Application Programming Research Articles

Abstract LB141: Harmonization and integration of regional based prospective cohorts: A preliminary report of the African Caribbean Cancer Consortium

Abstract Introduction: The promise of achieving cancer control both nationally and globally rest on rigorous data collection, data harmonization and data sharing to catalyze scientific knowledge and understanding to more quickly advance our progress to addressing cancer disparities. In response, the LIFE Project aimed to establish a cancer-focused robust, functional platform for data harmonization and sharing across national and international studies with a focus on low-resourced settings and LMIC. Methods: Established in 2022, the integrated database platform hosts data collected from the ongoing Jamaican LIFE and US CAP3 projects. Both studies focus on investigating biologic and socioecologic risk factors for non-communicable chronic diseases (NCDs) and cancers. Participants self-reported data via REDCap. A one-to one mapping of the variables from both questionnaires was conducted and stored in the integrated REDCap file. An automated REDCap Application Programming Interface (API) script weekly imports the new entries into the integrated database. To ensure data accuracy, on a weekly schedule, data importation and data integration are monitored and assessed by random sampling of original source data entries and the corresponding entries in the integrated database. Any additional errors in data integration are resolved via consultations with the programming team. Results: As of June 2023, 3,369 participants were enrolled - 2,561 enrolled from the LIFE study and 808 from the CAP3 study. Overall, the combined participants were 89% Afro-Caribbean or African American, and comprised 40% males with a median age of 54 years and 60% females with median age being 51 years. For the LIFE study, median age is 50 years, and for the CAP3 the median age is 55. The prevalence of self-reported health conditions are as follows: diabetes - 13% LIFE, 13% CAP3; hypertension - 37% LIFE, 19% CAP3; dyslipidemia - 15% LIFE, 21% CAP3. Conclusion: With the continuing need for precision public health, there is a dire need to address disparities through innovative ways that promote comprehensive data linkage and analyses. This platform can be used in mapping individual cohort data with demonstrated success for the LIFE and CAP3 studies. Our web-based query tool to interface that interfaces with our REDCap database also allows us to query the available variables in both the separate and combined cohort databases and will facilitate hypothesis development. The impact of our study goes beyond the data harmonization, and robust data integration platform that links the LIFE and CAP3 studies. This allows for US comparative studies. Novel. We will also have the ability to harmonize with large US cohort studies, as well as interrogate research questions examining commonalities and differences among African Americans, Afro-Caribbean Americans and Afro-Caribbeans in the Caribbean. These comparative studies will help to elucidate the causes increased health risks, and accelerate the science to remedy the global Black NCD and cancer disparities. Citation Format: Camille C. R. Ragin, Janeil Williams, Olga Tchuvatkina, Joette Mckenzie, Kimlin Ashing, Marshall Tulloch-Reid. Harmonization and integration of regional based prospective cohorts: A preliminary report of the African Caribbean Cancer Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB141.

OpenMP-based parallel MLPG solver for analysis of heat conduction

PurposeIn the present work, we focus on developing an in-house parallel meshless local Petrov-Galerkin (MLPG) code for the analysis of heat conduction in two-dimensional and three-dimensional regular as well as complex geometries.Design/methodology/approachThe parallel MLPG code has been implemented using open multi-processing (OpenMP) application programming interface (API) on the shared memory multicore CPU architecture. Numerical simulations have been performed to find the critical regions of the serial code, and an OpenMP-based parallel MLPG code is developed, considering the critical regions of the sequential code.FindingsBased on performance parameters such as speed-up and parallel efficiency, the credibility of the parallelization procedure has been established. Maximum speed-up and parallel efficiency are 10.94 and 0.92 for regular three-dimensional geometry (343,000 nodes). Results demonstrate the suitability of parallelization for larger nodes as parallel efficiency and speed-up are more for the larger nodes.Originality/valueFew attempts have been made in parallel implementation of the MLPG method for solving large-scale industrial problems. Although the literature suggests that message-passing interface (MPI) based parallel MLPG codes have been developed, the OpenMP model has rarely been touched. This work is an attempt at the development of OpenMP-based parallel MLPG code for the very first time.

