Application Programming Research Articles

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.

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.

Integrated Blockchain and Digital Twin Framework for Sustainable Building Energy Management

The building sector remains a major contributor to increasing energy consumption and emissions. Meanwhile, the energy system is becoming more complex due to the transition to clean energy sources. Current tools and policies struggle to manage this complexity, as the existing infrastructure was not designed for such large dynamic distributed energy resources. This creates an urgent need to adopt emerging technologies for enhancing building energy management systems. The objective of this research is to develop a framework that integrates Blockchain and Digital Twin technologies to provide an efficient and trusted energy management platform that supports smart cities communities and to effectively contribute to enhancing the progress of UN Sustainability Development Goals (SDGs), specifically SDG11 and SDG13. The proposed framework comprises four main elements: Blockchain platform, Digital Twin platform, Application Program Interfaces (APIs), and building energy model. Blockchain platform automates energy billing by utilizing digital currency and smart contracts with pre-set pricing tiers and feed in tariffs. Digital Twin platform provides interactive communication and visualization with physical assets. APIs enables seamless interconnectivity between both platforms. The building energy model acts as a prediction tool, and the simulation results are fed to Digital Twin platform to alert system participants in case actual consumption deviates from optimum values. The viability of the proposed framework is demonstrated using a case study of a residential apartment.

Artificial Intelligence Scribe and Large Language Model Technology in Healthcare Documentation: Advantages, Limitations, and Recommendations.

Artificial intelligence (AI) scribe applications in the healthcare community are in the early adoption phase and offer unprecedented efficiency for medical documentation. They typically use an application programming interface with a large language model (LLM), for example, generative pretrained transformer 4. They use automatic speech recognition on the physician-patient interaction, generating a full medical note for the encounter, together with a draft follow-up e-mail for the patient and, often, recommendations, all within seconds or minutes. This provides physicians with increased cognitive freedom during medical encounters due to less time needed interfacing with electronic medical records. However, careful proofreading of the AI-generated language by the physician signing the note is essential. Insidious and potentially significant errors of omission, fabrication, or substitution may occur. The neural network algorithms of LLMs have unpredictable sensitivity to user input and inherent variability in their output. LLMs are unconstrained by established medical knowledge or rules. As they gain increasing levels of access to large corpora of medical records, the explosion of discovered knowledge comes with large potential risks, including to patient privacy, and potential bias in algorithms. Medical AI developers should use robust regulatory oversights, adhere to ethical guidelines, correct bias in algorithms, and improve detection and correction of deviations from the intended output.

Large Language Models in Otolaryngology Residency Admissions: A Random Sampling Analysis.

To investigate potential demographic bias in artificial intelligence (AI)-based simulations of otolaryngology, residency selection committee (RSC) members tasked with selecting one applicant among candidates with varied racial, gender, and sexual orientations. This study employed random sampling of simulated RSC member decisions using a novel Application Programming Interface (API) to virtually connect to OpenAI's Generative Pre-Trained Transformers (GPT-4 and GPT-4o). Simulated RSC members with diverse demographics were tasked with ranking to match 1 applicant among 10 with varied racial, gender, and sexual orientations. All applicants had identical qualifications; only demographics of the applicants and RSC members were varied for each simulation. Each RSC simulation ran 1000 times. Chi-square tests analyzed differences across categorical variables. GPT-4o simulations additionally requested a rationale for each decision. Simulated RSCs consistently showed racial, gender, and sexual orientation bias. Most applicant pairwise comparisons showed statistical significance (p < 0.05). White and Black RSCs exhibited greatest preference for applicants sharing their own demographic characteristics, favoring White and Black female applicants, respectively, over others (all pairwise p < 0.001). Asian male applicants consistently received lowest selection rates. Male RSCs favored White male and female applicants, while female RSCs preferred LGBTQIA+, White and Black female applicants (all p < 0.05). High socioeconomic status (SES) RSCs favored White female and LGBTQIA+ applicants, while low SES RSCs favored Black female and LGBTQIA+ applicants over others (all p < 0.001). Results from the newest iteration of the LLM, ChatGPT-4o, indicated evolved selection preferences favoring Black female and LGBTQIA+ applicants across all RSCs, with the rationale of prioritizing inclusivity given in >95% of such decisions. Utilizing publicly available LLMs to aid in otolaryngology residency selection may introduce significant racial, gender, and sexual orientation bias. Potential for significant and evolving LLM bias should be appreciated and minimized to promote a diverse and representative field of future otolaryngologists in alignment with current workforce data. NA Laryngoscope, 135:87-93, 2025.

