Application Programming Research Articles

Temporalities behind the paywall: examining patterns of data flow and temporalities within social media platform APIs

ABSTRACT Multiple temporalities emerge with the participation of technologies and digital objects in shaping how time is perceived and articulated. By employing the concepts of data mobility and data friction, this paper scrutinizes the multiple temporalities generated and shaped in the movement of social media data via Application Programming Interfaces (APIs). Based on a document analysis of Twitter/X API documentation and changelogs, this study identifies three patterns of data mobility, each of which is accompanied by frictions that represent the conflicts between different data flows, data formats, temporal logics, and protocols. Specifically, the first pattern refers to social media data moving in fragments with different levels of granularity, which shapes fragmented and asynchronous temporalities. The second pattern refers to the composition of data movement and processes of verification of digital identities, which generates a compound temporality due to the combination of data and practice flows with different temporal logics. The third pattern refers to the asynchronicity between data collection and data analysis, setting real-timeness as the platform's primary goal. The findings enrich the examination of network time in the context of social media data movement. This article also reveals that rather than moving in an uninterrupted, real-time, and frictionless way, social media data moves in fragmented, asynchronous, and compound ways maintained by the platform's API paywall. By constructing these patterns of data mobility, the platform participates in shaping how time is perceived and how multiple temporalities are assembled.

Diversity in the medical research ecosystem: a descriptive scientometric analysis of over 49 000 studies and 150 000 authors published in high-impact medical journals between 2007 and 2022

ObjectivesHealth research that significantly impacts global clinical practice and policy is often published in high-impact factor (IF) medical journals. These outlets play a pivotal role in the worldwide dissemination of...

ПРОДУКТИВНОСТЬ И ЗАПАСЫ СЫРЬЯ БОРЩЕВИКА РАССЕЧЕННОГО В ХАБАРОВСКОМ КРАЕ

The purpose of research is to study the productivity and develop accounting standards (productivity and possible collection) and harvesting of dissected hogweed (Heracleum dissectum Ledeb.) on the example of the Khabarovsk Region. The object of the study is the food raw material of the dissected hogweed. The laying of registration sites for the collection of food raw materials of hogweed was carried out on the territory of the Hekhcirskoe forestry of the Khabarovsk Region (5 registration sites 1×1 min size were laid). The study was conducted from 2019 to 2022. Hogweed dissected was evaluated by the method of model specimens. When assessing the yield by the method of model specimens, 2 indicators were established: the number of accounting specimens (plant stems) per unit area and the average mass of raw materials obtained from one specimen, the number of bud inflorescences on the plant was also taken into account. This method was used because individual specimens varied in degree of development. The mass of young shoots was determined by weighing in wet and air-dry conditions. The food raw materials of the dissected hogweed were dried to an air-dry state at 95 °C in a ShS-80-01 drying cabinet. The yield of raw materials in the air-dry state, expressed as a percentage, was obtained by the ratio of the mass of dry raw materials to the mass of freshly harvested. The data were processed using the analysis package of the Statistica application program. The yield of air-dry food raw materials of hogweed was 25 %, and bud inflorescences – 16 %. On one hectare of cenoses with the participation of cow parsnip, the yield of the product with 100 stems is 3–5 t/ha; with 500 stems – 7–8 t/ha. Hogweed dissected is represented by ash-elm, poplar, white birch, aspen and alder formations. Possible collection of hogweed dissected in plantations (0.3–0.4) at a thicket density of 0.5–1.5 pcs/m2 – 99–290 kg/ha; 1.6–2.5 pcs/m2 – 330–550 kg/ha; 2.6–3.5 pcs/m2 – 520–720 kg/ha. Possible collection in open spaces (0.1) – at the degree of density of thickets 0.5–1.5 pcs/m2 – 75–210 kg/ha; 1.6–2.5 pcs/m2 – 300–400 kg/ha; 2.6–3.5 pcs/m2 – 450–600 kg/ha.