ANALYSIS OF THE EFFICIENCY OF THE USE OF PROTECTIVE SCREENS TO REDUCE THE LEVEL OF AIR POLLUTION

The task of assessing the areas of chemical pollution near the highway, where protective screens of different geometric shapes are located, is considered. The purpose of the work is to develop numerical models for calculating pollution zones formed near protective screens, as well as conducting a laboratory experiment to analyze the patterns of formation of pollution zones near screens of a complex geometric shape. For mathematical modeling of the process of formation of pollution zones near the protective screen, the equation of convective-diffusion transfer of impurities is used. This equation takes into account atmospheric diffusion, wind speed, emission intensity of a chemically hazardous substance, the location of the emission source, and the shape of the protective screen. The Navier-Stokes equation and the Laplace equation for the velocity potential are used to solve the problem of aerodynamics. Finite-difference methods are used for numerical integration of modeling equations. A package of application programs was created on the basis of the developed numerical models. Numerical models and a package of programs have been built, allowing to study the process of the formation of areas of pollution near the highway in almost real time. The results of the computational experiment are presented.

PENGGUNAAN MEDIA PEMBELAJARAN BERBASIS APLIKASI (KAHOOT) TERHADAP HASIL BELAJAR PESERTA DIDIK PADA MATA PELAJARAN FIQIH KELAS VII DI MTS MUHAMMADIYAH 06 BANYUTENGAH TAHUN PELAJARAN 2020/2021

This research is motivated by the increasing development of technologyin the era of the industrial revolution 4.0, educators can innovate learning tomake the learning process more interesting, namely by utilizing various programsthat can produce an interactive learning media. The lack of use of learning mediaused, especially in Fiqh learning, makes students' learning motivation decreaseand feel bored in the learning process which tends to use conventional methods.One of the latest application programs that can be used as interactive learningmedia is the Kahoot educational game application. The purpose of this study is todetermine whether there is an effect of application-based learning media(Kahoot) on student learning outcomes at MTs Muhammadiyah 06 Banyutengah.This study used a quantitative approach with a One-group pretest-posttest designand the number of respondents was 26 respondents, using sample data. Datacollection using pre-test and post-test questions. The collected data were analyzedby validity test, reliability test, normality test, homogeneity test, correlation test,significance test and coefficient of determination. Based on the results ofcalculations obtained manually with hypothesis testing using the product momentcorrelation formula, the results obtained rcount = 0.730> rtable = 0.3882. Whilefor the significance test, the results obtained tcount = 5.228> ttable = 2.063 atthe 5% significance level. From the discussion as mentioned, it can be concludedthat Ho is rejected and Ha is accepted or there is a positive and significantinfluence between Application-Based Learning Media (Kahoot) on StudentLearning Outcomes in Fiqh Class VII Subjects at MTs Muhammadiyah 06Banyutengah 2020/2021 Study Year

SecureLaw Portal: Empowering Citizens with Transparent Legal Records via Blockchain and Cloud eVault

In the digital age, where data integrity is essential, the management of legal data requires innovative solutions. SecureLaw is a new project that combines the immutable power of blockchain, the flexibility of Cloud. The agility of the Cloud, Express.js, React.js, Node .js suite. This integration forms the backbone of the transformation of data protection laws, providing unparalleled security, transparency and easy access. Highlights of this project: SecureLaw is instrumental in redefining information law in India. Traditional systems often face security breaches and lack of transparency, undermining the integrity of the legal process. SecureLaw solves these problems by integrating blockchain technology, which provides immutable data that ensures legal documents are not tampered with and ensure their authenticity. The introduction of smart contracts automates the process, controls permissions and makes data immutable, thus increasing efficiency and reducing errors. Leveraging Cloud services such as S3 for storage, API gateways for seamless communications, Lambda for serverless computing, and DynamoDB for scalable databases provides a powerful, scalable, and cost-effective infrastructure. Scalability is important to accommodate legal system growth, and Cloud’s Dynamic Services provide a solution. The AERN group (AWS, Express.js, React.js, Node.js) strengthens the foundation of the project. Express.js is a simple and easy backend framework that seamlessly integrates for efficient data processing and processing. React.js is a front-end library with many features that make it user responsive and intuitive. Node.js is known for its performance that enables dynamic recovery and makes data fast and recoverable. SecureLaw's user centric approach includes managing access to personal and legal personal details. Personal auditors, regional directors, directors (DGs), judges, etc. It deals with many user roles, personal control panels, and access rights. This change promotes cooperation, transparency and communication in the legal system. Features of a self-explanatory legal system encourage people to understand the legal process, fostering transparency, trust, and collaborative knowledge. Keywords: Blockchain, Application Programming Interface (API), Cloud , Express.js, React.js, Node.js

