Application Programming Research Articles

Thought-Controlled Computer Applications: A Brain–Computer Interface System for Severe Disability Support

This study introduces an integrated computational environment that leverages Brain–Computer Interface (BCI) technology to enhance information access for individuals with severe disabilities. Traditional assistive technologies often rely on physical interactions, which can be challenging for this demographic. Our innovation focuses on creating new assistive technologies that use novel Human–Computer interfaces to provide a more intuitive and accessible experience. The proposed system offers four key applications to users controlled by four thoughts: an email client, a web browser, an e-learning tool, and both command-line and graphical user interfaces for managing computer resources. The BCI framework translates ElectroEncephaloGraphy (EEG) signals into commands or events using advanced signal processing and machine learning techniques. These identified commands are then processed by an integrative strategy that triggers the appropriate actions and provides real-time feedback on the screen. Our study shows that our framework achieved an 82% average classification accuracy using four distinct thoughts of 62 subjects and a 95% recognition rate for P300 signals from two users, highlighting its effectiveness in translating brain signals into actionable commands. Unlike most existing prototypes that rely on visual stimulation, our system is controlled by thought, inducing brain activity to manage the system’s Application Programming Interfaces (APIs). It switches to P300 mode for a virtual keyboard and text input. The proposed BCI system significantly improves the ability of individuals with severe disabilities to interact with various applications and manage computer resources. Our approach demonstrates superior performance in terms of classification accuracy and signal recognition compared to existing methods.

QCManyBody: A flexible implementation of the many-body expansion.

While the many-body expansion (MBE) and counterpoise treatments are commonly used to mitigate the high scaling of accurate abinitio methods, researchers may need to piece together tools and scripts if their primary chosen software does not support targeted features. To further modular software in quantum chemistry, the arbitrary-order, multiple-model-chemistry, counterpoise-enabled MBE implementation from Psi4 has been extracted into an independent, lightweight, and open-source Python module, QCManyBody, with new schema underpinning, application programming interface, and software integrations. The package caters to direct users by facilitating single-point and geometry optimization MBE calculations backed by popular quantum chemistry codes through the QCEngine runner and by defining a schema for requesting and reporting many-body computations. It also serves developers and integrators by providing minimal, composable, and extensible interfaces. The design and flexibility of QCManyBody are demonstrated via integrations with geomeTRIC, OptKing, Psi4, QCEngine, and the QCArchive project.

Temporal and Thematic Analysis of Promotional Waterpipe-Related Posts on Twitter/X in the US.

Waterpipe tobacco smoking (WTS), also known as hookah, shisha, or narghile, is particularly popular among young people in the United States (US). WTS poses serious health risks similar to those of cigarette smoking. Using the Twitter/X streaming API (Application Programming Interface), we collected 4,853,562 tweets between March 9, 2021, and March 14, 2023, using waterpipe-related keywords, such as "hookah" and "waterpipe". After geographical filtering to identify tweets from the US and keyword filtering for the promotional content, we identified 23,803 promotional waterpipe-related tweets. We examined trends in the posting time of these promotional waterpipe-related tweets and identified prevalent topics from these tweets using the BERTopic (Bidirectional Encoder Representations from Transformers) modeling. The number of promotional waterpipe-related tweets showed an overall decreasing trend during the study period. The posting of promotional waterpipe-related tweets was more active later in the day. Major topics in the promotional tweets included "Promotion from hookah lounges and online hookah business" (63.97%, 15,227/23,803), "promoting hookah parties and events" (32.26%, 7,679/23,803), and "promoting engineered and durable hookah products" (3.77%, 897/23,803). Twitter/X accounts posting waterpipe-related promotional content have substantial variations in the number of relevant tweets (mean = 2.28, SD = 12.22) and followers (mean = 5,937, SD = 76,770). This study demonstrates a significant social media activity in promoting waterpipe tobacco smoking. Our findings underscore the urgent need to regulate the promotional content of WTS on social media and promote public health education messages on social media to counteract the promotion of WTS.

