Analysis of the shear lag effect of cantilever box girder

The theoretical method for solving the shear lag effect of cantilever box girder is applicable through exerting the principle of minimum potential energy and combining the variation method, from which the computation expressions of longitudinal displacement function and maximum angle displacement difference function are deduced. The computational formulas of the shear lag coefficient, additional bending moment and deflection are also deduced under condition that the cantilever box girder is acting both on the concentrated force load and uniformly distributed load. The simulated analysis model is built using a finite element method. The analysis indicates that if the external load produces a constant shear flow within the section of the cantilever beam, only the positive shear lag effect will be produced ;but if such load produces a varying or reverse shear flow within the section of the cantilever beam, the negative shear lag effect will be produced. As the magnitude of the shear lag coefficient is proportional to the wide-span ratio, the structural deflection will be increased either by a positive or negative shear lag effect. . The theoretical analysis is consistent with the analysis conclusion of the finite element method

Training on Thin Air: Improve Image Classification with Generated Data

Acquiring high-quality data for training discriminative models is a crucial yet challenging aspect of building effective predictive systems. In this paper, we present Diffusion Inversion, a simple yet effective method that leverages the pre-trained generative model, Stable Diffusion, to generate diverse, high-quality training data for image classification. Our approach captures the original data distribution and ensures data coverage by inverting images to the latent space of Stable Diffusion, and generates diverse novel training images by conditioning the generative model on noisy versions of these vectors. We identify three key components that allow our generated images to successfully supplant the original dataset, leading to a 2-3x enhancement in sample complexity and a 6.5x decrease in sampling time. Moreover, our approach consistently outperforms generic prompt-based steering methods and KNN retrieval baseline across a wide range of datasets. Additionally, we demonstrate the compatibility of our approach with widely-used data augmentation techniques, as well as the reliability of the generated data in supporting various neural architectures and enhancing few-shot learning

Techniques for improving the water-flooding of oil fields during the high water-cut stage

International audienceThe multi-layer co-exploitation method is often used in offshore oilfields because of the large spacing between the injection and production wells. As oilfields gradually enter the high water-cut stage, the contradiction between the horizontal and vertical directions becomes more prominent, and the distribution of the remaining oil is more complex. Oilfields are facing unprecedented challenges in further enhancing oil recovery. Using oilfield A, which is in the high water-cut stage, as the research object, we compiled a detailed description of the remaining oil during the high water-cut stage using the information collected during the comprehensive adjustment and infilling of the oilfield. In addition various techniques for tapping the potential reservoir, stabilizing the oil, and controlling the water were investigated. A set of key techniques for the continuous improvement of the efficiency of water injection after comprehensive adjustment of high water-cut fields was generated. Based on the determined configuration of the offshore deltaic reservoir, a set of detailed descriptive methods and tapping technology for extracting the remaining oil in the offshore high water-cut oilfield after comprehensive adjustment was established. By considering the equilibrium displacement and using a new quantitative characterization method that includes displacement, a new technique for determining the quantity of water that needs to be injected into a stratified injection well during the high water-cut stage was established. Based on the principle of flow field intensity reconfiguration, a linear, variable-intensity, alternating injection and withdrawal technique was proposed. With the application of this series of techniques, the increase in the water content was controlled to within 1%, the natural reduction rate was controlled to within 9%, and the production increased by 1.060 × 107 m3

Comprehensive analysis of clinical significance of stem-cell related factors in renal cell cancer

<p>Abstract</p> <p>Background</p> <p>C-MYC, LIN28, OCT4, KLF4, NANOG and SOX2 are stem cell related factors. We detected whether these factors express in renal cell carcinoma (RCC) tissues to study their correlations with the clinical and pathological characteristics.</p> <p>Methods</p> <p>The expressions of c-MYC, LIN28, SOX2, KLF4, OCT4 and NANOG in 30 RCC patients and 5 non-RCC patients were detected with quantitative real-time reverse transcription-PCR (qRT-PCR). The data were analyzed with Wilcoxon signed rank sum test and x²test.</p> <p>Results</p> <p>In RCC group, c-MYC expression was significantly higher in RCC tissues compared with normal tissues (P < 0.05). The expression levels of OCT4, KLF4, NANOG and SOX2 were significantly lower in RCC tissues compared with normal tissues (P < 0.05). LIN28 expression level was not significant. No difference was observed when it comes to clinical and pathological characteristics such as gender, age, tumor size, cTNM classification and differentiation status (P > 0.05). Also the expression levels of all above factors were not significantly changed in non-RCC group (P > 0.05).</p> <p>Conclusions</p> <p>The present analysis strongly suggests that altered expression of several stem cell related factors may play different roles in RCC. C-MYC may function as an oncogene and OCT4, KLF4, NANOG and SOX2 as tumor suppressors.</p

Large Language Models Are Human-Level Prompt Engineers

By conditioning on natural language instructions, large language models (LLMs) have displayed impressive capabilities as general-purpose computers. However, task performance depends significantly on the quality of the prompt used to steer the model, and most effective prompts have been handcrafted by humans. Inspired by classical program synthesis and the human approach to prompt engineering, we propose Automatic Prompt Engineer (APE) for automatic instruction generation and selection. In our method, we treat the instruction as the "program," optimized by searching over a pool of instruction candidates proposed by an LLM in order to maximize a chosen score function. To evaluate the quality of the selected instruction, we evaluate the zero-shot performance of another LLM following the selected instruction. Experiments on 24 NLP tasks show that our automatically generated instructions outperform the prior LLM baseline by a large margin and achieve better or comparable performance to the instructions generated by human annotators on 19/24 tasks. We conduct extensive qualitative and quantitative analyses to explore the performance of APE. We show that APE-engineered prompts can be applied to steer models toward truthfulness and/or informativeness, as well as to improve few-shot learning performance by simply prepending them to standard in-context learning prompts. Please check out our webpage at https://sites.google.com/view/automatic-prompt-engineer