Smart City Projects Financing

The implementation of advanced digital technologies and innovations in smart cities for the provision of more efficient, sustainable, and inclusive city services, the development of infrastructure, and the improvement of citizens’ quality of life require appropriate financing technologies. In addition to purely public (for example, green bonds, social impact bonds, state subsidies, etc.) and purely private (venture investments, leasing, etc.) financial instruments, various options for public-private partnerships and financing with and against the participation of the community (participatory ), which increase the efficiency of budget financing due to the agreed distribution of risks and responsibilities between stakeholders. The bibliometric analysis of publications indexed by Scopus using the keywords “smart city” and “public-private partnership” using the VOSviwer tool allowed establishing the periods of growth (2013-2019 and 2022-2023) and decline (2019-2022) of the popularity of this topic among scientists, countries-leaders of research activity (USA, India, China, Italy, Spain, Great Britain, Canada and Germany), dominant directions of cross-sectoral research. The analysis of the Smart City Index Report (IMD Smart City Index Report) showed that in 2023, the top 5 leaders were Switzerland (Zurich), Norway (Oslo), Australia (Canberra), Denmark (Copenhagen), and Great Britain (London). With the help of the Google Trends toolkit, an analysis of the dynamics of Internet requests over the past 10 years from citizens of these countries, as well as Ukraine and the world, was carried out using identical keywords. The analysis proved that both in these countries and in the world as a whole, public interest in the development of the smart grid was recorded (in Ukraine, the highest peak (100 GT Scale) was in 2019; in Great Britain and Switzerland – in 2018, in Denmark – in 2017 and 2022, in Australia and Norway – in 2016) against the background of an almost absolute (except for Great Britain and Australia) lack of public interest in issues of public-private partnership. A regression model was developed to study the impact of participatory financing on the readiness level of Ukraine’s regions (as of 2020) to implement smart local development technologies. An integral indicator was used as the resulting variable, within which data on the assessment of the availability of electronic public services and the level of automation and digitization of public services (the number of state and local self-government bodies that provided the possibility of using electronic democracy tools), the level of use electronic platforms for communication with state bodies (the number of registered “E-appeals”, published “E-petitions” and reports on “E-consultations”), the availability of the Internet (the number of Internet subscribers and the share of households that have access to Internet services home). In the modeling process, adjustments were made for the gross regional product and the population of the respective region, the weighting factors were determined by the method of principal components, the estimation of the model parameters was carried out by the method of least squares, the calculations were made using the MS Excel spreadsheet and the Statistica application program package.

The National Healthcare Safety Network's digital quality measures: CDC's automated measures for surveillance of patient safety.

This article presents the National Healthcare Safety Network (NHSN)'s approach to automation for public health surveillance using digital quality measures (dQMs) via an open-source tool (NHSNLink) and piloting of this approach using real-world data in a newly established collaborative program (NHSNCoLab). The approach leverages Health Level Seven Fast Healthcare Interoperability Resources (FHIR) application programming interfaces to improve data collection and reporting for public health and patient safety beginning with common, clinically significant, and preventable patient harms, such as medication-related hypoglycemia, healthcare facility-onset Clostridioides difficile infection, and healthcare-associated venous thromboembolism. The NHSN's FHIR dQMs hold the promise of minimizing the burden of reporting, improving accuracy, quality, and validity of data collected by NHSN, and increasing speed and efficiency of public health surveillance.

