Application Programming Research Articles

RadLab: An open science resource for radiation studies relevant to human spaceflight

In response to the growing need of the space life sciences community for a publicly available single access point for radiation physics data relevant to human space exploration, an open data repository and analysis platform, RadLab, has been developed. RadLab consists of a database and a user-friendly data retrieval, visualization, and analysis toolkit, including a graphical user interface (GUI) and an application programming interface (API). RadLab complements the space biology data in the NASA Open Science Data Repository (OSDR) and aims to provide open, centralized access to radiation physics data relevant to spaceflight. Analysis tools contained in RadLab will facilitate intercomparison among different detectors and radiation data sets to better understand the radiation environment in spacecraft and planetary habitats, and facilitate the use of space radiation data by space biology investigators and radiation instrument developers.

CFD Simulation of the Influence of the Type of Gas Distribution in the Burners on Thermal Aerodynamic Processes in the DKVR 10-13 Boiler

Topics related to fuel combustion and its impact on the environment will never lose their relevance, as the issues of efficient combustion and emission reduction are key in power generation and environmental protection. The countries of the European Union are massively abandoning the use of natural gas as a fuel for thermal power stations. However, in Asian countries, the ease of using natural gas as the main fuel and its environmental friendliness compared to coal made it possible to widely use natural gas in industry and energy sector. Comparing natural gas with alternative combustible gases (generator, blast furnace, mine, biogas), the main conclusion that it has the most attractive characteristics for its use in industry, including energy facilities, can be drawn. Therefore, it is impossible to replace it with alternative fuels in the chemical, heavy industry and energy sector in the near future. The paper is devoted to CFD modeling of stabilized combustion without premixing in a burner with low swirl for two operating modes of the boiler unit - nominal one and at 60% capacity. The study was carried out using numerical methods with the ANSYS-Fluent application program package. The object of the study is a burner built according to the technology based on the use of jet-niche systems with gas distribution of fuel by circular jets fed perpendicularly into the flow of the oxidizer through a single-row system of holes. Hydrodynamics and heat exchange processes were chosen as the subject of research, based on the analysis of which a model of NOx generation in jet-niche systems was obtained. The authors of the paper believe that replacing the regular burners of the DKVR-10-13 water heating boiler with a jet-niche ones can contribute to better mixing of fuel and air and ensure more complete combustion. In this paper, two types of burners are considered. In one of the burners, fuel is supplied through rectangular slits, in the other – through round holes arranged in a row. Air is supplied to both burners through rectangular slits. It was determined that gas distribution through round holes increases the spraying of the mixture and increases the area of combustion products spraying. Visualization of the distribution of pressure, temperature, kinetic energy profiles of turbulent pulsations and vorticity was carried out. The obtained results indicate that there are no changes in the flow regime, flame displacement or its instability. It was determined that both the axial velocity and the tangential velocity of the flow affect the distribution of combustion products and harmful impurities such as NOx. Gas distribution in circular jets stabilizes combustion and reduces flame expansion.

The Role of API Security in Modern Enterprise Platforms

This article explores the critical role of API security in modern enterprise platforms, addressing the growing importance of protecting Application Programming Interfaces (APIs) as they become increasingly central to digital ecosystems. We examine the fundamental challenges organizations face in securing their APIs, including authentication, data encryption, rate limiting, and version management. The paper presents a comprehensive overview of best practices for API security, such as implementing OAuth and token-based authentication, utilizing Transport Layer Security (TLS), deploying API gateways, and conducting regular audits. We discuss implementation strategies that integrate security throughout the API development lifecycle and highlight the importance of developer training. Through case studies, we illustrate both successful API security implementations and lessons learned from security breaches. The article also looks ahead to future trends in API security, considering the impact of emerging technologies like AI and quantum computing, as well as the evolving threat landscape. By providing a thorough analysis of current practices and future directions, this paper aims to equip organizations with the knowledge needed to develop robust, secure, and scalable API infrastructures that can support innovation while protecting critical data and resources in an increasingly interconnected digital world.