CDN modelling with API integration in public information systems of Indonesia: A simulation-based approach

This study investigates the integration of Content Delivery Networks (CDNs) and Application Programming Interfaces (APIs) within Indonesia's public information systems, modelled across the country’s administrative hierarchy. The simulation measures key performance metrics such as Time-to-Live (TTL), Cache Hit Ratio (CHR), Latency, Throughput, and Bandwidth Consumption across five different scenarios. These scenarios involve varying points of origin for content delivery, from the Central Government down to individual villages, reflecting Indonesia's administrative structure. The study demonstrates that CDN integration significantly improves data delivery efficiency by reducing latency, optimizing bandwidth consumption, and increasing cache effectiveness. These results align with the Indonesian government’s goals of enhancing public information dissemination and supporting digital transformation efforts.

The influences of social support expressed from doctors and disclosed from peers on patient decision-making: an analysis from the online health community

Patients are turning to OHCs to deal with the stresses and complications of infertility. As a stigmatized disease, infertile patients may value informational support and emotional support differently, which is ignored in existing studies. Based on social support theory, this study aims to reveal the role of informational support and emotional support of doctors in infertile patient decision-making. We select Haodf.com as our data source and we collected information for all new patients of 2,989 doctors. we use Jieba word segmentation tool and TF-IDF algorithm to segment word and find keywords. Then random forest algorithm is used to classify 750 training texts and 250 test texts. We measure all the treatment experience of these new patients between September and December 2022 and then take the average value (TreatExpi). The sentiment analysis application program interface from Baidu AI-NLP is used to measure emotion state of patients and then the average value is included (EmoStai). In order to eliminate the influence of other factors, control variables including the number of patients online (#Patienti), the recommendation (Recommendationi), hospital level (Hleveli), doctor medical title (Mtitlei) and service price (Serpi). First, the effects of social support expressed from doctor and social support disclosed from peers show heterogeneity on patient decision-making. Second, the influences of informational support and emotional support also show difference. Third, the above relationships change with the characteristics of patients. Detecting different types and different sources of social support activities via text mining contributes to between understanding patient decision behavior in OHCs. This study contributes social support theory and OHC studies, and helps the management and design OHCs.

Enabling Seamless Interoperability of Digital Systems in Smart Cities Using API: A Systematic Literature Review

ABSTRACT Smart cities are envisioned to achieve an urban space where systems are connected to provide value added services to citizens and stakeholders, improve quality of life, and enhance institutional effectiveness. In smart cities, digital systems are deployed that comprise a wide range of service providers who collaborate to provide digital services to citizens. Hence, there is a need for accessible data that can be used to develop innovative applications and services to improve citizens’ quality of life. Unfortunately, achieving interoperability of digital systems is challenging due to isolated systems referred to as vertical silos, which cannot communicate with each other due to lack of standardization of interfaces. Thus, interoperability is seen as a barrier for the seamless transfer of data between systems deployed in smart cities. In order to facilitate interoperability of systems in smart cities, this article conducts a Systematic Literature Review (SLR) to investigate the role of Application Programming Interface (API) to enable seamless interoperability in smart cities. An SLR was employed to guide the selection of studies retrieved to adequately address the formulated research questions. Findings from this study present the importance of interoperability in smart urban environments and help in providing an overview and categorization of existing interoperability solutions to make cities smarter. The findings also identified prior interoperability approaches in smart cities. Findings present a case scenario that depicts how APIs support seamless interoperability among digital systems in a smart city environment. Lastly, the findings elaborate on available API management tools and further suggest that APIs can significantly contribute to improve interoperability between digital systems and decrease barriers in providing innovative digital services in smart cities.

Describing ZYN-related content on TikTok: content analysis.