Evaluation of Maternal-Fetal Medicine Fellowship Program Websites.

Introduction Due to the COVID-19 pandemic,the American Association of Medical Colleges (AAMC) recommended that all interviews for residencies and fellowships be conducted in a virtual format. As of March 2024, the Society of Maternal-Fetal Medicine (SMFM) continues to request that all fellowship interviews occur virtually. Without in-person interviews, prospective Maternal-Fetal Medicine (MFM) fellowship applicants must largely rely on program websites to gain insight into each program's offerings, culture, and application requirements. The purpose of this study was to evaluate the content of American College of Graduate Medical Education (ACGME)-accredited Maternal-Fetal Medicine (MFM) fellowship program websites and assess if regional differences exist among website content. Methods All ACGME-accredited MFM fellowship programwebsites were assessed for 21 defined criteria as of March 2024 and further compared by geographic regions (Midwest, Northeast, South, and West). Analyses were completed using chi-squared univariate tests, with a p < 0.05. Results Of the 108 accredited MFM fellowship programs, 106 programs had a dedicated website (98.15%). Most MFM programs (over 80%) included contact information (102/106), program director name (98/106), faculty names (95/106), application requirements (92/106), current fellow names (91/106), and the program coordinator name (89/106) on their website. Less programs (less than 30%) included diversity, equity, inclusion (DEI) content (28/106), interview dates (28/106), and current fellow research projects or publications (27/106). Western programs were less likely to include the program coordinator's name (12/18 (67%), p = 0.046), but more likely to include DEI content (10/18 (56%), p = 0.005). Northeastern programs were less likely to include their application requirements (24/32 (75%), p = 0.049) and less likely to include pictures of their current fellows (20/32 (63%), p = 0.045). Southern programs were more likely to include the yearly rotation schedule (19/32 (59%), p = 0.040). Midwestern programs were more likely to include information on fellowship benefits or salary (15/24 (63%), p = 0.046). Conclusion This study demonstrated that the content available on MFM fellowship websites varies greatly between programs and geographic regions. Efforts should be made by MFM training institutions to enhance website DEI content, curriculum information, recent fellow publications, and information on program alumni. A detailed and well-structured website may helpapplicants compare individual programs more equitablyin the age of virtual interviewing.

Preference Signaling in the Orthopaedic Surgery Match: Applicant and Residency Program Attitudes, Behaviors, and Outcomes.

The orthopaedic surgery match has experienced a consistent increase in both the number of applicants and applications submitted per applicant. Preference signaling was implemented during the 2022 to 2023 application cycle in part to curtail the rising application burden on both applicants and residency programs. Our aim was to explore the impact of the preference signaling system on applicant and residency program leader attitudes, behaviors, and outcomes. We distributed surveys to American Orthopaedic Association/Council of Orthopaedic Residency Directors member program leaders (program directors, assistant program directors, and program coordinators) and applicants registered for the Electronic Standardized Letter of Recommendation after Universal Interview Offer Day 2022 (Fall Survey) and Match Day 2023 (Spring Survey). The surveys contained multiple-choice and numeric response questions on attitudes, behaviors, and outcomes that were analyzed and reported as percentages and medians, respectively. Open-text responses were reviewed for dominant themes. One hundred program leaders and 378 applicants (47%) completed the Fall Survey, and 146 program leaders and 290 applicants (36%) completed the Spring Survey. A majority of applicants (71%) and program leadership (91%) support the continued use of signaling. Applicants reported a 16% reduction in the number of programs to which they applied. Program directors largely used signaling as a tool for screening applications (75%), with few programs using signaling in the ranking process (20%). Applicants reported that 81% of their interviews were from programs they signaled. Slightly more than half of programs (53%) reported filling their last slot at a higher rank order position than the average of the previous 5 years. Qualitative analysis suggests a need for more transparency in the use of signals, consideration of application and/or interview caps, and reconsideration of the other components of the application. Preference signaling in the orthopaedic surgery match was met with positive feedback and led to a reduction in the number of applications. Future research will examine the continued impact of preference signaling and assess alterations for optimizing the match process.

