Jingchu University of Technology

This paper proposes a manufacturing task allocation model based on bi-level planning to address the multi-agent distributed manufacturing task allocation problem within the equipment group. This model considers the group headquarters as the upper-level decision-making entity and its subsidiaries as the lower-level decision-making entity. It comprehensively considers key factors such as time, cost, and quality, and constructs a bi-level optimization objective function. The model is solved using the NSGA-II algorithm, achieving a scientific and rational allocation of manufacturing tasks. Case analysis results show that the proposed method effectively optimizes resource allocation, reduces production costs, shortens production cycles, improves product quality, and enhances the manufacturing capabilities and customer service satisfaction of subsidiaries, providing an effective solution for manufacturing task allocation within the equipment group.

The reduction of N2 to ammonia is a primary pathway for artificial nitrogen fixation. The use of photocatalysts to achieve this reduction is currently considered as an environmentally friendly and efficient approach. However, one of the major challenges in photocatalytic nitrogen fixation is the adsorption of N2 onto the catalyst surface and the subsequent electron transfer for reduction. Molybdenum (Mo) has garnered significant attention due to its crucial role in facilitating N2 adsorption and promoting the escape of photogenerated electrons. This review begins by examining the dual role of Mo in both biological and artificial photocatalytic nitrogen fixation. It systematically summarizes the application of various Mo-based photocatalytic materials in the field of nitrogen fixation, such as metal molybdates, molybdenum chalcogenides, and Mo-based MOFs/MXenes, along with modification strategies like Mo doping, Mo single atoms, and Mo metallenes. By elucidating the underlying mechanisms, the critical function of Mo in the nitrogen fixation process is highlighted. Finally, the challenges and future prospects of Mo-containing photocatalysts are discussed, providing a theoretical foundation for the development and selection of highly efficient Mo-containing materials for artificial nitrogen fixation.

Background: There is a substantial number of research exploring the application of artificial intelligence (AI) in identifying electrocardiogram (ECG) abnormalities related to heart rhythm or conduction with the 12-channel format. However, there is a scarcity of studies focusing on refined differentiation of serials of ECG abnormalities with wide QRS complexes in a simplified channel format. Methods: We constructed an ECG dataset (standard 10-s, 12-channel format) from adult patients from Tongji Hospital of Huazhong University of Science and Technology, Wuhan, China. This dataset was consisted of 5 kinds of ECG abnormalities with wide QRS complexes in the normal heartbeat (60 to 100 beats per minute) and the normal ECGs. Convolutional neural network was developed to classify these abnormalities. Four-channel (I, II, V1, and V5) and 8-channel (I, II, and V1 to V6) formats, compared to the standard 12-channel format (I, II, III, aVR, aVL, aVF, and V1 to V6), were chosen as the input channel format of the model. Other unreplicated ECGs from Tongji Hospital (TJ-Test set), annotated by a committee of board-certified cardiologists, served as the test dataset. The F1 score, area under the receiver operating characteristic curve (AUROC), and accuracy were calculated to assess the performance of the model, which were further compared with diagnoses of 6 ECG cardiologists who were informed that the final objective was classifying among 6 classes with the 12-channel format. In addition, a dataset of 291 ECGs from The First People’s Hospital of Jiangxia District (JX-Test set) and a public dataset of 64 ECGs were used to assess model generalizability Results: The dataset consisted of 11,808 ECGs from 8,542 patients from 2012 January 1 to 2020 November 30 and divided into training and validation datasets in the ratio of 9:1. The test dataset included unreplicated 480 ECGs from 480 new adult patients recorded from 2014 January 1 to 2017 November 30. The model shows a superior performance in the 8-channel format compared to that of 4- and 12-channel formats. As for the 8-channel format, the model obtained an accuracy of 95.0%, a mean F1 score of 0.969 (0.943 to 0.997), and a mean AUROC score of 0.997 (0.975 to 1.00) compared to an accuracy of 89.9%, an F1 score of 0.898 (0.863 to 0.932), and an AUROC score of 0.941 (0.918 to 0.963) of physicians assessing the same datasets. The model exhibited a mean F1 score of 0.917 (0.943 to 0.997) and a mean AUROC score of 0.994 (0.975 to 1.00) on the JX-Test set, and mean F1 scores of 0.708 for the left bundle branch block and 0.828 for the right bundle branch block for the external published validation data both with the 8-channel format. Conclusion: Our model distinguishes a range of distinct abnormalities focusing on abnormal morphology on QRS complexes in normal heartbeats with high accuracy, providing a foundation for AI-aided clinical decision-support systems in ECG differential diagnosis.

In recent years, the research on the preparation of carbon dots (CDs) from biomass waste has been increasing. Consequently, there is a growing emphasis on developing green and convenient synthetic...