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This paper considers the order-fulfillment coalitions among an e-tailer and two third-party sellers in a hybrid e-commerce platform. The e-tailer uses a self-supported logistics system to fulfill the orders, and the seller can either use the former or turn to third-party logistics systems. By investigating the equilibrium order quantities, the strong preference relations, and the possible deviations for all the possible coalition structures, we discuss both myopic stability and farsighted stability for all the coalition structures. We find that if economies of scale are absent, fulfilling the orders independently is unique Nash stable and farsightedly stable; however, if alliances generate economies of scale, there may not exist Nash stable, but the farsightedly stable outcome always exists, which further depends on the interaction between the e-tailer's order-fulfillment cost advantage and economies of scale. Further discussions on extremely high economies of scale and the proportional commission fee are also conducted. [ABSTRACT FROM AUTHOR]

Background: Airway pressure release ventilation (APRV) has become increasingly popular for the management of acute respiratory distress syndrome (ARDS); however, its clinical impact remains a topic of debate. Furthermore, there is a gap between the guidelines and the actual clinical practices in mechanical ventilation management for ARDS. This survey aimed to explore the utilization of APRV and mechanical ventilation strategies for ARDS in Chinese intensive care unit (ICU) clinicians. Methods: A comprehensive 34‐item survey was distributed online platforms amongst ICU clinicians across mainland China from June to August 2019. Results: A total of 420 valid responses were collected, with 57.4% (241) originating from academic hospitals and 42.6% (179) from non‐academic hospitals. Of the respondents, 98.6% (414) recognized the significance of low tidal volume ventilation for ARDS prognosis, 85.2% adhered to a tidal volume below 8 mL/kg predicted body weight, and most (46.4%) selected the initial positive end‐expiratory pressure within the range of 5–10 cmH2O based on experience. Among the respondents, 62.1% (261) reported familiarity with APRV and 41.9% (176) had implemented APRV. Of those who had utilized APRV, 93.2% (164) believed in its effectiveness for ARDS patients and 69.3% (122) advocated for early application of APRV. Substantial variations were noted regarding APRV initiation settings and the preservation of spontaneous breathing during APRV. Academic hospitals exhibited higher usage rates of lung recruitment, neuromuscular blockade, prone ventilation, and acquaintance with and utilization of APRV compared to non‐academic hospitals (all p values ≤ 0.001). Conclusions: Our findings highlight opportunities for improvement in mechanical ventilation management for ARDS, particularly in non‐academic hospitals. Additionally, a significant proportion of clinicians demonstrated limited knowledge of APRV, and there was a lack of consensus on its application. Further training and larger‐scale clinical trials are required to validate the efficacy and utilization of APRV in managing ARDS. [ABSTRACT FROM AUTHOR]

Green supply chain management (GSCM) is one of the key directions for the sustainable development of steel enterprises. However, due to the low energy efficiency and high emissions of steel enterprises, there are still difficulties in implementing GSCM. To this end, a comprehensive performance analysis method was proposed. To analyze the performance level of enterprises implementing GSCM, a performance evaluation model integrating Three-scale Analytic Hierarchy Process (TAHP) and Levenberg-Marquardt back propagation (LMBP) is proposed. Furthermore, considering the influencing factors of GSCM performance of steel enterprises, a factor analysis model based on the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is proposed. Through the analysis of key driving factors affecting performance, suggestions are made for enterprises to improve GSCM. This approach takes the GSCM of a steel enterprise as an example to verify the feasibility of the method. Influencing factors, such as corporate sustainable development image, goods supply, inventory management, production management have been identified as the driving factors affecting GSCM performance, and effective measures to improve the performance of GSCM of steel enterprises have been proposed for the driving factors. The results can provide support for the effective implementation of GSCM in steel enterprises. [ABSTRACT FROM AUTHOR]

License plate detection is an important task in Intelligent Transportation Systems (ITS) and has a wide range of applications in vehicle management, traffic control, and public safety. In order to improve the accuracy and speed of mobile recognition, an improved lightweight YOLOv5s model is proposed for license plate detection. First, an improved Stemblock network is used to replace the original Focus layer in the network, which ensures strong feature expression capability and reduces a large number of parameters to lower the computational complexity; then, an improved lightweight network, ShuffleNetv2, is used to replace the backbone network of the YOLOv5s, which makes the model lighter and ensures the detection accuracy at the same time. Then, a feature enhancement module is designed to reduce the information loss caused by the rearrangement of the backbone network channels, which facilitates the information interaction in the feature fusion process; finally, the low‐, medium‐ and high‐level features in the Shufflenetv2 network structure are fused to form the final high‐level output features. Experimental results on the CCPD dataset show that compared to other methods this paper obtains better performance and faster speed in the license plate detection task, in which the average precision mean value reaches 96.6%, and can achieve a detection speed of 43.86 frame/s, and the parameter volume is reduced to 5.07 M. [ABSTRACT FROM AUTHOR]

