Search

" AI for Science " (AI4S) is a new scientific research paradigm that deeply integrates AI technology with scientific research to promote the discovery of new knowledge and the solution of scientific problems. As the application of AI4S in the natural sciences and humanities and social sciences advances, its development line, opportunities and challenges, needs and tasks, and ways of realization deserve further discussion. In order to advance AI4S research, promote scientific and technological (S&T) innovation and progress, and facilitate the effective strengthening of the discipline of information resources management, our journal has invited seven experts to organize this academic conversation on AI4S. 1) Supporting knowledge services for AI4S: In the current landscape of intelligent knowledge services, the requirements for supporting AI4S have increased, including the need for multi-level knowledge discovery and acquisition, cross-disciplinary research and innovation, and user-friendly participatory services. In addition, knowledge service scenarios are moving towards diversification, complexity, depth, specialization, and personalization in ubiquitous knowledge discovery, generative content services, and multi-round interactive service exploration. In response, professional science and technology information organizations need to reassess the role of knowledge services in the AI4Science environment and their significance in comprehensively supporting the S&T innovation process. This involves establishing a broad literature perspective, deepening full-text knowledge elements, balancing universal and specialized depth, autonomously developing core products, and deeply engaging with professional fields to support interdisciplinary innovation. 2) Building the knowledge base of AI4S: The essence of artificial intelligence (AI) lies in the acquisition and use of knowledge, and scientific and technological (S&T) literature is the primary carrier of human knowledge. Fully recognizing the paradigm shift in scientific research brought about by AI, the Document Information Center of the China Academy of Sciences has proposed the concept of building a S&T literature knowledge base for AI4S. It is actively exploring the scientific knowledge and high-quality data contained in the S&T literature, strives to build a domain intelligent knowledge base for AI4S, and transforms the "S&T literature database" into a "scientific knowledge engine" that supports intelligent services such as query evidence, situational awareness, inference prediction, and generation of insights required by AI4S. 3) Powering AI4S with scientific data: Effective aggregation of scientific data is the foundation for unleashing the powerful capabilities of AI4S. This is essential for libraries to adapt their roles and functions in the AI era and is a crucial prerequisite for catalyzing the transformation of scientific research services, deepening scientific research support, and accelerating S&T innovation. Currently, libraries face various macro and meso challenges in effectively aggregating valuable scientific data to provide support for AI4S. To address these challenges, the following ways can be pursued: defining the roles and functions of libraries in scientific data management; promoting a conducive environment for scientific data management; establishing a collaborative network for scientific data management; and enhancing the service capacity of scientific data management. 4) AI4S and intelligent language modeling for classical literature: AI4S technology can be used to analyze documents and texts, enabling a faster and more comprehensive understanding of a vast amount of historical documents and cultural materials. The development of intelligent language modeling for classical literature represents a significant breakthrough in the field of ancient literature research, bringing new opportunities and challenges. With the increasing popularity of multimodal and generative GPT models in the context of AI4S, the intelligent language modeling of classical literature will focus on integrating diverse information, enhancing adaptability, improving knowledge representation, and addressing a wider range of application scenarios. 5) Library Digital Scholarly Services for AI4S: The concept of using LLM-based AI4S and AIGC to drive the development of smart libraries is consistent with the vision for digital scholarly services in libraries, and presents both opportunities and challenges. Given the trends towards AI4S platformization and the characteristics of "middle-end" digital scholarly service, as well as the longstanding tradition of libraries in serving scholarly research, the reengineering path for the library's digital scholarly services platform includes three approaches: building an AI4S service platform independently, purchasing and utilizing third-party AI4S platforms, and promoting embedded knowledge services as a component of scientific intelligence. This innovative approach addresses the dilemmas of financial resources, human resources, cognitive and practical gaps, and emphasizes the importance of user needs in the AI4S environment. It also focuses on knowledge organization and service delivery to meet user needs in the AI4S landscape. 6) Historical evolution and logical structure of the scientific intelligence paradigm (AI4S): AI4S is a scientific paradigm change dominated by the full application of AI technology to various disciplines, and its logical structure includes "data+model"-driven, knowledge ecology created by machine conjecture, and application scenarios expanded by algorithmic thinking. In the era of digital civilization, AI4S-driven scientific progress and social development must carry forward the value of science and technology for the good, effectively select the theoretical arguments and proposals for extending AI4S to the field of social sciences and humanities, and improve the series of mechanisms for integrating human decision-making and machine intelligence. 7) Development opportunities and prospects of AI4S in the era of generative AI: With the advances in generative AI, pre-training algorithms and large-scale pre-trained models have provided significant opportunities for AI4S in various disciplinary domains. These technologies have shown immense potential and value for applications in diverse fields such as industrial inspection, robotics, and medicine. Additionally, it is crucial to emphasize the importance of key factors such as the constraints of technical implementation conditions for large pre-trained models, the sustainability of data/computing resources, and the transparency, fairness, and accessibility of the technology.

