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This article discusses a novel artificial intelligence (AI)-enabled approach to the rapid screening and diagnosis of acute promyelocytic leukemia (APL) using bone marrow morphology. A convolutional neural network (CNN) with embedded attention mechanisms was developed to recognize APL in microscope images. The model was trained and evaluated using a large dataset of bone marrow smear images, achieving high accuracy and performance. The AI-assisted system, called CELLSEE, showed potential for improving the speed and accuracy of APL diagnosis, enabling early identification and treatment. The study highlights the importance of AI in medical diagnostics and its potential for reducing mortality rates in APL cases. [Extracted from the article]

The propagation of water waves can be manipulated via artificial structures. However, such structures usually need to possess a large volume ratio in water, and it may not be easy to build and move them in practical ocean engineering. Here, we theoretically propose and experimentally demonstrate a fixed, shallow-draft cylinder array that can refract and focus water waves within a wide range of wavelengths. When impinging upon the cylinder array from a region of water without structures, water waves can be slightly reflected at the surface of the array and speeded up in the array. As a result, favorable focusing performances such as a short focal length and high intensity at the focal point are observed. [ABSTRACT FROM AUTHOR]

Systematic studies on the mechanism underlying Fe2O3-catalyzed NO reduction in a reducing atmosphere during sludge combustion remain limited. In this study, density functional theory was employed to investigate the adsorption properties of NH3, CO, and NO on the α-Fe2O3(001) surface, and the mechanisms underlying the NH3 and CO reduction of NO during the adsorption process. The results demonstrated that NH3, CO, and NO chemically adsorbed on the surface Fe top site, thereby generating distinctly high adsorption energies. NO exhibited the highest adsorption energy. With regard to the catalytic mechanisms of NH3 and CO during NO reduction, the α-Fe2O3(001) surface exhibited different characteristics. NH3 reduction of NO tended to follow the Eley–Rideal (E-R) mechanism. The dissociation of -NH2NO is the rate-determining step for the NH3 reduction of NO. The presence of α-Fe2O3(001) reduced the dissociation energy barriers of NH3 and NH2NO, thereby catalyzing the reduction reaction. In contrast, NO dissociation was more challenging during the CO reduction of NO. The α-Fe2O3(001) surface reduced the dissociation barrier of the NO-NO dimer from 2.04 to 1.53 eV. Two adsorbed NO molecules first formed NO-NO dimers; these then dissociated into N2O and atomic oxygen, thereby catalyzing the reduction reaction. [ABSTRACT FROM AUTHOR]

With the increasing emphasis on sustainable development, low-carbon education has become an important work in colleges, emphasizing the practice of low-carbon life and letting students know that low-carbon is a good living habit. Under this guidance, the ideological and political workers in colleges need to combine education with low-carbon background, cultivate and develop their life planning, direction and values, which is of great and long-term significance to improve the living environment quality, moral quality and social responsibility of college students. We attempt to use the theory of behavioural science, starting from professor Fogg’s behaviour model, with the convenience of digital tools in the internet era, focusing on the motivation improvement, difficulty reduction and trigger factors of low-carbon consumption of college students, to explore the path of using mobile education to do a good job in low-carbon education, hoping to provide innovative education methods and useful suggestions for ideological and political education in colleges.