Digitalization-Based Geometric Design of Open Pit Mining Blast Using Visual Block Programming Method

Gilgal Batu Alam Lestari (PT. GILBAL) is a company specializing in granodiorite rock mining. In its mining operations, PT. GILBAL employs blasting to break down materials, simplifying the loading and processing stages. The design of blast hole geometry requires precise calculations to achieve an optimal configuration, ensuring that blast fragmentation aligns with operational needs. To facilitate the blast hole geometry calculations, an application is needed that delivers accurate results and can be accessed anytime, anywhere. One approach to developing this application is through visual block programming using the Kodular tool. The research aims to design a system programming concept for an application capable of processing calculations for both blast geometry design and blast fragmentation outcomes, implementing this application program for actual blasting operations in the field, and achieving high accuracy in the application design's performance when applied in real-world field conditions. Application testing for blast hole geometry calculations demonstrated that the R.L. Ash method achieved an average accuracy of 97.39%, the C.J. Konya method reached 98.49%, the Anderson method achieved 99.93%, and the Langefors method reached 99.95%. In terms of calculating blast fragmentation, the R.L. Ash method attained an average accuracy of 89.57%, the C.J. Konya method 93.49%, the Anderson method 86.50%, and the Langefors method 99.81%.

API management and cloud integration model for SMEs

In the rapidly evolving technological landscape, Small and Medium-sized Enterprises (SMEs) face significant challenges in maintaining efficient and scalable IT systems. This review outlines a strategic model for API management and cloud integration designed to enhance system performance and optimize cloud-native features. The proposed model focuses on the redesign and optimization of Application Programming Interfaces (APIs) to leverage cloud capabilities effectively, addressing common issues faced by SMEs in system integration and performance. The model begins with a comprehensive assessment of existing API architectures to identify inefficiencies and performance bottlenecks. It emphasizes the need for a cloud-native approach, which includes the adoption of microservices architecture and serverless computing. By redesigning APIs to align with cloud-native principles, the model aims to achieve greater scalability, flexibility, and responsiveness in handling varying workloads. Key components of the model include API versioning strategies, improved security protocols, and enhanced data management practices. The integration of cloud-native features such as containerization, orchestration, and automated scaling ensures that APIs are optimized for performance and reliability. This approach not only streamlines system operations but also reduces the overhead associated with traditional server-based architectures. Furthermore, the model incorporates best practices for continuous integration and continuous deployment (CI/CD), facilitating seamless updates and maintenance of APIs. It also includes monitoring and analytics tools to provide real-time insights into API performance and user interactions, enabling proactive management and optimization. By implementing this strategic model, SMEs can achieve significant improvements in system performance, reduce latency, and enhance overall cloud integration. The model supports the efficient utilization of cloud resources, leading to cost savings and improved operational efficiency. This approach positions SMEs to better compete in the digital economy by leveraging advanced technologies and optimizing their IT infrastructure.

Pengaruh Kompensasi dan Lingkungan Fisik terhadap Kepuasan Kerja dan Kinerja Karyawan

This research proposal was put forward to identify how the effect of compensation on job satisfaction, the effect of the physical environment on job satisfaction, the effect of job satisfaction on employee performance. The type of research that will be carried out is explanatory research or explanatory research where this research aims to analyze the relationship between exogenous variables and endogenous variables. Exogenous variables in this study include compensation, physical environment and work satisfaction, while the endogenous variables are employee performance. The population to be analyzed in this research proposal is 133 production and office employees of CV Garuda Plastik in the Demak area. The sample in this study was determined using a purposive sampling method with a total of 100 respondents. For the research analysis method used is a quantitative analysis method using multiple linear regression analysis. The supporting application program used is SPSS 22. The results of the study show that compensation has a positive and significant effect on job satisfaction. The physical environment has a positive and significant effect on job satisfaction. Job satisfaction has a positive and significant effect on employee performance

Examining the Popularity, Content, and Intersections With the Substance Abuse and Mental Health Services Administration's Definition of Recovery in a Nonclinical Online Cannabis Cessation Community: Infodemiology Study of Reddit Posts.