Objective: To observe the influence of tidal volume(VT) variation on the inspiratory flow index during pressure controlled ventilation (PCV) in different lung models. Methods: The Hamilton C3 ventilator was connected to an ASL5000 lung simulator, which simulated lung mechanics in patients with healthy adult, patients with mild and severe chronic obstructive pulmonary disease (COPD) and acute respiratory distress syndrome (ARDS), and 4 respiratory mechani-cs models were constructed. mild and severe chronic obstructive pulmonary disease (COPD)The [system compliance (Crs) was 30.0 and 60.0 mL/cmH2O, the airway resistance (Raw) was 5.0, 10.0 and 20.0 cmH2O/(L•s)]. The Hamilton C3 ventilator was operated in PCV mode were actived with output VT at 5.0, 7.0 and 10.0 ml/kg, positive end-expiratory pressure (PEEP) was set at 5.0 cmH2O, breathing rate at 10 bpm, and inspiratory time was set at 3.0 sec. The performance characteristics were collected, and the inspiratory flow index and expiratory time constant(RCexp) were estimated by specical equationscalculated. Results: The flow index was not aboveless than 1.0 in all four lung mechanics profiles models during passive ventilation with PCV mode. Peak inspiratory flow (PIF), peak expiratory flow (PEF) and inspiratory driving pressure (DP) were increased gradually with the increment of tidal volume, whereas the flow index was decreased. There were the similar value in the estimation of The flow index in health adult and of the ARDS lung models was similar to that of healthy adults, but RCexp was significantly reduced in severe restrictive model. Flow index and end-inspiratory flow (EIF) were significantly higher in severe COPD model than in the other lung models, which with flow index was close toreaching 0.80 when VT was at 5.0 mL/kg. Conclusions: Flow index has the characteristics ofis an non-invasive derivative parameter and continuous can be monitoringed continuously, and is affected by the alteration of tidal volume and respiratory mechanical properties. During pressure controlled ventilation, the estimation continuous monitoring of flow index is useful to evaluate the severity of airflow limitation and the association between the patient’s inspiratory effort and ventilator outputted assistance level.

● Gasification of cow dung was evaluated using Aspen Plus software.● Optimum reaction conditions were utilized to maximize hydrogen production.● Steam gasification can effectively increase hydrogen production.● Optimum hydrogen production was achieved at 800 °C and steam/biomass of 1.5 and 0.1 MPa. In this study, a biomass gasification model was developed and simulated based on Gibbs free energy minimization by using software Aspen Plus. Two reactors, RYIELD and RGIBBS, were moslty used. The biomass feedstock used was cow dung. The model was validated. The composition, H2/CO ratio and low heating value (LHV) of the resulting synthetic gas (also known as syngas) was estimated by changing the operating parameters of gasification temperatures, steam and biomass ratios and pressures. Simulation results showed that increased gasification temperature helped to elevate H2 and CO content and H2 peaked at 900 °C. When steam increased as the gasification agent, H2 production increased. However, the steam/biomass (S/B) ratio negatively affected CO and CH4, resulting in lower LHV. The optimal S/B ratio was 1.5. An increase in pressure lead to a decrease in H2 and CO content, so the optimal pressure for gasification was 0.1 MPa.