Building a community-driven bioinformatics platform to facilitate Cannabis sativa multi-omics research.

The web portal can be accessed at www.icgrc.info.

Framework to enable and test conversational assistant for APIs and RPAs

AbstractIn the realm of business automation, conversational assistants are emerging as the primary method for making automation software accessible to users in various business sectors. Access to automation primarily occurs through application programming interface (APIs) and robotic process automation (RPAs). To effectively convert APIs and RPAs into chatbots on a larger scale, it is crucial to establish an automated process for generating data and training models that can recognize user intentions, identify questions for conversational slot filling, and provide recommendations for subsequent actions. In this paper, we present a technique for enhancing and generating natural language conversational artifacts from API specifications using large language models (LLMs). The goal is to utilize LLMs in the “build” phase to assist humans in creating skills for digital assistants. As a result, the system does not need to rely on LLMs during conversations with business users, leading to efficient deployment. Along with enabling digital assistants, our system employs LLMs as proxies to simulate human interaction and automatically evaluate the digital assistant's performance. Experimental results highlight the effectiveness of our proposed approach. Our system is deployed in the IBM Watson Orchestrate product for general availability.

De-centring the interface. Towards the integrated study of interfacial relations

ABSTRACT Digital interfaces have become the primary way people relate to technology. While tangible and graphical user interfaces have long attracted scholarly attention, a variety of different interfaces connect cityscapes, homes, and brains with the digital realm. These connections may be lifesaving for some and a vehicle for invasive surveillance for others. Interfaces are thus also sites where layered relations, both digital and analogue, become translated, contested, and maintained. Furthermore, interfaces lie at the heart of new forms of machine-to-machine interaction and coordination. Software interfaces such as Application Programming Interfaces, have come to play a pivotal role in shaping platform economies, or automating entire sectors such as stock trading. Despite their ubiquity and variety, interfaces are largely reduced to a user-facing device, for example, a screen or a keyboard. In this article, we endeavour to go beyond this narrow notion of the interface. Drawing on an emerging body of literature that takes interfaces as the starting point for studies of the digital, we lay the groundwork for the integrated study of interfaces that conceptualises them as processual, distributed, and constitutively political. From this perspective, we focus not on what interfaces are but what they do. This shift towards processes of interfacing expands the scope of interface studies beyond the user-machine relation and towards the body-machine and machine-to-machine relation. Ultimately, we contend that interface studies must account for the layering of these interfacial relations.

A tool for integrating agrometeorological observation data for digital agriculture: A Minnesota case study

AbstractAgrometeorological data are essential for understanding production using digital agriculture techniques. However, integrating agrometerological observations from multiple sources remains a challenge. Often, digital agriculture scientists download and clean the same datasets many times. We present a prototype system that simplifies the process of collecting, cleaning, integrating, and aggregating data from meteorological data sources by providing a simplified user interface, database, and application programming interface. The prototype provides a standard interface for querying multiple geospatial formats (raster and vector) and integrates observation networks including the National Oceanic and Atmospheric Administration Global Historical Climatology Network (NOAA GHCN), NOAA NClim‐Grid (NOAA's Gridded Climate Normals), and Ameriflux BASE. The system automatically checks and updates data, saving storage space and processing time, and allows users to summarize data spatially and temporally. Provided as open source code and browser‐based user interface, the application and integration system can be run across Windows, Linux, and Mac environments to support broader use of multi‐source agrometeorology data.

Optimising availability and geographical accessibility to emergency obstetric care within a sub-national social health insurance scheme in Nigeria.

