Supplemental Resource Research Articles

Global trends in the burden of rheumatoid arthritis by sociodemographic index: a joinpoint and age–period–cohort analysis based on the Global Burden of Disease Study 2019

ObjectiveTo describe temporal trends in the incidence and disability-adjusted life years (DALYs) of rheumatoid arthritis (RA) across sociodemographic index (SDI) quintiles and sexes from 1990 to 2019.MethodsData on RA incidence...

Performance evaluation of ChatGPT 4.0 on cardiovascular questions from the medical knowledge self-assessment program

Abstract Background Medical trainees are increasingly using online chat-based artificial intelligence (AI) platforms as supplementary resources for board exam preparation and clinical decision support. Prior studies have evaluated the performance of AI chatbots like ChatGPT on various general standardized tests such as the United States Medical Licensing Examination (USMLE), but little is known about their performance on subspecialty-focused exam questions, particularly related to clinical management and treatment. Objective This study aims to evaluate the performance of ChatGPT version 4.0 on the cardiovascular questions from the Medical Knowledge Self-Assessment Program (MKSAP) 19, a widely used resource for board exam preparation in the United States. Methods We submitted all cardiovascular questions from MKSAP 19 to ChatGPT 4.0, covering a broad range of cardiology topics in a multiple-choice format. Performance was gauged against both the official MKSAP answer key and average trainee scores obtained from the MKSAP website. Out of 129 questions, 4 were invalidated due to post-publication data, and 18 were excluded due to reliance on visual aids, leaving 107 questions for analysis. Results ChatGPT 4.0 correctly answered 93 out of 107 questions, reflecting an 87% accuracy rate, compared to a 60% accuracy rate averaged among all human users for the same questions (p<0.0001). ChatGPT accuracy rates for each question category (e.g., heart failure, electrophysiology, etc.) are provided in the figure. On the 14 questions that ChatGPT answered incorrectly, human users averaged a 47% accuracy rate. All 14 incorrectly answered questions were related to clinical management and treatment (as opposed to diagnosis or epidemiology), and the majority (10/14) involved choosing treatment medications or choosing between medications versus interventions. Conclusion ChatGPT 4.0 surpassed the typical 70% accuracy threshold required for passing the internal medicine board certification but performed less well in answering questions about clinical management and treatment. This performance suggests that ChatGPT 4.0 could be an effective adjunct tool for cardiology education, but raises some concerns about its use in clinical decision support for trainees.

Assessing Chat Generative Pre-training Transformer’s Proficiency in Identifying, Diagnosing, and Managing Orthopedic Fractures

Abstract Background: The exploration of Chat generative pre-training transformer’s (GPT’s) applications in medicine is gaining momentum, with artificial intelligence, particularly ChatGPT, showing promise in enhancing orthopedic care. ChatGPT can assist clinicians by providing relevant information based on patient symptoms, medical history, and radiological findings, aiding in differential diagnosis, and suggesting appropriate imaging modalities. This study focuses on evaluating the effectiveness of ChatGPT-4 in diagnosing and managing common orthopedic fractures. Methods: This study involved inputting a diverse set of fracture images into the ChatGPT-4 model. The process commenced by prompting ChatGPT with four questions: “What does the radiograph show?” The second prompt instructed ChatGPT with the actual diagnosis, followed by inquiries on how a clinician should manage the condition and potential complications. All generated responses underwent grading by two authors (musculoskeletal radiologist and orthopedic trainee), utilizing a 5-point Likert scale. Intraclass correlation coefficient (ICC) analysis measurements were performed to assess inter-rater reliability. Descriptive statistical analysis was then employed to provide a comprehensive summary of the study’s findings. Results: ChatGPT demonstrated limitations in identifying diagnoses based on inputted X-ray images. However, it excelled in providing comprehensive information about fracture management and potential complications. There was excellent interobserver reliability with a kappa of 0.9. Conclusion: Our study underscores the utility of ChatGPT as a valuable tool for aiding in the management of common fractures, offering a comprehensive overview of diagnosis, management, and potential complications. The findings highlight its potential role as a supplementary resource in orthopedic practice.

