Intelligent technologies in operating room nursing: A bibliometric analysis of research.

The research is devoted to solving the urgent problem of industrial production intellectualization based on the creation of an intelligent HMI display using a unified artificial immune system (UAIS) with neuro-endocrine interaction technologies for the control of complex objects and equipment diagnostics in the oil and gas industry. The developed intelligent adaptive display with a predictive alarm system based on a digital twin of the technological process allows reducing the internal and external load on the operator during the operation of high-tech equipment at oil and gas processing plants. Control of the alarm system is of key importance for ensuring safety and maintaining the efficient operation of the production process, i.e. homeostasis. Of extreme importance is the problem of adequate reaction to decision-making by the operator in case of possible failures in the technological process and equipment operation. This issue is especially acute in the automation of large-scale complex production. The control of the alarm system is dynamic and can change depending on many factors. To process multidimensional information about the state of a complex object and predict the behavior of the system, a unified artificial immune system is used in interaction with an artificial neural network to identify informative features when working with historical data, as well as endocrine regulation of homeostasis in the system. With the help of an intelligent HMI display built on these principles, the internal load on the operator is reduced, which allows for effective decision-making on process management. The results of experiments and modeling using the proposed technology and the developed intelligent HMI display on Honeywell Experion PKS equipment in the Honeywell laboratory of the School of Information Technology and Engineering, Kazakh-British Technical University in Almaty, Republic of Kazakhstan are presented. This UAIS technology can be used with industrial equipment from other vendors for SCADA (Supervisory Control and Data Acquisition) and DCS (Distributed Control System) systems with support for web technologies HTML (HyperText Markup Language) and CSS (Cascading Style Sheets) in the development of displays for workstations and operator panels.

ABSTRACT The COVID-19 pandemic triggered a swift transition to online learning, reshaping tertiary education in Ghana. This study explores how students at a Ghanaian technical university perceive online teaching effectiveness in the post-COVID era. Using a qualitative design and semi-structured interviews, it investigates students’ experiences, attitudes, and challenges before, during, and after the pandemic. Findings reveal that while online learning enhanced flexibility, digital literacy, and resource access, unstable internet, limited interaction, and unsuitability for practical courses impeded effectiveness. Guided by Social Learning and Expectation Confirmation Theories, results show that students value online education but hesitate to fully embrace it due to technological and pedagogical gaps. The study advocates for blended learning models, stronger digital infrastructure, and faculty training, emphasising equitable access, engagement strategies, and institutional support to sustain online education in Ghana’s tertiary sector.

The article examines the phenomenon of anthropomorphism in the use of artificial intelligence among students using the example of Bauman Moscow State Technical University students, since in the context of active digitalization, artificial intelligence is increasingly being integrated into students' educational and daily practices, acquiring the features of a social actor. The theoretical and methodological basis of the research is the actor-network theory of Bruno Latour, as well as modern concepts of technology anthropomorphism. The empirical base consisted of data from an online survey of 434 student AI users. The results suggest that the anthropomorphization of artificial intelligence in the student environment is situational and practice-oriented.

This article presents the results of a pedagogical experiment on the development and testing of a comprehensive model for fostering foreign language academic speaking skills in non-linguistic major students. The relevance of the study is driven by the growing demands of academic and professional communication, which require modern specialists to confidently master the skills of publicly presenting scientific concepts in a foreign language. The theoretical foundation of the research is the theory of the cognitotype, understood as a mental-linguistic schema that organizes a text according to a frame structure and reflects the genre features of an utterance. Based on this theory, the author proposes an original five-level methodology aimed at the sequential formation of key competencies: conceptual (working with and transforming scientific information), genre-compositional (assimilating structural models of scientific texts), linguistic-stylistic (mastering the linguistic means and clichés of the academic style), metacognitive (conscious use of knowledge about scientific discourse for text production), and pragmatic-communicative (effective application of the developed skills in real situations of professional communication, including the use of visual aids and argumentation). The pedagogical experiment, conducted at the Novosibirsk State Technical University (NSTU), confirmed the high effectiveness of this integrative approach. A comparative analysis of the results from the control and experimental groups showed that students trained according to the proposed model demonstrated significant improvement in their skills of composing scientific presentations in a foreign language, increased adherence to genre criteria in their speeches, and enhanced the quality of visual support usage. The most notable progress was observed in aspects such as compliance with the norms of the academic style and the logical structure of the presentation. At the same time, expanding their active vocabulary, despite the overall positive outcome, required students to undertake additional targeted work. In conclusion, it is argued that the developed model provides a targeted and effective means of preparing non-linguistic major students for successful oral scientific presentations in a foreign language.

