Specific Field Research Articles

Comparison of Gemini Advanced and ChatGPT 4.0's Performances on the Ophthalmology Resident Ophthalmic Knowledge Assessment Program (OKAP) Examination Review Question Banks.

Background With advancements in natural language processing, tools such as Chat Generative Pre-Trained Transformers (ChatGPT) version 4.0 and Google Bard's Gemini Advanced are being increasingly evaluated for their potential in various medical applications. The objective of this study was to systematically assess the performance of these language learning models (LLMs) on both image and non-image-based questions within the specialized field of Ophthalmology. We used a review question bank for the Ophthalmic Knowledge Assessment Program (OKAP) used by ophthalmology residents nationally to prepare for the Ophthalmology Board Exam to assess the accuracy and performance of ChatGPT and Gemini Advanced. Methodology A total of 260 randomly generated multiple-choice questions from the OphthoQuestions question bank were run through ChatGPT and Gemini Advanced. A simulated 260-question OKAP examination was created at random from the bank. Question-specific data were analyzed, including overall percent correct, subspecialty accuracy, whether the question was "high yield," difficulty (1-4), and question type (e.g., image, text). To compare the performance of ChatGPT-4 and Gemini on the difficulty of questions, we utilized the standard deviation of user answer choices to determine question difficulty. In this study, a statistical analysis of Google Sheets was conducted using two-tailed t-tests with unequal variance to compare the performance of ChatGPT-4.0 and Google's Gemini Advanced across various question types, subspecialties, and difficulty levels. Results In total, 259 of the 260 questions were included in the study as one question used a video that any form of ChatGPT could not interpret as of May 1, 2024. For text-only questions, ChatGPT-4.0.0 correctly answered 57.14% (148/259, p < 0.018), and Gemini Advanced correctly answered 46.72% (121/259, p < 0.018). Both versions answered most questions without a prompt and would have received a below-average score on the OKAP. Moreover, there were 27 questions utilizing a secondary prompt in ChatGPT-4.0 compared to 67 questions in Gemini Advanced. ChatGPT-4.0 performed 68.99% on easier questions (<2 on a scale from 1-4) and 44.96% on harder questions (>2 on a scale from 1-4). On the other hand, Gemini Advanced performed 49.61% on easier questions (<2 on a scale from 1-4) and 44.19% on harder questions (>2 on a scale from 1-4). There was a statistically significant difference in accuracy between ChatGPT-4.0 and Gemini Advanced for easy (p < 0.0015) but not for hard (p < 0.55) questions. For image-only questions, ChatGPT-4.0 correctly answered 39.58% (19/48, p < 0.013), and Gemini Advanced correctly answered 33.33% (16/48, p < 0.022), resulting in a statistically insignificant difference in accuracy between ChatGPT-4.0 and Gemini Advanced (p < 0.530). A comparison against text-only and image-based questions demonstrated a statistically significant difference in accuracy for both ChatGPT-4.0 (p < 0.013) and Gemini Advanced (p < 0.022). Conclusions This study provides evidence that ChatGPT-4.0 performs better on the OKAP-style exams and is improved compared to Gemini Advanced within the context of ophthalmic multiple-choice questions. This may show an opportunity for increased worth for ChatGPT in ophthalmic medical education. While showing promise within medical education, caution should be used as a more detailed evaluation of reliability is needed.

The importance of financial ratios in determining profit distribution policies for a sample of Iraqi banks

This study aims primarily to demonstrate the importance of four types of financial ratios, namely profitability ratios, turnover rates, activity cost ratios, and financial leverage ratios, as well as to explain their impact on cash dividend distribution policies for a sample of Iraqi banks traded on the Iraqi Stock Exchange for the period (2010 - 2017). The number of banks was chosen from the Iraq Stock Exchange (13 banks), and the data for the aforementioned time period was taken according to the bulletins and financial statements issued by the official website of the market. After conducting statistical analysis of the data using the statistical package program (SPSS), where the sample size used was (104) observations, and the number of independent variables was (9), which represent the financial ratios, it was found that there is a significant impact of these financial ratios on determining profit distribution policies, and the study recommended to the specialists In the financial field, using these ratios and focusing on them in the financial analysis aspect.

