SEPARATENESS AND CONNECTEDNESS: A STUDY OF WAR NARRATIVE IN VAN BOOY’S THE ILLUSION OF SEPARATENESS

Simon Van Booy, an emerging British novelist, continues to write about war but narrows it down to the separateness and connectedness of war in his novel The Illusion of Separateness. Van Booy takes advantage of a series of narrative strategies to create the illusion of separateness at the surface level, but at the deep level of the novel, he reveals that war makes people closely connected with each other, which can be seen in the interlaced, elliptical character relationship diagram of three generations. Therefore, this study, drawing on narrative theory, endeavors to investigate Van Booy’s war writing in The Illusion of Separateness and explore how the writer uses narrative devices to emphasize the natural elements of war, namely separateness and connectedness. By expounding on these elements and the war narrative in this novel, we can see Van Booy’s unique thinking on war and also have a deeper understanding of war

DO THEY COMMIT PERJURY?: A STUDY OF REPEATING NARRATIVE OF A CRIME SCENE IN PETER DEXTER’S PARIS TROUT

In Paris Trout, a novel based on actual cases, American writer Pete Dexter arranges a crime scene to be told eight times from different perspectives. A close look at repeating narratives leads to discovering certain discrepancies between the narrator’s account and the characters, especially the criminals’. Dexter renders the criminals’ statements questionable by giving the omniscient heterodiegetic narrator authority and letting his account exert the primary effect. Based on the related laws, this essay finds out that the criminals commit perjury in their statements to exonerate themselves. Moreover, Dexter reveals that their illicit doings are under the defense lawyer’s instructions. By doing so, Dexter puts lawyers’ professional ethics at the center of the story. Showing the truth or winning the lawsuit for the customer? This question is an ethical issue that every lawyer ponders. In order to vigorously promote this kind of thinking, the novelist purposely forms a huge difference in characterization. The defense lawyer is modeled on a lawyer of integrity and honesty who is committed to revealing the truth. Through the ironic change in characterization, Dexter criticizes defense lawyers who don’t have professional ethics, a situation rampant in American society in the 1980s

Effect of Strain-gradient Plasticity in Engineering Fracture Assessments

AbstractThis study implements the conventional mechanism-based strain gradient plasticity (CMSG) in the engineering fracture assessment of structural steels, to estimate both the near-tip opening displacements and the probability of brittle fracture. The CMSG theory recognizes the dependence of the material hardening on both the strain and its gradient, for plastic deformations occurring at micron or sub-micron levels, through a material length scale. The CMSG presents a more realistic description of the stress, strain and displacement field in the immediate vicinity of the crack tip, than does the classical plasticity. This study therefore examines the near-tip opening displacement, commonly used in the assessment for ductile fracture in structural steels. This study also integrates the CMSG theory in calculating the microscopic crack driving force in a cleavage fracture assessment framework, namely the Weibull stress approach. The accuracy of the scalar Weibull stress relies significantly on the gradient- dependent, near-tip stress field, which subsequently impinges on the failure probability estimated using the Weibull stresses

Cleavage fracture assessment incorporating strain gradient plasticity

Ph.DDOCTOR OF PHILOSOPH

Less is more: Ensemble Learning for Retinal Disease Recognition Under Limited Resources

Retinal optical coherence tomography (OCT) images provide crucial insights into the health of the posterior ocular segment. Therefore, the advancement of automated image analysis methods is imperative to equip clinicians and researchers with quantitative data, thereby facilitating informed decision-making. The application of deep learning (DL)-based approaches has gained extensive traction for executing these analysis tasks, demonstrating remarkable performance compared to labor-intensive manual analyses. However, the acquisition of Retinal OCT images often presents challenges stemming from privacy concerns and the resource-intensive labeling procedures, which contradicts the prevailing notion that DL models necessitate substantial data volumes for achieving superior performance. Moreover, limitations in available computational resources constrain the progress of high-performance medical artificial intelligence, particularly in less developed regions and countries. This paper introduces a novel ensemble learning mechanism designed for recognizing retinal diseases under limited resources (e.g., data, computation). The mechanism leverages insights from multiple pre-trained models, facilitating the transfer and adaptation of their knowledge to Retinal OCT images. This approach establishes a robust model even when confronted with limited labeled data, eliminating the need for an extensive array of parameters, as required in learning from scratch. Comprehensive experimentation on real-world datasets demonstrates that the proposed approach can achieve superior performance in recognizing Retinal OCT images, even when dealing with exceedingly restricted labeled datasets. Furthermore, this method obviates the necessity of learning extensive-scale parameters, making it well-suited for deployment in low-resource scenarios.Comment: Ongoing wor

Human subtelomeric duplicon structure and organization

The sequence divergence within subtelomeric duplicon families varies considerably, as does the organization of duplicon blocks at subtelomere alleles; a class of duplicon blocks was identified that are subtelomere-specific

Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images

Chest imaging plays an essential role in diagnosing and predicting patients with COVID-19 with evidence of worsening respiratory status. Many deep learning-based approaches for pneumonia recognition have been developed to enable computer-aided diagnosis. However, the long training and inference time makes them inflexible, and the lack of interpretability reduces their credibility in clinical medical practice. This paper aims to develop a pneumonia recognition framework with interpretability, which can understand the complex relationship between lung features and related diseases in chest X-ray (CXR) images to provide high-speed analytics support for medical practice. To reduce the computational complexity to accelerate the recognition process, a novel multi-level self-attention mechanism within Transformer has been proposed to accelerate convergence and emphasize the task-related feature regions. Moreover, a practical CXR image data augmentation has been adopted to address the scarcity of medical image data problems to boost the model's performance. The effectiveness of the proposed method has been demonstrated on the classic COVID-19 recognition task using the widespread pneumonia CXR image dataset. In addition, abundant ablation experiments validate the effectiveness and necessity of all of the components of the proposed method.Comment: Accepted by the IEEE Journal of Biomedical and Health Informatic, doi: 10.1109/JBHI.2023.324794