Purpose This study examines how diversity and gender equality workers at technical universities understand their possibilities for effecting change, focusing on how discourses of diversity, intersectionality and Scandinavian exceptionalism shape equality work in practice. Design/methodology/approach The study is based on semi-structured interviews with DEI workers from technical universities in Denmark, Norway and Sweden. The interviews were analyzed by finding shared discourses among the interviewees. Findings The analysis shows that diversity and intersectionality are often conflated in DEI work at technical universities. Diversity is commonly framed as organizational value and the management of difference, while intersectionality is acknowledged in principle but remains difficult to operationalize in practice. These tensions are shaped by Scandinavian discourses of exceptionalism regarding gender and ethnic equality, which position equality as largely achieved and render structural power relations less visible. As a result, diversity and intersectionality are frequently adapted to fit meritocratic, measurable and non-disruptive organizational logics, limiting their transformative potential. Originality/value The study contributes to research on gender equality and diversity in Scandinavian technical universities by theorizing exceptionalism as a key discursive lens shaping DEI work. It highlights how exceptionalist narratives limit intersectional and transformative approaches, emphasizing the need for contextual understandings and power analysis in diversity, equity and inclusion work.

Enterococcus faecium can cause severe infections and is often resistant to the first-line antibiotic ampicillin. Consequently, clinicians usually prescribe broad-spectrum antibiotics, promoting the selection of multidrug-resistant bacteria. We investigated whether machine learning models can detect ampicillin susceptibility directly from MALDI-TOF/MS to enable earlier optimised treatment in ampicillin-susceptible E. faecium infections. Two datasets of clinical E. faecium MALDI-TOF spectra and their resistance phenotype were analysed: our own Technical University of Munich (TUM) dataset and the publicly available MS-UMG dataset. We evaluated logistic regression (LR) and Light gradient boosting machine (GBM) models and explored transferability including a target-domain-adapted external validation. Discriminatory MALDI-TOF peaks were investigated using liquid chromatography-tandem mass spectrometry (LC-MS/MS). LightGBM slightly outperformed LR in identifying ampicillin-susceptible isolates in both datasets (area under the precision-recall curve 0.907 ± 0.016 vs. 0.902 ± 0.030 for TUM; 0.902 ± 0.029 vs. 0.899 ± 0.054 for MS-UMG). Target-domain-adapted training demonstrated good transferability of LightGBM models (area under the precision-recall curve of 0.869 ± 0.013 when trained on TUM plus 30% MS-UMG, tested on the remaining 70% MS-UMG). SHapley Additive exPlanations (SHAP) analysis consistently identified a MALDI-TOF spectral peak at m/z ≈ 5091 as the most discriminative, which LC-MS/MS analysis mapped to bacteriocin T8. LightGBM and LR models can identify ampicillin-susceptible E. faecium isolates from MALDI-TOF spectra and generalise well to unseen datasets. Bacteriocin T8 serves as a key discriminatory feature associated with ampicillin resistance. While clinical implementation currently still requires confirmatory testing, the addition of larger datasets will support the development of more robust machine learning models.