특수교사의 인공지능교육 관심도 분석

Objectives This study sought to investigate special education teachers' interest in artificial intelligence education(hereinafter referred to as AI education) in the introduction of special education, and to derive implications for effectively applying AI education to special education fields. Methods In order to investigate the interest in AI education of special education teachers, 237 current special education teachers were surveyed online using the SoCQ test tool. For data processing, the general characteristics of the study subjects and the intensity percentile score for each SoCQ sub-stage were investigated through frequency and ratio, and t-test and ANOVA were conducted to compare the difference in AI education interest by special education teachers' background variables. Results The results of the study are as follows. First, special education teachers' interest in AI education was similar to the non-user profile and their interest in the impact of AI education was relatively low. Second, there was no statistically significant difference in interest in AI education according to gender and working institution and in terms of student disability type, the interest of teachers teaching students with sensory and physical disabilities was high in all stages except stage 3(operation), a significant difference appeared. Third, in terms of teaching experience, the higher the teaching experience, the higher the interest in AI education. In terms of educational background, the higher the educational level, the higher the interest in AI education. There was a significant difference in both teaching experience and educational background. Fourth, in terms of SW education-related training hours and AI education-related training hours, the more training hours there were, the higher the level of interest in both SW education and AI education. Fifth, in terms of SW education competency, the higher the SW education competency, the higher the interest in AI education, and in terms of AI education support, the higher the support for AI education, the higher the interest. Conclusions According to these results, as a way to increase special education teachers' interest in AI education, promote AI education to special education teachers so that they can be effective and interested in students with disabilities, and provide sufficient AI education methods and teaching and learning materials according to the type of disability. Needs to be. In addition, national-level policy support is needed, such as providing opportunities for expert collaboration and establishing new training courses so that special education teachers can develop AI education-related capabilities.

DATA DRIVEN AND CELL SPECIFIC DETERMINATION OF NUCLEI-ASSOCIATED ACTIN STRUCTURE

In the specialized field of cellular mechanosignaling, the intricate relationship between the cytoskeleton and nucleus is crucial for determining the behavior and fate of mesenchymal stem cells (MSCs). Existing analytical methods for filamentous actin fibers (F-actin) suffer from issues of accuracy and reproducibility. To address this, our research introduces a significant advancement in image analysis by utilizing two deep-learning segmentation Convolutional Neural Networks (CNNs) based on U-Net architecture. This algorithm precisely quantifies F-actin and offers reproducible data from 3D confocal microscopy images. Our CNNs automatically extract the 3D shape of both the nucleus and individual actin fibers, thereby enabling the geometric reconstruction of these critical cellular components. We applied this methodology to MSCs exposed to low-intensity vibration, and hypothesized that mechanical stimuli will induce perinuclear actin remodeling. Confocal Z-stack images of MSCs were captured using a 40x oil lens and a Zeiss LSM 900 microscope. Nucleus and F-actin structures were stained with Hoechst 33342 and Phalloidin, respectively. Nucleus-based images were employed to mask and isolate actin fibers within the nuclear vicinity. A specialized deep-learning segmentation model was engineered within the PyTorch framework. For model training, a selective 2% of nuclei and F-actin images were annotated. Specifically, three layers from each of six representative nuclei, among approximately 500 cross-sectional layers, were meticulously selected; a similar approach was employed for actin. The model underwent 200 epochs of training. Post-training, the deep-learning model was employed to process all collected cross-sectional images of F-actin and nuclei. A subsequent analytical step involved the scrutiny of intersecting segmented fibers between consecutive layers for geometrically accurate actin fiber reconstruction. We analyzed 102 control and 110 vibration-treated mesenchymal stem cells (MSCs) by utilizing our deep-learning-based reconstruction algorithm on confocal Z-stack images. Our data revealed an 8% decrease in nuclear height from 5.73 µm to 5.28 µm (p &lt; 0.001) upon application of low-intensity vibration. In contrast, the two groups observed no significant difference in nuclear volume. Accompanying these changes at the nuclear level, the following changes in the F-actin cytoskeleton were observed upon LIV application. The total number of actin filaments per cell increased from 117 to 150. The average volume of individual actin fibers rose from 0.40 µm³ to 0.43 µm³. Notably, the volume of fibers at the apical nuclear surface increased from 0.35 µm³ to 0.46 µm³. While the average length of actin fibers in the apical nuclear surface remained largely unchanged due to the addition of smaller fibers in LIV groups, the LIV group had a larger number of fibers that were larger than 15µm.

