H2-Promoted Benign Coke Strategy for Dimethyl Ether Carbonylation with Long-Term Stability and High Activity.

Zeolite-catalyzed dimethyl ether (DME) carbonylation provides a novel route to producing methyl acetate (MeOAc). Mordenite (MOR) has drawn significant interest because of its remarkable MeOAc selectivity in DME carbonylation, albeit with limited catalytic stability. Herein, novel MOR-based DME carbonylation catalysts, distinguished by long-term stability and high activity were successfully developed, based on an H2-promoted benign coke strategy. Both the H2 cofeeds and the presence of metal species with hydrogenation capability are demonstrated to be crucial for the regulation of coke depositions. The coke deposits can potentially cover the acid sites in the 12-MR main channels, thereby mitigating the occurrence of undesirable methanol-to-hydrocarbon side reactions. Meanwhile, the elimination of ultralarge coke species under the assistance of H2 and Cu species could ensure smooth mass transfer within the catalyst, contributing to its remarkable catalytic performance. The most highlighted DME carbonylation performance was achieved on coke-mediated CuZn-HMOR with a high MeOAc yield of 0.4-0.5 g·gcat-1·h-1 for over 520 h (over 50× enhancement versus HMOR), exhibiting promising industrial application potential. The current strategy is expected to inspire further research into zeolite-catalyzed reactions, which could be potentially improved by the presence of benign coke.

Fog-Edge Collaborative Task Offloading Strategy Based on Chaotic Teaching and Learning Particle Swarm Optimization.

To improve the contradiction between the surge of business demand and the limited resources of MEC, firstly, the "cloud, fog, edge, and end" collaborative architecture is constructed with the scenario of smart campus, and the optimization model of joint computation offloading and resource allocation is proposed with the objective of minimizing the weighted sum of delay and energy consumption. Second, to improve the convergence of the algorithm and the ability to jump out of the bureau of excellence, chaos theory and adaptive mechanism are introduced, and the update method of teaching and learning optimization (TLBO) algorithm is integrated, and the chaos teaching particle swarm optimization (CTLPSO) algorithm is proposed, and its advantages are verified by comparing with existing improved algorithms. Finally, the offloading success rate advantage is significant when the number of tasks in the model exceeds 50, the system optimization effect is significant when the number of tasks exceeds 60, the model iterates about 100 times to converge to the optimal solution, the proposed architecture can effectively alleviate the problem of limited MEC resources, the proposed algorithm has obvious advantages in convergence, stability, and complexity, and the optimization strategy can improve the offloading success rate and reduce the total system overhead.

Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning.

BACKGROUND: There is progress to be made in building artificially intelligent systems to detect abnormalities that are not only accurate but can handle the true breadth of findings that radiologists encounter in body (chest, abdomen, and pelvis) computed tomography (CT). Currently, the major bottleneck for developing multi-disease classifiers is a lack of manually annotated data. The purpose of this work was to develop high throughput multi-label annotators for body CT reports that can be applied across a variety of abnormalities, organs, and disease states thereby mitigating the need for human annotation. METHODS: We used a dictionary approach to develop rule-based algorithms (RBA) for extraction of disease labels from radiology text reports. We targeted three organ systems (lungs/pleura, liver/gallbladder, kidneys/ureters) with four diseases per system based on their prevalence in our dataset. To expand the algorithms beyond pre-defined keywords, attention-guided recurrent neural networks (RNN) were trained using the RBA-extracted labels to classify reports as being positive for one or more diseases or normal for each organ system. Alternative effects on disease classification performance were evaluated using random initialization or pre-trained embedding as well as different sizes of training datasets. The RBA was tested on a subset of 2158 manually labeled reports and performance was reported as accuracy and F-score. The RNN was tested against a test set of 48,758 reports labeled by RBA and performance was reported as area under the receiver operating characteristic curve (AUC), with 95% CIs calculated using the DeLong method. RESULTS: Manual validation of the RBA confirmed 91-99% accuracy across the 15 different labels. Our models extracted disease labels from 261,229 radiology reports of 112,501 unique subjects. Pre-trained models outperformed random initialization across all diseases. As the training dataset size was reduced, performance was robust except for a few diseases with a relatively small number of cases. Pre-trained classification AUCs reached > 0.95 for all four disease outcomes and normality across all three organ systems. CONCLUSIONS: Our label-extracting pipeline was able to encompass a variety of cases and diseases in body CT reports by generalizing beyond strict rules with exceptional accuracy. The method described can be easily adapted to enable automated labeling of hospital-scale medical data sets for training image-based disease classifiers.