Dual-Channel Supply Chain Network Equilibrium Model with Consumer-Driven

In this paper, we study designing and managing effective dual-channel supply chain network equilibrium model to optimize the profit of each node in dual-channel supply chain network and satisfy seamlessly customer demand. The customer demand in each channel is driven by the heterogeneous consumer characteristic attributes. In our proposed model, Multinomial Logit (MNL) function is used to make a purchase decision for customers considering selling price, operation time and retail services. Furthermore, the Variational Inequality is used to express the equilibrium solution in dual-channel supply chain network. A numerical example in dual-channel supply chain network is presented to show the MNL function can be a good replacement for the demand function when customers are heterogeneous and the proposed model can be helpful to avoid time trap

Transport properties of dense deuterium-tritium plasmas

Consistent descriptions of the equation of states, and information about transport coefficients of deuterium-tritium mixture are demonstrated through quantum molecular dynamic (QMD) simulations (up to a density of 600 g/cm$^{3}$ and a temperature of $10^{4}$ eV). Diffusion coefficients and viscosity are compared with one component plasma model in different regimes from the strong coupled to the kinetic one. Electronic and radiative transport coefficients, which are compared with models currently used in hydrodynamic simulations of inertial confinement fusion, are evaluated up to 800 eV. The Lorentz number is also discussed from the highly degenerate to the intermediate region.Comment: 4 pages, 3 figure

Textual analysis and machine leaning: Crack unstructured data in finance and accounting

In finance and accounting, relative to quantitative methods traditionally used, textual analysis becomes popular recently despite of its substantially less precise manner. In an overview of the literature, we describe various methods used in textual analysis, especially machine learning. By comparing their classification performance, we find that neural network outperforms many other machine learning techniques in classifying news category. Moreover, we highlight that there are many challenges left for future development of textual analysis, such as identifying multiple objects within one single document

Revisiting Multi-modal 3D Semantic Segmentation in Real-world Autonomous Driving

LiDAR and camera are two critical sensors for multi-modal 3D semantic segmentation and are supposed to be fused efficiently and robustly to promise safety in various real-world scenarios. However, existing multi-modal methods face two key challenges: 1) difficulty with efficient deployment and real-time execution; and 2) drastic performance degradation under weak calibration between LiDAR and cameras. To address these challenges, we propose CPGNet-LCF, a new multi-modal fusion framework extending the LiDAR-only CPGNet. CPGNet-LCF solves the first challenge by inheriting the easy deployment and real-time capabilities of CPGNet. For the second challenge, we introduce a novel weak calibration knowledge distillation strategy during training to improve the robustness against the weak calibration. CPGNet-LCF achieves state-of-the-art performance on the nuScenes and SemanticKITTI benchmarks. Remarkably, it can be easily deployed to run in 20ms per frame on a single Tesla V100 GPU using TensorRT TF16 mode. Furthermore, we benchmark performance over four weak calibration levels, demonstrating the robustness of our proposed approach.Comment: 7 pages, 3 figure

Functional and postoperative outcomes after high-intensity interval training in lung cancer patients: A systematic review and meta-analysis

ObjectiveThe study evaluated the effects of high-intensity interval training (HIIT) on postoperative complications and lung function in patients with lung cancer compared to usual care.MethodsWe searched electronic databases in April 2022, including PubMed, Embase, the Cochrane Library, Web of Science, and the China National Knowledge Infrastructure (CNKI). Two authors independently applied the Cochrane Risk of Bias tool to assess the quality of RCTs. The postoperative complications, length of hospitalization, and cardiopulmonary functions from the studies were pooled for statistical analysis.ResultsA total of 12 randomized controlled trials were eligible for inclusion and were conducted in the meta-analysis. HIIT significantly increased VO2peak (MD = 2.65; 95% CI = 1.70 to 3.60; I2 = 40%; P <0.001) and FEV1 (MD = 0.12; 95% CI = 0.04 to 0.20; I2 = 51%; P = 0.003) compared with usual care. A subgroup analysis of studies that applied HIIT perioperatively showed significant improvement of HIIT on FEV1 (MD = 0.14; 95% CI = 0.08 to 0.20; I2 = 36%; P <0.0001). HIIT significantly reduced the incidence of postoperative atelectasis in lung cancer patients compared with usual care (RD = −0.16; 95% CI = −0.24 to −0.08; I2 = 24%; P <0.0001). There was no statistically significant effect of HIIT on postoperative arrhythmias (RD = −0.05; 95% CI = −0.13 to 0.03; I2 = 40%; P = 0.22), length of hospitalization (MD = −1.64; 95% CI = −3.29 to 0.01; P = 0.05), and the six-minute walk test (MD = 19.77; 95% CI = −15.25 to 54.80; P = 0.27) compared to usual care.ConclusionHIIT may enhance VO2peak and FEV1 in lung cancer patients and reduce the incidence of postoperative atelectasis. However, HIIT may not reduce the incidence of postoperative arrhythmia, shorten the length of hospitalization, or improve the exercise performance of patients with lung cancer.Systematic review registrationPROSPERO, CRD4202233544

CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysis

Nuclear Magnetic Resonance (NMR) spectroscopy has served as a powerful analytical tool for studying molecular structure and dynamics in chemistry and biology. However, the processing of raw data acquired from NMR spectrometers and subsequent quantitative analysis involves various specialized tools, which necessitates comprehensive knowledge in programming and NMR. Particularly, the emerging deep learning tools is hard to be widely used in NMR due to the sophisticated setup of computation. Thus, NMR processing is not an easy task for chemist and biologists. In this work, we present CloudBrain-NMR, an intelligent online cloud computing platform designed for NMR data reading, processing, reconstruction, and quantitative analysis. The platform is conveniently accessed through a web browser, eliminating the need for any program installation on the user side. CloudBrain-NMR uses parallel computing with graphics processing units and central processing units, resulting in significantly shortened computation time. Furthermore, it incorporates state-of-the-art deep learning-based algorithms offering comprehensive functionalities that allow users to complete the entire processing procedure without relying on additional software. This platform has empowered NMR applications with advanced artificial intelligence processing. CloudBrain-NMR is openly accessible for free usage at https://csrc.xmu.edu.cn/CloudBrain.htmlComment: 11 pages, 13 figure