Electronic structure and properties of isoelectronic magic clusters: Al13X (X=H,Au,Li,Na,K,Rb,Cs)

The equilibrium structure, stability, and electronic properties of the Al13X (X=H,Au,Li,Na,K,Rb,Cs) clusters have been studied using a combination of photoelectron spectroscopy experiment and density functional theory. All these clusters constitute 40 electron systems with 39 electrons contributed by the 13 Al atoms and 1 electron contributed by each of the X (X=H,Au,Li,Na,K,Rb,Cs) atom. A systematic study allows us to investigate whether all electrons contributed by the X atoms are alike and whether the structure, stability, and properties of all the magic clusters are similar. Furthermore, quantitative agreement between the calculated and the measured electron affinities and vertical detachment energies enable us to identify the ground state geometries of these clusters both in neutral and anionic configurations

Prioritized experience replay-based DDQN for Unmanned Vehicle Path Planning

Path planning module is a key module for autonomous vehicle navigation, which directly affects its operating efficiency and safety. In complex environments with many obstacles, traditional planning algorithms often cannot meet the needs of intelligence, which may lead to problems such as dead zones in unmanned vehicles. This paper proposes a path planning algorithm based on DDQN and combines it with the prioritized experience replay method to solve the problem that traditional path planning algorithms often fall into dead zones. A series of simulation experiment results prove that the path planning algorithm based on DDQN is significantly better than other methods in terms of speed and accuracy, especially the ability to break through dead zones in extreme environments. Research shows that the path planning algorithm based on DDQN performs well in terms of path quality and safety. These research results provide an important reference for the research on automatic navigation of autonomous vehicles.Comment: 4 pages, 6 figures, 2024 5th International Conference on Information Science, Parallel and Distributed System

Automatic News Generation and Fact-Checking System Based on Language Processing

This paper explores an automatic news generation and fact-checking system based on language processing, aimed at enhancing the efficiency and quality of news production while ensuring the authenticity and reliability of the news content. With the rapid development of Natural Language Processing (NLP) and deep learning technologies, automatic news generation systems are capable of extracting key information from massive data and generating well-structured, fluent news articles. Meanwhile, by integrating fact-checking technology, the system can effectively prevent the spread of false news and improve the accuracy and credibility of news. This study details the key technologies involved in automatic news generation and factchecking, including text generation, information extraction, and the application of knowledge graphs, and validates the effectiveness of these technologies through experiments. Additionally, the paper discusses the future development directions of automatic news generation and fact-checking systems, emphasizing the importance of further integration and innovation of technologies. The results show that with continuous technological optimization and practical application, these systems will play an increasingly important role in the future news industry, providing more efficient and reliable news services

The effect of childhood sexual abuse on depressive symptoms in female college students: a serial mediation model

ObjectiveChildhood sexual abuse (CSA) can have a negative impact on women’s psychological, emotional and social functioning. The purpose of this study was to explore the relationship between CSA and depressive symptoms in female college students, as well as the mediating roles of negative core schema and experiential avoidance.Methods515 female college students responded to the Sexual Abuse subscale of the Childhood Trauma Questionnaire, the Depression subscale of the Depression Anxiety Stress Scale, the Brief Core Schema Scales, and the Acceptance and Action Questionnaire – II. The structural equation modeling was used for the mediation analysis.ResultsThere was a significant positive correlation between CSA and depressive symptoms in female college students. The theoretical model was well fitted, χ2/df = 3.422, RMSEA = 0.069, CFI = 0.929, TLI = 0.919. The negative core schema played a mediating role between CSA and depressive symptoms. Experiential avoidance played a mediating role between CSA and depressive symptoms. The negative core schema and experiential avoidance played a serial mediating role between CSA and depressive symptoms.ConclusionThese results deepen our understanding of the relationship between CSA and depressive symptoms in female college students, and provide theoretical guidance for the prevention of depression in female college students. Attention should be paid to female college students who have experienced CSA, to eliminate the adverse influence of negative core schema on these students. Meanwhile, we should teach female college students to accept themselves as they are, and thereby reduce their use of experiential avoidance strategies

SynAsk: Unleashing the Power of Large Language Models in Organic Synthesis

The field of natural language processing (NLP) has witnessed a transformative shift with the emergence of large language models (LLMs), revolutionizing various language tasks and applications, and the integration of LLM into specialized domains enhances their capabilities for domain-specific applications. Notably, NLP has made significant strides in organic chemistry, particularly in predicting synthetic tasks, paving the way for the development of LLMs tailored to the organic chemistry field. In this work, we introduce SynAsk, a comprehensive organic chemistry domain-specific LLM platform developed by AIChemEco Inc. By finetuning an LLM with domain-specific data and integrating it with a chain of thought approach, SynAsk seamlessly accesses our knowledge base and advanced chemistry tools in a question-and-answer format. This includes functionalities such as a basic chemistry knowledge base, molecular information retrieval, reaction performance prediction, retrosynthesis prediction, chemical literature acquisition, and more. This novel methodology synergizes fine-tuning techniques with external resource integration, resulting in an organic chemistry-specific model poised to facilitate research and discovery in the field. Accessible via http://synask.aichemeco.com, SynAsk represents a significant advancement in leveraging NLP for synthetic applications

