A Marxist Reading of Mary Barton

 Mary Barton is a realistic novel written by Mrs. Gaskell. It assumes in the reader not only imaginative sympathy with the distress of the Manchester workers, but also knowledge of the social and political movement of the 1840s, which so often offered hopes to the oppressed. By applying Marxist literary theory to the reading of this novel one can find how subtle the authoress’ depiction of growing hope and then crushing despair of the workingmen. Also, by using Marxist critique one can discover the authoress, while trying to write from the working people’s viewpoint, can not simply empty herself of all her inherited middle-class attitude, which results in the unconvincing and doubtful ending of the novel

Basic Cognitive Abilities in Interpreting and Their Changes over Human Life Span -- A Developmental Psychology Perspective

Interpreting is a special language processing activity involving a multiplicity of abilities, among which cognitive abilities are of key importance and received the most attention from interpreting researchers. By drawing on cognitive psychology and developmental psychology, this paper has briefly surveyed three major cognitive abilities that are functioning at the core of the interpreting process. As these cognitive abilities are subject to the law of aging, this paper has further discussed the possible impact that aging-associated decline of these cognitive abilities might exert on the performance of interpreters

Robust Deep Multi-Modal Sensor Fusion using Fusion Weight Regularization and Target Learning

Sensor fusion has wide applications in many domains including health care and autonomous systems. While the advent of deep learning has enabled promising multi-modal fusion of high-level features and end-to-end sensor fusion solutions, existing deep learning based sensor fusion techniques including deep gating architectures are not always resilient, leading to the issue of fusion weight inconsistency. We propose deep multi-modal sensor fusion architectures with enhanced robustness particularly under the presence of sensor failures. At the core of our gating architectures are fusion weight regularization and fusion target learning operating on auxiliary unimodal sensing networks appended to the main fusion model. The proposed regularized gating architectures outperform the existing deep learning architectures with and without gating under both clean and corrupted sensory inputs resulted from sensor failures. The demonstrated improvements are particularly pronounced when one or more multiple sensory modalities are corrupted.Comment: 8 page

Changes of Serum TNF-α in Acute Pancreatitis Patients and Its Clinical Significance

Objective: The aimed of this study is to investigate the changes in serum levels of tumor necrosis factor (TNF-α) in the patients with acute pancreatitis treated with octreotide and its clinical significance. Method: Total of 65 patients of acute pancreatitis were selected as a case study, in which 30 patients with mild acute pancreatitis (MAP) and 35 severe acute pancreatitis (SAP) patients were treated with octreotide. 60 healthy subjects as control group and 65 case group was subjected to performed double antibody sandwich enzyme-linked immunosorbent assay (ELISA) to detect the serum levels of TNF-α. Results: The serum TNF-α level in the case group was (12.67 ± 3.45) pg/mL and the control group was (1.56 ± 0.57) pg/mL. Case group was significantly higher than control group (p < 0.05). Serum level of acute pancreatitis (AP) before treatment was (8.96 ± 2.12) pg/mL. After treatment, SAP group was (17.34 + 4.56) pg/mL, MAP group was significantly lower than SAP group, and the difference was statistically significant (p < 0.05). Conclusion: The serum levels of TNF-α in patient with acute pancreatitis were significantly higher than those of normal healthy people, and their serum level was closely related to the severity of illness

Fairness in online vehicle-cargo matching: An intuitionistic fuzzy set theory and tripartite evolutionary game approach

This paper explores the concept of fairness and equitable matching in an on-line vehicle-cargo matching setting, addressing the varying degrees of satisfaction experienced by shippers and carriers. Relevant indicators for shippers and carriers in the on-line matching process are categorized as attributes, expectations, and reliability, which are subsequent quantified to form satisfaction indicators. Employing the intuitionistic fuzzy set theory, we devise a transformed vehicle-cargo matching optimization model by combining the fuzzy set's membership, non-membership, and uncertainty information. Through an adaptive interactive algorithm, the matching scheme with fairness concerns is solved using CPLEX. The effectiveness of the proposed matching mechanism in securing high levels of satisfaction is established by comparison with three benchmark methods. To further investigate the impact of considering fairness in vehicle-cargo matching, a shipper-carrier-platform tripartite evolutionary game framework is developed under the waiting response time cost (WRTC) sharing mechanism. Simulation results show that with fairness concerns in vehicle-cargo matching, all stakeholders are better off: The platform achieves positive revenue growth, and shippers and carriers receive positive subsidy. This study offers both theoretical insights and practical guidance for the long-term and stable operation of the on-line freight stowage industry.Comment: 36 pages, 15 figure

Accelerated degradation modeling considering long-range dependence and unit-to-unit variability

Accelerated degradation testing (ADT) is an effective way to evaluate the reliability and lifetime of highly reliable products. Existing studies have shown that the degradation processes of some products are non-Markovian with long-range dependence due to the interaction with environments. Besides, the degradation processes of products from the same population generally vary from each other due to various uncertainties. These two aspects bring great difficulty for ADT modeling. In this paper, we propose an improved ADT model considering both long-range dependence and unit-to-unit variability. To be specific, fractional Brownian motion (FBM) is utilized to capture the long-range dependence in the degradation process. The unit-to-unit variability among multiple products is captured by a random variable in the degradation rate function. To ensure the accuracy of the parameter estimations, a novel statistical inference method based on expectation maximization (EM) algorithm is proposed, in which the maximization of the overall likelihood function is achieved. The effectiveness of the proposed method is fully verified by a simulation case and a microwave case. The results show that the proposed model is more suitable for ADT modeling and analysis than existing ADT models