Conversion Model Research Articles

Democratizing the Development of Chatbots to Improve Public Health: Feasibility Study of COVID-19 Misinformation.

Chatbots enable users to have humanlike conversations on various topics and can vary widely in complexity and functionality. An area of research priority in chatbots is democratizing chatbots to all, removing barriers to entry, such as financial ones, to help make chatbots a possibility for the wider global population to improve access to information, help reduce the digital divide between nations, and improve areas of public good (eg, health communication). Chatbots in this space may help create the potential for improved health outcomes, potentially alleviating some of the burdens on health care providers and systems to be the sole voices of outreach to public health. This study explored the feasibility of developing a chatbot using approaches that are accessible in low- and middle-resource settings, such as using technology that is low cost, can be developed by nonprogrammers, and can be deployed over social media platforms to reach the broadest-possible audience without the need for a specialized technical team. This study is presented in 2 parts. First, we detailed the design and development of a chatbot, VWise, including the resources used and development considerations for the conversational model. Next, we conducted a case study of 33 participants who engaged in a pilot with our chatbot. We explored the following 3 research questions: (1) Is it feasible to develop and implement a chatbot addressing a public health issue with only minimal resources? (2) What is the participants' experience with using the chatbot? (3) What kinds of measures of engagement are observed from using the chatbot? A high level of engagement with the chatbot was demonstrated by the large number of participants who stayed with the conversation to its natural end (n=17, 52%), requested to see the free online resource, selected to view all information about a given concern, and returned to have a dialogue about a second concern (n=12, 36%). This study explored the feasibility of and the design and development considerations for a chatbot, VWise. Our early findings from this initial pilot suggest that developing a functioning and low-cost chatbot is feasible, even in low-resource environments. Our results show that low-resource environments can enter the health communication chatbot space using readily available human and technical resources. However, despite these early indicators, many limitations exist in this study and further work with a larger sample size and greater diversity of participants is needed. This study represents early work on a chatbot in its virtual infancy. We hope this study will help provide those who feel chatbot access may be out of reach with a useful guide to enter this space, enabling more democratized access to chatbots for all.

BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials.

The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge is systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model is finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further strengthenedwith enhanced reasoning ability, as well as with Retrieval-Augmented Generation (RAG) to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model shows impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains.

Discourse Structure Analysis of Making Request in Japanese Conversation

Making requests (irai) is a genre of spoken interaction that is taught from the basic level of learning Japanese as a foreign language. A request is one of the speech acts that may raise face-threatening potentials. Understanding the stages of request appropriate to Japanese culture is thus essential for Japanese learners to achieve successful conversation. Therefore, conversation pedagogy by using a discourse approach is essential. This study investigates a potential structure gap in Japanese making-requests conversations realized in actual settings and textbook conversational models. By employing genre theory and interpersonal discourse of “Negotiation” as a qualitative discourse analytic method from the Systemic Functional Linguistics (SFL) perspective, this paper describes the gaps and some factors that potentially influence the structure of Japanese making-request conversation. Data were obtained from conversational texts in the Japanese language corpus named Japanese Natural Conversation Corpus and Japanese textbooks for elementary and middle adult learners. Regarding the structure, the results show no difference between conversations in textbooks and authentic ones at the stage level, but both differ at the phase level. There is no introduction to the problem, additional explanation, and confirmation phases in textbook conversational models. In addition, the absence of the phases, the differences in pre-condition content between textbook and authentic conversations, also the length of the reasoning phase, are assumed to be influenced by relational status between participants (tenor) as well as the imposition degree of the requested object.

ChatGPT effects on cognitive skills of undergraduate students: Receiving instant responses from AI-based conversational large language models (LLMs)

This study investigated the impact of using ChatGPT, a state-of-the-art generative AI-based model, on the critical, creative, and reflective thinking skills of university students in Ghana. The study utilized a mixed-methods research approach, incorporating quantitative and qualitative data collection instruments, and an experimental procedure with a pretest-posttest control group. The study ultimately enlisted a sample of 125 students randomly allocated to either the experiment group (60 students) or the control group (65 students). The research was conducted in the context of a Research Methodology course, which had adopted the flipped classroom approach. The students in the experiment group engaged with ChatGPT for in-class tasks, while those in the control group used traditional databases and search engines for similar tasks. Data were collected using the Critical Thinking Scale, Creative Thinking Scale, Reflective Thinking Scale, and a student interview guide (semi-structured). The study's findings illustrated that incorporating ChatGPT influenced the students' critical, reflective, and creative thinking skills and their dimensions discernibly. As a result, the study provides suggestions for academics, instructional designers, and researchers working in educational technology.