Wastewater of domestic kitchen (KWW) may contain significant quantity of cooking oil residues and form problem of blocking domestic sewage pipe as cooking oil accumulated and clump inside the sewage pipe requiring cleaning and process for the removal of clumped oil residues. This study was designed to examine the efficiency of synthetic filter consisting of walnut shells in the removal of cooking oil residues from kitchen wastewater in addition to improve other wastewater variables such as pH, electric conductivity and total dissolved solids. where all collected kitchen wastewater samples were examined during November 2024 in Technical College of Al-Musaib lap. The synthetic filter was prepared by using a polyethylene plastic tube with 30 cm length and 10 cm diameter giving a volume of 2336.5 cm3. This tube had two ends where the upper end for receiving kitchen wastewater while the lower end for the draining. About 150 g of walnut shells with various sizes were placed in the synthetic filter where the upper end was linked to the pipe of kitchen sink while the lower end was connected to draining plastic container in order to examine the wastewater containing cooking oil residues. This study was proceeded at lab scale and the examination was carried out firstly via filtering wastewater using only Whatman filter paper to act as control and secondly via synthetic filter containing walnut shells which was repeated three times. A total of 2000 cm3 domestic kitchen wastewater was obtained and divided into 4 subsamples of 250 cm3 each where the sample was used for control test while the remaining 3 subsamples used for walnut shells filter. The obtained results have shown that mean pH value of control sample was 9.1 ± 0.2 while it was almost similar for the walnut shells filter samples and varied from 7.5 ± 0.2 to 7.6 ± 0.1. For wastewater electric conductivity electric conductivity ( EC), it was found that control sample had higher mean value of 3192.5 ± 317.7 µs/cm and the walnut shell filter samples have had lower mean values varying from 2425.3 ± 295.0 µs/cm to 2754.4 ± 44.55 µs/cm. Regarding wastewater total dissolved solid (TDS) content, it was recorded that control sample had much higher mean value (3072.67 ± 47.5 ppm) than those of walnut shells samples which ranged from minimum value of 1381.0 ±13.0 to maximum value of 1414.0 ± 74.0 ppm. In case of cooking oil residues, the study has recorded significantly higher mean value of 7.3 ± 0.8 gm in control wastewater sample while walnut sells filtered samples had much lower mean values varying from 2.94 ± 0.08 to 3.3 ±0.2 gm. It seems very clearly that walnut shell filter has removed significant quantity (Probability ≤ 0.05) of cooking oil residues from the kitchen wastewater.

The increasing complexity of industrial machinery necessitates a standardized approach to safety and maintenance training within technical education. Objective: This study aimed to assess students' activities regarding safety measures for inclusion in a capacity-building module designed for teaching milling machine maintenance in technical colleges. Methodology: A descriptive survey research design was adopted, gathering data from technical educators and workshop instructors in Delta State. The sample comprised 70 respondents, including 26 technical teachers and 44 workshop instructors, drawn from four government technical colleges. Data collection was facilitated through structured questionnaires, while mean, standard deviation, and t-test statistics were employed for comprehensive data analysis. Findings: The results revealed that both technical teachers and workshop instructors strongly agreed that the identified safety measures, instructional materials, and methodologies were highly appropriate for inclusion in the developed module. Furthermore, no significant differences were found in the responses of the two groups, underscoring a professional consensus on the variables required to enhance effective teaching and safe practices. Conclusion & Recommendations: The study concludes that the developed capacity-building module serves as a critical tool for improving teaching and learning outcomes in milling machine maintenance. It is recommended that technical teachers strictly adopt these safety measures in their instructional activities. Additionally, curriculum planners and the National Board for Technical Education (NBTE) should integrate the module into formal programs, supported by government and administrative resources to ensure successful implementation.

We developed an e-learning tool in augmentative and alternative communication (AAC) for medical students in Germany and tested it at Rhenish-Westphalian Technical University, Aachen. Our cross-sectional evaluation study is an innovative approach since the topic is yet to be implemented in medical education. Other AAC tools do not target medical students. Our study underlines the importance of AAC for medical doctors. Universities should include AAC in their lectures to prepare medical students for their clinical practice. The "AACHEN" tool is a first step.

The ubiquity of generative artificial intelligence (AI) in higher education presents both opportunities and significant pedagogical challenges. In graphic communication design, students increasingly rely on AI to conduct research, prepare presentations, generate design concepts, and produce videos—tasks traditionally requiring competence in computer-aided design (CAD) software. While these tools can enhance productivity, some students use them in ways that bypass learning, submitting AI-generated work without understanding underlying design principles or technical vocabulary. This study reports a qualitative case study conducted at Tamale Technical University in Northern Ghana, where 38 third-year graphic design students completed two presentation-based assessments requiring oral defense of their work. The study addressed three questions: (1) To what extent do students use generative AI in ways that hinder learning? (2) What patterns of terminology deficit, inconsistency, and anxiety emerge during oral defense? (3) How do students perceive the effectiveness of presentation-based assessment? Findings indicate that 81.6% of students used AI extensively, 89.5% could not define basic terminology from their own submissions, and 71.1% showed inconsistencies between submitted work and oral explanation. Despite initial anxiety, 71.1% reported that presentation-based assessment improved their learning. The study offers context-specific recommendations for design educators, including structuring interactive presentations, formulating probing questions, and clearly distinguishing between permitted and unethical AI use. It suggests that oral defense can reveal learning gaps, while treating claims about detecting unethical AI use cautiously and acknowledging alternative explanations such as language anxiety and prior instruction.

