In creative industries, AIGC music technologies hold promise for advancing organizational knowledge creation and reuse, but face barriers in resource efficiency, collaborative controllability, and alignment with knowledge management goals. This study develops a hybrid Transformer-GNN framework with a dual-loop control mechanism, tailored to support KM processes: knowledge capture, transfer, and reuse. Evaluations on MAESTRO-v3, Lakh-Clean, and a custom dataset show the framework outperforms mainstream models in Pitch Accuracy (85.3%), Harmonic Coherence (0.78), and Structure Completeness (0.73), with optimized resource use (36-hour training, 18.8 GB peak GPU memory). User studies confirm a 26% reduction in manual revisions, enhancing knowledge transfer between AI and human teams. This work provides a deployable AIGC solution bridging technical innovation and organizational KM needs in creative contexts.

This article serves as an introduction to the series of articles included in the SJIAOS special issue about the comprehensive updates made to the System of National Accounts 2025 (2025 SNA) and the Integrated Balance of Payments and International Investment Position Manual, Seventh Edition (BPM7). The article provides an overview of the increasing complexity of economic activity, incorporating new forms of value creation such as digital platforms, cryptocurrencies, and intangible assets. It notes that updated standards enhance consistency and integration between national accounts and external sector statistics, facilitating more accurate measurement and analysis for policymakers. The article outlines the key themes included in the special issue such as the explicit recognition of data as a produced asset, expanded indicators for digitalization, and improved classification of financial instruments and institutional sectors. The article underscores the collaborative nature of these updates, achieved through international cooperation and extensive consultation, ensuring global relevance and acceptance. It concludes by advocating for ongoing revision and harmonization of statistical manuals to maintain relevance and support effective policy decisions.

The changeable nature of chemicals requires constant monitoring. In a self-driving laboratory (SDL), manual revision of chemicals should be replaced by automated compliance checks. Infrared spectroscopy is an SDL compatible tool that can be integrated into a robotic arm to record a spectrum for each compound scheduled to be used in a synthetic transformation. In the case of any changes happening with a chemical, the IR-spectrum would include both signals of the main compound and an impurity, resulting in failure in decoding and further synthesis. Here, a series of complicated IR-spectra of mixtures of compounds was generated to train a neural network. The mixtures were identified based on chemical intuition. An F1-score of 0.94 for purity determination and 0.97 for functional group determination was achieved for the generated spectra. The trained model was tested using IR-spectra of real mixtures of compounds. An F1-score of 0.99 for functional group determination was achieved for the real spectra and 0.95 for the purity determination task was observed. In fact, the model was able to determine whether a compound was pure enough for further use or contaminated with the identified substance and should be purified.

Pride In All Who Served (PRIDE) is the first manualized, evidence-based group intervention developed for lesbian, gay, bisexual, transgender, queer, intersex, asexual, and/or other diverse sexual/gender identities (LGBTQIA+) Veterans. PRIDE has spread to over 70 Veterans Administration Medical Centers (VAMCs) in every region of the country and continues to scale to new sites. In 2023, a PRIDE Manual Revision Team was formed to create a new version of the PRIDE Facilitation Manual (Version 3) that updated information and best practices for the LGBTQIA+ Veteran population and improved ease of facilitation for staff. The purpose of this study was to describe the iterative process used to incorporate input from over 270 Veterans, clinicians, and subject matter experts and to track modifications using the Framework for Reporting Adaptations to Evidence-Based Interventions (FRAME)-a novel application of this framework. Results revealed 76 distinct modifications to improve the feasibility, fit, satisfaction, engagement, and effectiveness of the PRIDE group for LGBTQIA+ Veterans. All modifications occurred during the "Scale-Up" phase of PRIDE, reflecting the program's current progress implementing across the national health care system. This work used a multishareholder modification approach within a large, complex health care setting and provides an example for effectively tracking changes to ensure fidelity of adaptations and promote sustainability in a rapidly expanding field. As additional interventions are disseminated to new settings, systematically tracking modifications can increase understanding of their impact on health outcomes and contribute to successful implementation and sustainment efforts.