Conversion in Ossetian Language (Iron and Digor Dialects)

This article analyzes the converted lexicon of the Iron and Digor dialects of the Ossetian language. The study uses lexical units extracted through a random sampling method from Ossetian language dictionaries. The relevance of the research lies in identifying the primary conversion models necessary for solving problems related to the differentiation of parts of speech in the Ossetian language. The novelty of the study is that it attempts a scientific description of Ossetian conversion models using linguistic material from the Iron and Digor dialects. The study reveals that the most active forms of conversion in the Ossetian language are substantivization, adjectivization, and adverbialization (the direction of productivity may vary). The study also shows that the conversion model in the Ossetian language often includes nouns, adjectives, and participles. Conversion relationships between autosynthetic and synsemantic words in the Ossetian language are mainly represented by adverbs and postpositions, which are due to the developed system of postpositions in the Ossetian language. Analysis of linguistic material from the two main dialects of the Ossetian language suggests that there are no significant differences in the mechanisms of converting Iron and Digor lexicons.

Supporting the Demand on Mental Health Services with AI-Based Conversational Large Language Models (LLMs)

The demand for psychological counselling has grown significantly in recent years, particularly with the global outbreak of COVID-19, which heightened the need for timely and professional mental health support. Online psychological counselling emerged as the predominant mode of providing services in response to this demand. In this study, we propose the Psy-LLM framework, an AI-based assistive tool leveraging large language models (LLMs) for question answering in psychological consultation settings to ease the demand on mental health professions. Our framework combines pre-trained LLMs with real-world professional questions-and-answers (Q&A) from psychologists and extensively crawled psychological articles. The Psy-LLM framework serves as a front-end tool for healthcare professionals, allowing them to provide immediate responses and mindfulness activities to alleviate patient stress. Additionally, it functions as a screening tool to identify urgent cases requiring further assistance. We evaluated the framework using intrinsic metrics, such as perplexity, and extrinsic evaluation metrics, including human participant assessments of response helpfulness, fluency, relevance, and logic. The results demonstrate the effectiveness of the Psy-LLM framework in generating coherent and relevant answers to psychological questions. This article discusses the potential and limitations of using large language models to enhance mental health support through AI technologies.

Below average ChatGPT performance in medical microbiology exam compared to university students

BackgroundThe transformative potential of artificial intelligence (AI) in higher education is evident, with conversational models like ChatGPT poised to reshape teaching and assessment methods. The rapid evolution of AI models requires a continuous evaluation. AI-based models can offer personalized learning experiences but raises accuracy concerns. MCQs are widely used for competency assessment. The aim of this study was to evaluate ChatGPT performance in medical microbiology MCQs compared to the students’ performance.MethodsThe study employed an 80-MCQ dataset from a 2021 medical microbiology exam at the University of Jordan Doctor of Dental Surgery (DDS) Medical Microbiology 2 course. The exam contained 40 midterm and 40 final MCQs, authored by a single instructor without copyright issues. The MCQs were categorized based on the revised Bloom’s Taxonomy into four categories: Remember, Understand, Analyze, or Evaluate. Metrics, including facility index and discriminative efficiency, were derived from 153 midterm and 154 final exam DDS student performances. ChatGPT 3.5 was used to answer questions, and responses were assessed for correctness and clarity by two independent raters.ResultsChatGPT 3.5 correctly answered 64 out of 80 medical microbiology MCQs (80%) but scored below the student average (80.5/100 vs. 86.21/100). Incorrect ChatGPT responses were more common in MCQs with longer choices (p = 0.025). ChatGPT 3.5 performance varied across cognitive domains: Remember (88.5% correct), Understand (82.4% correct), Analyze (75% correct), Evaluate (72% correct), with no statistically significant differences (p = 0.492). Correct ChatGPT responses received statistically significant higher average clarity and correctness scores compared to incorrect responses.ConclusionThe study findings emphasized the need for ongoing refinement and evaluation of ChatGPT performance. ChatGPT 3.5 showed the potential to correctly and clearly answer medical microbiology MCQs; nevertheless, its performance was below-bar compared to the students. Variability in ChatGPT performance in different cognitive domains should be considered in future studies. The study insights could contribute to the ongoing evaluation of the AI-based models’ role in educational assessment and to augment the traditional methods in higher education.