The article discusses the pedagogical aspects of integrating mathematical methods of signal analysis into the training of technical university students in the context of modern energy-efficient technologies and Internet of Things (IoT) systems. The relevance of the study is due to the growing demands on engineers to be able to work with large amounts of real data, perform analytical processing, and make informed engineering decisions in the field of computer-integrated heating control systems for “smart homes.” Particular attention is paid to the problem of insufficient practical orientation of teaching mathematical disciplines in technical education and the gap between theoretical knowledge and real engineering tasks. The purpose of the article is to justify and experimentally verify a step-by-step methodology for teaching student’s mathematical methods of analyzing heating parameters based on real IoT data using Fourier and wavelet transforms. The work applies competency-based, interdisciplinary, and practice-oriented approaches to teaching. The proposed methodology involves a sequential transition from the analysis of time temperature signals to spectral and time-frequency analysis, with the subsequent use of the results obtained to optimize the operation of heating systems. The article defines a list of key mathematical methods necessary for analyzing heating parameters in IoT systems and establishes their connection with specific laboratory work. A complete cycle of classes based on real or near-real data from a “smart home” is described. Considerable attention is paid to the physical and engineering interpretation of the spectral characteristics of temperature signals, which contributes to the formation of systematic engineering thinking in students. The pedagogical effectiveness of the methodology is confirmed by the results of an experimental study using quantitative indicators: levels of professional competence, results of educational testing, and assessment of practical skills. The data obtained indicate a statistically significant increase in the level of student training, an improvement in the quality of mastery of mathematical methods, and the ability to apply them to solve real engineering problems in the field of energy-efficient IoT systems.

Importance. The integration of artificial intelligence technologies into various spheres of human life is becoming increasingly active every day. These technologies have particular potential in the field of foreign language teaching. To date, a substantial body of Russian and international research on the application of artificial intelligence (AI) in various aspects of foreign language teaching has been formed. However, oral speech has been the subject of study significantly less frequently compared to written speech. This necessitates a systematic investigation into the development of students’ oral foreign language communicative skills using modern AI-based technological solutions. The development of a methodology for developing students’ oral speech skill through practice with the Janitor AI web application has not yet been the subject of individual research, which determines the relevance of this work. The aim of the study is to develop stage-bystage methodology for the development of students’ foreign language communicative skills through speech practice with artificial intelligence. The achieve this goal, the following tasks were set: a) to determine the list of students’ communicative skills to be developed; b) to develop a stage-by-stage methodology for the development of students’ oral foreign language speech skills based of additional speech practice with a chatbot powered by the Janitor AI web application; c) to test the effectiveness of the proposed methodology in an experiment. Definition of Concepts. The key concepts in this study are: “chatbot”, “web application” and “foreign language communicative skills”. Materials and Methods. The experiment involved 50 first-year students enrolled in “Linguistics” and “Translation and Translation Studies” programs (at Lipetsk State Pedagogical P. SemenovTyan-Shansky University and Lipetsk State Technical University). In the control group ( N = 25), training followed the traditional methodology. In the experimental group ( N = 25), in addition to classroom lessons, students participated in oral foreign language communication with a chatbot on the Janitor AI twice a week. The control aspects were 15 foreign language communicative skills. The SPSS Statistics software (Student’s t-test) was used for statistical analysis. Results and Discussion. The conducted study largely proved the effectiveness of the proposed author’s methodology for developing students’ foreign language oral speech skills based on additional practice with the Janitor AI web application. For all 15 controlled skills, the statistical gain in the EG exceeded that in the control group. Interesting results were obtained for the skills of starting, maintaining and ending a conversation and turning to the interlocutor for clarification. In the control group, no statistically significant increase was recorded for these skills (control group: t = 0.57 at p = 0.29 for the skill of starting, maintaining and ending a conversation and control group: t = 1.0 at p = 0.16 for the skill of turning to the interlocutor for clarification), while in the experimental group, a statistical gain was recorded for these skills (experimental group: t = 3.56 at p = 0.001 for the skill of starting, maintaining and ending a conversation and experimental group: t = 4.82 at p = 0.0001 for the skill of turning to the interlocutor for clarification respectively). This is probably due to the fact that these skills were already developed in students at an appropriate level at earlier stages of education, however, practice interacting with AI, which due to its technical features cannot judge a student for mistakes made, increases the students’ overall level of confidence during the dialogue and encourages them to use new communication strategies. The data on communicative skill of expressing one’s emotions on the topic under discussion deserves special attention. Dynamics for this skill were present in both groups, but in the EG it was twice as high as in the control group (experimental group: t = 8.3 at p = 0.0001; control group: t = 3.94 at p = 0.0004) This may also be related to the fact that students who interacted with AI generally felt more confident, which might have encouraged them to share their own emotions on the discussed topic more often. Conclusion. The novelty of the research lies in the development and approbation of an original methodology for the development of students’ oral speech skills based on additional speech practice with artificial intelligence (the Janitor AI web application). The obtained results indicate that interaction with AI, by reducing the psychological barrier and increasing students’ confidence, can contribute to more effective mastery of complex skills related to initiative, flexible switching of communicative roles, a deeper understanding of the interlocutor’s position, and expression of one’s own emotions and feelings. The proposed teaching methodology can be used in secondary schools, as well as in linguistic and non-linguistic universities.

