An Autonomous Large Language Model Agent for Chemical Literature Data Mining

Chemical synthesis, which is crucial for advancing material synthesis and drug discovery, impacts various sectors including environmental science and healthcare. The rise of technology in chemistry has generated extensive chemical data, challenging researchers to discern patterns and refine synthesis processes. Artificial intelligence (AI) helps by analyzing data to optimize synthesis and increase yields. However, AI faces challenges in processing literature data due to the unstructured format and diverse writing style of chemical literature. To overcome these difficulties, we introduce an end-to-end AI agent framework capable of high-fidelity extraction from extensive chemical literature. This AI agent employs large language models (LLMs) for prompt generation and iterative optimization. It functions as a chemistry assistant, automating data collection and analysis, thereby saving manpower and enhancing performance. Our framework's efficacy is evaluated using accuracy, recall, and F1 score of reaction condition data, and we compared our method with human experts in terms of content correctness and time efficiency. The proposed approach marks a significant advancement in automating chemical literature extraction and demonstrates the potential for AI to revolutionize data management and utilization in chemistry

Formation of Maillard Reaction Products in Heat-Treated Torreya grandis cv. Merrilli Seed Oil and Their Effects on Two Analytical Methods for the Determination of Total Phenolics

The occurrence of Maillard reaction in heated Torreya grandis cv. Merrilli seed oil was verified by detecting browning index and the contents of the Maillard reaction products (MRPs) 3-deoxyglucosone, methylglyoxal and 5-(hydroxymethyl)furfural, and the effects of the MRPs on the quantification of total phenolics using the Folin-Ciocalteu and Fast Blue BB assays were explored. The Folin-Ciocalteu assay showed that the content of total phenolics in the oil increased after treatment at 150 ℃ for 90 to 120 min, while the Fast Blue BB assay showed the opposite result. It was also observed that the absorbance of the oil at a wavelength of 294 nm increased after long-term low-temperature or high-temperature heat treatment. 3-Deoxyglucosone (0.21–0.47 μg/g) and 5-(hydroxymethyl)furfural (0.06–0.40 μg/g) were detected only in the 150 ℃ treated oil, while methylglyoxal (0.67–1.73 μg/g) existed in both oil samples. In the Folin-Ciocalteu assay, the absorbance at a wavelength of 765 nm of 3-deoxyglucosone and methylglyoxal linearly increased with an increase in their concentrations, and the decreasing order of the absorbance of the MRPs at the same concentration was 3-deoxyglucosone > methylglyoxal > 5-(hydroxymethyl)furfural. In the presence of 3-deoxyglucosone, the Folin-Ciocalteu result was greater than the actual value, and the degree of interference was not related to the content of total phenolics in samples, but instead was positively correlated with the concentration of 3-deoxyglucosone. In the Fast Blue BB assay, there was no significant difference in absorbance at 420 nm among the three MRPs. For food matrices prone to the formation of 3-deoxyglucosone and methylglyoxal during processing, the Fast Blue BB assay can be selected instead of the Folin-Ciocalteu assay to mitigate the interferences from the two substances in the quantification of total phenolics

Design and implementation of quantitative remote sensing monitoring and intelligent analysis system for mine ecological environment

Mine ecological environment monitoring and governance is a critical requirement for national ecological civilization construction and the dual carbon goal. The informatization and intelligent construction of the mine ecological environment have become an important part of Digital China driven by the new generation of information technology, and it is also an inevitable trend in the development of the current era. However, existing mine ecological environment monitoring systems are still in the primary stage with a single theme, incomplete elements, basic measurement, and local management, and they cannot meet the demand for multi-element, long-term, high-frequency monitoring and analysis of the mine ecological environment. To address this problem, the quantitative remote sensing monitoring and intelligent analysis system for the mine ecological environment under B/S architecture is proposed, called Mine Ecology Remote Eyes. The development requirements, technical framework, key technologies, and core functions of the system are further described in detail. The system utilizes satellite remote sensing technology and other monitoring methods to obtain and aggregate mine ecological big data from different sources, forming a map of mine distribution and data resource services. Using quantitative remote sensing to invert ecological parameters of mine environments, a set of long-term and multi-element monitoring products can be generated. These products cover various ecological elements such as human activities, natural geographical conditions, and “vegetation-soil-water-atmosphere” parameters. The system provides a range of tools for GIS spatial and temporal analysis, statistical analysis, and comprehensive quantitative evaluation. With these tools, users can monitor spatial changes in ecological parameters such as land use and normalized difference vegetation index (NDVI) in mining areas along with mining activities, as well as query and visualize historical statistical values of ecological elements such as soil water content and suspended solids concentration in water under different spatiotemporal locations or regions. Additionally, the system enables comprehensive quantitative evaluation of the quality of the mine ecological environment taking into account multiple ecological elements. Finally, the system generates a monitoring report on ecological disturbance and governance of the mine. The application of Mine Ecology Remote Eyes will facilitate the change monitoring, data management, intelligent analysis, and decision-making applications of the mine ecological environment. This system has the potential to improve the efficiency and intelligence level of monitoring and governance of the mine ecological environment, and provides a reference for promoting the informatization of ecological civilization

