Machine Learning-Assisted Synchronous Fluorescence Sensing Approach for Rapid and Simultaneous Quantification of Thiabendazole and Fuberidazole in Red Wine.

Rapid analysis of components in complex matrices has always been a major challenge in constructing sensing methods, especially concerning time and cost. The detection of pesticide residues is an important task in food safety monitoring, which needs efficient methods. Here, we constructed a machine learning-assisted synchronous fluorescence sensing approach for the rapid and simultaneous quantitative detection of two important benzimidazole pesticides, thiabendazole (TBZ) and fuberidazole (FBZ), in red wine. First, fluorescence spectra data were collected using a second derivative constant-energy synchronous fluorescence sensor. Next, we established a prediction model through the machine learning approach. With this approach, the recovery rate of TBZ and FBZ detection of pesticide residues in red wine was 101% ± 5% and 101% ± 15%, respectively, without resorting complicated pretreatment procedures. This work provides a new way for the combination of machine learning and fluorescence techniques to solve the complexity in multi-component analysis in practical applications.

Freeze-Cast Ni-MOF Nanobelts/Chitosan-Derived Magnetic Carbon Aerogels for Broadband Electromagnetic Wave Absorption.

The exceptional benefits of carbon aerogels, including their low density and tunable electrical characteristics, infuse new life into the realm of creating ultralight electromagnetic wave absorbers. The clever conceptualization and straightforward production of carbon-based aerogels, which marry aligned microporous architecture with nanoscale heterointerfaces and atomic-scale defects, are vital for effective multiscale microwave response. We present an uncomplicated synthesis method for crafting aligned porous Ni@C nanobelts anchored on N, S-doped carbon aerogels (Ni@C/NSCAs), featuring multiscale structural intricacies─achieved through the pyrolysis of freeze-cast Ni-MOF nanobelts and chitosan aerogel composites. The well-ordered porous configuration, combined with multiple heterointerfaces adopting a "nanoparticles-nanobelts-nanosheets" contact schema, along with a wealth of defects, adeptly modulates conductive, polarization, and magnetic losses to realize an equilibrium in impedance matching. This magnetically doped carbon aerogel showcases an impressive effective absorption bandwidth of 8.96 GHz and a minimum reflection loss of -68.82 dB, while maintaining an exceptionally low filler content of 1.75 wt %. Additionally, the applied coating exhibits an astonishing radar cross-section reduction of 51.7 dB m2, signifying its superior radar wave scattering capabilities. These results offer key insights into the attainment of broad-spectrum microwave absorption features by enhancing the multiscale structure of current aerogels.

Knowledge and attitudes toward artificial intelligence in nursing among various categories of professionals in China: a cross-sectional study.

Objectives: The application of artificial intelligence (AI) in healthcare is an important public health issue. However, few studies have investigated the perceptions and attitudes of healthcare professionals toward its applications in nursing. This study aimed to explore the knowledge, attitudes, and concerns of healthcare professionals, AI-related professionals, and others in China toward AI in nursing. Methods: We conducted an online cross-sectional study on nursing students, nurses, other healthcare professionals, AI-related professionals, and others in China between March and April 2024. They were invited to complete a questionnaire containing 21 questions with four sections. The survey followed the principle of voluntary participation and was conducted anonymously. The participants could withdraw from the survey at any time during the study. Results: This study obtained 1,243 valid questionnaires. The participants came from 25 provinces and municipalities in seven regions of China. Regarding knowledge of AI in nursing, 57% of the participants knew only a little about AI, 4.7% did not know anything about AI, 64.7% knew only a little about AI in nursing, and 13.4% did not know anything about AI in nursing. For attitudes toward AI in nursing, participants were positive about AI in nursing, with more than 50% agreeing and strongly agreeing with each question on attitudes toward AI in nursing. Differences in the numbers of participants with various categories of professionals regarding knowledge and attitudes toward AI in nursing were statistically significant (p < 0.05). Regarding concerns and ethical issues about AI in nursing, every participant expressed concerns about AI in nursing, and 95.7% of participants believed that it is necessary to strengthen medical ethics toward AI in nursing. Conclusion: Nursing students and healthcare professionals lacked knowledge about AI or its application in nursing, but they had a positive attitude toward AI. It is necessary to strengthen medical ethics toward AI in nursing. The study's findings could help develop new strategies benefiting healthcare.

Highly effective and tunable microwave absorber integrating multiscale attenuation behaviours derived from prussian blue analogue/graphene oxide aerogel.

Tunable and efficient absorption of graphene-based microwave absorbers are essential on the realms of electromagnetic compatibility and protection in various application scenarios. However, challenges arise owing to their limited microwave attenuation behaviors. Herein, CoNiFe Prussian blue analogue (PBA)-derived magnetic alloy@carbon nanocubes anchored on N-doped reduced graphene oxide (rGO) aerogels were achieved via CoNiFe-PBA nanocubes assisting assembly of GO and subsequent thermal annealing approach. Such three-dimensional (3D) graphene-based macroscopic architecture integrates multiple attenuation behaviours occurred across multiple length scales. Attributed to the synergy of multiple scattering, conduction loss, multiple heterogeneous interface and dipolar polarizations, and magnetic loss, the optimized CoNiFe-PBA/GO aerogel derivative simultaneously exhibits strong reflection loss and wide effective bandwidth with an ultralow filling content (1.1 wt%) at both X band (-66.01 dB and 5.2 GHz at 3.2 mm) and Ku band (-66.23 dB and 6.6 GHz at 2.6 mm). Multiscale assembly strategy of graphene-based electromagnetic functional materials from molecular level to macroscale proposed and demonstrated by this work shows promise for exploring tunable and efficient microwave absorbers.

