The intelligent social event observer: Multi-source continuous event integration, discovery, and induction with LLMs

Exosomal biomarkers in cancer: Insights from Multi-OMIC approaches.

Radiotherapy, a cornerstone of cancer treatment, is critically limited by tumor radioresistance and off-target toxicity. Lanthanide-based nanomaterials (Ln-NPs) have recently emerged as a versatile and promising class of theranostic radiosensitizers to overcome these hurdles. This review comprehensively outlines the state-of-the-art in Ln-NP-enabled radiotherapy, beginning with their fundamental physicochemical properties and synthesis and then delving into the multi-level mechanisms of radiosensitization, including high-Z element-mediated physical dose amplification, catalytic generation of reactive oxygen species (ROS), and disruption of DNA damage repair pathways. The unique capacity of certain Ln-NPs to serve as MRI contrast agents is highlighted as the foundation for image-guided, dose-painting radiotherapy. We critically summarize the preclinical and clinical progress of representative systems, benchmarking them against other high-Z nanomaterials. Finally, this work discusses the ongoing challenges, such as biocompatibility, targeted delivery, and regulatory hurdles, and envisages future directions, including combinatorial strategies with immunotherapy and the development of personalized nanotheranostic paradigms. Through this synthesis, this review aims to provide a clear roadmap for the continued development and clinical integration of lanthanide nanotheranostics in oncology.

Background: Effective reservoir management require integrating multiple geological and technological parameters to optimize decision-making. Traditional approaches, while useful, often struggle with the complexity and volume of reservoir data, highlighting the need for more advanced analytical methods. Aim: This article examines various methodologies for data-driven comparative analysis and its application for selection of drilling points for production and water flooding operations. Materials and methods: Advanced computational techniques, including machine learning applications, are explored for their role in improving evaluation accuracy. Additionally, this study compares different comparative analysis approaches used in the industry, highlighting their strengths, limitations, and adaptability to various geological conditions. Results: The synthesis of recent research demonstrates the potential of multimodal analysis approaches to enhance predictive accuracy and decision-making efficiency. Comparative evaluations reveal that while traditional methods remain valuable in certain contexts, data-driven techniques provide superior adaptability and scalability. Future advancements are identified in integrating real-time data streams and cross-disciplinary modeling. Conclusion: Data-driven comparative analysis, particularly when supported by machine learning, shows significant promise in improving reservoir management practices. By enabling more accurate drilling point selection and more effective water flooding operations, these approaches can drive both economic and operational efficiency. The study emphasizes the importance of continuous innovation and integration of computational tools to address the evolving complexity of reservoir systems.

This study investigated the effectiveness of the iSCan learning model combined with Mobile Seamless Learning (MSL) in improving students’ scientific thinking skills on static and dynamic fluid topics. A quantitative approach using a quasi-experimental pretest–posttest control group design was applied. The study involved 102 eleventh-grade students at SMA Muhammadiyah 10 GKB, Gresik, who were randomly assigned to an experimental group (n = 51) and a control group (n = 51). The experimental group received instruction through the iSCan–MSL model across four seamless learning phases: informal, formal 1, formal 2, and combined formal–informal. The control group followed conventional instruction. Data were collected using a validated two-tier scientific thinking test, which had a content validity score of 90% and a reliability coefficient of Cronbach’s α = 0.869. The results showed a statistically significant improvement in scientific thinking skills for students in the iSCan–MSL group compared to the control group. These findings suggest that the continuous integration of value-based learning and mobile technology can effectively strengthen scientific reasoning and enhance student engagement in 21st-century physics learning.

Legacy software delivery practices pose difficulties in regulated industries such as financial services, healthcare, and government, where organizations must comply with governance requirements throughout the software delivery lifecycle while protecting sensitive information. Most common continuous integration and continuous delivery CI/CD pipelines lack auditability and traceability and include manual processes, which are a bottleneck in the release process to production systems. Environment inconsistencies lead to deployment failures and configuration drift across infrastructure tiers. The article presents an architectural framework combining Kubernetes orchestration with GitOps methodology for regulated enterprise environments. Declarative configuration management establishes Git repositories as authoritative sources for infrastructure state. Pull-based deployment models eliminate direct pipeline access to production clusters. Zero-trust security principles ensure continuous verification of access requests regardless of network origin. Policy-driven automation embeds compliance validation throughout the build and deployment stages. Admission controllers enforce governance rules at deployment time without manual intervention. Comprehensive observability mechanisms provide audit capabilities satisfying regulatory examination requirements. The framework enables organizations to accelerate deployment frequency while preserving rigorous change management controls. Separation of duties occurs naturally through pull request approval workflows. The architectural patterns presented address fundamental gaps in traditional CI/CD implementations for highly regulated operational contexts.