Peningkatan Pelayanan Perizinan Online Melalui Inovasi Aplikasi Surabaya Single Window (SSW) Alpha

In order to maximise public services as a support system for government administration, ongoing innovation is needed to raise the standard of public services. This study uses a qualitative methodology that involves creating a complex description, looking up words, compiling in-depth reports from respondents' perspectives, and studying real-world scenarios linked to enhancing online licencing services in the city of Surabaya via the SSW Alfa application programme. Research methods with qualitative descriptive and study focus include relative advance, Compatibility, Complexity, Trialability and Observability. Data analysis is based on the interactive data model from Miles and Huberman which consists of three activity flows that occur simultaneously, namely: data condensation, data presentation, and verification withdrawal. The results of the study show that SSW Alpha is based on: 1) Relative Advantage, the community is given the advantage of having easy access to permits; 2) Compatibility, there is a discrepancy in the flow of licensing and authority signing with previous innovations, therefore old innovations are no longer used; 3) Complexity, still complicated and trouble occurs and there are still incomplete files; 4) Triability, through the trial stages before the application is launched to the public; 5) Observability, making it easy for the public to observe and monitor the permit filing process and the convenience of officials to easily obtain licensing data.

Comparison of Conventional CNN Sequential API and Functional API for Microalgae Identification

Microalgae is widely known for its application in producing biodiesel and other health supplements. However, microalgae are also the leading cause of harmful algae blooms that may affect consumers and sea wildlife. The current microalgae identification method requires professionals, resources, budgets, technologies, and time. Therefore, a novel approach to identifying microalgae has been produced by implementing deep learning, specifically the Convolutional Neural Network (CNN). Due to the blooming of research in the deep learning field for microalgae identification, this research aims to compare application programming interface (API) use and study its effects on the accuracy and loss of a model. Using a light microscope, the microalgae images' datasets are self-collected from the AlBio laboratory at the Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia. The microalgae species were Acutodesmus obliquus, Monoraphidium sp, Spirullina sp, Tetradesmus deserticola, and Desmodesmus perforatus. The architecture used to identify the microalgae in this research was the conventional CNN with different APIs, functional and sequential. The functional API resulted in 0.85 accuracies and a loss score of 3.77. On the other hand, the sequential scored 0.89 and a loss of 0.32. This study concluded that the sequential API was better than the functional API for a linear convolutional neural network. However, further improvement to the model could be applied by applying better hyperparameters and parameters to prevent underfitting and improve the model’s accuracy.

Social Network Analysis of e-Cigarette-Related Social Media Influencers on Twitter/X: Observational Study.