Appropriateness of Online Chat-Based Artificial Intelligence (ChatGPT) Answers to Common Questions on Inguinal Hernia Repair.

ChatGPT is a conversational AI model developed by OpenAI designed to generate human-like text based on the input it receives. ChatGPT has become increasingly popular, and the general public may use this tool to ask questions about different medical conditions. There is a lack of data showing if ChatGPT is able to provide reliable information on medical conditions to the general public. The aim of our study is to assess the accuracy and appropriateness of ChatGPT answers to questions on inguinal hernia management.

Enhancement of reaction rate prediction of biomass: A focus on experimental and numerical simulation approaches

While the biomass gasification and cofiring technology is promising for future energy, including green technologies, such as carbon capture and storage and bioresource feedstock, negative carbon emissions are simultaneously possible using these technologies. However, despite these functions, our previous study established that for simulating a biomass reactor, a new model needed to be designed that could improve the accuracy of both real and lab-scale simulations for carbonaceous fuels, such as coal-based applications. Hence, in this study, a biomass experiment utilizing a thermogravimetric analyzer was conducted to obtain varying reaction rate data over carbon conversion, which may then be used to simulate the biomass conversion model and 3D simulation was also conducted for a 500 MWe coal-biomass cofiring boiler. Gasification was obtained at 1000 °C and 1200 °C using CO2 at constant temperature for different biomasses. Compared to the peak exhibited by the random pore model, biomass gasification reaction rates shifted into the later stage of the reaction, thereby yielding irregular reaction rate developments and poor prediction accuracies. With flexibility-enhanced random pore model(FERPM), we observed that correlation factors increased from the previously reported value of 0.7–0.95. In the coal-biomass cofiring boiler, the difference between the Kinetics/Diffusion-limited model and FERPM in biomass combustion aspect was examined, and it was ultimately determined that characteristics of particle reactions through FERPM are valid in 3D analysis. Therefore, this paper proposed a flexibility-enhanced random pore model proven to enhance the prediction accuracy of a simulation.

Artificial intelligence at the pen's edge: Exploring the ethical quagmires in using artificial intelligence models like ChatGPT for assisted writing in biomedical research.

Chat generative pretrained transformer (ChatGPT) is a conversational language model powered by artificial intelligence (AI). It is a sophisticated language model that employs deep learning methods to generate human-like text outputs to inputs in the natural language. This narrative review aims to shed light on ethical concerns about using AI models like ChatGPT in writing assistance in the health care and medical domains. Currently, all the AI models like ChatGPT are in the infancy stage; there is a risk of inaccuracy of the generated content, lack of contextual understanding, dynamic knowledge gaps, limited discernment, lack of responsibility and accountability, issues of privacy, data security, transparency, and bias, lack of nuance, and originality. Other issues such as authorship, unintentional plagiarism, falsified and fabricated content, and the threat of being red-flagged as AI-generated content highlight the need for regulatory compliance, transparency, and disclosure. If the legitimate issues are proactively considered and addressed, the potential applications of AI models as writing assistance could be rewarding.

ChatGPT Performance in Diagnostic Clinical Microbiology Laboratory-Oriented Case Scenarios.