Applications of Explainable AI in Natural Language Processing

This paper investigates and discusses the applications of explainable AI in natural language processing. It first analyzes the importance and current state of AI in natural language processing, then focuses on the role and advantages of explainable AI technology in this field. It compares explainable AI with traditional AI from various angles and elucidates the unique value of explainable AI in natural language processing. On this basis, suggestions for further improvements and applications of explainable AI are proposed to advance the field of natural language processing. Finally, the potential prospects and challenges of explainable AI in natural language processing are summarized, and future research directions are envisaged. Through this study, a better understanding and application of explainable AI technology can be achieved, providing beneficial references for the development of the natural language processing field

Chimeric Antigen Receptor T Cells to Target cd79B in B-Ceall Lymphomas

BACKGROUND: Chimeric antigen receptor (CAR) T cells targeting CD19 mediate potent and durable effects in B-cell malignancies. However, antigen loss or downregulation is a frequent cause of resistance. Here, we report development of a novel CAR T-cell therapy product to target CD79b, a pan B-cell antigen, widely expressed in most B-cell lymphomas. METHODS: We generated a novel anti-CD79b monoclonal antibody by hybridoma method. The specificity of the antibody was determined by testing against isogenic cell lines with human CD79b knock-in or knock-out. A single-chain variable fragment derived from the monoclonal antibody was used to make a panel of CD79b-targeting CAR molecules containing various hinge, transmembrane, and co-stimulatory domains. These were lentivirally transduced into primary T cells and tested for antitumor activity in in vitro and in vivo B-cell lymphoma models. RESULTS: We found that the novel anti-CD79b monoclonal antibody was highly specific and bound only to human CD79b and no other cell surface protein. In testing the various CD79b-targeting CAR molecules, superior antitumor efficacy in vitro and in vivo was found for a CAR consisting CD8α hinge and transmembrane domains, an OX40 co-stimulatory domain, and a CD3ζ signaling domain. This CD79b CAR specifically recognized human CD79b-expressing lymphoma cell lines but not CD79b knock-out cell lines. CD79b CAR T cells, generated from T cells from either healthy donors or patients with lymphoma, proliferated, produced cytokines, degranulated, and exhibited robust cytotoxic activity in vitro against CD19 CONCLUSION: Our results indicated that this novel CD79b CAR T-cell therapy product has robust antitumor activity against B-cell lymphomas. These results supported initiation of a phase 1 clinical trial to evaluate this product in patients with relapsed or refractory B-cell lymphomas

Review of advanced road materials, structures, equipment, and detection technologies

As a vital and integral component of transportation infrastructure, pavement has a direct and tangible impact on socio-economic sustainability. In recent years, an influx of groundbreaking and state-of-the-art materials, structures, equipment, and detection technologies related to road engineering have continually and progressively emerged, reshaping the landscape of pavement systems. There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies. Therefore, Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of “advanced road materials, structures, equipment, and detection technologies”. This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars, all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering. It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering: advanced road materials, advanced road structures and performance evaluation, advanced road construction equipment and technology, and advanced road detection and assessment technologies

Creep mechanical behavior and damage model of layered slate under combined thermal-hydraulic-mechanical action

The long-term stability of the surrounding rock of geothermal wellbore traversing groundwater and layered rock formations is commonly influenced by the combined effects of thermal, hydraulic, and mechanical factors. In order to mitigate the collapse risk associated with traversing layered rock formations in geothermal wellbore surroundings, this study examines the influence of thermal-hydraulic-mechanical interactions on the creep mechanical characteristics of layered shale. The objective is to provide an assessment of its long-term stability and risk profile. This study focuses on analyzing the creep behavior of layered shale under these combined influences. A nonlinear creep damage model is developed, considering high temperature, pore water pressure, and bedding plane effects through the incorporation of a variable-order fractional element and statistical damage. The variations in model parameters are examined. The findings indicate a gradual decrease in creep mechanical parameters with increasing water pressure and temperature. Additionally, anisotropic behavior is observed in the creep parameters across different angles. The proposed creep damage model shows good agreement with experimental curves, with the fitted parameters exhibiting a linear function relationship with temperature