Core components of infectious disease outbreak response

Outbreak response, as a technical and specialized field of practice, is struggling to keep pace with the evolving landscape of public health emergencies. Here, we analyze 235 different multisectoral activities that comprise outbreak preparedness and response. We explore the conditions under which these activities are applicable, including different phases of response, different operating circumstances, and different disease etiologies, and find that the core activities required for outbreak response largely apply across etiology and scale, but are more substantial during the early phases of response. To validate this framework with real-world examples, we then examine 246 reports from the WHO Disease Outbreak News (DON), a narrative record of outbreak history through time, and examine which of our activities are reported or implied in these narratives. We find that the core components of response are applicable across the vast majority of outbreaks, especially as they relate to basic epidemiology, infection prevention, and governance, and that many different kinds of real-world outbreaks require the same core set of responses. These findings point to a nearly-universal set of outbreak response activities that could be directly incorporated into national and international response plans, significantly reducing the risk and impact of infectious disease outbreaks.

Enhancing plastic scintillator performance through advanced injection molding techniques

The production of plastic scintillators with superior optical properties, including high light yield and optimal emission spectra, plays a pivotal role in facilitating their extensive applicability across various domains. In addition, ensuring robust radiation hardness is essential for these scintillators to serve diverse purposes effectively. Given the significant impact of the optical gap on the optical characteristics and radiation resistance of scintillator products, we embarked on optimizing the injection molding technique, focusing on this critical parameter as part of the plastic scintillator fabrication process. Our findings indicate that the injection molding technique not only accelerates the fabrication process, but also yields products with comparable light yield and improved radiation hardness, outperforming samples produced by traditional thermal polymerization. These outcomes not only offer valuable insights and guidance for the streamlined production of high-quality plastic scintillators but also signify a notable advancement in this specialized field of study.

Reflexiones sobre el clima laboral: estudio de caso en una empresa de capacitación profesional

The site of the case study was the professional training academy, which is responsible for the provision of online training services. It employs specialists in various fields, such as English, Robotics, and Computer Science, accredited by the Ministry of Labor and the Andes Technological Institute. Despite the virtual nature of the academy’s services, it employs workers who perform specific functions in person, justifying the need to investigate the work environment in which these professionals operate. The general objective was to analyze the work environment of a professional training academy using a data collection instrument to provide elements for designing an improvement proposal. Researchers administered Workplace Climate Scale (SL-SPC) questionnaire, which revealed that the main problem included a lack of job involvement or self-realization and poor communication among employees. It led to proposed actions to promote a culture of open communication that recognizes and rewards employee achievements, encourages assertive communication, and promote work-life balance.

A machine learning based EMA-DCPM algorithm for production scheduling

Some special manufacturing fields such as aerospace may encounter mixed production of multiple research and development projects and multiple batch production projects. Under these special production conditions resource conflicts are more severe, resulting in uncertain operating times that are difficult to predict. In addition, a single project may have tens of thousands of supporting products, making it difficult to effectively control the total construction process. To address these challenges this paper proposes new methods. A model, EMA-DCPM (dynamic critical path method) incorporating attention mechanisms in Enterprise Resource Planning and Mechanical Engineering Society) has been proposed. This model predicts product job time through machine learning methods and discovers the predictive advantage of the attention mechanism through data comparison. The CPM control algorithm was improved to enhance its robustness and an efficient modeling method, “5+X” was proposed. This new method is suitable for mixed line planning management in sophisticated manufacturing projects and has value for practical applications.