Seasonality in Sonoran pronghorn survival and movement within a managed rangeland

AbstractThe endangered Sonoran pronghorn (Antilocapra americana sonoriensis) has a large portion of its range within a military operations area in southwestern Arizona, USA. Sonoran pronghorn have been actively managed in this region for more than 20 years. Recovery efforts have included release of captive‐born pronghorn into areas both currently and historically occupied by wild pronghorn. These areas provide habitat for federally endangered essential and experimental non‐essential, referred to as 10(j), pronghorn populations. More than 20 years of monitoring efforts have resulted in a large dataset on their spatial occurrence and movement within these areas. To synthesize long‐term trends and seasonality in Sonoran pronghorn movement and mortality, we employed a suite of quantitative analyses that characterized the pronghorns' 1) individual and population‐level home ranges, 2) occurrence near active military targets, 3) trends in seasonal group sizes and composition, 4) sex‐ and group‐specific survivorship, and 5) changes in movement‐based behavior following release from captivity. We found strong seasonal trends throughout, including sex‐specific differences in seasonal movement rates, home ranges, survival, and group composition. Further, captive‐born pronghorn released into the endangered population exhibited markedly higher survival compared to those released in 10(j) areas. Captive‐born pronghorn exhibited significant seasonal use of active military target areas. These analyses, which represent the most comprehensive analysis of Sonoran pronghorn movement and survival to date, have important implications for the future conservation and management of this endangered species. Our results indicate that active management of endangered Sonoran pronghorn has successfully increased survival, and we recommend maintaining or increasing current recovery efforts, such as captive breeding and placement of supplemental resources throughout their range (e.g., freestanding water and supplemental forage), to further boost the survivorship of both wild‐ and captive‐born pronghorn. Moreover, future management efforts could use our seasonality results to create dynamic prediction models of when and where pronghorn are likely to overlap with military activities to best manage interactions. Our methods have added application to other mobile species of conservation interest occurring in disturbed and resource‐limited landscapes.

Assessment of ChatGPT-4 in Family Medicine Board Examinations Using Advanced AI Learning and Analytical Methods: Observational Study.

This research explores the capabilities of ChatGPT-4 in passing the American Board of Family Medicine (ABFM) Certification Examination. Addressing a gap in existing literature, where earlier artificial intelligence (AI) models showed limitations in medical board examinations, this study evaluates the enhanced features and potential of ChatGPT-4, especially in document analysis and information synthesis. The primary goal is to assess whether ChatGPT-4, when provided with extensive preparation resources and when using sophisticated data analysis, can achieve a score equal to or above the passing threshold for the Family Medicine Board Examinations. In this study, ChatGPT-4 was embedded in a specialized subenvironment, "AI Family Medicine Board Exam Taker," designed to closely mimic the conditions of the ABFM Certification Examination. This subenvironment enabled the AI to access and analyze a range of relevant study materials, including a primary medical textbook and supplementary web-based resources. The AI was presented with a series of ABFM-type examination questions, reflecting the breadth and complexity typical of the examination. Emphasis was placed on assessing the AI's ability to interpret and respond to these questions accurately, leveraging its advanced data processing and analysis capabilities within this controlled subenvironment. In our study, ChatGPT-4's performance was quantitatively assessed on 300 practice ABFM examination questions. The AI achieved a correct response rate of 88.67% (95% CI 85.08%-92.25%) for the Custom Robot version and 87.33% (95% CI 83.57%-91.10%) for the Regular version. Statistical analysis, including the McNemar test (P=.45), indicated no significant difference in accuracy between the 2 versions. In addition, the chi-square test for error-type distribution (P=.32) revealed no significant variation in the pattern of errors across versions. These results highlight ChatGPT-4's capacity for high-level performance and consistency in responding to complex medical examination questions under controlled conditions. The study demonstrates that ChatGPT-4, particularly when equipped with specialized preparation and when operating in a tailored subenvironment, shows promising potential in handling the intricacies of medical board examinations. While its performance is comparable with the expected standards for passing the ABFM Certification Examination, further enhancements in AI technology and tailored training methods could push these capabilities to new heights. This exploration opens avenues for integrating AI tools such as ChatGPT-4 in medical education and assessment, emphasizing the importance of continuous advancement and specialized training in medical applications of AI.