Road transport systems are central to sustainable mobility and the energy transition because they account for a large share of final energy use and remain heavily dependent on fossil fuels. With more than 90% of transport energy still supplied by petroleum-based fuels, improving energy efficiency and reducing emissions in road networks has become a strategic priority. This review compares Australia, Hong Kong, and the United Kingdom to examine how road-design standards and emerging digital technologies can improve energy performance across planning, design, operations, and maintenance. Using Australia’s Austroads Guide to Road Design, Hong Kong’s Transport Planning and Design Manual (TPDM), and the UK’s Design Manual for Roads and Bridges (DMRB) as core reference frameworks, we apply a rubric-based document analysis that codes provisions by mechanism type (direct, indirect, or emergent), life-cycle stage, and energy relevance. The findings show that energy-relevant outcomes are embedded through different pathways: TPDM most strongly supports urban operational efficiency via coordinated/adaptive signal control and public-transport prioritization; DMRB emphasizes strategic-network flow stability and whole-life carbon governance through managed motorway operations and life-cycle assessment requirements; and Austroads provides context-sensitive, performance-based guidance that supports smoother operations and active travel, with implementation varying by jurisdiction. Building on these results, the paper proposes an AI-enabled benchmarking overlay that links manual provisions to comparable energy and carbon indicators to support cross-jurisdictional learning, investment prioritization, and future manual revisions toward safer, more efficient, and low-carbon road transport systems.

Accurate interpretation of cone-beam computed tomography (CBCT) scans is critical for oral diagnosis and treatment planning. Existing methods for automated tooth segmentation in CBCT face challenges, such as difficulties in generalizing across imaging artifacts and anatomical variations, as well as requiring manual revisions in many cases. To address these limitations, this study introduces ToothSeg, a fully automated approach for tooth instance segmentation and numbering in CBCT using deep learning and self-correction. ToothSeg combines semantic and instance segmentation into a unified method where their respective strengths are complemented. In particular, self-correction is employed when combining the segmentations, resolving merged or split teeth and determining the optimal sequence of tooth numbers for each dental arch. We conducted a comprehensive evaluation using a diverse in-house dataset (n = 1282, 25+ devices) and the publicly available ToothFairy2 challenge dataset (n = 480, 1 device), including an ablation study, a comparison to state-of-the-art methods, and an analysis of challenging cases. Compared to an optimized semantic segmentation model, including instance segmentation and self-correction consistently improved tooth segmentation (True Positive Dice: 93.6% to 94.3%) and tooth detection and numbering (multiclass instance F1: 94.2% to 95.5%). Furthermore, ToothSeg outperformed the other methods on both datasets (True Positive Dice: $\boldsymbol{\ge }$ +0.4%, multiclass instance F1: $\boldsymbol{\ge }$ +1.8%), particularly for challenging cases. This study provides a promising approach for automated tooth segmentation and numbering in CBCT, which is significant for reducing manual workload and supporting scalable, data-driven research in oral and craniofacial health. Code and models are publicly available at https://github.com/MIC-DKFZ/ToothSeg.

To evaluate whether tranexamic acid (TXA) administration reduces the prevalence of severe postpartum hemorrhage (sPPH), defined as a hemoglobin drop of ≥ 3g/dL, in primiparous women undergoing vacuum-assisted vaginal delivery (VAVD). A retrospective cohort study was conducted at a large tertiary medical center, including all primiparous women undergoing VAVD between January 2021 and December 2022. TXA (1g IV within 30min of delivery) was administered at the discretion of the attending clinician, such that some women received TXA while others did not. The primary outcome was sPPH. Secondary outcomes included postpartum transfusion of blood products, absolute decline in hemoglobin levels, and additional clinical interventions related to hemorrhage, such as manual removal of the placenta or administration of uterotonic agents for the treatment of uterine atony. Initial comparisons were performed between TXA-treated and untreated women in the overall cohort. To account for baseline differences in the likelihood of receiving TXA, propensity score matching was performed using relevant clinical predictors; neonatal birthweight, prolonged second or third stage of labor, manual uterine revision. Logistic regression models were used for adjusted analyses. During the study period, 6,580 primiparous women delivered, of whom 1,048 (15.9%) met the inclusion criteria and comprised the study cohort (N = 1,048). Of these, 383 (36.5%) received TXA, and 274 (26.1%) experienced sPPH. TXA-treated women had higher sPPH rates compared to untreated women (33.5% vs. 22.1%, p < 0.001), greater mean hemoglobin drop (2.54 ± 1.3 vs. 2.18 ± 1.3g/dL, p < 0.001), and increased postpartum blood transfusion rates (3.7% vs. 1.5%, p = 0.031). Propensity score matching (367 pairs) yielded similar results, with sPPH remaining more prevalent in the TXA group (31.7% vs. 18.8%, p < 0.001). Primiparous women undergoing VAVD are at increased risk for sPPH. Administration of 1 gram of TXA within 30min of delivery was not associated with a reduction in the prevalence of sPPH or the need for postpartum blood transfusion. Given the non-randomized design and retrospective nature of the study, it was not possible to determine whether TXA was administered prophylactically or in response to active bleeding. Nevertheless, TXA did not appear to reduce the prevalence of sPPH. Further research is needed to identify effective interventions for sPPH prevention in this high-risk population.