Factors Influencing Polyol Liquefaction of Nut Shells of Different Camellia Species

The liquefaction rates and kinetics of nut shells of different Camellia species in PEG400/glycerol/H2SO4 liquefying solvent were investigated. Changes in major components including cellulose, hemicellulose, and lignin as well as cellulose crystallinity of the nut shells were determined. The compositions of the liquefaction residues were analyzed. Results indicated that, under the same conditions, the liquefaction rates of nut shells of different Camellia species were noticeably different and the PEG400/glycerol/H2SO4 liquefaction agent was not suitable for the liquefaction of the nut shells of all Camellia species. The burst liquefaction of Camellia nut shells (CNSs) that occurred during the first stage was due to the rapid degradation of hemicellulose, acid-soluble lignin, and amorphous cellulose. The liquefaction during the second stage became very slow, mostly because the swelling and decomposition of crystalline cellulose was very difficult to achieve with the liquefying agent and the liquefaction products inhibited liquefaction at later stages. The liquefaction residues of CNSs were composed of crystalline cellulose, small amounts of hemicellulose, acid-insoluble lignin, and ash. Ash was partially dissolved in the liquefying agent. The liquefaction rates of all CNSs tested in this study showed linear relationships with time, with coefficients of determination (R2) greater than 0.7082, indicating that the liquefaction of CNS was a pseudo-first-order reaction

Physicochemical Properties of Camellia Nut Shell and its Thermal Degradation Characteristics

Camellia nut shell (CNS) is known as an important bio-resource that has great potential as a biomaterial. The elemental composition, chemical structure, crystallinity, and pyrolysis characteristics were analyzed in this paper for six species of CNS. The concentration of organic carbon, N, K, and Na in CNS ranges from 44.40 to 48.60%, 2.91 to 4.42 mg.g-1, 7.67 to 13.80 mg.g-1, and 0.02 to 0.26 mg.g-1, respectively. The content of lignin, cellulose, hemicellulose, and ash varies between 30.07 and 36.23%, 13.87 and 20.95%, 35.15 and 49.34%, as well as 2.00 and 4.75%, respectively. Camellia nut shell cellulose crystalline structure belongs to typical cellulose type I, and the cellulose crystallinity index for the six species ranges from 37.4 to 62.3%. The CNS pyrolysis process can be divided into three phases, and the substantial degradation occurs within the temperature range of 200 to 430 °C, with nearly 60% loss of weight. The temperature could be reduced greatly during pyrolysis under acidic conditions with PEG 400/glycerol as a solvent. The degradation rate was impacted by K concentration. Increasing cellulose crystallinity negatively affected the degradation rate

The Actor-Dueling-Critic Method for Reinforcement Learning

Model-free reinforcement learning is a powerful and efficient machine-learning paradigm which has been generally used in the robotic control domain. In the reinforcement learning setting, the value function method learns policies by maximizing the state-action value (Q value), but it suffers from inaccurate Q estimation and results in poor performance in a stochastic environment. To mitigate this issue, we present an approach based on the actor-critic framework, and in the critic branch we modify the manner of estimating Q-value by introducing the advantage function, such as dueling network, which can estimate the action-advantage value. The action-advantage value is independent of state and environment noise, we use it as a fine-tuning factor to the estimated Q value. We refer to this approach as the actor-dueling-critic (ADC) network since the frame is inspired by the dueling network. Furthermore, we redesign the dueling network part in the critic branch to make it adapt to the continuous action space. The method was tested on gym classic control environments and an obstacle avoidance environment, and we design a noise environment to test the training stability. The results indicate the ADC approach is more stable and converges faster than the DDPG method in noise environments.Peer reviewe