Artificial intelligence (AI)-based tools can reshape healthcare practice. This includes ChatGPT which is considered among the most popular AI-based conversational models. Nevertheless, the performance of different versions of ChatGPT needs further evaluation in different settings to assess its reliability and credibility in various healthcare-related tasks. Therefore, the current study aimed to assess the performance of the freely available ChatGPT-3.5 and the paid version ChatGPT-4 in 10 different diagnostic clinical microbiology case scenarios. The current study followed the METRICS (Model, Evaluation, Timing/Transparency, Range/Randomization, Individual factors, Count, Specificity of the prompts/language) checklist for standardization of the design and reporting of AI-based studies in healthcare. The models tested on December 3, 2023 included ChatGPT-3.5 and ChatGPT-4 and the evaluation of the ChatGPT-generated content was based on the CLEAR tool (Completeness, Lack of false information, Evidence support, Appropriateness, and Relevance) assessed on a 5-point Likert scale with a range of the CLEAR scores of 1-5. ChatGPT output was evaluated by two raters independently and the inter-rater agreement was based on the Cohen's κ statistic. Ten diagnostic clinical microbiology laboratory case scenarios were created in the English language by three microbiologists at diverse levels of expertise following an internal discussion of common cases observed in Jordan. The range of topics included bacteriology, mycology, parasitology, and virology cases. Specific prompts were tailored based on the CLEAR tool and a new session was selected following prompting each case scenario. The Cohen's κ values for the five CLEAR items were 0.351-0.737 for ChatGPT-3.5 and 0.294-0.701 for ChatGPT-4 indicating fair to good agreement and suitability for analysis. Based on the average CLEAR scores, ChatGPT-4 outperformed ChatGPT-3.5 (mean: 2.64±1.06 vs. 3.21±1.05,P=.012, t-test). The performance of each model varied based on the CLEAR items, with the lowest performance for the "Relevance" item (2.15±0.71 for ChatGPT-3.5 and 2.65±1.16 for ChatGPT-4). A statistically significant difference upon assessing the performance per each CLEAR item was only seen in ChatGPT-4 with the best performance in "Completeness", "Lack of false information", and "Evidence support" (P=0.043). The lowest level of performance for both models was observed with antimicrobial susceptibility testing (AST) queries while the highest level of performance was seen in bacterial and mycologic identification. Assessment of ChatGPT performance across different diagnostic clinical microbiology case scenarios showed that ChatGPT-4 outperformed ChatGPT-3.5. The performance of ChatGPT demonstrated noticeable variability depending on the specific topic evaluated. A primary shortcoming of both ChatGPT models was the tendency to generate irrelevant content lacking the needed focus. Although the overall ChatGPT performance in these diagnostic microbiology case scenarios might be described as "above average" at best, there remains a significant potential for improvement, considering the identified limitations and unsatisfactory results in a few cases.

Evaluating AI performance in answering questions related to thoracic anatomy

Introduction: ChatGPT, an AI-enabled conversational language model, holds immense promise for applications across healthcare education, research, and clinical practice. A number of recent reports have highlighted promising outcomes from using ChatGPT for answering multiple-choice questions (MCQs), hinting at a potential reshaping of educational methodologies. Aim: The objective of this particular study was to quantitatively assess the performance of ChatGPT-3.5 and ChatGPT-4 in the context of answering anatomical questions focusing on the Thorax in a Gross Anatomy course for medical students. Methods: The research conducted for this study was focused on a comprehensive examination of ChatGPT-3.5 and ChatGPT-4 capabilities in answering 50 multiple choice questions MCQs designed in USMLE style. These questions were randomly selected from Gross Anatomy course exam database for medical students and reviewed by three independent experts. No questions with images were included in this study. The selected questions had different levels of difficulty. Findings: The analysis revealed that ChatGPT-3.5 exhibited a remarkable ability to answer anatomical MCQs, achieving an average accuracy of 45.6±3.8%, significantly surpassing random guessing at 18.8±4.4%. The subsequent ChatGPT-4 demonstrated a 30% improvement, with an even higher accuracy of 75.6±1.7%. Discussion: ChatGPT demonstrates considerable potential as an engaging educational tool for students immersed in the study of anatomy. Its unique strength lies in its ability to incite student engagement and arouse curiosity through an interactive, conversational mode of answer delivery. Conclusion: It is vital to remember that ChatGPT should not be viewed as a replacement for the pivotal role teachers play in the educational journey. Instead, it should be visualized as an auxiliary instrument poised to enrich the educational experience. Future studies should aim to determine clear, holistic guidelines that illuminate the best ways to leverage and apply ChatGPT within the scope of anatomy instruction.

Estimation of grain filling rate of winter wheat using leaf chlorophyll and LAI extracted from UAV images