Ultralight Magnetic and Dielectric Aerogels Achieved by Metal-Organic Framework Initiated Gelation of Graphene Oxide for Enhanced Microwave Absorption.

HIGHLIGHTS: Metal-organic frameworks (MOFs) are used to directly initiate the gelation of graphene oxide (GO), producing MOF/rGO aerogels. The ultralight magnetic and dielectric aerogels show remarkable microwave absorption performance with ultralow filling contents. The development of a convenient methodology for synthesizing the hierarchically porous aerogels comprising metal-organic frameworks (MOFs) and graphene oxide (GO) building blocks that exhibit an ultralow density and uniformly distributed MOFs on GO sheets is important for various applications. Herein, we report a facile route for synthesizing MOF/reduced GO (rGO) aerogels based on the gelation of GO, which is directly initiated using MOF crystals. Free metal ions exposed on the surface of MIL-88A nanorods act as linkers that bind GO nanosheets to a three-dimensional porous network via metal-oxygen covalent or electrostatic interactions. The MOF/rGO-derived magnetic and dielectric aerogels Fe3O4@C/rGO and Ni-doped Fe3O4@C/rGO show notable microwave absorption (MA) performance, simultaneously achieving strong absorption and broad bandwidth at low thickness of 2.5 (- 58.1 dB and 6.48 GHz) and 2.8 mm (- 46.2 dB and 7.92 GHz) with ultralow filling contents of 0.7 and 0.6 wt%, respectively. The microwave attenuation ability of the prepared aerogels is further confirmed via a radar cross-sectional simulation, which is attributed to the synergistic effects of their hierarchically porous structures and heterointerface engineering. This work provides an effective pathway for fabricating hierarchically porous MOF/rGO hybrid aerogels and offers magnetic and dielectric aerogels for ultralight MA.

Derivative Matrix-Isopotential Synchronous Spectrofluorimetry and Hantzsch Reaction: A Direct Route to Simultaneous Determination of Urinary δ-Aminolevulinic Acid and Porphobilinogen.

Early and sensitive detection of δ-aminolevulinic acid (δ-ALA) and porphobilinogen (PBG) is the cornerstone of diagnosis and effective treatment for acute porphyria. However, at present, the quantifying strategies demand multiple solvent extraction steps or chromatographic approaches to separate δ-ALA and PBG prior to quantification. These methods are both time-consuming and laborious. Otherwise, in conventional spectrofluorimetry, the overlapping spectra of the two analytes cause false diagnosis. To overcome this challenge, we present a two-step approach based on derivative matrix-isopotential synchronous fluorescence spectrometry (DMISFS) and the Hantzsch reaction, realizing the simple and simultaneous detection of δ-ALA and PBG in urine samples. The first step is chemical derivatization of the analytes by Hantzsch reaction. The second step is the determination of the target analytes by combining MISFS and the first derivative technique. The proposed approach accomplishes following advantages: 1) The MISFS technique improves the spectral resolution and resolves severe spectral overlap of the analytes, alleviating tedious and complicated pre-separation processes; 2) First derivative technique removes the background interference of δ-ALA on PBG and vice versa, ensuring high sensitivity; 3) Both the analytes can be determined simultaneously via single scanning, enabling rapid detection. The obtained detection limits for δ-ALA and PBG were 0.04 µmol L-1 and 0.3 µmol L-1, respectively. Within-run precisions (intra and inter-day CVs) for both the analytes were <5%. Further, this study would serve to enhance the availability of early and reliable quantitative diagnosis for acute porphyria in both scientific and clinical laboratories.

Understanding via Exploration: Discovery of Interpretable Features With Deep Reinforcement Learning.

Understanding the environments through interactions has been one of the most important human intellectual activities in mastering unknown systems. Deep reinforcement learning (DRL) has already been known to achieve effective control through human-like exploration and exploitation in many applications. However, the opaque nature of deep neural network (DNN) often hides critical information about feature relevance to control, which is essential for understanding the target systems. In this article, a novel online feature selection framework, namely, the dual-world-based attentive feature selection (D-AFS), is first proposed to identify the contribution of the inputs over the whole control process. Rather than the one world used in most DRL, D-AFS has both the real world and its virtual peer with twisted features. The newly introduced attention-based evaluation (AR) module performs the dynamic mapping from the real world to the virtual world. The existing DRL algorithms, with slight modification, can learn in the dual world. By analyzing the DRL's response in the two worlds, D-AFS can quantitatively identify respective features' importance toward control. A set of experiments is performed on four classical control systems in OpenAI Gym. Results show that D-AFS can generate the same or even better feature combinations than the solutions provided by human experts and seven recent feature selection baselines. In all cases, the selected feature representations are closely correlated with the ones used by underlying system dynamic models.