Digitising a Card Game for Teaching Pragmatic Markers

Most language learning applications focus on pronunciation, vocabulary, and grammar of a language. However, learning the pragmatics enables learners to interpret contextual meanings of specific words uttered by native speakers during conversations. An educational card game called Mind You! was developed for non-native English-speaking international students in Australia to help them learn English pragmatic markers. The card game was designed to encourage social constructivism and situated learning, where learners construct their own sentences based on their combined interpretations of pragmatic markers, images and scenarios displayed on cards to answer hypothetical questions. A mobile application of the card game is being developed as a supplementary resource when face-to-face or in-person learning is neither possible nor practical. Learners can listen to audios of pragmatic markers in sentences and type or record their own sentences based on their interpretations of the cards and share them online with other learners. This provides a platform for distance collaborative learning between other language learners where they share sentences based on different interpretations of different card combinations. The objective of the mobile application is to train learners in using pragmatic markers for English conversations outside the game. This work-in-progress manuscript outlines how the card game can be digitised into a mobile application.

A thorough examination of ChatGPT-3.5 potential applications in medical writing: A preliminary study.

Effective communication of scientific knowledge plays a crucial role in the advancement of medical research and health care. Technological advancements have introduced large language models such as Chat Generative Pre-Trained Transformer (ChatGPT), powered by artificial intelligence (AI), which has already shown promise in revolutionizing medical writing. This study aimed to conduct a detailed evaluation of ChatGPT-3.5's role in enhancing various aspects of medical writing. From May 10 to 12, 2023, the authors engaged in a series of interactions with ChatGPT-3.5 to evaluate its effectiveness in various tasks, particularly its application to medical writing, including vocabulary enhancement, text rewriting for plagiarism prevention, hypothesis generation, keyword generation, title generation, article summarization, simplification of medical jargon, transforming text from informal to scientific and data interpretation. The exploration of ChatGPT's functionalities in medical writing revealed its potential in enhancing various aspects of the writing process, demonstrating its efficiency in improving vocabulary usage, suggesting alternative phrasing, and providing grammar enhancements. While the results indicate the effectiveness of ChatGPT (version 3.5), the presence of certain imperfections highlights the current indispensability of human intervention to refine and validate outputs, ensuring accuracy and relevance in medical settings. The integration of AI into medical writing shows significant potential for improving clarity, efficiency, and reliability. This evaluation highlights both the benefits and limitations of using ChatGPT-3.5, emphasizing its ability to enhance vocabulary, prevent plagiarism, generate hypotheses, suggest keywords, summarize articles, simplify medical jargon, and transform informal text into an academic format. However, AI tools should not replace human expertise. It is crucial for medical professionals to ensure thorough human review and validation to maintain the accuracy and relevance of the content in case they eventually use AI as a supplementary resource in medical writing. Accepting this mutually symbiotic partnership holds the promise of improving medical research and patient outcomes, and it sets the stage for the fusion of AI and human knowledge to produce a novel approach to medical assessment. Thus, while AI can streamline certain tasks, experienced medical writers and researchers must perform final reviews to uphold high standards in medical communications.

The Kinomatics Australian Film Production Dataset: A Curated Dataset Describing Australian Feature Film Titles and Personnel 1975–2022

Abstract This article presents a novel, extensive, and thoroughly documented dataset describing Australian feature films and the personnel filling ten key production roles on those films. The dataset is curated from public information in multiple sources and draws on further supplemental resources to verify, validate and consolidate this information. In total, the data describes 22,720 roles filled by 9,397 distinct people across 1,877 films, covering an important 47-year period in the Australian film industry. The authors outline how the dataset solves several problems for scholars interested in data that provides a historical record of the collaborative filmmaking process. In particular, to address concerns about known coverage problems with popular sources such as the Internet Movie Database, this dataset has undergone extensive manual checking to ensure that it is reliable as a source of information on a national film industry. Moreover, the authors have carefully and manually linked each person appearing in the dataset, which allows the dataset to provide a rich source of information for exploring the relationality of filmmaking collaborations. The inclusion of ten key filmmaking roles further expands the utility of the dataset beyond existing datasets which tend to focus on actors and/or directors, writers and producers.