Background: The thinking healthy program (THP) is an evidence-based psychological intervention for perinatal depression designed for delivery by nonspecialist health workers. To ensure its relevance in Nepal, we adapted THP using the mental health Cultural Adaptation and Contextualization for Implementation (mhCACI) framework. Methods: Using mhCACI's 10-step process, we applied a participatory approach involving a multidisciplinary team to adapt both content and implementation strategies. A qualitative study nested within a pilot trial was conducted to assess feasibility and acceptability of adapted THP through in-depth interviews with perinatal women (n=20), family members (n=11) and focus group discussions with Female Community Health Volunteers (FCHVs) (n=16). Results: FCHVs were selected as delivery agents. Implementation adaptations included reducing the number of THP sessions from 16 to 8, integration of additional 2.5-day Foundational Helping Skills training and skill-based training methods. Manual revisions included simplified language, cultural idioms, visual aids and locally relevant examples. Referral pathways for gender-based violence, suicide and severe mental illness were included. The adapted THP was well received by providers and recipients. Conclusion: The adaptation demonstrates how global interventions can be contextually tailored for low-resource settings while preserving therapeutic integrity, offering a scalable model for community-based mental health care.

Students frequently deal with large volumes of study materials, which makes manual note-making and revision both difficult and time-consuming. A lack of easy access to personalized question-answer support and structured study plans further complicates exam preparation. This project, “AI-Powered Smart Study Assistant,” presents a web-based application implemented using Python, NLP, and Machine Learning to support students in their academic learning. The assistant integrates multiple critical learning features into a single platform: summarization of study materials, interactive Q&amp;A support and personalized study planning. The primary goal is to build an intelligent, lightweight, and user-friendly tool that functions as a virtual tutor, helping students to learn more efficiently and effectively.

BackgroundDue to the heavy medical workload of medical staff and the complicated procedure of medical science popularization, there are certain difficulties and resistance in medical science popularization. The main objective of this study was to evaluate the feasibility of generative artificial intelligence (AI) models in the medical science popularization of urological diseases.MethodsChatGPT 4.0 was used to generate relevant content on the pathogenesis, clinical manifestations, diagnosis and treatment of different urological diseases, and the generated content was evaluated and analyzed in multiple dimensions including scientificity, recency, comprehensiveness, understandability, conciseness and interest. The unreasonable contents of ChatGPT 4.0 were revised. Then all the revised contents were evaluated again from the six dimensions and compared with the initial evaluation.ResultsChatGPT 4.0 generated content scored high in scientificity, recency, and comprehensiveness, while scoring relatively low in understandability, conciseness, and interest. There was no significant difference in the scores of the generated content in different diseases. There was no significant difference in the scores of pathogenesis, clinical manifestations, diagnosis and treatment. Manual revision could significantly improve the comprehensiveness and conciseness of the generated content.ConclusionsGenerative AI tools such as ChatGPT may assist urologists in popularizing knowledge of urological diseases, and the generated content needs to be manually reviewed to ensure the accuracy and readability of the content.