Cannabis consumption has increased in recent years, as has cannabis use disorder. While researchers have explored public online community discussions of active cannabis use, less is known about the popularity and content of publicly available online communities intended to support cannabis cessation. This study aims to examine the level of engagement and dominant content of an online community for cannabis cessation through 3 specific aims. First, we examine the use of a subreddit cannabis cessation community (r/leaves) over time to evaluate the popularity of this type of resource for individuals who want to stop using cannabis. Second, we examine the content of posts in the community to identify popular topics related to cessation. Third, we compare the thematic findings relative to the 4 domains of recovery defined by the Substance Abuse and Mental Health Services Administration (SAMHSA). By examining these 3 gaps, we take the initial steps toward understanding the experiences being shared online among individuals interested in cannabis cessation and compare them with the principles outlined in the SAMHSA definition of recovery. Using the Pushshift application programming interface, we collected the count of posts by year between 2011 and 2021 and the narrative of the 100 posts with the most comments per year in a popular cannabis cessation-focused subreddit (r/leaves). A linear model and a nonlinear model were compared to evaluate change in the number of posts by year. Mixed natural language processing and qualitative analyses were applied to identify top terms, phrases, and themes present in posts over time. Overlap between themes and the 4 SAMHSA domains of recovery (health, purpose, community, and home) were examined. The number of annual posts in r/leaves increased from 420 in 2011 to 34,841 in 2021 (83-fold increase), with exponential growth since 2018. The term that was the most common across posts was "smoke" (2019 posts). Five major themes were identified, and a narrative arc was represented, from motivations and perceived benefits of cannabis use to the negative consequences of use, strategies to change behaviors, and the positive and negative consequences of change. There was substantial overlap between these 5 themes and 3 of SAMHSA's 4 domains of recovery: health, purpose, and community. However, the domain of home was less commonly identified. Engagement in this online cannabis support community appears to be increasing. Individuals using this forum discussed several topics, including multiple aspects of recovery defined by the SAMHSA. Online communities, such as this one may, serve as an important pathway for individuals seeking to reduce or cease their consumption of cannabis.

Current problems of using artificial intelligence inHR management in Kazakhstan

The article provides an understanding of artificial intelligence in the field of HR, and it is said that the digitization of the industry is reflected in the widespread use of artificial intelligence. The theory and history of the field of artificial intelligence is systematized by reviewing the works of researchers. The possibilities of using artificial intelligence are clarified and its efficiency is considered. As a result of the study, it was determined that artificial intelligence is used in four main categories in the field of HR, and it was found that there is a high demand for automated resume search, analysis, and selection services. At the same time, in order to invite candidates for a vacant position, including the identification of qualified talented employees, chatbots, Skype interviews, the use of information on online recruiting platforms such as HeadHunter, notification of vacancies at fairs, and pilot projects introduced by the state will be analyzed. The features of artificial intelligence programs introduced in the Kazakhstan labor exchange, and changes brought to the HR system, the advantages and disadvantages will be discussed. Especially as the most effective programs in the field of artificial intelligence in recent years: IBM's Deep Blue program, Alvinn program for driving, and Microsoft Application and Services Group's talking robot program, the importance of research on the specificity of programs in the field of medicine and pharmaceuticals will be analyzed and considered as examples of regional enterprises.

Automated Mass Extraction of Over 680,000 PICOs from Clinical Study Abstracts Using Generative AI: A Proof-of-Concept Study.

Generative artificial intelligence (GenAI) shows promise in automating key tasks involved in conducting systematic literature reviews (SLRs), including screening, bias assessment and data extraction. This potential automation is increasingly relevant as pharmaceutical developers face challenging requirements for timely and precise SLRs using the population, intervention, comparator and outcome (PICO) framework, such as those under the impending European Union (EU) Health Technology Assessment Regulation 2021/2282 (HTAR). This proof-of-concept study aimed to evaluate the feasibility, accuracy and efficiency of using GenAI for mass extraction of PICOs from PubMed abstracts. Abstracts were retrieved from PubMed using a search string targeting randomised controlled trials. A PubMed clinical study 'specific/narrow' filter was also applied. Retrieved abstracts were processed using the OpenAI Batch application programming interface (API), which allowed parallel processing and interaction with Generative Pre-trained Transformer 4 Omni (GPT-4o) via custom Python scripts. PICO elements were extracted using a zero-shot prompting strategy. Results were stored in CSV files and subsequently imported into a PostgreSQL database. The PubMed search returned 682,667 abstracts. PICOs from all abstracts were extracted in < 3 h, with an average processing time of 200 s per 1000 abstracts. A total of 395,992,770 tokens were processed, with an average of 580 tokens per abstract. The total cost was $3390. On the basis of a random sample of 350 abstracts, human verification confirmed that GPT-4o accurately and comprehensively extracted 342 (98%) of all PICOs, with only outcome elements rarely missed. Using GenAI to extract PICOs from clinical study abstracts could fundamentally transform the way SLRs are conducted. By enabling pharmaceutical developers to anticipate PICO requirements, this approach allows for proactive preparation for the EU HTAR process, or other health technology assessments (HTAs), streamlining efficiency and reducing the burden of meeting these requirements.