Nicotine pouches are the fastest-growing oral smokeless tobacco or nicotine product category in the United States, and there are concerns about their potential appeal to youth. Few studies have surveilled discussions about nicotine pouches on TikTok, an audiovisual platform popular among youth. To address this gap, this study conducted a content analysis of TikTok posts related to the leading nicotine pouch brand, ZYN. Publicly available posts (N=665) from 10 ZYN-related hashtags were collected from the TikTok Application Programming Interface between January 2023 and February 2024. The number of likes, comments, shares, and plays were collected for each post and the number of followers for each account profile was recorded. Posts were coded for themes like product marketing and use, health warnings, and demographics. The most prevalent theme was humor (n=581, 87.4%), followed by product use (n=378, 56.8%), and branded merchandise (n=378, 56.8%). Young people (i.e., persons in the video who appeared younger than 30) were identified in 48.3% (n=321) of posts, and 61.5% (n=409) of posts featured people who appeared to be male. Cessation (n=12, 1.8%) was an uncommon theme. Most posts came from non-commercial accounts (n=420, 63.1%), followed by commercial accounts (n=158, 23.8%), and content creators/influencers (n=87, 13.1%). This study showed that ZYN-related posts on TikTok often featured pro-tobacco themes, such as humor and product use, while rarely mentioning anti-tobacco themes like cessation. Future research should examine the relationship between exposure to pro-tobacco content on TikTok and tobacco-related attitudes and behaviors. This study highlighted the potential role of TikTok in normalizing tobacco and nicotine product use, and in particular ZYN product use, including among youth. While increased enforcement of online tobacco control policies can help reduce exposure to ZYN-related content in youth, prevention programs and interventions that incorporate tobacco-related digital media literacy are needed to inoculate adolescents and young adults from the pro-tobacco content found online.

DSKIPP: A Prompt Method to Enhance the Reliability in LLMs for Java API Recommendation Task

ABSTRACTIn the realm of software development, selecting the appropriate Java application programming interfaces (APIs) from a vast pool remains a significant challenge for developers. This research addresses this complexity by tackling the limitations of current API recommendation methods, which often struggle to align API suggestions with the specific queries and development contexts. In this paper, we introduce a novel prompt method named DSKIPP (Development Scenario, key Knowledge and Intention's Progressive Prompt), designed to enhance the efficiency of large language models (LLMs) in Java API recommendations. Firstly, we devise an overview of DSKIPP which conducts LLMs through a sequential process: first, inferring the package level, followed by the class level, and ultimately the method level as an API comprises three distinct components at varying levels—package, class and method. Secondly, at each level, DSKIPP assists LLMs in deducing the development scenario associated with a query and the essential key knowledge relevant to that scenario. This approach enables LLMs to gain a more profound contextual understanding of the query's intention. Moreover, during the inference process at the class and method level, we implement a self‐check mechanism enabling LLMs to validate the results and ensure a more reasoned and reliable outcome. To validate the efficiency of DSKIPP, comparison and ablation experiments are both conducted within Java programming environment. The comparison results affirm that our method outperforms the current state‐of‐the‐art technologies in API recommendation tasks, while the ablation results shed light on why DSKIPP can enhance the reliability of API recommendations in LLMs. This research contributes to the field by offering a more reliable and context‐sensitive solution for API recommendation in software development.

The Weather On-Demand Framework

This paper describes the Weather On-Demand (WOD) forecasting framework which is a software stack used to run operational and on-demand weather forecasts. The WOD framework is a distributed system for the following: (1) running the Weather Research and Forecast (WRF) model for data assimilation and forecasts by triggering either scheduled or on-demand jobs; (2) gathering upstream weather forecasts and observations from a wide variety of sources; (3) reducing output data file sizes for permanent storage; (4) making results available through Application Programming Interfaces (APIs); (5) making data files available to custom post-processors. Much effort is put into starting processing as soon as the required data become available, and in parallel where possible. In addition to being able to create short- to medium-range weather forecasts for any location on the globe, users are granted access to a plethora of both global and regional weather forecasts and observations, as well as seasonal outlooks from the National Oceanic and Atmospheric Administration (NOAA) in the USA through WOD integrated-APIs. All this information can be integrated with third-party software solutions via WOD APIs. The software is maintained in the Git distributed version control system and can be installed on suitable hardware, bringing the full flexibility and power of the WRF modelling system to the user in a matter of hours.

Can large language models fully automate or partially assist paper selection in systematic reviews?

Background/aimsLarge language models (LLMs) have substantial potential to enhance the efficiency of academic research. The accuracy and performance of LLMs in a systematic review, a core part of evidence building,...

The Expert System Application to Diagnose Computer Damage Using UML Model (Unified Modeling Language)

The expert system application are developed in line with the existence of information technology. The development of expert systems aims to be a means of assistance to provide solutions in our lives. This expert system can help technicians get solutions quickly and can save time. Expert systems use computer technology to integrate, manipulate and display information or characteristics. Expert systems can also help in making better solutions. With the very rapid technological advances today, an idea or idea has emerged from the author to try to implement one of the expert system application programs into the quality of service activities of computer technicians. The author tries to build an application that will help to facilitate the provision of solutions to computer damage to hardware so that it can make it easier for users or technicians to get solutions quickly. The system to be created is "Designing an Expert System for Diagnosing Computer Problems Using Visual Basic" This system will use the prototype method and tools for modeling using UML (Unified Modeling Language). This system is built using the Visual Basic 6.0 application to process the Microsoft Access database.