An e-cigarette uses a battery to heat a liquid that generates an aerosol for consumers to inhale. e-Cigarette use (vaping) has been associated with respiratory disease, cardiovascular disease, and cognitive functions. Recently, vaping has become increasingly popular, especially among youth and young adults. The aim of this study was to understand the social networks of Twitter (now rebranded as X) influencers related to e-cigarettes through social network analysis. Through the Twitter streaming application programming interface, we identified 3,617,766 unique Twitter accounts posting e-cigarette-related tweets from May 3, 2021, to June 10, 2022. Among these, we identified 33 e-cigarette influencers. The followers of these influencers were grouped according to whether or not they post about e-cigarettes themselves; specifically, the former group was defined as having posted at least five e-cigarette-related tweets in the past year, whereas the latter group was defined as followers that had not posted any e-cigarette-related tweets in the past 3 years. We randomly sampled 100 user accounts among each group of e-cigarette influencer followers and created corresponding social networks for each e-cigarette influencer. We compared various network measures (eg, clustering coefficient) between the networks of the two follower groups. Major topics from e-cigarette-related tweets posted by the 33 e-cigarette influencers included advocating against vaping policy (48.0%), vaping as a method to quit smoking (28.0%), and vaping product promotion (24.0%). The follower networks of these 33 influencers showed more connections for those who also post about e-cigarettes than for followers who do not post about e-cigarettes, with significantly higher clustering coefficients for the former group (0.398 vs 0.098; P=.005). Further, networks of followers who post about e-cigarettes exhibited substantially more incoming and outgoing connections than those of followers who do not post about e-cigarettes, with significantly higher in-degree (0.273 vs 0.084; P=.02), closeness (0.452 vs 0.137; P=.04), betweenness (0.036 vs 0.008; P=.001), and out-of-degree (0.097 vs 0.014; P=.02) centrality values. The followers who post about e-cigarettes also had a significantly (P<.001) higher number of followers (n=322) than that of followers who do not post about e-cigarettes (n=201). The number of tweets in the networks of followers who post about e-cigarettes was significantly higher than that in the networks of followers who do not post about e-cigarettes (93 vs 43; P<.001). Two major topics discussed in the networks of followers who post about e-cigarettes included promoting e-cigarette products or vaping activity (55.7%) and vaping being a help for smoking cessation and harm reduction (44.3%). Followers of e-cigarette influencers who also post about e-cigarettes have more closely connected networks than those of followers who do not themselves post about e-cigarettes. These findings provide a potentially practical intervention approach for future antivaping campaigns.

Data Processing Engine (DPE): data analysis tool for particle tracking and mixed radiation field characterization with pixel detectors Timepix

Hybrid semiconductor pixelated detectors from the Timepix family are advanced detectors for online particle tracking, offering energy measurement and precise time stamping capabilities for particles of various types and energies. This inherent capability makes them highly suitable for various applications, including imaging, medical fields such as radiotherapy and particle therapy, space-based applications aboard satellites and the International Space Station, and industrial applications. The data generated by these detectors is complex, necessitating the development and deployment of various analytical techniques to extract essential information.For this purpose, and to aid the Timepix user community, it was designed and developed the“Data Processing Engine” (DPE) as an advanced tool for data processing designed explicitly for Timepix detectors. The functionality of the DPE is structured into three distinct processing levels: i) Pre-processing: this phase involves clusterization and the application of necessary calibrations and corrections. ii) Processing: this stage includes particle classification, employing machine learning algorithms, and the recognition of radiation fields. iii) Post-processing: involves various analyses, such as directional analysis, coincidence analysis, frame analysis, Compton directional analysis, and the generation of physics products, are performed. The core of the DPE is supported by an extensive experimental database containing calibrations and referential radiation fields of typical environments, including protons, ions, electrons, gamma rays and X rays, as well as thermal and fast neutrons. To enhance accessibility, the DPE is implemented into various user interface platforms such as a command-line tool, an application programming interface, and as a graphical user interface in the form of a web portal. The DPE's broad utility is exemplified through its integration into various applications and developments.

KFFPDet: Android malicious application detection system with assisted detection of adversarial samples

Machine learning methods are widely used in Android malware detection, but carefully scrambled adversarial samples can effectively evade detection. Adversarial defence methods such as retraining and ensemble learning have high training costs and significant errors. This paper proposes a novel Android malware detection system, KFFPDet, to detect adversarial samples. It can detect adversarial samples efficiently. Firstly, this paper uses K-Nearest Neighbor (KNN) as the primary detection algorithm. In addition, this paper proposes a three-stage adversarial sample detection method, which saves the detection cost and reduces the error through prior knowledge. In the first stage, the frequency difference between benign typical features and malicious typical features is used to filter adversarial samples. In the second stage, the feature continuous values of benign typical features are used for adversarial sample filtering. The third stage filters adversarial samples by detecting isolation permissions and application programming interface (API). Experiments on various benchmark datasets verify the detection effect of KFFPDet. KFFPDet achieves 96% accuracy of adversarial sample detection on the VirusShare dataset. Compared with other advanced methods, KFFPDet ’s adversarial sample detection accuracy is improved by 4.43% to 94.53%. The experimental results show that the proposed three-stage adversarial sample detection method is useful and will help design a more effective adversarial sample detection scheme.