The development of educational gerontology in Ukraine under martial law

The full-scale military invasion of the Russian Federation aggressor country into Ukraine has led to the mobilisation of Ukrainian citizens of the draft age and caused a reduction in the labour market of young and middle-aged specialists. To support the labour market and economy of Ukraine, the need arose to introduce elderly people into the educational system. Therefore, the main aim of this article was to determine the specific grounds for developing educational gerontology during the full-scale military invasion of Ukraine. The data for the research were assessed using quantitative and qualitative methods, including questionnaires, observation, and analysis of data and documents. The study of the main fields of educational gerontology chosen by elderly people revealed the following: digital technology, management, health and wellness, language and culture, creative arts, engineering and infrastructure, agriculture, and logistics and supply. Researchers of educational gerontology in the USA and European countries (including Ukraine up to February 2022) identified the main reasons for the need to include elderly persons in the educational process, namely: loneliness, mental health support, and socialisation. After February 2022, the main reasons for including the elderly in the educational process expanded to include the shortage of specialists in various fields of the economy due to the mobilisation of young and middle-aged adults into the Armed Forces of Ukraine, the tragic loss of life from the bombings of Ukrainian cities and villages, and the outflow of human resources abroad. These circumstances necessitate the development of adult learning strategies and the establishment of a network of educational gerontology institutes, which will provide mental health support for the elderly and include them in the socio-economic environment

Copolymerization of novel self-promoted curing phthalonitrile with epoxy resin and its thermal property

The disadvantage of poor thermal performance of epoxy resin limits its application in special fields, therefore we improve the thermal performance of epoxy resin by copolymerizing it with phthalonitrile resin. A novel self-promoted curing phthalonitrile monomer containing pyridine rings and amino groups (APNH) was successfully synthesized using 2-amino-4-hydroxypyridine and 4-nitrophthalonitril, and characterized by FTIR and NMR spectra. DSC confirmed that the APNH monomer exhibits curing behavior that is self-promoting. Copolymerizing the APNH monomer with epoxy resin enhances the thermal performance of the epoxy resin. The curing behavior of the EPNH copolymer was studied using DSC, which revealed two distinct curing peaks. FTIR analysis showed that the EPNH copolymer has formed structures such as triazine, phthalocyanine, and isoindoline. The presence of cyano groups significantly enhances the thermal properties of the copolymer, surpassing those of traditional epoxy resins. This enhancement in thermal performance amplifies with an increase in the content of the APNH monomer. The research indicates that the EPNH copolymer exhibits superior thermal stability and elevated glass transition temperatures, facilitating the application of epoxy resin in specialized areas.

AI-Enabled Medical Assessment and Assistance for Vocal Disorders: A Comparative Study

Vocal disorders present significant challenges for patients and clinicians, impacting communication and quality of life. The development of artificial intelligence (AI) technologies offers promising possibilities for improving the assessment and management of vocal disorders. This study aims to evaluate the effectiveness and applicability of different AI approaches in this field through a comparative study of AI-enabled medical assessment and assistance for vocal disorders. Various AI techniques, including machine learning algorithms, deep learning models, and natural language processing methods, are explored in the context of diagnosing vocal disorders, planning treatments, and managing patients. The insights gained from this comparative study contribute to understanding the role of AI in transforming healthcare delivery for vocal disorders, highlighting opportunities, challenges, and future directions for utilizing AI to enhance medical assessment and assistance in this specialized field.

Applications of Silver Nanoparticles in Patent Research.

Silver nanoparticles (AgNPs) have been widely applied in research and industrial fields, finding applications in nanomedicine, drug delivery, biomedical devices, electronics, the energy sector, and environmental protection. Patents provide information on the industrial viability of product technologies, and the number of patent documents provides an estimate of the evolution of a specific technological field. The present work aims to describe the current trends in AgNPs patent applications. In addition, a retrospective study of published patents in Brazil is presented. Analyses of AgNPs-related patents were conducted using the free platform for patent search Lens® in 2010-2019 and articles published in same period using the Scholar® base. The patent applications and their evolution over time, major depositors and holders, and the main technological areas associated with AgNP applications have been described. China and United States are the major patent applicants for nanotechnologies. The worldwide distribution of publications of journal articles shows that China, India, and the United States are the leading countries in the total number of articles published, in that order. Our study of patent applications and published articles confirmed the growing global increase in new technologies involving NPs and AgNPs, particularly in the biotechnology area, in the fields of medicine and agriculture.

Developing a prognostic signature: identifying differentially expressed genes in cardia and non-cardia gastric cancer for immunity and therapeutic sensitivity analysis.