There are very few organ transplants from brain-dead donors (BD transplants) in Japan compared with other countries. The neuroemergency section of the Japanese Society of Neurology (JSN) conducted a survey on the participation of neurologists in BD determination. The target of the survey was educational and quasi-educational institutions of the JSN. It was found that neurologists served as doctors to legally declare BD in 38% of institutions. Many institutions had neurological staffs with the skills required for BD determination, such as EEG reading (97%), neurological evaluations of BD (93%), judgment of the apnea test (75%), or interpretation of auditory brainstem responses (67%). As many as 96% of the responders considered that neurologists should participate in legal BD determination, although 20% felt that the lack of human resources prevented them from active participation. An inquiry to the Japan Organ Transplant Network revealed that neurologists served as a doctor to legally declare BD in around 80% of legal BD determination cases so far. The neuroemergency section conducted another survey regarding the duration of high-sensitivity and bipolar recording in EEG for BD determination. This was because simplification of EEG recording was planned for the revision of the official manual for legal BD determination. It was found that high-sensitivity and bipolar recording was conducted for 15 minutes or shorter in 52% of institutions. Many existing overseas as well as Japanese guidelines require 30-minute EEG recording for BD determination. However, the only basis was the reports of two cases with drug intoxication in whom EEG reappeared after 20 minutes' electrocerebral inactivity: such patients would not be candidates for donors according to the Japanese guideline. Based on the present results, the minimal required duration of (high-sensitivity and bipolar) EEG recording for legal BD determination was shortened to 15 minutes in the revised manual.

BackgroundAccreditation of laboratories offering diagnostic services improves the operation of clinical as well as research performance.ObjectiveThis case report describes the journey of Hararghe Health Research Laboratory from it’s inception to the International Organization for Standardization 15189:2012 accreditation by the Ethiopian Accreditation Service.MethodsAn external consultant conducted a baseline audit in November 2019 following the World Health Organization African Region’s Stepwise Laboratory Quality Improvement Process Towards Accreditation guideline. The follow-up internal audit was conducted in January 2021. Then, an on-site laboratory assessment was conducted by experts from Ethiopian Accreditation Service towards the end of 2022.FindingsThe Hararghe Health Research laboratory received multiple remarks during audit by external consultant and drew up a corrective action plan. Some of the actions were revision of quality policy manual, managerial and technical documents, participation in the United Kingdom National External Quality Assessment Scheme and implementation of the International Organization for Standardization 15189:2012 accreditation checklist. The internal audit revealed a total of 26 gaps in the microbiology and 16 in the molecular biology sections and these were filled by the end of April 2022.The laboratory was cited for nine minor non-conformities during an assessment by experts from the Ethiopian Accreditation Service. The laboratory developed a corrective action plan, cleared non-conformities by end of February 2023 and received the accreditation certificate on 3rd May 2023. The laboratory’s accreditation achievement in less than five years is a significant milestone and serves as a model for other institutions to achieve it in a similar time frame.

Integrating deep learning (DL) for auto-contouring has significantly improved organ-at-risk (OAR) delineation in adult radiotherapy. However, its application in paediatric radiotherapy remains limited. This study evaluates DL-based auto-contouring of OARs, followed by manual revisions, for paediatric flank irradiation, focusing on delineation time, accuracy, and inter-observer variability (IOV). Twelve paediatric radiation oncologists from nine countries affiliated with the SIOP Renal Tumour Study Group participated in a two-day workshop. Participants were randomly divided into two groups: one performed manual delineation first, followed by DL-based revision, while the other group performed in reverse order. Eight thoracoabdominal OARs were delineated on non-contrast CTs of renal tumour patients (ages 1-6). DL-based contours were generated using a model for paediatric abdominal cases. Delineation time was recorded, accuracy and IOV were assessed using the Dice similarity coefficient (DSC), 95th percentile Hausdorff distance, mean surface distance against a STAPLE consensus (threshold=0.95), and an expert reference. In total, 122 manual delineations and 254 DL-based revisions were collected. DL-based auto-contouring reduced delineation time by 59%, from 25.5 to 10.2min. The mean DSC of all eight OARs improved from 0.91 to 0.97 using STAPLE reference and from 0.89 to 0.93 using expert reference. The pancreas exhibited the largest gain, with mean DSC increases ranging from 0.18 to 0.25. Delineation accuracy was significantly improved for seven OARs (p<0.05), while IOV significantly decreased for the pancreas and heart in both references (p<0.05). Manually revising DL-based auto-contouring reduces delineation time, enhances accuracy, and reduces inter-observer variability in paediatric CT-based OAR delineation.

