Description Method of Outdoor Climate Characteristics Considering the Comprehensive Effect on Indoor Climate

What human and buildings perceived the environmental information is comprehensive information. However, existing indoor environment design methods are often simplified to single parameters for indoor and outdoor environmental prediction and indoor environment design. In order to describe the indoor climate characteristics of the comprehensive impact of outdoor climate, this study uses the ensemble empirical mode decomposition (EEMD) method to establish a multi-parameter integrated outdoor comprehensive environmental information description method based on the information-response theory. The outdoor climate feature description method is applied to the analysis of the amplitude and frequency characteristics of outdoor comprehensive information, which provides a research basis for further exploring the indoor and outdoor environmental response under the multi-parameters interaction

Characterization of Electronic Cigarette Aerosol and Its Induction of Oxidative Stress Response in Oral Keratinocytes.

In this study, we have generated and characterized Electronic Cigarette (EC) aerosols using a combination of advanced technologies. In the gas phase, the particle number concentration (PNC) of EC aerosols was found to be positively correlated with puff duration whereas the PNC and size distribution may vary with different flavors and nicotine strength. In the liquid phase (water or cell culture media), the size of EC nanoparticles appeared to be significantly larger than those in the gas phase, which might be due to aggregation of nanoparticles in the liquid phase. By using in vitro high-throughput cytotoxicity assays, we have demonstrated that EC aerosols significantly decrease intracellular levels of glutathione in NHOKs in a dose-dependent fashion resulting in cytotoxicity. These findings suggest that EC aerosols cause cytotoxicity to oral epithelial cells in vitro, and the underlying molecular mechanisms may be or at least partially due to oxidative stress induced by toxic substances (e.g., nanoparticles and chemicals) present in EC aerosols

GAMMA: Revisiting Template-based Automated Program Repair via Mask Prediction

Automated program repair (APR) aims to fix software bugs without human intervention and template-based APR has been widely investigated with promising results. However, it is challenging for template-based APR to select the appropriate donor code, which is an important repair ingredient for generating candidate patches. Inappropriate donor code may cause plausible but incorrect patch generation even with correct fix patterns, limiting the repair performance. In this paper, we aim to revisit template-based APR, and propose GAMMA, to directly leverage large pre-trained language models for donor code generation. Our main insight is that instead of retrieving donor code in the local buggy file, we can directly predict the correct code tokens based on the context code snippets and repair patterns by a cloze task. Specifically, (1) GAMMA revises a variety of fix templates from state-of-the-art template-based APR techniques (i.e., TBar) and transforms them into mask patterns. (2) GAMMA adopts a pre-trained language model to predict the correct code for masked code as a fill-in-the-blank task. The experimental results demonstrate that GAMMA correctly repairs 82 bugs on Defects4J-v1.2, which achieves 20.59\% (14 bugs) and 26.15\% (17 bugs) improvement over the previous state-of-the-art template-based approach TBar and learning-based one Recoder. Furthermore, GAMMA repairs 45 bugs and 22 bugs from the additional Defects4J-v2.0 and QuixBugs, indicating the generalizability of GAMMA in addressing the dataset overfitting issue. We also prove that adopting other pre-trained language models can provide substantial advancement, e.g., CodeBERT-based and ChatGPT-based GAMMA is able to fix 80 and 67 bugs on Defects4J-v1.2, indicating the scalability of GAMMA. Overall, our study highlights the promising future of adopting pre-trained models to generate correct patches on top of fix patterns.Comment: Accepted to 38th IEEE/ACM International Conference on Automated Software Engineering (ASE2023

A Critical Review of Large Language Model on Software Engineering: An Example from ChatGPT and Automated Program Repair

Large Language Models (LLMs) have been gaining increasing attention and demonstrated promising performance across a variety of Software Engineering (SE) tasks, such as Automated Program Repair (APR), code summarization, and code completion. For example, ChatGPT, the latest black-box LLM, has been investigated by numerous recent research studies and has shown impressive performance in various tasks. However, there exists a potential risk of data leakage since these LLMs are usually close-sourced with unknown specific training details, e.g., pre-training datasets. In this paper, we seek to review the bug-fixing capabilities of ChatGPT on a clean APR benchmark with different research objectives. We first introduce {\benchmark}, a new benchmark with buggy and the corresponding fixed programs from competitive programming problems starting from 2023, after the training cutoff point of ChatGPT. The results on {\benchmark} show that ChatGPT is able to fix 109 out of 151 buggy programs using the basic prompt within 35 independent rounds, outperforming state-of-the-art LLMs CodeT5 and PLBART by 27.5\% and 62.4\% prediction accuracy. We also investigate the impact of three types of prompts, i.e., problem description, error feedback, and bug localization, leading to additional 34 fixed bugs. Besides, we provide additional discussion from the interactive nature of ChatGPT to illustrate the capacity of a dialog-based repair workflow with 9 additional fixed bugs. Inspired by the findings, we further pinpoint various challenges and opportunities for advanced SE study equipped with such LLMs (e.g.,~ChatGPT) in the near future. More importantly, our work calls for more research on the reevaluation of the achievements obtained by existing black-box LLMs across various SE tasks, not limited to ChatGPT on APR