ChatGPT-3.5 and -4.0 Do Not Reliably Create Readable Patient Education Materials for Common Orthopaedic Upper- and Lower-Extremity Conditions

PurposeTo investigate if ChatGPT 3.5 and 4.0 can serve as a viable tool to create readable patient education materials (PEMs) for patients with common orthopaedic upper and lower extremity conditions. MethodsUtilizing ChatGPT versions 3.5 and 4.0, the authors asked the AI program a series of two questions pertaining to patient education for 50 common orthopaedic upper extremity and 50 common orthopaedic lower extremity pathologies. Two templated questions were created and used for all conditions. Readability scores were calculated using the Python library TextStat. Multiple readability test scores were generated, and a consensus reading level was created taking into account the results of 8 reading tests. ResultsChatGPT 3.5 produced only 2% and 4% of responses at the appropriate reading level for upper and lower extremity conditions, respectively, compared to 54% produced by ChatGPT 4.0 for both upper and lower extremity conditions (both p<0.0001). Following a priming phase, ChatGPT 3.5 did not produce any viable responses for either the upper or lower extremity conditions, compared to 64% for both upper and lower extremity conditions by ChatGPT 4.0 (both p<0.0001). Additionally, ChatGPT 4.0 was more successful than ChatGPT 3.5 in producing viable responses both pre- and post-priming phase based on all available metrics for reading level (all p<0.001), including Automated Readability scores, Coleman Liau Index, Dale Chall Score, Flesch Kincaid Grade, Flesch Reading Ease, Gunning Fog, Linsear Write Formula Score, and Simple Measure of Gobbledygook (SMOG) Index. ConclusionsOur results indicate that ChatGPT unreliably created readable PEMs for common orthopaedic upper and lower extremity conditions at the time of the study. Clinical RelevanceThe findings of this study suggest that ChatGPT, while constantly improving as evidenced by the advances from 3.5 to 4.0, should not be substituted for traditional methods of patient education at this time and in its current state may be utilized as a supplemental resource at the discretion of providers.

Developing translational products for adapting evidence-based physical activity interventions in rural communities

The development and distribution of educational materials is a key strategy to support the implementation of evidence-based interventions (EBIs). Rural communities have higher rates of physical inactivity and face higher burden of many diseases that increased physical activity can prevent. To support the translation of a developed physical activity intervention for adults in micropolitan communities (10,000-50,000 people), called Active XXXX, the University of XXXX Prevention Research Center for Rural Health (PRC-RH) created a toolkit and supplemental resources designed to guide implementers through the implementation of the intervention. Through a community-engaged process, the PRC-RH underwent three phases of review and evaluation of the developed products. The first phase involved the Community Advisory Board from the pilot intervention, the second involved the PRC-RH State Advisory Board and public health practitioners from across the state, and the third involved micropolitan leaders and micropolitan health department staff. The feedback received through these three phases resulted in changes to the developed products to improve usability, readability, and clarity. The feedback also resulted in the development of additional materials to further support the implementation of the intervention. The success the PRC-RH experienced in the review process can be attributed to the strong, established partnerships with practitioners across the state who represented a variety of community roles and organizations. The developed materials can be used to improve physical activity rates in rural and micropolitan communities, in turn reducing chronic diseases and improving the quality of life for rural residents.

The Use of Digital Interventions for Children and Adolescents with Autism Spectrum Disorder-A Meta-Analysis.

This comprehensive meta-analysis aimed to assess the effectiveness of digital interventions in improving developmental skills for children and adolescents with autism spectrum disorder (ASD). We conducted a systematic literature search based on three databases. A pre-test adjusted between-group standardized effect size was computed for effect size synthesis. We utilized a robust variance estimation model to analyze overall treatment effect. Moderator analyses and publication bias were also addressed. Twenty-eight studies (150 effect sizes) using randomized control trials (RCT; n = 22) or quasi-experimental designs (QED; n = 6) were included. Most studies (n = 22) included social-emotional skills as primary outcomes. The meta-analysis revealed a medium to large overall effect size, with Hedges' g = 0.62, 95% CI [0.36, 0.88], p < 0.001. We found that digital interventions have a statistically significantly large effect on enhancing social-emotional skills compared with language and communication skills, cognitive skills, daily living skills, and physical skills. The results of moderator analyses indicated that computer-based interventions have larger effect sizes in comparison to tablet/smartphone-based interventions. No statistically significant differences were observed between studies utilizing RCT and those using QED. We recommended the integration of digital interventions as supplemental resources in behavioral and educational interventions. Further research needs to focus on more females, young children, and adolescents with ASD in digital intervention research.