Health insurance is a key instrument for a health system on its path to achieving universal health coverage (UHC) and protects individuals from catastrophic health expenditures, especially in health emergencies. However, there are other dimensions to care access beyond financial accessibility. In this study, we assess the geographical accessibility of comprehensive emergency obstetric care (CEmOC) within the Lagos State Health Insurance Scheme. We geocoded functional public and private CEmOC facilities, established facilities registered on the insurance panel as of December 2022, and assembled population distribution for women of childbearing age. We used Google Maps Platform's internal directions application programming interface to obtain driving times to facilities. State- and local government area (LGA)-level median travel time (MTT) and a number of CEmOC facilities reachable within 30 min were obtained for peak travel hours. Across Lagos State, MTT to the nearest public CEmOC was 25 min, reduced to 17 min with private facilities added to the insurance panel. MTT to the nearest public facility in LGAs ranged from 9 min (Lagos Island) to 51 min (Ojo) (median = 25 min). With private facilities added, MTT ranged from 5 min (Agege and Ajeromi-Ifelodun) to 36 min (Ibeju-Lekki) (median = 13 min). On average, no public CEmOC facility was reachable within 30 min of driving for women living in 6 of 20 LGAs. With private facilities included in the scheme, reachable facilities within 30 min remained zero in one LGA (Ibeju-Lekki). Our innovative approach offers policy-relevant evidence to optimise insurance coverage, support efforts in advancing UHC, ensure coverage for CEmOC, and improve health system performance.

CIC 2023: Enhancing Referrals to the Supplemental Nutrition Assistance Program (SNAP) through Clinical Integration of a Standards-Based Decision Support System.

The Supplemental Nutrition Assistance Program (SNAP) is one of the most successful national programs to reduce poverty and improve health outcomes, but millions of Americans who qualify still do not have access to SNAP, and limited data is available to determine how referrals to the program can be completed successfully. We aimed to design and develop a standards-based digital care coordination platform to support closed-loop social services referrals between patients and social and health care providers; and demonstrate the feasibility to screen, diagnose, plan, and complete interventions with selected patients in real-settings. We partnered with community members to design the platform through Community Engagement Studios and with a Federally Qualified Health Center, Local Mental Health Authority, and Food Bank to determine the features and workflow requirements of the platform design. We customized currently available systems to use Fast Healthcare Interoperability Resources (FHIR) Application Programming Interfaces (APIs) that could exchange information in real-time across providers and participants. The platform was successfully demonstrated through a pilot where patients were recruited in clinical settings and referred to a Food Bank that provided SNAP application assistance to study participants. We translated the requirements of the platform to national standards and required workflows of providers and patients. Study participants were notified electronically of the specific steps to follow to complete their SNAP applications while receiving support from specialists from the Food Bank. The pilot demonstrated the feasibility of collecting granular social service referral information that can be used to better address gaps in social care. The pilot also highlighted the importance of further coordination on the usage and harmonization of needs assessment nationally and that current digital systems are still not ready to fully utilize national Social Determinants of Health (SDOH) data standards.

Early diagnosis of stroke risk factors in high school students in Makassar, South Sulawesi, Indonesia

The number of stroke patients in Indonesia is ranked as the first country to experience the most strokes in all of Asia. The prevalence of stroke in Indonesia reaches 8.3 out of 1000 population. This prevalence rate increases with increasing age. Indonesian national data shows that stroke is the highest cause of death, at 15.4%, with approximately 750,000 strokes per year in Indonesia and 200,000 recurrent strokes. People who are physically inactive (those who exercise less than three times per week, each for 30 minutes) have an almost 50% increased risk of stroke compared to those who are active. Physical inactivity can lead to weight problems and increased blood pressure and is associated with diabetes, all of which are important risk factors for stroke. Inactivity also contributes to the onset of early atherosclerosis and other cardiovascular diseases, such as heart attacks. This study is an analytical observational study with a cross sectional design to see and observe the risk factors for stroke that exist in school children in Makassar city. Data processing was carried out using computer application programs and SPSS version 22.0 with data analysis methods using the Chi Square Test with the Yate's Correlation formula in SPSS called Continuity Correction and Risk. The number of respondents studied was 896 people. Hypertension is a major risk factor for health problems in society, especially in socioeconomic transition. Hypertension is a primary risk factor for the onset of heart disease and stroke. Doing physical activity will prevent us from various diseases, by doing adequate physical activity we can suppress the increase in blood sugar levels, cholesterol levels, and avoid obesity and strengthen the heart. All of which are risk factors for stroke. So thus by doing adequate physical activity we can avoid stroke. There is a relationship between gender, obesity, hypertension, family history of disease, stress, physical activity, risky diet and smoking on stroke risk factors and there is no relationship between age and knowledge with stroke risk factors.