Individual DOI minting for Open Repository: a script for creating a DOI on demand for a DSpace repository

Digital Object Identifiers (DOIs) are a key persistent identifier in the publishing landscape to ensure the discoverability and citation of research products. Minting DOIs can be a time-consuming task for repository librarians. This process can be automated since the metadata for DOIs is already in the repository record and DataCite, a DOI minting organization, and Open Repository, a DSpace repository platform, both have application programming interfaces (APIs). Existing software enables bulk DOI minting. However, the institutional repository at UMass Chan Medical School contains a mixture of original materials that need DOIs (dissertations, reports, data, etc.) and previously published materials that already have DOIs such as journal articles. An institutional repository librarian and her librarian colleague with Python experience embarked on a paired programming project to create a script to mint DOIs on demand in DataCite for individual items in the institution’s Open Repository instance. The pair met for one hour each week to develop and test the script using combined skills in institutional repositories, metadata, DOI minting, coding in Python, APIs, and data cleaning. The project was a great learning opportunity for both librarians to improve their Python coding skills. The new script makes the DOI minting process more efficient, enhances metadata in DataCite, and improves accuracy. Future script enhancements such as automatically updating repository metadata with the new DOI are planned after the repository upgrade to DSpace 7.

Exploring Digital-Only Banking in Malaysia: Technological Advancements, Regulatory Landscape, and Business Implications

Digital banking in Malaysia is undergoing rapid transformation, driven by technological advancements, regulatory frameworks, and evolving consumer preferences. This shift is marked by the adoption of critical technologies such as Application Programming Interfaces (APIs), low-code technology, cloud computing and artificial intelligence (AI) which are redefining how financial services are delivered. These innovations enhance operational efficiency, enable seamless integrations, and offer personalized customer experiences, positioning digital banking as a cornerstone of Malaysia’s financial ecosystem. Concurrently, regulatory measures introduced by Bank Negara Malaysia (BNM), including digital banking licenses and consumer protection policies, have facilitated a conducive environment for growth while ensuring financial inclusion and stability. Despite these advancements, the transition to digital-only banking is not without challenges. Cybersecurity threats, compliance complexities and technological dependency pose significant risks to both financial institutions and consumers. Addressing these issues requires a robust cybersecurity framework, continuous innovation and collaborative efforts among policymakers, industry players and technology developers. This paper reviews critical technologies shaping the industry, explores the regulatory landscape, evaluates security concerns, and examines the business implications and opportunities. The findings provide insights into the future of digital banking in Malaysia, focusing on innovations, challenges and potential growth areas.

DETERMINING THE EFFECTIVENESS OF GROWTH REGULATORS IN TECHNOLOGICAL SCHEMES FOR SWEET POTATO (IPOMOEA BATATAS) CULTIVATION IN THE CONDITIONS OF THE FOREST-STEPPE OF UKRAINE