Methods of treatment of trophic skin defect in patients with chronic venous insufficiency in stage C6

Annotation. Chronic venous insufficiency (CVI) of the lower extremities is currently the most common pathology of the human vascular system. The frequency of development of venous insufficiency per 1000 population is 1.48-3.05 people. The aim of the work – to improve the results of treatment of patients with chronic venous insufficiency of the lower extremities in stage C6 by accelerating the epithelialization of active trophic venous ulcers. The study is based on the analysis of examination and treatment of 97 patients with CVI. According to the purpose of the study, all patients were divided into the main and control groups. The main study group included 47 patients who underwent classical surgery (crossectomy+phlebectomy according to Babcock and/or Narat) in combination with the developed method of treating trophic wounds. The control group included 50 patients who underwent classical surgical treatment (ligation of perforating veins according to Cockett’s method, phlebectomy according to Narat). Quantitative indicators were processed statistically. Statistical processing of materials and research results was carried out with the help of the Statistica application program package from the company “StatSoft” (USA). After the treatment in the main group, the rapid dynamics of healing of wound surfaces was observed within 1-3 months after surgical treatment. The speed of epithelization of trophic ulcers of venous etiology in patients of two groups varied from 6 to 50 days, and the daily increase of epithelization - from 1.79% to 17.21%. The lowest rate of epithelization was observed in trophic ulcers larger than 20 cm2. Thanks to the application of our own developed method of surgical treatment of patients with a decompensated form of CVI complicated by trophic skin defects, it was possible to significantly reduce the healing time of active trophic wounds after surgical treatment.

MedShapeNet- a large-scale dataset of 3D medical shapes for computer vision.

The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models). However, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instruments is missing. We present MedShapeNet to translate data-driven vision algorithms to medical applications and to adapt state-of-the-art vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. We present use cases in classifying brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. By now, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. MedShapeNet contains medical shapes from anatomy and surgical instruments and will continue to collect data for benchmarks and applications. The project page is: https://medshapenet.ikim.nrw/.

REMOTE INTELLIGENT ROBOT MANIPULATOR CONTROL SUBSYSTEM

The issue of expanding the functionality of an intelligent software and hardware subsystem for capturing an object by a robot manipulator was considered. It is shown that the task of expanding the functionality of the subsystem for free-form object capture by a robot manipulator with technical vision based on the operations of building a depth map from the obtained stereo image is insufficiently developed and can be solved by integrating a remote robot cntrol subsystem connected to a local network or the Internet into the robotic complex. The subsystem was developed using a Raspberry Pi microcomputer, a Stereo Pi board, a board for connecting eight Adafruit PCA9865 servos, Raspberry PI cameras, and a camera module. The structure and electrical diagram of the subsystem, a description of the software, and the main code snippet of the web application program are presented. Based on the analysis of the research results, it is established that the proposed remote control subsystem provides an extension of the functionality of the intelligent software and hardware subsystem for capturing an object by a robot manipulator, since in addition to the functions of automatic object capture, due to remote adjustment of the accuracy and manufacturability of object capture, it provides the functions of high-precision positioning of objects relative to each other and especially the effort on the robot manipulator to perform precise operations. The reliability of the object capture is also increased due to the fact that the subsystem allows for the additional involvement of a team of competent users. The proposed system increases the degree of automation, provides the possibility of moderation, collective administration, and user feedback during robot operation.

Education of the Principles and Tools of Laparoscopy: Introduction of the Laparoscopic Co-Surgeon Educational Application

Introduction: With the significant increase in the number and variety of minimally invasive surgical procedures and the growing complexity of tools, equipment, and techniques in laparoscopy, the need for innovative educational methods such as mobile-based learning (M-Learning) has become essential. This study introduced design and development the Laparoscopic Co-Surgeon application. Methods: The developmental study was conducted in two phases. In the first phase, application designed after explaining the goals and educational content in the expert panel. In the design phase, game-based learning principles, puzzles, and conceptual maps were used. In the second phase, the production of electronic application was developed by experts and its validity was assessed by experts. Results: The Laparoscopic Co-Surgeon application was developed in six panels. This application was developed using gamification strategies, a puzzle panel with a matching method, a quiz panel, and panels for concept maps, instrument recognition, and surgeries based on conceptual maps, along with animated images and short videos. Conclusion: The current results confirm the validity of the Co- surgeon application program in teaching surgical technology students related to the identification of laparoscopic surgery tools and their uses. It is suggested to use the Co-Surgeon application as an educational tool in the clinical and specialized training of the disciplines involved in laparoscopic surgeries.

Popularity Bias in Correlation Graph-based API Recommendation for Mashup Creation

The explosive growth of the Application Programming Interfaces (APIs) economy in recent years has led to a dramatic increase in available APIs. Mashup development, a dominant approach for creating data-centric applications based on APIs, has experienced a surge in popularity. However, the vast array of choices poses a challenge for mashup developers when selecting appropriate API compositions to meet specific business requirements. Correlation graph-based recommendation approaches have been designed to assist developers in discovering related and compatible API compositions for mashup creation. Unfortunately, these approaches often suffer from popularity bias issues, leading to an inequality in API usage and potential disruptions to the entire API ecosystem. To address these challenges, our research begins with a theoretical analysis of the popularity bias introduced by correlation graph-based API recommendation approaches. Subsequently, we empirically validate the presence of popularity bias in API recommendations through a data-driven study. Finally, we introduce the p opularity b ias aware w eb A PI r ecommendation ( PB-WAR ) approach to mitigate popularity bias in correlation graph-based API recommendations. Experimental results over a real-world dataset demonstrate that PB-WAR offers the optimal tradeoff between accuracy and debiasing performance compared to other competitive methods.