Gastric cancer (GC) can be anatomically categorized into two subtypes; that is, cardia gastric cancer (CGC) and non-cardia gastric cancer (NCGC), which have distinct molecular mechanisms and prognoses. At present, the majority of pharmacological interventions for GC adhere to non-specific treatment regimens. The stratification of GC based on molecular disparities between CGC and NCGC has important clinical guidance value and could help in the development of precision therapies tailored to individual patient needs. Nevertheless, research in this specialized field remains notably limited. This study aims to investigate the molecular differences between CGC and NCGC and to leverage these differences to develop a prognostic risk scoring model (PRSM). We used patient data from The Cancer Genome Atlas (TCGA) and performed a differentially expressed gene (DEG) analysis between CGC and NCGC. A PRSM was developed from the prognosis-associated DEGs identified through Cox regression analyses and was well validated using Gene Expression Omnibus (GEO) data. A total of 339 DEGs were identified between CGC and NCGC, and four prognosis-associated genes were used to construct the PRSM. Using the risk coefficients and expression levels of signature genes, a median risk score (RS) was calculated to classify patients into high- and low-risk groups. The high-risk group had a significantly worse prognosis than the low-risk group. An in-depth analysis revealed that TP53 mutations were more prevalent in the high-risk group, and MUC16 mutations were more prevalent in the low-risk group. A gene set enrichment analysis (GSEA) and the CIBERSORT algorithm were used to assess the differences in the significantly enriched pathways and immune microenvironment in the high- and low-risk groups, respectively. The inhibitory concentration (IC50) values of the chemotherapy drugs for GC also varied between the two groups. This study elucidated the unique molecular characteristics of GC subtypes based on the anatomical site and provided a preliminary contribution for the development of precision medicine for GC.

An Analysis of Graduate Theses Conducted in the Field of Special Education in Türkiye

Special education is a field that has been valued in Türkiye especially in recent years. The aim of this study is to examine the types of postgraduate theses conducted in the field of special education in Türkiye between 2000 and 2023, their intensity in the field of special education and their distribution according to years. The research is a literature review designed in qualitative research model. The keywords of special education, autism, down syndrome, learning disability, intellectual disability, mental deficiency, giftedness, superior intelligence and special talented were determined to serve the purpose of the research. The keywords were searched from the National Thesis Center database. The data were analyzed using descriptive analysis. Considering the titles of the theses, 2991 postgraduate theses including master's, doctorate, specialization in medicine and proficiency in art were reached. The types of postgraduate theses reached, the distribution of theses among keywords and the change of theses according to years are presented in graphs. As a result, it was observed that master's theses were predominant in postgraduate theses in the field of special education between 2000 and 2023, the most focused special education field was autism, and there was a general increase in theses by years, except for 2020.

DESCRIPTION AND FORMATION OF THE TERMINOLOGICAL CONCEPTS IN THE RURAL AND AGROTOURISM

In this article, the concept of the terminosphere and thefunctionality of the terms "Tourism and Tourism Industry" is discussed, the conceptof "terminosphere" and "terminosistem", linguistic analysis of tourism terms, as wellas specialized, special field terms, basic concepts and universal lexical system,including special knowledge It is explained about distinguishing hierarchical levels of peripheral terms that can also be used in thefield.

Crystallization, mechanical, and heat resistance performances of copolymer polypropylene modified by α nucleating agent

AbstractPolypropylene (PP) was a very practical and important thermoplastic, which was widely used in various fields due to its low cost, universal, and corrosion resistance. However, the moderate mechanical and heat resistance properties of PP limited its application in some special fields. In this paper, nucleating agents NP‐657 were added to further improve its mechanical and heat resistance performances of PP (300H). Furthermore, the heterogeneous nucleation of NP‐657 reduced the crystal size and increased the crystal density of 300H. With the rise of NP‐657 content, the crystallization rate of different PP systems increased significantly, such as the t1/2 of 300H‐0.2 reduced by 31.9 min in comparison with that of 300H at 140°C. At the same time, the crystallinity of 300H‐0.05 with 0.05 wt.% NP‐657 increased by about 5.96% compared with 300H. In addition, 300H‐0.05 has the highest tensile strength (23.9 ± 0.6 MPa), flexural modulus (1273.3 ± 67.3 MPa), and the highest thermal deformation temperature (88.5 ± 7.7°C) in different PP systems. In general, this paper provided a reference for the fabrication of PP with satisfactory mechanical and heat resistance performances.Highlights 300H‐0.05 had a maximum crystallinity of 40.8 ± 0.2% in different PP systems. The flexural modulus of 300H‐0.05 was 434 MPa higher than that of 300H. The tensile strength of 300H‐0.05 increased to 23.9 ± 0.6 MPa. 300H had the highest thermal deformation temperature of 88.5 ± 7.7°C.