Developing a smart system for infant incubators using the internet of things and artificial intelligence

This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R&lt;sup&gt;2&lt;/sup&gt; value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R&lt;sup&gt;2&lt;/sup&gt; value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.

112 Flight Tracker: A REDCap Tool to Streamline Career Development Grant Preparation and Reporting

OBJECTIVES/GOALS: Compiling information about characteristics and progress of scholars is required for career development applications and progress reports. The range of information is substantial, and preparation is onerous. We sought to create a tool to facilitate gathering key data about trainees and mentors who participate in programs like NIH K- and T-awards. METHODS/STUDY POPULATION: Using the REDCap platform, we developed forms and surveys to support intake of applicants, updates from scholars, and information about their participation in activities and use of resources. We deployed application programming interfaces (APIs) to automate capture of publicly available data about publications, impact metrics, and federal grant funding. Similar tools capture descriptions of mentor expertise including grant funding, prior trainees, and publications with mentees We also built modules to 1) allow connection to institutional grant and contract data to capture foundation and other funding; 2) pre-populate follow-up surveys to update information about career trajectories with minimal scholar effort; and 3) support mentee-mentor agreements as living documents. RESULTS/ANTICIPATED RESULTS: After a pilot period at our institution, we disseminated Flight Tracker to more than 50 academic institutions, most of whom are CTSA hubs. They track scholars in TL1/T32s, KL2/K12s, MSTP programs, and academic groups. Beyond federal reporting, uses now include publication impact factors (relative citation ratios, Altmetrics scores), grant funding of groups, maps of network relationships among investigators, scholar receipt of internal pilot awards, and statistics about transition to independence and time-to-promotion. Scholars can be separated into smaller cohorts by demographics, training dates, and funding dates. Over 34,000 scholars are tracked nationally among over 260 programs. Having structured data supports program evaluation, continuous improvements, and documents program strengths. DISCUSSION/SIGNIFICANCE: We informally estimate Flight Tracker reduces staff and leadership effort in preparation of program data by 75%, preserving time to focus on service to scholars. Ready access to data over time and within and across institutions creates new opportunities for collaborative data analysis to support evidence-based career development.

Visual Question Generation from Remote Sensing Images Using Gemini API

Abstract: Visual Question Generation Extracting Information from Remote Sensing Images Remote Sensing Images plays a vital role in understanding and extracting information from aerial and satellite images. Utilizing Bidirectional Encoder Representation from Transformers (BERT) for extracting valuable insights from remote sensing images. Gemini Application Programming Interface(API), and Convolution Neural Networks (CNNs) are used. First, The proposed methodology employs CNN to extract high-level features from remote sensing images, capturing spatial data and generatingquestions. Similarly, the Gemini Application Programming Interface(API) integrates contextual understanding into the question-generation process by providing relevant environmental data. Lastly, BERT functions as a language model in which employees enhance and refine the generated questions by taking into account both the syntax and semantics. Hence, by combining all these techniques we are capable of generating required relevant questions from remote sensing images in an enhanced and efficient way.

Effectiveness of Motor Vehicle Tax Payment Services Through the SAMBARA Application at P3DW Cirebon Regency 1 Sumber

Information technology facilitates access and interactive communication. Digital-based public services cut queues and increase efficiency. Good governance requires excellent service. The SAMBARA application simplifies vehicle tax payments, provides information, and improves service effectiveness. The purpose of this study is to determine how effective the Motor Vehicle Tax Payment Service through the SAMBARA Application is at the Regional Revenue Management Center of Cirebon Regency 1 Sumber. This study applied a descriptive qualitative approach using data collection techniques such as observation, interviews, and documentation. Data analysis used is data reduction, data presentation, and conclusions. The informants of this research are regulators and the public who use the SAMBARA Application. The theory used is Campbell's theory with five indicators: Program Success, Goal Success, Program Satisfaction, Input and Output Levels, Overall Goal Achievement. The success of the SAMBARA Application program in paying Motor Vehicle Tax is influenced by practicality and efficiency, providing solutions for users by avoiding queues at service offices. Despite obstacles such as unstable internet connection, the success of this program depends on clear regulations and intensive socialization by related parties. The high level of satisfaction is reflected in its ease, speed, and practicality, increasing the effectiveness of services without consuming much time and human resources. The implementation of SAMBARA provides significant outputs by facilitating payments, reducing queues, and providing easy access to tax information. This success is a successful step in achieving organizational goals, increasing the regularity of tax payments, preventing the practice of brokers, and facilitating access to information for the public.