Gearbox Fault Prediction of Wind Turbines Based on a Stacking Model and Change-Point Detection

The fault diagnosis and prediction technology of wind turbines are of great significance for increasing the power generation and reducing the downtime of wind turbines. However, most of the current fault detection approaches are realized by setting a single alarm threshold. Considering the complicated working conditions of wind farms, such methods are prone to ignore the fault, send out a false alarm, or leave insufficient troubleshooting time. In this work, we propose a gearbox fault prediction approach of wind turbines based on the supervisory control and data acquisition (SCADA) data. A stacking model composed of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBOOST) was constructed as the normal behavior model to describe the normal conditions of the wind turbines. We used the Mahalanobis distance (MD) instead of the residual to measure the deviation of the current state from the normal conditions of the turbines. By inputting the MD series into the proposed change-point detection algorithm, we can obtain the change point at which the fault symptom begins to appear, and thus achieving the fault prediction of the gearbox. The proposed approach is validated on the historical data of 5 wind turbines in a wind farm, which proves its effectiveness to detect the fault in advance

Long-term efficacy and stability of miniscrew-assisted rapid palatal expansion in mid to late adolescents and adults: a systematic review and meta-analysis

Abstract Background The purpose of this study is to investigate the long-term efficacy and stability of Miniscrew-assisted Rapid Palatal Expansion (MARPE), including its primary outcomes, namely the nasomaxillary complex transverse skeletal and dental expansion, and related secondary outcomes. Methods Electronic databases and manual literature searches, up to October 31, 2022, were performed. The eligibility criteria were the following: studies on patients with transverse maxillary deficiency treated with MARPE in adults and adolescents over 13.5 years of age. Results Ultimately, twelve articles were included in the analysis, one prospective and eleven retrospective observational studies. Five studies showed a moderate risk of bias, while the remaining seven studies were at a serious risk of bias. The GRADE quality of evidence was very low. MARPE is an effective treatment modality for transverse maxillary deficiency (mean success rate: 93.87%). Patients showed increased mean in the skeletal and dental transverse expansion. The basal bone composition, mean alveolar bone and mean dental expansion accounted for 48.85, 7.52, and 43.63% of the total expansion, respectively. There was a certain degree of skeletal and dental relapse over time. MARPE could also cause dental, alveolar, and periodontal side effects, and have an impact on other craniofacial bones, upper airway, and facial soft tissue. Conclusions MARPE is an effective treatment for transverse maxillary deficiency, with a high success rate and a certain degree of skeletal and dental relapse over time

Stachydrine, a Bioactive Equilibrist for Synephrine, Identified from Four <i>Citrus</i> Chinese Herbs

Four Chinese herbs from the Citrus genus, namely Aurantii Fructus Immaturus (Zhishi), Aurantii Fructus (Zhiqiao), Citri Reticulatae Pericarpium Viride (Qingpi) and Citri Reticulatae Pericarpium (Chenpi), are widely used for treating various cardiovascular and gastrointestinal diseases. Many ingredients have already been identified from these herbs, and their various bioactivities provide some interpretations for the pharmacological functions of these herbs. However, the complex functions of these herbs imply undisclosed cholinergic activity. To discover some ingredients with cholinergic activity and further clarify possible reasons for the complex pharmacological functions presented by these herbs, depending on the extended structure–activity relationships of cholinergic and anti-cholinergic agents, a simple method was established here for quickly discovering possible choline analogs using a specific TLC method, and then stachydrine and choline were first identified from these Citrus herb decoctions based on their NMR and HRMS data. After this, two TLC scanning (TLCS) methods were first established for the quantitative analyses of stachydrine and choline, and the contents of the two ingredients and synephrine in 39 samples were determined using the valid TLCS and HPLC methods, respectively. The results showed that the contents of stachydrine (3.04‰) were 2.4 times greater than those of synephrine (1.25‰) in Zhiqiao and about one-third to two-thirds of those of Zhishi, Qingpi and Chenpi. Simultaneously, the contents of stachydrine, choline and synephrine in these herbs present similar decreasing trends with the delay of harvest time; e.g., those of stachydrine decrease from 5.16‰ (Zhishi) to 3.04‰ (Zhike) and from 1.98‰ (Qingpi) to 1.68‰ (Chenpi). Differently, the contents of synephrine decrease the fastest, while those of stachydrine decrease the slowest. Based on these results, compared with the pharmacological activities and pharmacokinetics reported for stachydrine and synephrine, it is indicated that stachydrine can be considered as a bioactive equilibrist for synephrine, especially in the cardio-cerebrovascular protection from these citrus herbs. Additionally, the results confirmed that stachydrine plays an important role in the pharmacological functions of these citrus herbs, especially in dual-directionally regulating the uterus, and in various beneficial effects on the cardio-cerebrovascular system, kidneys and liver

Correction: Characterization of Electronic Cigarette Aerosol and Its Induction of Oxidative Stress Response in Oral Keratinocytes.

[This corrects the article DOI: 10.1371/journal.pone.0154447.]