Development of an interactive platform using an Application Programming Interface to facilitate social connections

Development of an interactive platform using an Application Programming Interface to facilitate social connections

Modeling of thermo-aerodynamic and environmental parameters on the example of a two-pipe water heating boiler that has worked out the park resource

Topics related to fuel combustion and its impact on the environment will never lose their relevance, as the issues of efficient combustion and emission reduction are key in power generation and environmental protection. The countries of the European Union are massively abandoning the use of natural gas as a fuel for thermal power station. However, in Asian countries, the ease of using natural gas in industry as the main fuel, its environmental friendliness compared to coal, made it possible to widely use natural gas in industry and energy. Comparing natural gas with alternative combustible gases (generator, blast furnace, mine, biogas), the main conclusion can be drawn that it has the most attractive characteristics for its use in industry, including energy. Therefore, it is impossible to replace it with alternative fuels in the chemical, heavy industry and energy industry in the near future. The presented work is devoted to CFD modeling of stabilized combustion without premixing in a burner with low swirl for two operating modes of the boiler unit - nominal and at 60% capacity. The study was carried out using numerical methods using the ANSYS-Fluent application program package. The object of the study is a burner built according to the technology based on the use of jet-niche systems with gas distribution of fuel by circular jets fed perpendicularly into the flow of the oxidizer through a single- row system of holes. Hydrodynamics and heat exchange processes were chosen as the subject of research, based on the analysis of which a model of NO x generation in SNS was obtained. In this work, two types of burners are considered. In one of the burners, fuel is supplied through rectangular slits, in the other – through round holes arranged in a row. Air is supplied to both burners through rectangular slits. It was determined that gas distribution through round holes increases the spraying of the mixture and increases the area of spraying of combustion products. Visualization of the distribution of pressure, temperature, kinetic energy profiles of turbulent pulsations and vorticity was carried out. The obtained results indicate that there are no changes in the flow regime, flame displacement or its instability. It was determined that both the axial velocity and the tangential velocity of the flow affect the distribution of combustion products and harmful impurities such as NO x . Gas distribution in circular jets stabilizes combustion and reduces flame expansion.

Candidates resume analyzer using artificial intelligence

In the modern recruitment process, where the number of candidates and job vacancies constantly increases, automating resume analysis becomes critically important for efficient selection. This paper focuses on developing a system for automated resume analysis using artificial intelligence, specifically the OpenAI model (ChatGPT). The paper thoroughly examines the system's components, structure, and functional capabilities. The system consists of several key modules: the user interface ensures interaction with users, the Application Programming Interface Gateway facilitates data exchange between the interface and the server, and the authentication module controls system access, ensuring user security. The main part of the system's business logic is implemented on the server, which interacts with the natural language processing module, utilizing the ChatGPT model for processing resume text data. The use of natural language processing technologies allows the system to automatically extract and analyse key elements of resumes, such as education, work experience, skills, and certifications of candidates. This significantly speeds up the processing of large volumes of resumes, improving selection efficiency and accuracy. Data obtained during the analysis is stored in a PostgreSQL database, ensuring reliable storage and quick access for further processing. The paper also emphasizes the importance of integrating the system with other recruiting platforms and databases for more efficient management of the hiring process. Such integration allows for automatic synchronization of job vacancies and candidates, optimizing the search and selection process. Additionally, the paper highlights the key challenges associated with automating resume processing, such as the diversity of resume formats submitted by candidates, differences in data structure, and potential errors in automatically recognizing important information. Despite these challenges, the use of artificial intelligence and natural language processing methods significantly enhances the productivity and accuracy of recruitment processes. Thus, the developed automated resume analysis system based on Artificial Intelligence can become a significant tool in the modern candidate selection process. Its functionality not only enables quick and accurate resume analysis but also allows integration with other HR systems, simplifying the recruitment process, reducing data processing time, and improving selection accuracy.

Scheduling as a Constraint Satisfaction Problem (Using the Example of Open-Pit Minе Production Scheduling Problem)

The research described in the work is aimed at developing methods for Scheduling. The fundamental disadvantage of the existing methods of Mixed-Integer Linear Programming in application to the problems under consideration is the fact that they are too demanding on the amount of RAM. The difficulty of applying local search procedures to such high-dimensional problems is to develop an effective way to find an acceptable initial approximation and determine the neighboring state transition function, which would allow achieving the optimum fast enough. In the Operations Research Theory, adding additional conditions to a problem can lead to a fundamental change in the problem-solving scheme. The methods proposed in the study are implemented within the framework of the Constraint Programming Paradigm which makes it possible to represent the subject domain dependencies saving RAM, as well as to provide the ability to step-by-step take into account heterogeneous problem conditions without essentially changing the scheme of finding solutions. A significant part of the research deals with methods of logical inference on constraints to reduce the search space and speed up the computational process. The approach to scheduling is illustrated by the Open-Pit Mine Production Scheduling Problem, which was first proposed to be solved as a Constraint Satisfaction Problem. In order to find the first feasible solution, a «greedy» search method is proposed, the result of which can be improved using the developed hybrid method. Both methods rely on original procedures of inference on constraints. The proposed approach has proven its efficiency for block models with sizes of tens and hundreds of thousands of blocks.