Analysis and Research on Temporal and Spatial Variation of Color Steel Tile Roof of Munyaka Region in Kenya, Africa

In Africa, the distribution of color steel tile roof (CSTR) can reflect the living standard of residents, and the analysis of its temporal and spatial changes can reflect the local changes in local living conditions. It is helpful to analyze the change of the local economic level. By using the satellite remote sensing image processing method to obtain the temporal and spatial change characteristics of CSTR and to analyze the changes in residents’ living conditions in Munyaka, Eldoret, Kenya, Africa, the model of multifeature decision tree method (DTM) extraction was established. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Building Index (NDBI) were used to remove farmland from the difference of the CSTR. The Normalized Difference Surface Index (NCSI) was constructed, and the texture features were analyzed to eliminate wasteland and bare land, respectively. The research results show that the Kappa coefficient is 0.9223, and the user precision and mapping precision are 97.79% and 91.10%, respectively. At the same time, combined with the Erdoret municipal road project, the changes of CSTR before and after the project in 2016–2020 are studied. Compared the area change of CSTR in 2016–2018 with that in 2018–2020, the annual growth rate before the construction of the municipal road project is about 3.47%. After the completion of the project, the annual growth rate is 7.29%, more than twice the rate before the construction. This method can realize the dynamic monitoring of CSTR, reflect the changes of the residents’ living environment in the region, help analyze the improvement of poverty in Africa, and help understand the changes of African economic conditions

Study on compatibility optimization of resin-based permeable pavement bricks

The permeable pavement plays an important role in mitigating urban flooding. In order to explore the relationship between materials and properties to better guide the practical production of resin-based permeable bricks, 10 kinds of representative aggregate samples with obvious different characteristics were selected for preparation. In this study, Image Pro Plus was used to binarize the acquired image pictures of the aggregate so as to obtain particle group characteristic parameters. The properties and porosity of the brick were measured in order to describe the influence of the material. The results are as follows. The relative standard deviation of aggregate and the amount of cementing material are negatively related to the compressive strength of permeable bricks, but positively related to water permeability and filtration performance. The roundness and roughness of the aggregate are the opposite. Furthermore, the porosity of the permeable brick is the essential reason for this phenomenon, that is, as the porosity increase, the compressive strength decrease, but the water permeability and the filtration performance become better. In the end, an optimization method for the compatibility of resin-based pavement permeable bricks was proposed through reflecting all factors in a two-bit flat grayscale image which can be applied in performance prediction and guidance of material selection

Indoor and outdoor low-cost seamless integrated navigation system based on the integration of INS/GNSS/LIDAR system

Global Navigation Satellite System (GNSS) provides accurate positioning data for vehicular navigation in open outdoor environment. In an indoor environment, Light Detection and Ranging (LIDAR) Simultaneous Localization and Mapping (SLAM) establishes a two-dimensional map and provides positioning data. However, LIDAR can only provide relative positioning data and it cannot directly provide the latitude and longitude of the current position. As a consequence, GNSS/Inertial Navigation System (INS) integrated navigation could be employed in outdoors, while the indoors part makes use of INS/LIDAR integrated navigation and the corresponding switching navigation will make the indoor and outdoor positioning consistent. In addition, when the vehicle enters the garage, the GNSS signal will be blurred for a while and then disappeared. Ambiguous GNSS satellite signals will lead to the continuous distortion or overall drift of the positioning trajectory in the indoor condition. Therefore, an INS/LIDAR seamless integrated navigation algorithm and a switching algorithm based on vehicle navigation system are designed. According to the experimental data, the positioning accuracy of the INS/LIDAR navigation algorithm in the simulated environmental experiment is 50% higher than that of the Dead Reckoning (DR) algorithm. Besides, the switching algorithm developed based on the INS/LIDAR integrated navigation algorithm can achieve 80% success rate in navigation mode switching.Peer reviewe