The Experimental Research of low strength Concrete Considering Strain Rate under Uniaxial Compression

AbstractThe tests were implemented in low strength concrete with four kinds of strength (C20, C25, C30, C35) and with diffrent strain ratses (10-5/s-1, 10-4/s-1, 10-3/s-1 and 10-2/s-1. The stress-strain curves, strength, elastic modulus and Poisson's ratio are studied in different strain rate. The results show that: (1) With the increase of strain rate, the inflexion strength and ultimate strength of concrete were improved; (2) With the increase of strain rate, the static and dynamic elastic modulus increased slightly; (3) Poisson's ratio increased slightly with the growth strain rate. This conclusion is very important in civil engineering

Towards Enhancing In-Context Learning for Code Generation

In-context learning (ICL) with pre-trained language models (PTLMs) has shown great success in code generation. ICL does not require training. PTLMs take as the input a prompt consisting of a few requirement-code examples and a new requirement, and output a new program. However, existing studies simply reuse ICL techniques for natural language generation and ignore unique features of code generation. We refer to these studies as standard ICL. Inspired by observations of the human coding process, we propose a novel ICL approach for code generation named AceCoder. Compared to standard ICL, AceCoder has two novelties. (1) Example retrieval. It retrieves similar programs as examples and learns programming skills (e.g., algorithms, APIs) from them. (2) Guided Code Generation. It encourages PTLMs to output an intermediate preliminary (e.g., test cases, APIs) before generating programs. The preliminary can help PTLMs understand requirements and guide the next code generation. We apply AceCoder to six PTLMs (e.g., Codex) and evaluate it on three public benchmarks using the Pass@k. Results show that AceCoder can significantly improve the performance of PTLMs on code generation. (1) In terms of Pass@1, AceCoder outperforms standard ICL by up to 79.7% and fine-tuned models by up to 171%. (2) AceCoder is effective in PTLMs with different sizes (e.g., 1B to 175B) and different languages (e.g., Python, Java, and JavaScript). (3) We investigate multiple choices of the intermediate preliminary. (4) We manually evaluate generated programs in three aspects and prove the superiority of AceCoder. (5) Finally, we discuss some insights about ICL for practitioners

Improving Code Generation by Dynamic Temperature Sampling

Recently, Large Language Models (LLMs) have shown impressive results in code generation. However, existing decoding strategies are designed for Natural Language (NL) generation, overlooking the differences between NL and programming languages (PL). Due to this oversight, a better decoding strategy for code generation remains an open question. In this paper, we conduct the first systematic study to explore a decoding strategy specialized in code generation. With an analysis of loss distributions of code tokens, we find that code tokens can be divided into two categories: challenging tokens that are difficult to predict and confident tokens that can be easily inferred. Among them, the challenging tokens mainly appear at the beginning of a code block. Inspired by the above findings, we propose a simple yet effective method: Adaptive Temperature (AdapT) sampling, which dynamically adjusts the temperature coefficient when decoding different tokens. We apply a larger temperature when sampling for challenging tokens, allowing LLMs to explore diverse choices. We employ a smaller temperature for confident tokens avoiding the influence of tail randomness noises. We apply AdapT sampling to LLMs with different sizes and conduct evaluations on two popular datasets. Results show that AdapT sampling significantly outperforms state-of-the-art decoding strategy

An efficient power plant model of electric vehicles for unit commitment of large scale wind farms

AbstractAn efficient power plant model of electric vehicles (E-EPP) considering the travelling comfort levels of EV users is developed to investigate the contribution of EVs on the unit commitment (UC) of large scale wind farms. Firstly, a generic EV battery model (GEBM) is established considering the uncertainties of battery parameters. Then, a Monte Carlo Simulation (MCS) method is implemented within the E-EPP to obtain the available response capacity of EV charging load over time. And a UC strategy using the E-EPP based on power flow tracing is developed. Finally, a modified IEEE 118-bus system integrated with wind farms is used to verify the effectiveness of the E-EPP for the UC of large scale wind farms