The authors analyse the influence of reference sphere on the test accuracy of Ritchey–Common method during testing of flat mirror surface. A simulation model is established to simulate the influence of different radii of curvature of reference sphere on system wavefront. Then, the difference between the flat mirror surface result with reference sphere surface error to the actual flat mirror surface is examined by this model. The test accuracy of flat mirror result can be effectively improved by eliminating the influence of the surface error of reference sphere. When the surface accuracy of the reference sphere is better than 0.01 λ, the influence on RMS test accuracy is within 0.01 λ. R–C test path is built to test a 100 mm‐diameter flat mirror surface, and its results are compared with those of the flat mirror surface without surface error of reference sphere and with the test result of the interferometer. When the surface accuracy of the actual used area of reference sphere is better than 0.01 λ, the effect on flat mirror RMS accuracy is within 0.01 λ. The RMS test accuracy can reach 0.01 λ, when the surface error of reference sphere is retained. The experiment verifies the correctness of the simulation results and guarantees the improvement in the test accuracy of R–C method. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNAs) exhibit essential regulation in the malignant development of hepatocellular carcinoma (HCC). This study aims to investigate the physiological mechanisms of circ_0029343 encoded by scavenger receptor class B member 1 (SCARB1) involved in the growth and metastasis of HCC. Differentially expressed mRNAs in HCC were obtained, followed by the prediction of target genes of differentially expressed miRNAs and gene ontology and kyoto encyclopedia of genes and genomes analysis on the differentially expressed mRNAs. Moreover, the regulatory relationship between circRNAs encoded by SCARB1 and differentially expressed miRNAs was predicted. In vitro cell experiments were performed to verify the effects of circ_0029343, miR‐486‐5p, and SRSF3 on the malignant features of HCC cells using the gain‐ or loss‐of‐function experiments. Finally, the effects of circ_0029343 on the growth and metastasis of HCC cells in xenograft mouse models were also explored. It was found that miR‐486‐5p might interact with seven circRNAs encoded by SCARB1, and its possible downstream target gene was SRSF3. Moreover, SRSF3 was associated with the splicing of various RNA. circ_0029343 could sponge miR‐486‐5p to up‐regulate SRSF3 and activate PDGF‐PDGFRB (platelet‐derived growth factor and its receptor, receptor beta) signaling pathway by inducing p73 splicing, thus promoting the proliferation, migration, and invasion and inhibiting apoptosis of HCC cells. In vivo, animal experiments further confirmed that overexpression of circ_0029343 could promote the growth and metastasis of HCC cells in nude mice. circ_0029343 encoded by SCARB1 may induce p73 splicing and activate the PDGF‐PDGFRB signaling pathway through the miR‐486‐5p/SRSF3 axis, thus promoting the growth and metastasis of HCC cells. [ABSTRACT FROM AUTHOR]

A rolling bearing remaining useful life prognostics method based on improved Continuous hidden semi-Markov model（ CHSMM） is proposed,aiming at the problem that the CHSMM algorithm prognostics accuracy is low for remaining useful life of rolling bearings. The feature vectors of the time and time frequency domain are extracted from the vibration signal of bearing and then the PCA algorithm is used to reduce the dimension of the feature vectors. Then,the degradation state recognition model and the remaining useful life prediction model are established based on improved CHSMM into which the Gauss mixture probability density function is introduced aiming at solving the low accuracy of remaining useful life prediction caused by the dwell time probability density function which does not conform to reality. Finally,the whole life cycle data of the bearing is input into the model,and the degenerate state and residual life of the bearing are obtained. The experimental results show that the proposed method can accurately predict the remaining useful life of bearings.Compared with the original CHSMM algorithm,the accuracy of the degradation state recognition is increased by12%,and the accuracy of remaining useful life prediction is increased by 23%.

Large and extensive manufacturing systems consume a large proportion of manufacturing energy. A key component of energy efficiency management is the accurate prediction of energy efficiency. However, the nonlinear and vibration characteristics of machining systems' energy consumption (EC) pose a challenge to the accurate prediction of system EC. To address this challenge, an energy consumption prediction method for machining systems is presented, which is based on an improved particle swarm optimization (IPSO) algorithm to optimize long short-term memory (LSTM) neural networks. The proposed method optimizes the LSTM hyperparameters by improving the particle swarm algorithm with dynamic inertia weights (DIWPSO-LSTM), which enhances the prediction accuracy and efficiency of the model. In the experimental results, we compared several improved optimization algorithms, and the proposed method has a performance improvement of more than 30% in mean absolute error (MAE)and mean error(ME). [ABSTRACT FROM AUTHOR]