Malware Detection Based on API Call Sequence Analysis: A Gated Recurrent Unit–Generative Adversarial Network Model Approach

Malware remains a major threat to computer systems, with a vast number of new samples being identified and documented regularly. Windows systems are particularly vulnerable to malicious programs like viruses, worms, and trojans. Dynamic analysis, which involves observing malware behavior during execution in a controlled environment, has emerged as a powerful technique for detection. This approach often focuses on analyzing Application Programming Interface (API) calls, which represent the interactions between the malware and the operating system. Recent advances in deep learning have shown promise in improving malware detection accuracy using API call sequence data. However, the potential of Generative Adversarial Networks (GANs) for this purpose remains largely unexplored. This paper proposes a novel hybrid deep learning model combining Gated Recurrent Units (GRUs) and GANs to enhance malware detection based on API call sequences from Windows portable executable files. We evaluate our GRU–GAN model against other approaches like Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) on multiple datasets. Results demonstrated the superior performance of our hybrid model, achieving 98.9% accuracy on the most challenging dataset. It outperformed existing models in resource utilization, with faster training and testing times and low memory usage.

Empirical Analysis of Widely Used Website Automated Testing Tools

In today's software development, achieving product quality while minimising cost and time is critical. Automated testing is crucial to attaining these goals by lowering inspection efforts and discovering faults more effectively. This paper compares widely used automated testing tools, such as Selenium, Appium, Java Unit (JUnit), Test Next Generation (TestNG), Jenkins, Cucumber, LoadRunner, Katalon Studio, Simple Object Access Protocol User Interface (SoapUI), and TestComplete, based on functionality, ease of use, platform compatibility, and integration capabilities. Our findings show that no single tool is inherently superior, with each excelling in certain areas such as online, mobile, Application Programming Interface (API), or performance testing. While Selenium and Appium are the dominant online and mobile testing frameworks, TestComplete and Katalon Studio offer complete, user-friendly cross-platform testing solutions. Despite the benefits of automation, obstacles such as tool maintenance, scalability, and cost issues remain. The report finishes with advice for picking the best tool for the project and offers potential approaches for enhancing testing frameworks, such as AI-driven optimisation, cloud-based testing, and greater Continuous Integration/ Continuous Deployment (CI/CD) integration. This study offers useful information for developers and testers looking to optimise their testing methods and increase software quality.

Leveraging Large Language Model ChatGPT for enhanced understanding of end-user emotions in social media feedbacks

For software evolution, user feedback has become a meaningful way to improve applications. Recent studies show a significant increase in analyzing end-user feedback from various social media platforms for software evolution. However, less attention has been given to the end-user feedback for low-rating software applications. Also, such approaches are developed mainly on the understanding of human annotators who might have subconsciously tried for a second guess, questioning the validity of the methods. For this purpose, we proposed an approach that analyzes end-user feedback for low-rating applications to identify the end-user opinion types associated with negative reviews (an issue or bug). Also, we utilized Generative Artificial Intelligence (AI), i.e., ChatGPT, as an annotator and negotiator when preparing a truth set for the deep learning (DL) classifiers to identify end-user emotion. For the proposed approach, we first used the ChatGPT Application Programming Interface (API) to identify negative end-user feedback by processing 71853 reviews collected from 45 apps in the Amazon store. Next, a novel grounded theory is developed by manually processing end-user negative feedback to identify frequently associated emotion types, including anger, confusion, disgust, distrust, disappointment, fear, frustration, and sadness. Next, two datasets were developed, one with human annotators using a content analysis approach and the other using ChatGPT API with the identified emotion types. Next, another round is conducted with ChatGPT to negotiate over the conflicts with the human-annotated dataset, resulting in a conflict-free emotion detection dataset. Finally, various DL classifiers, including LSTM, BILSTM, CNN, RNN, GRU, BiGRU and BiRNN, are employed to identify their efficacy in detecting end-users emotions by preprocessing the input data, applying feature engineering, balancing the data set, and then training and testing them using a cross-validation approach. We obtained an average accuracy of 94%, 94%, 93%, 92%, 91%, 91%, and 85%, with LSTM, BILSTM, RNN, CNN, GRU, BiGRU and BiRNN, respectively, showing improved results with the truth set curated with human and ChatGPT. Using ChatGPT as an annotator and negotiator can help automate and validate the annotation process, resulting in better DL performances.