SummaryCell-free protein synthesis (CFPS) is a versatile tool for rapid biological prototyping. However, exploring the large number of component combinations is a very time-consuming process. Active learning (AL) is known to reduce the number of experiments required, but is rarely integrated into routine laboratory workflows. To address this, we developed a fully automated Design-Build-Test-Learn (DBTL) pipeline that streamlines this optimization process with an improved AL strategy that selects informative and diverse experimental conditions. The Design phase was created entirely using ChatGPT-4 without manual code revisions, dramatically reducing coding time. This pipeline was implemented in a modular way within the Galaxy platform, following the Findable-Accessible-Interoperable-Reusable (FAIR) principles. When applied to the optimization of colicin M and E1 in both Escherichia coli and HeLa-based CFPS systems, a 2- to 9-fold increase in yield was achieved in just four cycles. This framework enables reliable, automated workflows for routine synthetic biology.

This study aims to identify the types of paraphrases generated by the artificial intelligence tool QuillBot in Indonesian and to analyze its effectiveness in reducing plagiarism in academic writing. The research employed a descriptive qualitative method with documentation and data analysis techniques, based on Longacre’s (1995) paraphrase theory. The data were taken from 18 journal abstracts covering topics in linguistics, education, and artificial intelligence. Additionally, the abstracts were sourced from various study programs within the Faculty of Social Humanities at Universitas Bina Darma Palembang to ensure linguistic variety and enrich the analysis. All abstracts were paraphrased using QuillBot Premium and analyzed with Turnitin. The results show that from 123 sentences, QuillBot generated six of seven paraphrase types: Equivalent (74), Negative Antonym (2), Generic-to-Specific (1), Amplification (14), Specific-to-Generic (12), and Contraction (20). Furthermore, QuillBot was found effective in reducing plagiarism, with an average decrease of 28.89% based on Turnitin results. The greater the initial similarity, the higher the reduction after paraphrasing. As supporting data, the perceptions of five final-year students were gathered, and respondents stated that QuillBot helped simplify the paraphrasing process and reduce similarity scores, although manual revision was still needed to ensure accuracy and naturalness of meaning.

In recent years, educational technology has transformed how students develop academic writing skills. Digital tools not only improve efficiency but also enhance learning through interactive and adaptive feedback. Among these innovations, artificial intelligence–based writing assistants provide immediate feedback, linguistic accuracy, and stylistic improvement. Grammarly, in particular, has gained attention in higher education for its accessibility, user-friendly interface, and ability to address grammar, vocabulary, and coherence. Its widespread use among non-native English-speaking students underscores the need to understand how learners perceive and utilize such tools.This study examines English Education students’ perceptions of Grammarly at Universitas 17 Agustus 1945 Banyuwangi as a tool to enhance academic writing. Using a qualitative descriptive approach and purposive sampling, ten students participated in semi-structured interviews. The research explored usage frequency, perceived benefits, and limitations.Results indicate that students generally view Grammarly positively, especially for improving grammar, sentence structure, and vocabulary. Features like real-time feedback, tone detection, and clarity suggestions are valued for aiding learning. However, concerns include occasional contextual inaccuracies and the risk of overreliance, potentially reducing self-editing and critical thinking skills.The study concludes that Grammarly should supplement, not replace, teacher feedback or manual revision, and recommends further research on its long-term impact and integration with other writing resources.

Dictionaries are essential in natural language processing and provide significant value across tasks; however, their construction and maintenance are expensive. Leveraging manual revision histories to suggest automatic corrections for unedited terms offers a promising solution to enhance quality while reducing costs. This study proposes a method for automatically correcting metadata in a large-scale medical dictionary containing more than 500,000 terms. By utilizing large language models that excel in zero-shot settings, the system estimates the dictionary information without task-specific configurations. This method was demonstrated through experiments on variations in gene biomarker expression, a task that requires specialized medical knowledge. The results indicate that this approach can significantly reduce the dictionary maintenance burden.