Purpose: To determine the effectiveness of growth regulators in technological schemes for sweet potato cultivation, their impact on tuber yield and quality under the conditions of the Forest-Steppe of Ukraine. Methods: The study was conducted using field and laboratory experiments, as well as computational and statistical calculations. Results: The use of growth regulators DG-16, DAC-6, DG-53 + DG-53A, and fertilizers with growth-stimulating properties such as Gros Cornerist + Gros Amino-Magnesium, Gros Cornerist + Ecoline Phosphite K-Amino significantly increased the number of leaves per sweet potato plant by 24.4–28.7%, stem thickness by 21.6–27.5%, the number of tubers per plant by 31.6–90.8%, and the average tuber weight by 13.0–20.9% relative to the background application of mineral fertilizers. Positive effects of growth regulators and fertilizers were noted on the chlorophyll content in sweet potato leaves (increase of 16.6–22.9% compared to the background), nitrogen content in leaves (excluding DAC-6 and humic fertilizer "Rich Soil," the increase was 9.4–14.3%), phosphorus content (by 9.1–22.7%), potassium content (by 7.8–12.2%), carotene content in leaves (by 13.5–19.3%), and increased antioxidant activity by 10.8–19.3%. Maximum impact on these indicators was provided by DG-16, DG-53 + DG-53A, Gros Cornerist + Ecoline Phosphite K-Amino, and Gros Cornerist + Gros Amino-Magnesium. Maximum yield increase was achieved using DG-16 (an increase of 5.6 t/ha or 36.6%), DG-53 + DG-53A (an increase of 5.7 t/ha or 37.2%), and Gros Cornerist + Ecoline Phosphite K-Amino (an increase of 5.8 t/ha or 37.9%). No significant impact on yield was observed with Timac Agro fertilizer. A positive impact of growth regulators and fertilizers on the biochemical composition of sweet potato tubers was established: Timac Agro fertilizer increased dry matter content (22.97%), DG-16, DAC-6, Gros Cornerist + Gros Amino-Magnesium, and Gros Cornerist + Ecoline Phosphite K-Amino increased vitamin C content (4.88–5.49 mg/100 g), Timac Agro and "Rich Soil" increased carotene content (8.47–9.72 mg/100 g), and DG-16 and Gros Cornerist + Ecoline Phosphite K-Amino increased starch content in tubers (15.28–15.86%). Discussion: The results confirm the effectiveness of growth regulators. Particular attention should be paid to adapting growth regulator application programs to specific growing conditions and crop varieties. Conclusions: The study confirms the potential of using growth regulators and fertilizers with growth-stimulating properties as an effective measure to enhance productivity and quality of sweet potato production under the conditions of the Left-Bank Forest-Steppe of Ukraine.

Unipept in 2024: Expanding Metaproteomics Analysis with Support for Missed Cleavages and Semitryptic and Nontryptic Peptides.

Unipept, a pioneering software tool in metaproteomics, has significantly advanced the analysis of complex ecosystems by facilitating both taxonomic and functional insights from environmental samples. From the onset, Unipept's capabilities focused on tryptic peptides, utilizing the predictability and consistency of trypsin digestion to efficiently construct a protein reference database. However, the evolving landscape of proteomics and emerging fields like immunopeptidomics necessitate a more versatile approach that extends beyond the analysis of tryptic peptides. In this article, we present a significant update to the underlying index structure of Unipept, which is now powered by a Sparse Suffix Array index. This advancement enables the analysis of semitryptic peptides, peptides with missed cleavages, and nontryptic peptides such as those encountered in other research fields such as immunopeptidomics (e.g., MHC- and HLA-peptides). This new index benefits all tools in the Unipept ecosystem such as the web application, desktop tool, application programming interface (API), and command line interface. A benchmark study highlights significantly improved performance in handling missed cleavages, preserving the same level of accuracy.

US State Public Health Agencies' Use of Twitter From 2012 to 2022: Observational Study.

Twitter (subsequently rebranded as X) is acknowledged by US health agencies, including the US Centers for Disease Control and Prevention (CDC), as an important public health communication tool. However, there is a lack of data describing its use by state health agencies over time. This knowledge is important amid a changing social media landscape in the wake of the COVID-19 pandemic. The study aimed to describe US state health agencies' use of Twitter from 2012 through 2022. Furthermore, we organized our data collection and analysis around the theoretical framework of the networked public to contribute to the broader literature on health communication beyond a single platform. We used Twitter application programming interface data as indicators of state health agencies' engagement with the 4 key qualities of communication in a networked public: scalability, persistence, replicability, and searchability. To assess scalability, we calculated tweet volume and audience engagement metrics per tweet. To assess persistence, we calculated the portion of tweets that were manual retweets or included an account mention. To assess replicability, we calculated the portion of tweets that were retweets or quote tweets. To assess searchability, we calculated the portion of tweets using at least 1 hashtag. We observed a COVID-19 pandemic-era shift in state health agency engagement with scalability. The overall volume of tweets increased suddenly from less than 50,000 tweets in 2019 to over 94,000 in 2020, resulting in an average of 5.3 per day. Though mean tweets per day fell in 2021 and 2022, this COVID-19 pandemic-era low was still higher than the pre-COVID-19 pandemic peak. We also observed a more fragmented approach to searchability aligning with the start of the COVID-19 pandemic. More state-specific hashtags were among the top 10 during the COVID-19 pandemic, compared with more general hashtags related to disease outbreaks and natural disasters in years before. We did not observe such a clear COVID-19 pandemic-era shift in engagement with replicability. The portion of tweets mentioning a CDC account gradually rose and fell around a peak of 7.0% in 2018. Similarly, the rate of retweets of a CDC account rose and fell gradually around a peak of 5.4% in 2018. We did not observe a clear COVID-19 pandemic-era shift in persistence. The portion of tweets mentioning any account reached a maximum of 21% in 2013. It oscillated for much of the study period before dropping off in 2021 and reaching a minimum of 10% in 2022. Before 2018, the top 10 mentioned accounts included at least 2 non-CDC or corporate accounts. From 2018 onward, state agencies were much more prominent. Overall, we observed a more fragmented approach to state health agency communication on Twitter during the pandemic, prioritizing volume over searchability, formally replicating existing messages, and leaving traces of interactions with other accounts.