Adapting OGC's SensorThings API and data model to support data management and sharing for environmental sensors

Software is critical in managing environmental sensor data. The Open Geospatial Consortium (OGC) developed the “OGC SensorThings API” (STA) standard to address variability across sensors, observed variables, platforms, and protocols, facilitating development of sensing and Internet of Things applications. This paper details a Python/Django implementation of the STA application programming interface (API) and a PostgreSQL/Timescale implementation of the STA data model, enhancing availability of robust software for management and sharing of environmental sensor data. STA offers a RESTful interface with JSON data encoding, aligning with modern development patterns and facilitating interoperability. Integration of metadata from the Observations Data Model ensures data can be adequately described and interpreted. STA's flexibility allows lightweight query responses or comprehensive metadata inclusion, and a complementary data management API enhances use of STA within multi-user systems. Open-source code and deployment instructions in GitHub enable standalone or cloud deployments, enhancing accessibility and usability for researchers and practitioners.

Racial Biases Associated With Pulse Oximetry: Longitudinal Social Network Analysis of Social Media Advocacy Impact.

Pulse oximetry is a noninvasive method widely used in critical care and various clinical settings to monitor blood oxygen saturation. During the COVID-19 pandemic, its application for at-home oxygen saturation monitoring became prevalent. Further investigations found that pulse oximetry devices show decreased accuracy when used on individuals with darker skin tones. This study aimed to investigate the influence of X (previously known as Twitter) on the dissemination of information and the extent to which it raised health care sector awareness regarding racial disparities in pulse oximetry. This study aimed to explore the impact of social media, specifically X, on increasing awareness of racial disparities in the accuracy of pulse oximetry and to map this analysis against the evolution of published literature on this topic. We used social network analysis drawing upon Network Overview Discovery and Exploration for Excel Pro (NodeXL Pro; Social Media Research Foundation) to examine the impact of X conversations concerning pulse oximetry devices. Searches were conducted using the Twitter Academic Track application programming interface (as it was known then). These searches were performed each year (January to December) from 2012 to 2022 to cover 11 years with up to 52,052 users, generating 188,051 posts. We identified the nature of influencers in this field and monitored the temporal dissemination of information about social events and regulatory changes. Furthermore, our social media analysis was mapped against the evolution of published literature on this topic, which we located using PubMed. Conversations on X increased health care awareness of racial bias in pulse oximetry. They also facilitated the rapid dissemination of information, attaining a substantial audience within a compressed time frame, which may have impacted regulatory action announced concerning the investigation of racial biases in pulse oximetry. This increased awareness led to a surge in scientific research on the subject, highlighting a growing recognition of the necessity to understand and address these disparities in medical technology and its usage. Social media platforms such as X enabled researchers, health experts, patients, and the public to rapidly share information, increasing awareness of potential racial bias. These platforms also helped connect individuals interested in these topics and facilitated discussions that spurred further research. Our research provides a basis for understanding the role of X and other social media platforms in spreading health-related information about potential biases in medical devices such as pulse oximeters.

CannSeek? Yes we Can! Anopen-source single nucleotide polymorphism database and analysis portal for Cannabis sativa.

The CannSeek portal is available at https://icgrc.info/cannseek, https://icgrc.info/genotype_viewer.

