster scanning mode and continuum imaging mode. The raster scanning mode obtains full-Sun or region-of-interest spectral images from 6559.7 to 6565.9 [Angstrom] and from 6567.8 to 6570.6 [Angstrom] with 0.024 [Angstrom] pixel spectral resolution and 1 min temporal resolution. The continuum imaging mode obtains photospheric images in continuum around 6689 [Angstrom] with the full width at half maximum of 13.4 [Angstrom]. The CHASE mission will advance our understanding of the dynamics of solar activity in the photosphere and chromosphere. In this paper, we present an overview of the CHASE mission including the scientific objectives, HIS instrument overview, data calibration flow, and first results of on-orbit observations. space-based telescope, solar physics, chromosphere, photosphere PACS number(s): 95.55.Fw, 96.60.-j, 96.60.Na, 96.60.Mz

A method is described to achieve accurate quantitative detection of atrazine (ATZ) in maize by using lateral flow strips based on gold nanoparticles (GNPs) and a handheld scanning reader. GNPs of 15 nm in diameter were applied as label, and a lateral flow immune assay strip was prepared. The linear range was 5.01–95.86 ng mL −1 with a detection limit of 4.92 ng mL −1 in phosphate buffer, 4 times better than the readout by the naked eye. ATZ-spiked corn samples were also analysed. The accuracy of results of spiked samples was confirmed by ELISA and liquid chromatography-tandem mass spectrometry (HPLC), which proved the reliability of the proposed method. A handhold device with an optical scanning system was designed for on-site quantitative detection. Combined with the pretreatment, the assay could be completed in less than 20 min. Graphical abstract

Efficient inventory management remains a persistent challenge in research settings, where productivity and budget control are tightly linked to accurate tracking of consumables, reagents, and equipment for decision-making and successful research project execution. Many laboratories still rely on traditional methods, including handwritten logs or static spreadsheets, which are prone to human error and can result in stockouts, over-ordering, and inaccurate forecasting. Moreover, existing inventory systems often fall short in balancing affordability, convenience, and functionality. Here, the Laboratory Inventory Management Engine (LIME) is introduced as a system designed to bridge these deficiencies by leveraging accessible tools like in-the-cloud spreadsheet files and mobile devices. LIME uses a smartphone app to enable real time updates to the inventory log by scanning consumables QR codes and barcodes. The system allows inputting new items to be classified in predetermined catalog lists (e.g., 4º fridge, chemicals, antibodies, etc.). This dual smartphone and spreadsheet inventory prototype is anticipated to improve the operational efficiency of small academic research laboratories and early-stage startups to benefit their bottom lines.

To evaluate the antibacterial performance of laser-induced periodic surface structures (LIPSS) formed on SUS430 stainless steel (equivalent to AISI 430, ferritic stainless steel), surface patterns were fabricated using femtosecond laser pulses (wavelength: 1030 nm; pulse duration: 150 fs) with controlled fluence. Two types of LIPSS were produced with average ridge and valley widths of approximately 410-nm and 380-nm/750-nm mixed structure, respectively. Antibacterial performance was assessed against Escherichia coli using the film-attachment method in accordance with the JIS Z 2801:2021 standard. Compared with the untreated SUS430 surface, the 410-nm LIPSS surface exhibited a reduction of approximately 83% in bacterial colony count, while the 380-nm/750-nm mixed structure showed a reduction of only about 31%. Fast Fourier transform analysis of scanning electron microscopy images revealed that the 410-nm structures had finer ridge and valley widths and higher structural uniformity. These findings suggest that the geometric characteristics of LIPSS, particularly ridge and valley width and uniformity, play a crucial role in enhancing antibacterial performance. This study demonstrates the potential of femtosecond laser processing as a novel approach to impart antibacterial functionality to material surfaces without the use of chemical agents. Keywords: femtosecond laser, stainless steel, LIPSS, antibacterial performance