Development and Evaluation of a Mental Health Chatbot Using ChatGPT 4.0: Mixed Methods User Experience Study With Korean Users.

Mental health chatbots have emerged as a promising tool for providing accessible and convenient support to individuals in need. Building on our previous research on digital interventions for loneliness and depression among Korean college students, this study addresses the limitations identified and explores more advanced artificial intelligence-driven solutions. This study aimed to develop and evaluate the performance of HoMemeTown Dr. CareSam, an advanced cross-lingual chatbot using ChatGPT 4.0 (OpenAI) to provide seamless support in both English and Korean contexts. The chatbot was designed to address the need for more personalized and culturally sensitive mental health support identified in our previous work while providing an accessible and user-friendly interface for Korean young adults. We conducted a mixed methods pilot study with 20 Korean young adults aged 18 to 27 (mean 23.3, SD 1.96) years. The HoMemeTown Dr CareSam chatbot was developed using the GPT application programming interface, incorporating features such as a gratitude journal and risk detection. User satisfaction and chatbot performance were evaluated using quantitative surveys and qualitative feedback, with triangulation used to ensure the validity and robustness of findings through cross-verification of data sources. Comparative analyses were conducted with other large language models chatbots and existing digital therapy tools (Woebot [Woebot Health Inc] and Happify [Twill Inc]). Users generally expressed positive views towards the chatbot, with positivity and support receiving the highest score on a 10-point scale (mean 9.0, SD 1.2), followed by empathy (mean 8.7, SD 1.6) and active listening (mean 8.0, SD 1.8). However, areas for improvement were noted in professionalism (mean 7.0, SD 2.0), complexity of content (mean 7.4, SD 2.0), and personalization (mean 7.4, SD 2.4). The chatbot demonstrated statistically significant performance differences compared with other large language models chatbots (F=3.27; P=.047), with more pronounced differences compared with Woebot and Happify (F=12.94; P<.001). Qualitative feedback highlighted the chatbot's strengths in providing empathetic responses and a user-friendly interface, while areas for improvement included response speed and the naturalness of Korean language responses. The HoMemeTown Dr CareSam chatbot shows potential as a cross-lingual mental health support tool, achieving high user satisfaction and demonstrating comparative advantages over existing digital interventions. However, the study's limited sample size and short-term nature necessitate further research. Future studies should include larger-scale clinical trials, enhanced risk detection features, and integration with existing health care systems to fully realize its potential in supporting mental well-being across different linguistic and cultural contexts.

The Transformative Potential of Large Language Models in Mining Electronic Health Records Data: Content Analysis.