The grain filling rate (GFR) plays a pivotal role in assessing winter wheat grain development and guiding crop variety selection and cultivation strategies. Presently, GFR monitoring primarily relies on destructive sampling methods, which are impractical for swift assessments in extensive agricultural fields. Our study introduces a novel approach for estimating winter wheat grain filling rate (GFR) using leaf chlorophyll content (LCC) and Leaf Area Index (LAI) extracted from UAV imagery. Initially, we established a quantitative relationship between GFR and LAI as well as LCC in winter wheat. Subsequently, UAV multispectral imagery was employed to track the time-series dynamics of LAI and LCC during the grain-filling stage. Ultimately, we constructed an estimation model for winter wheat GFR and achieved plot-scale GFR mapping through UAV-based imaging technology. To identify sensitive vegetation index combinations for LAI and chlorophyll content (SPAD), we employed adaptive re-weighted sampling and continuous projection algorithms, respectively. Following this, we employed partial least squares and random forest algorithms to develop LAI and SPAD estimation models, which were then compared to full-band regression. Finally, GFR estimation was based on the conversion models of LAI and SPAD to GFR. Our findings revealed that, despite variations in water supply, there were no significant differences in GFR trends among the irrigated treatments. However, in the nitrogenous fertilizer application treatments, GFR initially declined with increased nitrogenous fertilizer supply, reaching its peak between 21 and 25 days post-flowering, during which period the differences in GFR among the various nitrogenous fertilizer application treatments were not significant. As the reproductive period advanced, higher nitrogenous fertilizer treatments maintained a higher GFR for a more extended period and experienced a slower decline compared to the lower nitrogenous fertilizer treatments. LAI validation yielded an R2 of 0.84, a root mean square error (RMSE) of 0.39, and a normalized root mean square error (NRMSE) of 10.9%. SPAD validation resulted in R2, RMSE, and NRMSE values of 0.79%, 1.30%, and 12.5%, respectively. The GFR estimation achieved an R2 of 0.77, an RMSE of 0.32 mg/d, and an NRMSE of 11.3%. Mapping the GFR distribution in the field, coupled with water and fertilizer management, supports UAVs in monitoring crop growth and refining field-level water and fertilizer decisions.

Applying the framework for developing and evaluating complex interventions to increase family communication about hereditary cancer.

Evaluate an intervention to increase family communication (FC) of positive hereditary cancer test results using the Framework for Developing and Evaluating Complex Interventions (FDECI). We developed 'programme theory' during the FDECI development phase by aligning intervention components with behavior change techniques (BCTs) and theoretical factors expected to improve FC. During the feasibility phase, we obtained feedback from 12 stakeholder interviews. Intervention components aligned with a total of 14 unique BCTs for which prior evidence links the BCT to theoretical factors that influence behavior change. Constructive stakeholder feedback included: more information desired, rewording to support autonomy by highlighting options, and improvements to navigation, visuals, and audio. Positive comments included: comprehensiveness of materials, modeling of conversations, and usefulness of the materials for helping a person prepare to share positive test results. The first FDECI phases were helpful for improving the intervention and planning our ongoing effectiveness and future implementation phases. Our application of the FDECI is novel, including plans to test our 'programme theory' using coincidence analysis (CNA) to determine who accesses which intervention materials, how utilizing certain materials impact the aligned theoretical factors, and whether these in turn make a difference in the behavioral outcome.

Generative Artificial Intelligence: Fundamentals

Generative language models have witnessed substantial traction, notably with the introduction of refined models aimed at more coherent user-AI interactions—principally conversational models. The epitome of this public attention has arguably been the refinement of the GPT-3 model into ChatGPT and its subsequent integration with auxiliary capabilities such as search features in Microsoft Bing. Despite voluminous prior research devoted to its developmental trajectory, the model’s performance, and applicability to a myriad of quotidian tasks remained nebulous and task specific. In terms of technological implementation, the advent of models such as LLMv2 and ChatGPT-4 has elevated the discourse beyond mere textual coherence to nuanced contextual understanding and real-world task completion. Concurrently, emerging architectures that focus on interpreting latent spaces have offered more granular control over text generation, thereby amplifying the model’s applicability across various verticals. Within the purview of cyber defense, especially in the Swiss operational ecosystem, these models pose both unprecedented opportunities and challenges. Their capabilities in data analytics, intrusion detection, and even misinformation combatting is laudable; yet the ethical and security implications concerning data privacy, surveillance, and potential misuse warrant judicious scrutiny.

Generation Coordination and Planning of New Power System Considering Industry Hydrogen Load

Considering the impact of industrial hydrogen load on the consumptive capacity of new energy, it is necessary to research the generation coordination and planning of new power systems considering industry hydrogen load. Firstly, considering the energy consumption in the hydrogen production and storage process, as well as the energy storage for hydrogen addition/use, hydrogen load transfer, and interruptible flexibility, an equivalent electrical load conversion model for multiple forms of hydrogen loads is established. Secondly, considering the impact of industrial hydrogen load on the absorption capacity of new energy, the investment decision-making problem of power supply, hydrogen production, and storage equipment is unified with the operation simulation problem of the system at the planning level, to minimize the annual cost of the system. To reduce the complexity of the model, the proposed model plan to the expansion capacity of various types of power sources from the perspective of power types. Finally, by using actual data from a power grid in a certain province as an example, the results verified the rationality and effectiveness of the model compared with the power grid planning without hydrogen load.