Features of the functional state of the endothelium and the hemocoagulation system in the formation of placental insufficiency in patients with gestational diabetes mellitus without insulin requirement

Background. There is a lot of data in the literature showing the state of the hemostatic system and endothelium separately in patients with gestational diabetes mellitus (GDM) and placental insufficiency (PI). However, with the combination of the above complications of pregnancy, there is very little research, therefore, the problem requires detailed study.Objective. To determine the contribution of the functional state of the endothelium and the hemocoagulation system to the formation of placental insufficiency in patients with gestational diabetes mellitus without insulin requirement.Materials and methods. A longitudinal cohort comparative study was conducted. The study included 120 patients in the II-III trimester of pregnancy with GDM without insulin requirement. The main group consisted of 70 women whose pregnancy was complicated by sub- and decompensated forms of PN. The comparison group included 50 pregnant women without pathology of the fetoplacental complex. The hemostasis system was studied using clotting tests and thromboelastometry. The concentrations of VEGF-A, total nitrite (NO2 total), endogenous nitrite (NO2 endogenous), nitric oxide (NO) in peripheral blood were determined by ELISA. Comparison of continuous quantitative data was carried out using the Mann-Whitney test, which was calculated using the MedCalc 15.8 application program. The null hypothesis was rejected at p&lt;0.05.Result. The value of the integral index of coagulation, onset time and initial speed of clot formation in the main group was statistically significantly higher than in patients in the comparison group, p&lt;0.05. The level of VEGF-A in pregnant women of the main group was statistically significantly lower than that in the comparison group, p&lt;0.05. The indicator of total NO2 and NO in the main group was statistically significantly lower than in the comparison group, p&lt;0.05. The endogenous NO2 indicator did not differ statistically significantly between groups.Conclusion. In patients with GDM on diet therapy and PN, endothelial dysfunction and, as a consequence, hypercoagulation occur.

Public Perception of the Tobacco 21 Amendment on Twitter in the United States: Observational Study.

Following the signing of the Tobacco 21 Amendment (T21) in December 2019 to raise the minimum legal age for the sale of tobacco products from 18 to 21 years in the United States, there is a need to monitor public responses and potential unintended consequences. Social media platforms, such as Twitter (subsequently rebranded as X), can provide rich data on public perceptions. This study contributes to the literature using Twitter data to assess the knowledge and beliefs of T21. Twitter data were collected from November 2019 to February 2021 using the Twitter streaming application programming interface with keywords related to vaping or e-cigarettes, such as "vape," "ecig," etc. The temporal trend of the T21 discussion on Twitter was examined using the mean number of daily T21-related tweets. Inductive methods were used to manually code the tweets into different sentiment groups (positive, neutral, and negative) based on the attitude expressed toward the policy by 3 coders with high interrater reliability. Topics discussed were examined within each sentiment group through theme analyses. Among the collected 3197 tweets, 2169 tweets were related to T21, of which 444 tweets (20.5%) showed a positive attitude, 736 (33.9%) showed a negative attitude, and 989 (45.6%) showed a neutral attitude. The temporal trend showed a clear peak in the number of tweets around January 2020, following the enactment of this legislation. For positive tweets, the most frequent topics were "avoidance of further regulation" (120/444, 27%), "Enforce T21" (110/444, 24.8%), and "health benefits" (81/444, 18.2%). For negative tweets, the most frequent topics were "general disagreement or frustration" (207/736, 28.1%) and "will still use tobacco" (188/736, 25.5%). Neutral tweets were primarily "public service announcements (PSA) or news posts" (782/989, 79.1%). Overall, we find that one-third of tweets displayed a negative attitude toward T21 during the study period. Many were frustrated with T21 and reported that underage consumers could still obtain products. Social media data provide a timely opportunity to monitor public perceptions and responses to regulatory actions. Continued monitoring can inform enforcement efforts and potential unintended consequences of T21.