We synthesized and developed 1,3-dihydro-2H-imidazo[4,5-b]phenazine-2-thione as a ratiometric chemosensor for [Hg.sup.2+] recognition in a DMSO/[H.sub.2]O (v/v = 9:1) binary solution. We rationally introduced the phenazine imidazole group as the fluorophore and the thione moiety as the recognition site to bind [Hg.sup.2+]. Interestingly, the chemosensor showed an ultrasensitive response to [Hg.sup.2+], and the lowest limit of detection was 0.167 nM. In addition, it can also separate [Hg.sup.2+] from aqueous solutions with excellent ingestion capacity, with an adsorption ratio of up to 96%. Furthermore, ion test strips based on chemosensors were fabricated for convenient and efficient detection of [Hg.sup.2+]. Key words: fluorometric, chemosensor, imidazo[4,5-b]phenazine-2-thione, ultrasensitive detection, separation, [Hg.sup.2+]. Nous avons synthetise la 1,3-dihydro-2H-imidazo[4,5-b]phenazine-2-thione afin de mettre au point un capteur chimique ratiometrique pour la reconnaissance des ions [Hg.sup.2+] dans une solution binaire de DMSO/[H.sub.2]O (v/v = 9:1). Nous avons selectionne de maniere rationnelle le groupe imidazophenazine comme fluorophore et le fragment thione comme site de reconnaissance afin qu'il se lie a l'ion [Hg.sup.2+]. Il est interessant de noter que le capteur chimique presente une reponse ultrasensible aux ions [Hg.sup.2+] et une limite inferieure de detection de 0,167 nM. De plus, il permet de separer les ions [Hg.sup.2+] de la solution aqueuse avec une excellente capacite d'ingestion, le rapport d'adsorption atteignant 96 %. En outre, nous avons fabrique des bandelettes d'analyse ioniques permettant de detecter facilement et efficacement les ions [Hg.sup.2+] a l'aide du capteur chimique. [Traduit par la Redaction] Mots-cles : fluorimetrique, capteur chimique, imidazo[4,5-b]phenazine-2-thione, detection ultrasensible, separation, [Hg.sup.2+]., Introduction Recently, ultrasensitive detection has received significant attention owing to its crucial biological, chemical, and environmental applications. (1-5) Over the past few years, numerous methods have been reported for ultrasensitive [...]

Abstract Background The plastid is a semiautonomous organelle with its own genome. Plastid genomes have been widely used as models for studying phylogeny, speciation and adaptive evolution. However, most studies focus on comparisons of plastid genome evolution at high taxonomic levels, and comparative studies of the process of plastome evolution at the infrageneric or intraspecific level remain elusive. Holcoglossum is a small genus of Orchidaceae, consisting of approximately 20 species of recent radiation. This made it an ideal group to explore the plastome mutation mode at the infrageneric or intraspecific level. Results In this paper, we reported 15 complete plastid genomes from 12 species of Holcoglossum and 1 species of Vanda. The plastid genomes of Holcoglossum have a total length range between 145 kb and 148 kb, encoding a set of 102 genes. The whole set of ndh-gene families in Holcoglossum have been truncated or pseudogenized. Hairpin inversion in the coding region of the plastid gene ycf2 has been found. Conclusions Using a comprehensive comparative plastome analysis, we found that all the indels between different individuals of the same species resulted from the copy number variation of the short repeat sequence, which may be caused by replication slippage. Annotation of tandem repeats shows that the variation introduced by tandem repeats is widespread in plastid genomes. The hairpin inversion found in the plastid gene ycf2 occurred randomly in the Orchidaceae.

In recent decades, the phenomenon of rapid urbanization in various parts of the world has led to a significant increase in PM2.5 concentration, which has emerged as a growing social concern. In order to achieve the objective of sustainable development, the United Nations Global Sustainable Development Goals (SDGs) have established the goal of creating inclusive, safe, resilient, and sustainable cities and human habitats (SDG 11). Goal 11.6 aims to decrease the negative environmental impact per capita in cities, with an emphasis on urban air quality and waste management. However, the global distribution of PM2.5 pollution varies due to disparities in urbanization development in different regions. The purpose of this paper is to explore the global spatial distribution and temporal variation of PM2.5 in cities with populations greater than 300,000 from 2000 to 2020, to gain insight into the issue. The findings indicate that PM2.5 concentrations are expected to continue increasing as urbanization progresses, but the rate of evolution of PM2.5 concentration varies depending on the continent, country, and city. From 2000 to 2020, PM2.5 concentration increased significantly in Asia and Africa, with the majority of the increased concentrations located in Asian countries and some African countries. On the other hand, most European and American countries had lower PM2.5 concentrations. The results of this study have the potential to inform urbanization policy formulation by providing knowledge about the spatial distribution of PM2.5 pollution during global urbanization. Addressing the issue of PM2.5 pollution is critical in achieving SDG 11.6 and promoting sustainable and coordinated development in cities worldwide. [ABSTRACT FROM AUTHOR]