Twitter Sentiment Analysis on Chatgpt Tweets

Abstract: This study investigates multilingual sentiment analysis within tweets on ChatGPT, an AI conversational model, employing Support Vector Machines (SVM) and BERT, an advanced language model. It aims to detect and classify emotions, including emoji identification, embedded within diverse messages across multiple languages on Twitter. By leveraging SVM's text classification and BERT's contextual understanding in various languages, the research delves into preprocessing techniques and feature engineering for sentiment analysis, encompassing multilingual and emoji detection. Furthermore, it explores the fusion of traditional SVM methods with BERT's state-of-the-art model for multilingual sentiment analysis, emphasizing emotion and emoji detection in AI-generated content on multilingual social media platforms like Twitter. This research yields insights into the successful detection of multilingual sentiment nuances and emotions, including emoji identification. It offers implications for advancing multilingual sentiment analysis in natural language processing across diverse linguistic contexts.

Application of Smart Overpressure Protection Devices to batch and semi-batch processes: The zero-emission mode

Chemical reactors in batch- and semi-batch processes are mainly protected by mechanical safety devices or safety integrity systems against critical runaway reactions. Both devices have in common, to limit with their fixed set conditions and discharge areas the productivity and flexibility of chemical plants. Besides, the trend in the industry is to reduce/avoid toxic and explosives to be discharges trough safety devices due to the increased requirements for climate- and environmental protection. A new idea in that regard is to connect the advantages of mechanical safety devices and safety integrated systems in so called smart overpressure protection devices (SmOP). Here, a predictive CSE conversion model (PreCoM) is online tracking the hazard potential of a chemical reaction and defines individual shutoff limits for the reaction. Its zero-emission mode ensures flexible, reaction independent application without fluid discharge. The smart relief mode is maximizing the plant utilization with individual shutoff limits and is minimizing the pollution with controlled discharge areas. In this article, the SmOP concept and the utilization of the PreCoM model for two different reactions and three industrial use cases for zero-emission application is being presented.

Research on English Foreign Propaganda Translation and External Communication Paths Based on Multimodal Analysis Approach

Abstract In this paper, a multimodal phrase corpus is created, enabling phrases to be searched automatically. The phrase structure is constructed through phrase centroids, and multilevel linguistic measurements are obtained for vocabulary, syntax, and parts of speech styles. A joint feature analysis model for the automatic conversion of English-Chinese machine translation is constructed through distributed feature fusion and joint parameter analysis. Linear statistical features and feature analysis detection are utilized to compute the optimal solution for English-Chinese machine automatic translation text. The graph embedding technique was used to pretrain the knowledge graph and obtain information about the network structure. Combined with the graph convolution technique, deep semantic information mining was carried out to achieve efficient content matching and cultural dissemination. It has been proved that the method of this paper has an AUC value of 0.921 and a satisfaction rate between 80% and 95% in English-translated movie data. The proposed method can accurately translate information from English to foreign propaganda content, which further promotes the development of international cultural exchange and communication.

Similarity and normalization study of fuel spray and combustion under ultra-high injection pressure and micro-hole diameter conditions–spray characteristics

The compression-ignition engine has been widely applied in various fields due to its high efficiency, fuel economy, and adaptability. With the increasing awareness of environmental issues, emission regulations have become more stringent, leading to a growing demand for high-pressure and small-sized engines. Since developing new engines is often both costly and time-consuming, leveraging the similarity between spray and combustion processes for new engine development can significantly reduce the required resources and time. However, to date, systematic research on the similarity and normalization of sprays and combustion processes, especially under ultra-high injection pressure and micro-hole diameter conditions, remains lacking. In this study, conversion models and normalization models for spray characteristics were developed based on both time scale and multi-dimensional space scale. The spray transformation and normalization were verified for different aperture sizes, injection pressures, ambient temperatures, densities, and pressures. The results show that the models exhibit good interchangeability and normalization effects. This research outcome will provide valuable references for optimizing and designing more efficient and environmentally friendly internal combustion engines.