This study aims to identify the multidimensional factors affecting employment quality of railway vocational education graduates in China. Using a mixed-methods approach, we surveyed 220 graduates from the School of Power Technology at Liuzhou Railway Vocational Technical College and conducted in-depth interviews with 15 faculty members. The research integrates collaborative governance theory, Maslow's hierarchy of needs, and social capital theory to analyze influencing factors at government, social, school, and individual levels. Results indicate that school-level factors (particularly employment guidance services, β=0.275, p<0.001) and individual-level factors (especially internship experiences, β=0.296, p<0.001) demonstrate the strongest predictive power for employment quality, with the four-level model explaining 46.7% of employment quality variance. Graduates show good performance in professional alignment (M=3.71) and formal employment arrangements (M=4.28), but face challenges in work-life balance (M=2.54-2.59) and career advancement opportunities (M=2.89). The study provides empirical evidence for enhancing railway vocational education employment support systems through multi-stakeholder collaboration. Key implications include prioritizing institutional career guidance services and structured internship programs while addressing work-life balance concerns in the railway industry.

Orientation: South African higher education institutions, including technical and vocational education and training (TVET) colleges, face persistent challenges in retaining skilled employees, with turnover intentions undermining work engagement, well-being and institutional performance. Research purpose: This study examined the factors influencing administrative staff retention at a public TVET college in Mpumalanga, South Africa. Motivation for the study: Research on employee retention in TVET colleges has largely overlooked administrative staff. Addressing this gap provides insights into an often-neglected workforce critical to institutional stability. Research design, approach and method: A quantitative, descriptive design was employed, using a stratified sample of 119 administrative employees at Nkangala TVET College. Data were collected through a structured, closed-ended questionnaire. Reliability was confirmed with Cronbach’s alpha, and Pearson’s correlations tested variable relationships. Main findings: Rewards and recognition, job security and career development significantly predict employee retention, while job satisfaction and performance appraisal were statistically insignificant. Practical/managerial implications: To retain administrative staff, TVET colleges should prioritise rewards and recognition as the strongest driver, alongside career development and job security, rather than relying on job satisfaction or performance appraisal. Contribution/value added: The study advances the understanding of administrative employee retention in South African TVET colleges and informs the development of evidence-based strategies to strengthen workforce stability in the sector.

At the current stage of the development of higher education, increasing importance is given to the development of entrepreneurial skills and innovations. At the same time, there are no established and widely applied methods for evaluating universities in terms of the entrepreneurship developed in them. Due to the identification of this problem, this article examines the policies and efforts of the European Union countries in relation to the promotion of entrepreneurship in education as a tool for achieving sustainable economic growth, striving for innovation and increasing the competencies of human resources. Various tools for improving entrepreneurial skills, improving the quality of education and thus increasing the competitiveness of EU member states are analysed. A hierarchical system and model for evaluating European Entrepreneurial Universities is proposed, which would support their effective functioning and adaptation to the conditions of a circular economy. Criteria for assessing entrepreneurial capacity are proposed, and three technical universities from Bulgaria are studied. The proposed model is verified by evaluating three leading Bulgarian universities. The results of the verification are presented.