Forensic Investigation, Challenges, and Issues of Cloud Data: A Systematic Literature Review

Cloud computing technology delivers services, resources, and computer systems over the internet, enabling the easy modification of resources. Each field has its challenges, and the challenges of data transfer in the cloud pose unique obstacles for forensic analysts, making it necessary for them to investigate and adjust the evolving landscape of cloud computing. This is where cloud forensics emerges as a critical component. Cloud forensics, a specialized field within digital forensics, focuses on uncovering evidence of exploitation, conducting thorough investigations, and presenting findings to law enforcement for legal action against perpetrators. This paper examines the primary challenges encountered in cloud forensics, reviews the relevant literature, and analyzes the strategies implemented to address these obstacles.

The evaluation of the performance of ChatGPT in the management of labor analgesia

ChatGPT4 is a leading large language model (LLM) chatbot released by OpenAI in 2023. ChatGPT4 can respond to free-text queries, answer questions and make suggestions regarding virtually any topic. ChatGPT4 has successfully answered anesthesia and even obstetric anesthesia knowledge-based questions with reasonable accuracy. However, ChatGPT4 has yet to be challenged in obstetric anesthesia clinical decision-making. Study Objective: In this study, we evaluated the performance of ChatGPT4 in the management of clinical labor analgesia scenarios compared to expert obstetric anesthesiologists. Intervention: Eight clinical questions with progressively increasing medical complexity were posed to ChatGPT4. Measurements: The ChatGPT4 responses were rated by seven expert obstetric anesthesiologists based on safety, accuracy and completeness of each response using a five-point Likert rating scale. Main Results:ChatGPT4 was deemed safe in 73% of responses to the presented obstetric anesthesia clinical scenarios (27% of responses were deemed unsafe). None of the ChatGPT4 responses were unanimously deemed to be safe by all seven expert obstetric anesthesiologists. Moreover, ChatGPT4 responses were overall partly accurate (score 4 out of 5) and somewhat incomplete (score 3.5 out of 5). Conclusions: In summary, approximately one quarter of all responses by ChatGPT4 were deemed unsafe by expert obstetric anesthesiologists. These findings may suggest the need for more fine-tuning and training of LLMs such as ChatGPT4 specifically for clinical decision making in obstetric anesthesia or other specialized medical fields. These LLMs may come to play an important future role in assisting obstetric anesthesiologists in clinical decision making and enhancing overall patient care.

БОЛОЧОКТОГУ МАТЕМАТИКА МУГАЛИМДЕРИН ОКУТУУНУН ЗАМАНБАП ТЕХНОЛОГИЯЛАРЫ

This article examines the impact of informatization and computerization on the development of science and economics in a modern digital society. The process of improving professional education and information plays a special role. Trends in informatization of education are aimed at training competent, highly qualified specialists in professional activities and related fields. Bul makala koldonuu mүmkүnchүlүktөrүnө arnalgan bilim beru maksatynda zamanbap ilim menen tehnikanyn zhetishkendikteri. Zamanbap koldonuunun on jaktary okuuchularga mathematicians okutuunun technologylars. modern science of mathematics prefers the introduction of new pedagogical technologies to traditional knowledge and skills in the field of Education. Educational technologies are also changing, and the introduction of information and communication technologies provides great opportunities for expanding the range of education in each subject in a general educational institution, including in the field of mathematics.

Leslie Matrix Model For Euphorbia Hirta L Population

Invasive species management has developed into a highly specialized field utilizing a systems approach. It requires knowledge of their life history, growth requirements, and population dynamics that integrate their biology and control. The foundation of strategic planning for the management of invasive species is laid by demographic studies, which record the birth, growth, reproduction, and death of individuals within a population. The present study makes use of the Discrete Leslie Matrix Model to analyze the growth in the age-structured population of Euphorbia hirta, an invasive species in agrosystems, identify critical stages in the species' life cycle, and project the structure and size of future population.