In this study, we evaluate the accuracy, efficiency, and cost-effectiveness of large language models in extracting and structuring information from free-text clinical reports, particularly in identifying and classifying patient comorbidities within oncology electronic health records. We specifically compare the performance of gpt-3.5-turbo-1106 and gpt-4-1106-preview models against that of specialized human evaluators. We specifically compare the performance of gpt-3.5-turbo-1106 and gpt-4-1106-preview models against that of specialized human evaluators. We implemented a script using the OpenAI application programming interface to extract structured information in JavaScript object notation format from comorbidities reported in 250 personal history reports. These reports were manually reviewed in batches of 50 by 5 specialists in radiation oncology. We compared the results using metrics such as sensitivity, specificity, precision, accuracy, F-value, κ index, and the McNemar test, in addition to examining the common causes of errors in both humans and generative pretrained transformer (GPT) models. The GPT-3.5 model exhibited slightly lower performance compared to physicians across all metrics, though the differences were not statistically significant (McNemar test, P=.79). GPT-4 demonstrated clear superiority in several key metrics (McNemar test, P<.001). Notably, it achieved a sensitivity of 96.8%, compared to 88.2% for GPT-3.5 and 88.8% for physicians. However, physicians marginally outperformed GPT-4 in precision (97.7% vs 96.8%). GPT-4 showed greater consistency, replicating the exact same results in 76% of the reports across 10 repeated analyses, compared to 59% for GPT-3.5, indicating more stable and reliable performance. Physicians were more likely to miss explicit comorbidities, while the GPT models more frequently inferred nonexplicit comorbidities, sometimes correctly, though this also resulted in more false positives. This study demonstrates that, with well-designed prompts, the large language models examined can match or even surpass medical specialists in extracting information from complex clinical reports. Their superior efficiency in time and costs, along with easy integration with databases, makes them a valuable tool for large-scale data mining and real-world evidence generation.

The International Master’s Program in Oceanography and Applications in Benin

The state and dynamics of the oceans and seas that surround Africa are changing at an increasing pace due to anthropogenic pressures. The livelihoods of many Africans depend on fishing and ocean-​driven monsoon rains, and some African coastlines are eroding rapidly, potentially with catastrophic results to populations and infrastructure. Yet few African scientists are prepared to engage in ocean-related research. To address this deficit, the Master’s program in Oceanography and Applications1 was launched in 2008 in Benin, targeting the West African region. This program is designed to build capacity in support of regional research programs in oceanography such as PIRATA (Prediction and Research Moored Array in the Tropical Atlantic; Bourlès et al., 2019), AMMA-EGEE (African Monsoon Multidisciplinary Analyses - Oceanic Circulation and Ocean-​Atmosphere Exchanges in the Gulf of Guinea; Redelsperger et al., 2006; Bourlès et al., 2007), and PROPAO (Regional Program of Physical Oceanography in West Africa; Sohou et al., 2020).

Knowledge-based planning, multicriteria optimization, and plan scorecards: A winning combination

Background and purposeThe ESTRO 2023 Physics Workshop hosted the Fully-Automated Radiotherapy Treatment Planning (Auto-RTP) Challenge, where participants were provided with CT images from 16 prostate cancer patients (6 prostate only, 6 prostate + nodes, and 4 prostate bed + nodes) across 3 challenge phases with the goal of automatically generating treatment plans with minimal user intervention. Here, we present our team’s winning approach developed to swiftly adapt to both different contouring guidelines and treatment prescriptions than those used in our clinic. Materials and methodsOur planning pipeline comprises two main components: 1) auto-contouring and 2) auto-planning engines, both internally developed and activated via DICOM operations. The auto-contouring engine employs 3D U-Net models trained on a dataset of 600 prostate cancer patients for normal tissues, 253 cases for pelvic lymph node, and 32 cases for prostate bed. The auto-planning engine, utilizing the Eclipse Scripting Application Programming Interface, automates target volume definition, field geometry, planning parameters, optimization, and dose calculation. RapidPlan models, combined with multicriteria optimization and scorecards defined on challenge scoring criteria, were employed to ensure plans met challenge objectives. We report leaderboard scores (0–100, where 100 is a perfect score) which combine organ-at-risk and target dose-metrics on the provided cases. ResultsOur team secured 1st place across all three challenge phases, achieving leaderboard scores of 79.9, 77.3, and 78.5 outperforming 2nd place scores by margins of 6.4, 0.4, and 2.9 points for each phase, respectively. Highest plan scores were for prostate only cases, with an average score exceeding 90. Upon challenge completion, a “Plan Only” phase was opened where organizers provided contours for planning. Our current score of 90.0 places us at the top of the “Plan Only” leaderboard. ConclusionsOur automated pipeline demonstrates adaptability to diverse guidelines, indicating progress towards fully automated radiotherapy planning. Future studies are needed to assess the clinical acceptability and integration of automatically generated plans.