Social-emotional abilities are essential competencies for teachers, and structured instructional interventions may support their systematic development in teacher education. Therefore, the purposes of this study were (1) to develop a learning module to enhance social-emotional abilities among pre-service teachers at Guangxi Modern Vocational and Technical College and (2) to examine the effects of implementing the developed learning module. The participants were 48 first-year students majoring in Primary Education, selected using cluster random sampling. The research instruments included a learning module based on anchored teaching, a Social and Emotional Ability Situational Test, and a student satisfaction questionnaire. The data were analyzed using descriptive statistics and paired-sample t-tests. The results of the study showed that students’ overall social-emotional abilities significantly improved after the implementation of the learning module, with notable gains in self-management, social interaction abilities, and responsible decision-making, while student satisfaction with the learning module was at a very high level. The findings contribute to teacher education by providing empirical evidence that social-emotional abilities can be effectively enhanced through a structured, module-based instructional approach.

The research aimed to create a hybrid model for assessing the carbon footprint of pilots’ education at a flight school, taking into account the level of implementation of green infrastructure by the educational institution, while excluding indirect emissions from the model. The study implemented an approach that combines fuzzy set theory with expert evaluation methods, utilizing membership functions and convolution mechanisms to incorporate subjective expert assessments into formalized numerical measures. The research was focused on two research questions: Does the proposed hybrid model allow for a practical assessment of a pilot’s carbon footprint during his training? Does the hybrid model provide the ability to automatically determine the level of carbon footprint of an aviation educational institution and generate substantiated recommendations for the strategic management of sustainable development of the educational process? The resulting model enables a quantitative assessment of individual CO2 emissions during pilot training and provides collective insights into the overall carbon footprint, accounting for the green infrastructure’s level of implementation. The hybrid model was tested and validated using real data from the Technical University of Košice (Slovakia) within the “PILOT” study program (2022–2025). The experimental calculations are based on the Viper SD4, a homogeneous aircraft type. The model is designed to account for multiple aircraft types through weighted aggregation, a feature that can be used in future institutional implementations. These recommendations are practical for managers and specialists at aviation educational institutions, environmental analysts, curriculum developers, and policymakers focused on sustainable development. At the current stage, the model primarily captures direct training-related and institution-level operational emissions, while indirect emissions were included only to a limited extent because of insufficiently available and non-systematically recorded data. Therefore, the proposed framework should be interpreted as an operational decision-support model rather than a full greenhouse gas inventory covering all indirect emission sources.

As the demand for attention to social problems, such as inequality and exclusion, grows, graduates find themselves in a work environment characterized by saturation in the public sector and limited salary conditions. Objective: To describe the labor market conditions for social work professionals- Technical University of Manabí during the academic period April-August/2025. A qualitative field methodology was used, with a non-experimental design and a descriptive scope. Data collection was carried out through semistructured interviews with 57 students, allowing direct perspectives on their training and job expectations to be obtained. The results reveal that, although graduates are committed to their vocation, they face multiple obstacles, such as lack of previous experience, saturation of the labor market in the public sector, and limited salary conditions, identifying a discrepancy between academic training and market demands. It is concluded that, despite the growing need for attention to social problems, job opportunities for social work professionals in Ecuador are limited and often do not align with their training.

The high-temperature reactor (HTR) code package, HCP, was initially codeveloped by Forschungszentrum Julich (FZJ) and RWTH Aachen University to address the need for an integral code package for HTR technologies, serving both licensing and safety evaluations. The code combines many previously independent analysis tools that were developed during HTR development in Germany, particularly at FZJ. The consolidation of the HCP code is part of the German research project KONHCP (KONsolidierung HCP) with the integration of central processes such as the reactivity control by control rods (Technical University of Munich) and the simulation of the fission products release and transport within the primary circuit in normal and accident conditions (Becker Technologies). This paper provides insights into the status of the integration in HCP of the Source Term Analysis Code system (STACY) module. In its original form, STACY is a standalone code in which users provide the data input, with several calculations needed to simulate different pebble histories. In the HCP framework, full-core calculations are now supported. For each fuel zone (node/batch), a STACY calculation is performed, with the other HCP modules automatically providing the input data required for the source term evaluation. A source term calculation for a small HTR in normal operation followed by a station blackout (SBO) using a single input in a single calculation run is presented. The simulation provides the maximum fuel temperature reached during the SBO transient as well as the fractional release of relevant nuclides. The analysis is supported by an uncertainty study. To assess the capabilities of HCP in simulating a HTR under normal and accident conditions, the HTR-10 reactor test case was selected and benchmarked on two scenarios: steady-state temperature at full power and SBO at 3 MW.