Smart technologies in banking

The article is aimed at the current issues of using smart technologies and innovative approach during evolution and transformation processes in banking. The study identifies the special place of this topic for achieving a high level of efficiency and competitiveness of banks and characterizes the impact of the introduction of technological approaches on the customer base and its perception of banking products. The main functions of banking innovations in this area are analyzed and the justification of their feasibility at the present stage of economic development is provided. A number of the most promising technologies and approaches to banking activities are allocated, namely: contactless payment, digital wallets, biometric identification, person-to-person payments, collective financing, omnichannel banking, interaction with FinTech companies, blockchain, big data, artificial intelligence, smart machines, Internet of Things, behavioral banking, retail bank, application programming interfaces, multi-component bank, open banking, augmented reality, robotic automation, hybrid clouds. The relevance of the identified areas is proved based on their perception by analyzing the popularity of the identified topics in Google search queries using the Google Trends tool. The perception of smart technologies in banking by Internet users in the world and specifically in Ukraine is investigated, which gave grounds to conclude that there is a significant interest in them, and therefore the expediency of further study and implementation in the activities of banks. It is identified that the most perspective technologies are biometric identification, blockchain, Internet of Things, big data analysis, artificial intelligence, etc. Several technologies have been identified, namely, collective financing (crowdfunding), application programming interfaces (APIs) and digital wallets, which are less popular in Ukraine than in the world in general, and therefore require detailed research and study of the relevance of their application in the domestic banking market. Possible directions for further innovative development of banking institutions based on the use of smart technologies are proposed. Based on panel data for 60 banks of Ukraine for the period 2014-2022, the author analyzes the correlations between the indicators of the use of digital technologies and the financial performance of banks and builds regression dependencies of financial indicators of banks on the indicator of the number of electronic means of payment in active circulation. The theoretical value of the study is to identify the most promising smart technologies and innovative approaches to banking business in modern conditions. The practical value lies in studying the level of perception of high-tech innovations in the field of banking services by the active public and identifying further directions for the development of this process. We consider it advisable to direct further research in the context of a detailed study of the possibilities of applying the identified technologies in specific banking products or business processes.

Open-source large language models in action: A bioinformatics chatbot for PRIDE database.

We here present a chatbot assistant infrastructure (https://www.ebi.ac.uk/pride/chatbot/) that simplifies user interactions with the PRIDE database's documentation and dataset search functionality. The framework utilizes multiple Large Language Models (LLM): llama2, chatglm, mixtral (mistral), and openhermes. It also includes a web service API (Application Programming Interface), web interface, and components for indexing and managing vector databases. An Elo-ranking system-based benchmark component is included in the framework as well, which allows for evaluating the performance of each LLM and for improving PRIDE documentation. The chatbot not only allows users to interact with PRIDE documentation but can also be used to search and find PRIDE datasets using an LLM-based recommendation system, enabling dataset discoverability. Importantly, while our infrastructure is exemplified through its application in the PRIDE database context, the modular and adaptable nature of our approach positions it as a valuable tool for improving user experiences across a spectrum of bioinformatics and proteomics tools and resources, among other domains. The integration of advanced LLMs, innovative vector-based construction, the benchmarking framework, and optimized documentation collectively form a robust and transferable chatbot assistant infrastructure. The framework is open-source (https://github.com/PRIDE-Archive/pride-chatbot).