Simulation of chemical reaction dynamics based on quantum computing

The molecular energies of chemical systems have been successfully calculated on quantum computers, however, more attention has been paid to the dynamic process of chemical reactions in practical application, especially in catalyst design, material synthesis. Due to the limited the capabilities of the noisy intermediate scale quantum (NISQ) devices, directly simulating the reaction dynamics and determining reaction pathway still remain a challenge. Here we develop the ab initio molecular dynamics based on quantum computing to simulate reaction dynamics by extending correlated sampling approach. And, we use this approach to calculate Hessian matrix and evaluate computation resources. We test the performance of our approach by simulating hydrogen exchange reaction and bimolecular nucleophilic substitution SN2 reaction. Our results suggest that it is reliable to characterize the molecular structure, property, and reactivity, which is another important expansion of the application of quantum computingComment: 8 pages, 4 figure

Effects of Water Quality Adjusted by Submerged Macrophytes on the Richness of the Epiphytic Algal Community

Submerged macrophytes and epiphytic algae play significant roles in the functioning of aquatic ecosystems. Submerged macrophytes can influence the epiphytic algal community by directly or indirectly modifying environmental conditions (nutrients, light, etc.). From December to June of the following year, we investigated the dynamics of the dominant winter species Potamogeton crispus, its epiphytic algae, and water quality parameters in the shallow Liangzi Lake in China. The richness of epiphytic algae had a trend similar to that of P. crispus coverage, which increased in the first four months and then decreased in the following three months. The structural equation model (SEM) showed that P. crispus affected the richness of epiphytic algae by reducing nutrient concentrations (reduction in total organic carbon, total nitrogen and chemical oxygen demand) and enhancing water transparency (reduction in turbidity and total suspend solids) to enhance the richness of epiphytic algae. The results indicated that high amounts of submerged macrophyte cover can increase the richness of the epiphytic algal community by changing water quality

MEMD-ABSA: A Multi-Element Multi-Domain Dataset for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis is a long-standing research interest in the field of opinion mining, and in recent years, researchers have gradually shifted their focus from simple ABSA subtasks to end-to-end multi-element ABSA tasks. However, the datasets currently used in the research are limited to individual elements of specific tasks, usually focusing on in-domain settings, ignoring implicit aspects and opinions, and with a small data scale. To address these issues, we propose a large-scale Multi-Element Multi-Domain dataset (MEMD) that covers the four elements across five domains, including nearly 20,000 review sentences and 30,000 quadruples annotated with explicit and implicit aspects and opinions for ABSA research. Meanwhile, we evaluate generative and non-generative baselines on multiple ABSA subtasks under the open domain setting, and the results show that open domain ABSA as well as mining implicit aspects and opinions remain ongoing challenges to be addressed. The datasets are publicly released at \url{https://github.com/NUSTM/MEMD-ABSA}

LAG-YOLO: Efficient road damage detector via lightweight attention ghost module

Road damage detection plays an important role in ensuring road safety and improving traffic flow. The dramatic progress of artificial intelligence (AI) technology offers new opportunities for this field. In this paper, we introduce lightweight attention ghost-you only look once (LAG-YOLO), an efficient deep-learning network for road damage detection. LAG-YOLO optimizes the network structure of YOLO, making it more suitable for real-time processing and lightweight deployment while ensuring high accuracy. In addition, a novel module called attention ghost is designed to reduce the model parameters and improve the model performance by the simple attention module (SimAM). LAG-YOLO achieves an impressive parameter reduction to 4.19 million, delivering remarkable mean average precision (mAP) scores of 45.80% on the Hualu dataset and 52.35% on the RDD2020 dataset. In summary, the proposed network performs satisfactorily on extensive road damage datasets with fewer parameters, making it more suitable to be deployed in practice

ChemiQ: A Chemistry Simulator for Quantum Computer

Quantum computing, an innovative computing system carrying prominent processing rate, is meant to be the solutions to problems in many fields. Among these realms, the most intuitive application is to help chemical researchers correctly de-scribe strong correlation and complex systems, which are the great challenge in current chemistry simulation. In this paper, we will present a standalone quantum simulation tool for chemistry, ChemiQ, which is designed to assist people carry out chemical research or molecular calculation on real or virtual quantum computers. Under the idea of modular programming in C++ language, the software is designed as a full-stack tool without third-party physics or chemistry application packages. It provides services as follow: visually construct molecular structure, quickly simulate ground-state energy, scan molecular potential energy curve by distance or angle, study chemical reaction, and return calculation results graphically after analysis.Comment: software,7 pages, 5 figure

Forest Phenology Dynamics and Its Responses to Meteorological Variations in Northeast China

Based on time series of Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data (2000–2009), we extracted forest phenological variables in Northeast China using a threshold-based method, which included the start of the growing season (SOS), end of the growing season (EOS), and length of the growing season (LOS). The spatial variation of phenological trends was analyzed using the linear regression method. In Northeast China, SOS was delayed at the rate of <1.5 days per year. The delay trend of EOS was well distributed in the entire region with almost the same rates. LOS increased slightly. The analysis of the relationship between forest phenology and meteorological variations shows that SOS was mainly affected by spring temperature, whereas SOS had a negative relationship with precipitation in the warm-temperate deciduous broadleaf forest region. The EOS in temperate steppe region was affected by temperature and precipitation in August, whereas the others were significantly affected by temperature. Because of the increased temperature in spring, the LOS of the temperate steppe region and temperate mixed forest region increased, and the LOS was positively correlated with the mean temperature of summer in the cool-temperate needleleaf forest region