Benefits and risks of FDA-approved amyloid-targeting antibodies for treatment of early Alzheimer's disease: Navigating clinician-patient engagement.

The emergence of the United States Food and Drug Administration (FDA)-approved amyloid-targeting therapies for Alzheimer's disease challenges clinicians and healthcare providers with a transformative landscape. Effectively communicating the risks, benefits, burdens, costs, and available support associated with these novel disease-modifying treatments to patients, families, and other healthcare providers is essential but complex. In response, the Alzheimer's Association's Clinical Meaningfulness Workgroup has proposed language surrounding treatment eligibility, benefits, amyloid-related imaging abnormalities (ARIA), apolipoprotein E (APOE) genotyping, and treatment costs, serving as a resource to healthcare professionals in navigating discussions with patients and their families. As the landscape evolves with the approval of new Alzheimer's therapies, this resource stands poised for updates, ensuring its continued relevance in facilitating informed and meaningful patient-provider dialogues. HIGHLIGHTS: Effective communication of risks, benefits, burdens, and costs of FDA-approved amyloid-targeting antibodies is essential to patients, families, and healthcare providers. The Alzheimer's Association's Clinical Meaningfulness Workgroup provides language for physicians and healthcare providers around treatment eligibility, benefits, ARIA, APOE genotyping, and treatment costs. This supplementary resource may be updated as new AD therapies become approved.

Impact of Long SARS-CoV-2 Omicron Infection on the Health Care Burden: Comparative Case-Control Study Between Omicron and Pre-Omicron Waves.

Following the initial acute phase of COVID-19, health care resource use has escalated among individuals with SARS-CoV-2 infection. This study aimed to compare new diagnoses of long COVID and the demand for health services in the general population after the Omicron wave with those observed during the pre-Omicron waves, using similar assessment protocols for both periods and to analyze the influence of vaccination. This matched retrospective case-control study included patients of both sexes diagnosed with acute SARS-CoV-2 infection using reverse transcription polymerase chain reaction or antigen tests in the hospital microbiology laboratory during the pandemic period regardless of whether the patients were hospitalized. We included patients of all ages from 2 health care departments that cover 604,000 subjects. The population was stratified into 2 groups, youths (<18 years) and adults (≥18 years). Patients were followed-up for 6 months after SARS-CoV-2 infection. Previous vaccination, new diagnoses, and the use of health care resources were recorded. Patients were compared with controls selected using a prospective score matched for age, sex, and the Charlson index. A total of 41,577 patients with a history of prior COVID-19 infection were included, alongside an equivalent number of controls. This cohort encompassed 33,249 (80%) adults aged ≥18 years and 8328 (20%) youths aged <18 years. Our analysis identified 40 new diagnoses during the observation period. The incidence rate per 100 patients over a 6-month period was 27.2 for vaccinated and 25.1 for unvaccinated adults (P=.09), while among youths, the corresponding rates were 25.7 for vaccinated and 36.7 for unvaccinated individuals (P<.001). Overall, the incidence of new diagnoses was notably higher in patients compared to matched controls. Additionally, vaccinated patients exhibited a reduced incidence of new diagnoses, particularly among women (P<.001) and younger patients (P<.001) irrespective of the number of vaccine doses administered and the duration since the last dose. Furthermore, an increase in the use of health care resources was observed in both adult and youth groups, albeit with lower figures noted in vaccinated individuals. In the comparative analysis between the pre-Omicron and Omicron waves, the incidence of new diagnoses was higher in the former; however, distinct patterns of diagnosis were evident. Specifically, depressed mood (P=.03), anosmia (P=.003), hair loss (P<.001), dyspnea (<0.001), chest pain (P=.04), dysmenorrhea (P<.001), myalgia (P=.011), weakness (P<.001), and tachycardia (P=.015) were more common in the pre-Omicron period. Similarly, health care resource use, encompassing primary care, specialist, and emergency services, was more pronounced in the pre-Omicron wave. The rise in new diagnoses following SARS-CoV-2 infection warrants attention due to its potential implications for health systems, which may necessitate the allocation of supplementary resources. The absence of vaccination protection presents a challenge to the health care system.

