NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC

Corrigendum to: The TianQin project: current progress on science and technology

In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Study on CO

CO2 emission control is of great urgency, and the decrease of heavy-duty diesel engines’ CO2 emission is one of the significant methods to reduce CO2 emission. This paper uses a full-flow constantvolume dilution sampling system to experiment on the China VI heavy-duty diesel engine in order to measure CO2 emissions under WHTC and WHSC cycles and different loads, with studying the instantaneous emissions characteristics of CO2, post-processing effects on CO2 emissions, influence factors of CO2 emissions. The study found that the CO2 emissions before the post-treatment of the WHSC cycle are 37% higher than that of the WHTC cycle, while emissions of CO2 are 3.45% higher than that before post-treatment. Simultaneously, cold start increases the CO2 emissions of the heavy-duty diesel engines by more than 1%. Post-treatment still increases the CO2 emissions of heavy-duty diesel engines by 3.5%. In addition, CO2 emissions have different trends with power at different speeds. CO2 emissions get an incremental within 600rpm and 900rpm, which gradually becomes slower until it reaches the peak, as engine power increases; the CO2 emissions initially increase, followed by a decrease, and then continue to increase within 1000rpm and 1400rpm; the CO2 emissions are almost not affected by the speed within 1500rpm and 1900rpm

Study on fuel consumption and emission characteristics of China VI heavy duty vehicle based on vehicle specific power

This study selects a China VI heavy duty vehicle for PEMS test, and Based on the measurement results of vehicle specific power (VSP) parameters, the VSP calculation formula applicable to this study is proposed , And analyzes the distribution characteristics of VSP, and at the same time according to the fuel consumption and emission data of the actual road driving process collected by the vehicle, The effect of VSP on vehicle fuel consumption and emission characteristics and the correlation between the two are studied. Results show that VSP of the vehicle are mainly concentrated in the interval -10 ≤ VSP ≤ 10kw / t, in which the vehicle driving time accounts for about 99.3% of the total time; the correlation coefficient between VSP and average fuel consumption is about 0.93, there is a strong correlation; The changes of CO, CO2, NOX and PN with VSP all show as that under the same absolute value of VSP, the pollutants emission rate in the VSP> 0 interval is higher than the VSP <0 interval, in which the correlation between VSP and CO, CO2, PN emissions is more strong, but poorly correlated with NOX emissions

Trajectories of Mediterranean Diet Adherence and Risk of Hypertension in China: Results from the CHNS Study, 1997–2011

Evidence indicates that longitudinal changes in dietary patterns may predict variations in blood pressure (BP) and risk of incident hypertension. We aimed to identify distinct trajectories in the levels of Mediterranean diet adherence (MDA) in China and explore their association with BP levels and hypertension risk using the China Health and Nutrition Survey 1997&#8315;2011 data. Three levels of MDA were constructed. The trajectories in these levels were constructed using group-based trajectory modeling. A Cox proportional hazards model was used to measure the association between MDA trajectory groups and the risk of incident hypertension after adjusting for covariates. Finally, 6586 individuals were included. Six distinct MDA trajectory groups were identified: persistently low and gradual decline; rapidly increasing and stabilized; persistently moderate; slightly increasing, steady, and acutely descending; slightly decreasing and acutely elevated; and persistently high. The systolic BP and diastolic BP were significantly lower in trajectory groups with rapidly increasing and stabilized MDA; slightly increasing, steady, and acutely descending MDA; and persistently high MDA. Cox regression analysis showed that the risks of developing hypertension were relatively lower in the group with slightly increasing, steady, and acutely descending MDA (hazard ratio (HR) = 0.17, 95% confidence interval (CI): 0.09&#8315;0.32) and the group with rapidly increasing and stabilized MDA (HR = 0.32, 95% CI: 0.23&#8315;0.42), but the risk was the highest in the trajectory with persistently moderate MDA (HR = 0.96, 95% CI: 0.84&#8315;1.08). In conclusion, MDA in China was categorized into six distinct trajectory groups. BP was relatively lower in trajectory groups with initially high or increasing MDA levels. Greater MDA was significantly associated with a lower risk of developing hypertension

Transient Emissions Forecasting of Off-Road Construction Machinery Based on Long Short-Term Memory Network

Off-road machinery is one of the significant contributors to air pollution due to its large quantity. In this study, a deep learning model was developed to predict the transient engine emissions of CO, NO, NO2, and NOx, which are the main pollutants emitted by off-road machinery. A portable emission measurement system (PEMS) was used to measure the exhaust emission features of four types of construction machinery. The raw PEMS data were preprocessed using data compensation, local linear regression, and normalization to ensure that the data could handle transient conditions. The proposed model utilizes the preprocessing PEMS data to estimate the CO, NO, NO2, and NOx emissions from off-road machinery using a recurrent neural network (RNN) based on a long short-term memory (LSTM) model. The experimental results show that the proposed method can effectively predict the emissions from off-road construction machinery under transient conditions and can be applied to controlling the emissions from off-road construction machinery

Involvement of 5mC DNA demethylation via 5-aza-2'-deoxycytidine in regulating gene expression during early somatic embryo development in white spruce (Picea glauca)

DNA methylation plays a crucial role in the development of somatic embryos (SEs) through the regulation of gene expression. To examine the impact of DNA methylation on gene expression during early SE development in Picea glauca, the demethylation reagent 5-aza-dC (5-aza-2′-deoxycytidine) was employed to modify DNA methylation regions and levels during the pre-maturation stage of somatic embryogenesis. The application of 2.0 µM 5-aza-dC did not induce toxicity to SEs in early development. Following treatment, the global DNA methylation level decreased significantly on the 7th day of pre-maturation and the 1st week of maturation. Methylated DNA immunoprecipitation (MeDIP) sequencing revealed that differentially methylated regions, as analyzed through Gene Ontology (GO), were related to plant development and reproduction and that they were hypomethylated on the 3rd day but hypermethylated on the 7th day in 5-aza-dC-treated embryogenic tissues. These findings indicate that 5-aza-dC treatment positively impacts early SE development, which was inhibited following 7 d of treatment. The expression of MSH7, JMJ14, and CalS10 was associated with DNA methylation, epigenetic regulation, and somatic embryogenesis. Further analysis of methylated regions revealed that the expression profiles of MSH7, JMJ14, and CalS10 were correlated with altered DNA methylation, suggesting DNA methylation at 5 mC may play a role in controlling the expression of these genes and regulating the early development of SEs in P. glauca. This study offers new insights into the regulation of somatic embryogenesis in conifers