Discursive Practices during an Electoral Cycle: Public Opinion and Political Disillusionment on Twitter

Electoral cycles are highly charged, politically intense moments that influence public discourse. Political elites, citizens, and the traditional news media seek to generate opinions and interactions on social networks. This research is motivated by the following questions: What are the communicative and deliberative practices used in these spaces? Is it possible to identify the characteristics that –through deliberative conversations– potentially foster or undermine democratic debate, particularly when using populist and polarizing discourse? Using a mixed methods approach, we apply a social network analysis tool to track conversations and identify the volume of political discourse (N=346,000). Using selective and staged filtering, we identified posts from nine Peruvian Twitter accounts during the September 2022 electoral campaign in Lima: three candidates, three media outlets, and three accounts with high levels of engagement. Our data comprised the comments from these nine accounts and was extracted using an Application Programming Interface (API). Subsequently, we carried out ethnographic content analysis on publications with more than 30 comments. In this phase, we analyzed Twitter comments using a codebook to identify deliberative practices and user responses. Our findings underscore the significant role of principal media outlets in shaping political conversation on Twitter. We also discovered that attempts to interact and deliberate were often overshadowed by heated comments attempting to impose opinions on others. Most importantly, our research reveals a widespread disillusionment with politics, political institutions, and even the political preferences of fellow citizens in Peru, highlighting a key challenge for democratic discourse on social media.

Алгоритм для численного решения диффузионно-реакционно-дрейфового уравнения с дробной производной по времени и координате

<p>The paper is devoted to the construction and program implementation of the computational algorithm for modeling a process of diffusion-drift nature based on the fractional diffusion approach. The mathematical model is formulated as an initial-boundary value problem for the time-space fractional diffusion-drift equation in a limited domain. Time and space fractional derivatives are considered in the sense of Caputo and Riemann – Liouville, respectively. A modified implicit finite-difference scheme is constructed. The concept of the considered mathematical problem provides an example of a deterministic model of the charging process of dielectric materials. An application program has been developed that implements the constructed numerical algorithm. The results were verified using the example of solving a test problem.</p>

PUSH AND PULL FACTORS OF FOREIGN TOURISTS IN CHOOSING TIRTA EMPUL SPIRITUAL TOURISM ATTRACTION, GIANYAR REGENCY, BALI

This research was conducted to determine the push factors and pull factors of foreign tourists in choosing Tirta Empul Spiritual Tourism Attraction. The type of data used is quantitative data with data sources using primary and secondary data. The population in this study are foreign tourists who have visited Tirta Empul Tourist Attraction. The sampling technique uses purposive sampling with a total of 100 respondents. Data collection was done by questionnaire, literature study, and observation. The data analysis technique used Exploratory Factor Analysis using the Statistical Program For Social Science version 25 (SPSS) application program. Based on the study's results, there are 5 driving factors and 2 pull factors for foreign tourist visits to Tirta Empul Spiritual Tourism Attraction. The main factor in the driving factor is Emotional Escape which has a total variance value of 33.623%. while the main factor in the pull factor is the Innovative Exploration factor which has a total variance value of 52.664%.

COMPARISON OF BONDS PERFORMANCE WITH SYARIAH BONDS (SUKUK)

The purpose of this study is to determine the comparison of bond performance with sharia bonds (sukuk) in 2010-2023. This study uses quantitative calculations with a comparative type of research. The data used are sourced from secondary data from OJK. This study uses a population, namely the capitalization of sharia bond performance data (sukuk) compared to the capitalization of bond performance data, using the Sharpe, Jensen method for the 2010-2022 period (monthly data) in the Indonesian Capital Market. Data analysis was carried out using the Sharpe and Jensen methods and using the MANOVA test with the help of the SPSS statistical application program. The results of this study indicate that the performance of sukuk with bonds using the Sharpe and Jensen methods is not significantly different. This shows that the sukuk return and the bonds based on the Sharpe and Jensen methods that will be received will be relatively the same.