Hubungan Kualitas Tidur dengan Derajat Keparahan Acne Vulgaris pada Siswa-Siswi Kelas X di Sma Muhammadiyah 2 Bandar Lampung Provinsi Lampung Tahun 2024

Acne vulgaris is a multifactorial chronic skin condition, characterized by inflammation in the pilosebaceous units, such as the formation of comedones, papules, pustules, nodules, and cysts, which typically present on the face, upper body, and upper arms. Sleep quality is defined by an individual's capacity to sleep adequately and achieve the necessary amount of rest. This study aims to identify the correlation between sleep quality and the severity of acne vulgaris among tenth grade students at Muhammadiyah 2 Senior High School of Bandar Lampung, Lampung Province in 2024.The research adopts a quantitative approach and analytical survey method with a cross-sectional design. The study population includes all 285 tenth-grade students at Muhammadiyah 2 Senior High School of Bandar Lampung, Lampung Province in 2024. A total of 74 respondents were selected using the Slovin formula based on inclusion and exclusion criteria, with a purposive sampling technique. The data were collected using the Pittsburgh Sleep Quality Index (PSQI) questionnaire and observations of the severity of acne vulgaris. Data analysis was conducted using univariate and bivariate analyses with the non-parametric chi- square test, utilizing SPSS version 26.The study revealed that 50 respondents (67.6%) had poor sleep quality, while 24 respondents (32.4%) experienced a moderate severity of acne vulgaris. The analysis indicates a significant correlation between sleep quality and the severity of acne vulgaris among tenth-grade students at Muhammadiyah 2 Senior High School in Bandar Lampung, Lampung Province in 2024, with a p-value of 0.000 ≤ 0.05. The results of this research provide useful information and may serve as a supplementary resource for future research.

A systematic analysis of binary blend cement concrete infused with lime sludge and fly ash

Lime sludge is a residual substance came from the paper sector. The current research investigates that the effects of adding lime sludge to concrete as a cementitious substance on its mechanical properties. Lime sludge replaces cement in various amounts to provide the appropriate mechanical properties. The resulting material is then compared to standard concrete. The research on supplemental cementation resources that equivalent Portland cements's potency without compromising stability. According to the Indian standard (IS 10262–2009), the study used M20, M30, and M40 concrete. Lime sludge replaced cement 0 ​%, 10 ​%, 20 ​%, and 30 ​%. Also investigated were lime sludge and fly ash mixtures of 10 ​%, 20 ​%, and 30 ​% by weight. Mechanical parameters of concrete classes M20, M30, and M40 were measured at 7 and 28 days. The grades show that the 10 ​% lime sludge out performed normal concrete. This work represents can be applied as unique additional cementitious substance up to the definite proportions.

Gamified Learning: Evaluating the Impact of Duolingo on Language Retention and Academic Performance in Secondary School Students

The study investigates the relationship between Duolingo usage language learning retention, and academic achievements among high school students. Duolingo, it is known for its user-friendly interface and gamified learning approach, has acquired popularity in the field of language instruction. The major goal of this study is to determine if Duolingo usage improves academic achievement and language retention in secondary school students. This study lasted four months and involved 40 students from SMA Istiqamah Muhammadiyah Samarinda. The collection of data includes Duolingo achievement assessments and questionnaires indicated to assess patterns of Duolingo usage, language retention, and app satisfaction. Statistical analysis, specifically the Pearson correlation coefficient, was employed to assess the level and course of the correlation between Duolingo use and language learning results. The findings indicate a significant positive correlation between the use of Duolingo and the retention of language learning, as well as an improvement in academic achievements. The results suggest that integrating Duolingo into the language learning curriculum is beneficial for students, enhancing both their engagement and learning outcomes. Furthermore, the study highlights the need for further research to explore the specific mechanisms through which Duolingo impacts language learning and to expand the findings to larger and more diverse student populations. This research contributes valuable insights for educators and policymakers seeking to incorporate digital learning tools into language education and underscores the potential of Duolingo as an effective supplementary resource for language learning in secondary schools.