In this study, the implementation and meaning of Neulbeom Schools, as experienced by key stakeholders—administrators, instructors, and parents—were analyzed using a phenomenological approach. Participants included four administrators working at Neulbeom Schools in Daejeon Metropolitan City, Sejong Special Self-Governing City, and Chungcheongnam-do, three program instructors, and two parents. The analysis yielded three central themes: the operating system and working conditions of Neulbeom Schools, the quality of care and education provided, and the limits and tensions surrounding the acceptance and implementation of the system. A total of eleven subthemes were identified across these categories. Based on the findings, the following conclusions were drawn. First, irrationalities and imbalances in role allocation were identified within the human resource management system. Second, parents tended to expect care and child-rearing support from Neulbeom Schools rather than focusing on their educational function. Third, the division of roles and administrative responsibilities between Neulbeom Schools and elementary schools was found to be ambiguous. In light of these conclusions, several recommendations are proposed, including the establishment of a professional human resource training system, the revision of operational manuals and support regulations, the securing of institutional independence, the redesign of administrative structures, and the need for follow-up research.

The article presents the results of clinical and statistical analysis and identifies relevant aspects of adenomyosis in women of reproductive age.The objective: to study the clinical and statistical analysis of women of reproductive age with adenomyosis and its impact on the development of the disease.Materials and methods. A clinical and statistical analysis was conducted on 90 women of reproductive age with adenomyosis (main group) and 30 gynecologically healthy women of reproductive age (control group). A numerical rating scale was used to determine the intensity of pain.Results. The conducted clinical and statistical analysis of the examined groups allowed to determine possible predictors of adenomyosis in women of reproductive age. 88.9% of women have a burdened heredity. Concomitant extragenital pathology: respiratory system diseases were found in 46.7% of women, gastrointestinal tract diseases – in 42.2%. Patients of the main group had menstrual cycle disorders (100%) and decreased generative and sexual function (dyspareunia 65.5%). Burdened gynecological history included: inflammatory processes of the uterus and appendages – 52.2% of cases, pathology of the cervix – 46.7%, ovarian cysts – 15.6%; artificial abortions – 77.8%, spontaneous abortion – 20.0%, ectopic pregnancy – 8.9%. Intrauterine surgical interventions were performed: curettage of the uterine cavity – in 17.8% of women, hysteroresectoscopy – 21.1%, pathological childbirth (manual revision of the uterine walls, obstetric forceps, maternal birth trauma) – 25.6%, uterine surgery (cesarean section, suturing of the perforated hole) – 30%, appendage surgery – in 18.9%, cervical surgery – in 35.6%.Conclusions. Severe gynecological heredity, extragenital pathology, impaired immune mechanisms, hormonal imbalance, inflammatory diseases of the female genital organs, abortions, pathological childbirth, and intrauterine surgeries create a favorable background for the development and progression of adenomyosis.

ABSTRACTIn the rapidly evolving field of education, the timely updating of course content is an essential task, especially in disciplines such as computer science and artificial intelligence, where knowledge evolves swiftly. However, traditional methods of course revision are labor‐intensive, time‐consuming, and struggle to keep up with the latest academic developments and industry practices. Moreover, directly utilizing content generated by agent tools often results in a disorganized structure and questionable quality. To address these challenges, this paper proposes a dynamic updating method of course content based on the collaboration of multiple intelligent agents. By integrating multiple intelligent agents and knowledge graph technology, this method enables automated retrieval, filtering, and integration of the latest knowledge points, thereby generating a structured, accurate, and comprehensive course content structure. For engineering education, this method can help educators quickly adapt to the needs of technological development, which not only effectively alleviates the burden on educators but also significantly enhances the efficiency and quality of course updating, offering a novel solution for educational content management and promoting the development of engineering education towards intelligence and dynamism. Experimental results indicate that compared to traditional manual revision and direct utilization of single agent, this method maintains high accuracy (over 95%) and high coverage (over 96%), while reducing the knowledge point update cycle to less than 0.5 h and controlling the task execution cycle within 1.5 h.