Paper-based artworks are prone to natural aging processes driven by chemical and biological processes. Numerous treatments have been developed to mitigate deterioration and prevent irreversible damage. In this study, we investigated the use of poly(acrylic acid)/TiO[sub.2] composite hydrogels, combining their cleaning and protective functions in a minimally invasive treatment. Hydrogels allow for controlled water flow and photocatalytic TiO[sub.2] nanoparticles enhance the hydrogel’s efficacy by enabling the removal of oxidation products and inactivating biological contaminants. Furthermore, this innovative material can act as a protective coating against UV-induced aging, preserving both color and stability of the paper. Raman spectroscopy and confocal laser scanning microscopy imaging techniques were employed to evaluate the treatments, allowing for us to differentiate between hydrolytic and oxidative aging processes. Our findings demonstrate that papers coated with poly(acrylic acid)/TiO[sub.2] composite hydrogels exhibit significant reductions in oxidative markers, an enhanced color stability, and an overall improved resistance to degradation compared to uncoated samples.

With the development of Computer-aided Diagnosis (CAD) and image scanning techniques, Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis. Therefore, WSI analysis has become the key to modern digital histopathology. Since 2004, WSI has been used widely in CAD. Since machine vision methods are usually based on semi-automatic or fully automatic computer algorithms, they are highly efficient and labor-saving. The combination of WSI and CAD technologies for segmentation, classification, and detection helps histopathologists to obtain more stable and quantitative results with minimum labor costs and improved diagnosis objectivity. This paper reviews the methods of WSI analysis based on machine learning. Firstly, the development status of WSI and CAD methods are introduced. Secondly, we discuss publicly available WSI datasets and evaluation metrics for segmentation, classification, and detection tasks. Then, the latest development of machine learning techniques in WSI segmentation, classification, and detection are reviewed. Finally, the existing methods are studied, and the application prospects of the methods in this field are forecasted.

Digital pathology (DP) is being increasingly employed in cancer diagnostics, providing additional tools for faster, higher-quality, accurate diagnosis. The practice of diagnostic pathology has gone through a staggering transformation wherein new tools such as digital imaging, advanced artificial intelligence (AI) algorithms, and computer-aided diagnostic techniques are being used for assisting, augmenting and empowering the computational histopathology and AI-enabled diagnostics. This is paving the way for advancement in precision medicine in cancer. Automated whole slide imaging (WSI) scanners are now rendering diagnostic quality, high-resolution images of entire glass slides and combining these images with innovative digital pathology tools is making it possible to integrate imaging into all aspects of pathology reporting including anatomical, clinical, and molecular pathology. The recent approvals of WSI scanners for primary diagnosis by the FDA as well as the approval of prostate AI algorithm has paved the way for starting to incorporate this exciting technology for use in primary diagnosis. AI tools can provide a unique platform for innovations and advances in anatomical and clinical pathology workflows. In this review, we describe the milestones and landmark trials in the use of AI in clinical pathology with emphasis on future directions.

With the recent availability and affordability of commercial depth sensors and 3D scanners, an increasing number of 3D (i.e., RGBD, point cloud) datasets have been publicized to facilitate research in 3D computer vision. However, existing datasets either cover relatively small areas or have limited semantic annotations. Fine-grained understanding of urban-scale 3D scenes is still in its infancy. In this paper, we introduce SensatUrban, an urban-scale UAV photogrammetry point cloud dataset consisting of nearly three billion points collected from three UK cities, covering 7.6 km2. Each point in the dataset has been labelled with fine-grained semantic annotations, resulting in a dataset that is three times the size of the previous existing largest photogrammetric point cloud dataset. In addition to the more commonly encountered categories such as road and vegetation, urban-level categories including rail, bridge, and river are also included in our dataset. Based on this dataset, we further build a benchmark to evaluate the performance of state-of-the-art segmentation algorithms. In particular, we provide a comprehensive analysis and identify several key challenges limiting urban-scale point cloud understanding. The dataset is available at http://point-cloud-analysis.cs.ox.ac.uk/.