A new fault diagnosis method using deep belief network and compressive sensing

Compressive sensing provides a new idea for machinery monitoring, which greatly reduces the burden on data transmission. After that, the compressed signal will be used for fault diagnosis by feature extraction and fault classification. However, traditional fault diagnosis heavily depends on the prior knowledge and requires a signal reconstruction which will cost great time consumption. For this problem, a deep belief network (DBN) is used here for fault detection directly on compressed signal. This is the first time DBN is combined with the compressive sensing. The PCA analysis shows that DBN has successfully separated different features. The DBN method which is tested on compressed gearbox signal, achieves 92.5 % accuracy for 25 % compressed signal. We compare the DBN on both compressed and reconstructed signal, and find that the DBN using compressed signal not only achieves better accuracies, but also costs less time when compression ratio is less than 0.35. Moreover, the results have been compared with other classification methods

Intermedin protects against myocardial ischemia-reperfusion injury in diabetic rats.

BACKGROUND: Diabetic patients, through incompletely understood mechanisms, endure exacerbated ischemic heart injury compared to non-diabetic patients. Intermedin (IMD) is a novel calcitonin gene-related peptide (CGRP) superfamily member with established cardiovascular protective effects. However, whether IMD protects against diabetic myocardial ischemia/reperfusion (MI/R) injury is unknown. METHODS: Diabetes was induced by streptozotocin in Sprague--Dawley rats. Animals were subjected to MI via left circumflex artery ligation for 30 minutes followed by 2 hours R. IMD was administered formally 10 minutes before R. Outcome measures included left ventricular function, oxidative stress, cellular death, infarct size, and inflammation. RESULTS: IMD levels were significantly decreased in diabetic rats compared to control animals. After MI/R, diabetic rats manifested elevated intermedin levels, both in plasma (64.95 +/- 4.84 pmol/L, p \u3c 0.05) and myocardial tissue (9.8 +/- 0.60 pmol/L, p \u3c 0.01) compared to pre-MI control values (43.62 +/- 3.47 pmol/L and 4.4 +/- 0.41). IMD administration to diabetic rats subjected to MI/R decreased oxidative stress product generation, apoptosis, infarct size, and inflammatory cytokine release (p \u3c 0.05 or p \u3c 0.01). CONCLUSIONS: By reducing oxidative stress, inflammation, and apoptosis, IMD may represent a promising novel therapeutic target mitigating diabetic ischemic heart injury

Construction of Trisubstituted Chromone Skeletons Carrying Electron-Withdrawing Groups Via PhIO-Mediated Dehydrogenation and Its Application to the Synthesis of Frutinone A

Abstract The construction of the biologically interesting chromone skeleton was realized by PhIO-mediated dehydrogenation of chromanones under mild conditions. Interestingly, this method also found application in the synthesis of the naturally occurring frutinone A

A novel method to quantify local CpG methylation density by regional methylation elongation assay on microarray

<p>Abstract</p> <p>Background</p> <p>DNA methylation based techniques are important tools in both clinical diagnostics and therapeutics. But most of these methods only analyze a few CpG sites in a target region. Indeed, difference of site-specific methylation may also lead to a change of methylation density in many cases, and it has been found that the density of methylation is more important than methylation of single CpG site for gene silencing.</p> <p>Results</p> <p>We have developed a novel approach for quantitative analysis of CpG methylation density on the basis of microarray-based hybridization and incorporation of Cy5-dCTP into the Cy3 labeled target DNA by using Taq DNA Polymerase on microarray. The quantification is achieved by measuring Cy5/Cy3 signal ratio which is proportional to methylation density. This methylation-sensitive technique, termed RMEAM (regional methylation elongation assay on microarray), provides several advantages over existing methods used for methylation analysis. It can determine an exact methylation density of the given region, and has potential of high throughput. We demonstrate a use of this method in determining the methylation density of the promoter region of the tumor-related gene <it>MLH1, TERT </it>and <it>MGMT </it>in colorectal carcinoma patients.</p> <p>Conclusion</p> <p>This technique allows for quantitative analysis of regional methylation density, which is the representative of all allelic methylation patterns in the sample. The results show that this technique has the characteristics of simplicity, rapidness, specificity and high-throughput.</p