Multiple habitat graphs: how connectivity brings forth landscape ecological processes

PurposeHabitat connectivity is integral to current biodiversity science and conservation strategies. Originally, the connectivity concept stressed the role of individual movements for landscape-scale processes. Connectivity determines whether populations can survive in sub-optimal patches (i.e., source-sink effects), complete life cycles relying on different habitat types (i.e., landscape complementation), and benefit from supplementary resources distributed over the landscape (i.e., landscape supplementation). Although the past decades have witnessed major improvements in habitat connectivity modeling, most approaches have yet to consider the multiplicity of habitat types that a species can benefit from. Without doing so, connectivity analyses potentially fail to meet one of their fundamental purposes: revealing how complex individual movements lead to landscape-scale ecological processes.MethodsTo bridge this conceptual and methodological gap, we propose to include multiple habitat types in spatial graph models of habitat connectivity, where nodes traditionally represent a single habitat type. Multiple habitat graphs will improve how we model connectivity and related landscape ecological processes, and how they are impacted by land cover changes.ResultsIn three case studies, we use these graphs to model (i) source-sink effects, (ii) landscape supplementation, and (iii) complementation processes, in urban ecosystems, agricultural landscapes, and amphibian habitat networks, respectively. A new version of the Graphab open-source software implements the proposed approach.ConclusionMultiple habitat graphs help address crucial conservation challenges (e.g., urban sprawl, biological control, climate change) by representing more accurately the dynamics of populations, communities, and their interactions. Our approach thereby extends the ecologist’s toolbox and aims at fostering the alignment between landscape ecology theory and practice.

Large Language Model Prompting Techniques for Advancement in Clinical Medicine.

Large Language Models (LLMs have the potential to revolutionize clinical medicine by enhancing healthcare access, diagnosis, surgical planning, and education. However, their utilization requires careful, prompt engineering to mitigate challenges like hallucinations and biases. Proper utilization of LLMs involves understanding foundational concepts such as tokenization, embeddings, and attention mechanisms, alongside strategic prompting techniques to ensure accurate outputs. For innovative healthcare solutions, it is essential to maintain ongoing collaboration between AI technology and medical professionals. Ethical considerations, including data security and bias mitigation, are critical to their application. By leveraging LLMs as supplementary resources in research and education, we can enhance learning and support knowledge-based inquiries, ultimately advancing the quality and accessibility of medical care. Continued research and development are necessary to fully realize the potential of LLMs in transforming healthcare.

Design and Implementation of Artificial Intelligence Online Learning Platform Based on Resource Scheduling Technology

Online learning (OL) is a new platform, which is used as a supplementary resource to improve the teaching and learning process in university education. The open educational resources provide supplementary resources for strengthening online learning platform. In this work, an artificial intelligence-based online learning platform is designed that can manage resources using scheduling technology. The OL platform automatically allocates resources through appropriate cluster-based scheduling technology. The proposed deep learning-based OL platform (DLOL) supports the sequential integration of the model by simplifying and adjusting the model operating environment. Combined with the fuzzy-based convolutional neural networks, the decision tree algorithm is used to implement OL platform using blockchain models. The research results proved that artificial intelligence can help students improve their learning ability and makes the learning process relatively easy and exciting.

The subsistence strategy transformation of the Yangshao culture (6900-4800BP) in the Guanzhong Area and Western Henan based on new faunal materials from the Miaodigou site

While the significance of the Yangshao culture in early China is widely recognized, systematic and detailed analysis and discussion of its subsistence strategy are lacking, especially for faunal materials. This paper examines the new and crucial faunal remains that include 3,400 identified specimens from the Miaodigou site unearthed in 2002. At that site, pig husbandry was predominant and wild animals were occasionally hunted as supplemental resources. In combination with published research including zooarchaeology, paleoethnobotany, and carbon and nitrogen stable isotope studies of 29 other sites mainly from the Guanzhong area and western Henan, we argue that subsistence strategy underwent a comprehensive and profound transformation during the middle period of Yangshao culture, namely Miaodigou period, as crop production and pig husbandry flourished in a wide range of areas. Domestic pigs accounted for over 80% of mammals, reaching an unprecedented proportion in the Neolithic age and manifesting a high level of consistency in feeding and slaughtering, and foxtail millet replaced broomcorn to become the most important crop at all sites, both of which demonstrate the breadth and depth of this transformation. The tension between the deterioration of climate, the decrease of surrounding wildlife resources due to excessive development and the continuous growth of the population may have contributed to this transformation. This study enriches our understanding of human-environment interactions in the embryonic stage of Chinese civilization and triggers further thoughts on how culture, society, the environment and subsistence were entangled in the development of civilization.