The role of SNAP and WIC in the bidirectional relationship between food insecurity and maternal depressive symptoms

BackgroundThe association between food insecurity and maternal depressive symptoms has been established by many cross-sectional and longitudinal studies however the understanding of the reciprocal relationship between them remains unclear. Further, previous research demonstrates that federal nutrition assistance decreases food insecurity and promotes maternal mental well-being but further research is needed to elucidate the moderating role of these programs in the association between food insecurity and maternal depressive symptoms. Therefore, the current study examined the bidirectional associations between maternal depression probability and food insecurity using cross-lagged models and then tested the main and moderating effects of SNAP and WIC. MethodsData were from the Future of Families and Child Wellbeing Study and the sample included 1948 mothers who participated in year 3 and year 5 of data collection. ResultsThe cross-lagged paths show that food insecurity at year 3 predicted maternal depression probability at year 5 and depression probability at year 3 predicted food insecurity at year 5. There was a significant moderating effect of WIC receipt on the association between food insecurity at year 3 and at year 5 such that when mothers with high food insecurity at year 3 received WIC, they were less food insecure at year 5. LimitationsThe study oversampled for unmarried mothers only two waves of data were used for the cross-lagged panel models. ConclusionsResults demonstrate the need to incorporate mental health services with the existing food assistance programs and the need to destigmatize the application process and program structure.

Building a Natural Language Interface for FHIR Clinical Terminology Server.

While Fast Healthcare Interoperability Resources (FHIR) clinical terminology server enables quick and easy search and retrieval of coded medical data, it still has some drawbacks. When searching, any typographical errors, variations in word forms, or deviations in word sequence might lead to incorrect search outcomes. For retrieval, queries to the server must strictly follow the FHIR application programming interface format, which requires users to know the syntax and remember the attribute codes they wish to retrieve. To improve its functionalities, a natural language interface was built, that harnesses the capabilities of two preeminent large language models, along with other cutting-edge technologies such as speech-to-text conversion, vector semantic searching, and conversational artificial intelligence. Preliminary evaluation shows promising results in building a natural language interface for the FHIR clinical terminology system.

Monte Carlo Workflow Unification for Nuclear Reactor Analysis with Metamodel-Driven Modeling

Monte Carlo (MC) transport codes are a cornerstone of nuclear reactor analysis frameworks, providing reference solutions and multigroup cross sections, and even as core components in multiphysics couplings. These applications can be seen in toolkits from the Nuclear Energy Advanced Modeling and Simulation program and the U.S. Nuclear Regulatory Commission’s BlueCRAB (Comprehensive Reactor Analysis Bundle). Contrary to their ubiquitousness in reactor physics modeling and simulation, popular MC codes, such as MCNP, Serpent, and KENO, are still reliant on antiquated textual input formats. These input languages use a plethora of keywords with terse syntax to specify all facets of a model, including its geometry, materials, physics settings, and tally options. This poses a steep learning curve and a poor user experience. Being tied to unique text-based input formats also significantly complicates programmatic input generation or modification that may be desired and/or required within a multiphysics framework. This work demonstrates how the development of programmatic interfaces for MC codes can support model unification and translation activities. Building on the Python application program interface (API) development of MCNPy, similar capabilities are being implemented for Serpent that are able to support model translation. In future work, the Serpent API capabilities will be made more robust and the work will be further expanded to include translations with KENO.

Navigating the spectrum of service in hospitality: hotel employees’ experiences toward guests with disabilities

ABSTRACT The primary goal of the present study was to investigate the experiences and challenges hotel employees face when interacting with guests with disabilities. The findings of this study, derived from data collected using Reddit’s Application Programming Interface, are not just significant but crucial and timely. Using Structural Topic Modelling and quadrant analysis, 12 topics emerged, each stratified based on topic frequency and sentiment scores. These topics delve into the daily interactions of hotel employees, the emotional and practical challenges they face, and the critical issues within the hotel industry, including disability accommodation, the role of service animals, and the complexities of reservation systems in accommodating special needs. This study also sheds light on the emotional toll accommodating guests with special needs entails on employees, emphasising the urgent need for better training and clear policies to support employees and guests with disabilities. The findings suggest that service interactions can be significantly improved by enhancing disability awareness and accommodation practices.

Review of emerging trends and projection of future developments in large language models research in ophthalmology

BackgroundLarge language models (LLMs) are fast emerging as potent tools in healthcare, including ophthalmology. This systematic review offers a twofold contribution: it summarises current trends in ophthalmology-related LLM research and...