Abstract Background Minimally invasive techniques for minor liver resections have advanced significantly, with robotic and laparoscopic approaches increasingly replacing traditional open surgery. While these methods are thought to improve patient outcomes and reduce healthcare costs, direct comparative data remain limited. Method A prospective dataset of 141 patients undergoing robotic (n = 47), laparoscopic (n = 47), and open (n = 47) minor liver resections at Southampton General Hospital (2022–2024) was analysed. Outcome measures included total hospital stay, HDU stay, intraoperative blood loss, transfusion requirement, readmission, grade ≥3 complications, and conversions to open surgery. Statistical analysis was performed using SPSS (Kruskal-Wallis and Chi-square tests). Cost effectiveness was evaluated based on per-procedure savings. Results Median total length of stay was significantly lower in the robotic and laparoscopic groups (3 days) compared to open surgery (5 days, p = .001). Robotic and laparoscopic procedures were associated with significantly lower intraoperative blood loss (100 mL) compared to open (525 mL, p < .001). Conversion to open surgery was significantly more common in the laparoscopic group (10 cases) than in the robotic group (2 cases, p = .013). Differences in transfusion rates (p = .091), readmission (p = .919), and major complications (p = .179) were not statistically significant. Estimated cost savings per procedure favoured robotic over open (£1374) and laparoscopic approaches (£488). Conclusions Robotic minor liver surgery demonstrated improved clinical outcomes and greater cost effectiveness compared to open and laparoscopic approaches. These findings support the continued integration of robotic surgery in hepatobiliary practice.

The rapid advancement of artificial intelligence (AI) has transformed teaching and learning practices in higher education. This study examines the impact of Artificial Intelligence (AI) on Lecturers’ Teaching Practices in the Fundamentals of Marketing Subject. Using a convenience sample of 23 lecturers, data were collected through an online questionnaire and analyzed using descriptive statistics. Findings indicate that lecturers generally perceive AI as highly useful, easy to use, and effective in improving teaching efficiency and student engagement. Most respondents expressed positive attitudes toward continued integration of AI into their teaching practices. The study contributes empirical insights into AI adoption in marketing education and offers recommendations for enhancing lecturers’ AI proficiency and classroom integration.

Cognitive flexibility relies on the continuous accumulation and integration of sensory evidence to guide adaptive behavior. In natural environments, behaviorally relevant information unfolds sequentially over time and is constantly evaluated against prior knowledge, task rules, and current demands. Integration of these inputs poses a computational challenge: How is temporally unfolding, predictive information integrated into a stable representation, while preserving the discriminability and flexibility to map individual stimuli to competing context-specific actions? Using large-scale human intracranial electroencephalography, we assessed how neural population activity integrates behaviorally relevant information across multiple sensory events that sequentially unfold over time and jointly determine the current context. The results uncover that the population geometry supports the emergence of conjunctive coding subspaces that integrate prior information with current sensory evidence and jointly define the temporal context that mediates behavioral benefits. Evidence accumulation diversifies the population responses distributed across the cortex, increasing the representational space that embeds context-dependent stimulus-action mappings. Hence, context-dependent sensory coding might constitute the neural basis underlying adaptive human behavior. In sum, these results demonstrate how neural population activity balances integrating predictive information with preserving stimulus discriminability to enable flexibility, while minimizing interference.

Enterprise digital transformation initiatives consistently prioritize speed and scalability while inadvertently creating security vulnerabilities that propagate through automated delivery pipelines. Traditional DevOps practices accelerate software releases but frequently defer security validation until late deployment stages, generating costly remediation cycles and compliance risks, particularly acute in regulated sectors. This article addresses this fundamental disconnect by presenting a comprehensive framework that embeds DevSecOps principles directly within cloud-native modernization architectures. Through practical implementation across mission-critical healthcare applications migrating to containerized infrastructure, the article demonstrates how automated security scanning, policy enforcement, and continuous compliance monitoring can be orchestrated within declarative CI/CD workflows using GitOps methodologies and Kubernetes orchestration. The article integrates four architectural layers encompassing source control governance, continuous integration with embedded security testing, automated deployment with rollback capabilities, and runtime compliance monitoring. Field validation spanning multiple production systems over an extended observation period revealed substantial improvements in deployment velocity, vulnerability prevention, and operational reliability. Comparative analysis against conventional CI/CD implementations highlighted the framework's effectiveness in eliminating critical security defects while accelerating release cadence. Cultural factors emerged as critical success determinants, with cross-functional collaboration between security and development teams proving essential for sustained improvement. The article establishes that security-driven modernization transforms enterprise delivery from reactive compliance toward proactive assurance, offering regulated industries a reproducible blueprint for achieving secure agility in cloud-native environments.

Federally regulated biomedical research institutions face persistent challenges when implementing modern software delivery pipelines due to stringent compliance frameworks that traditional DevOps methodologies fail to address adequately. The architectural gap between agile deployment practices and federal regulatory requirements creates operational bottlenecks where manual compliance verification processes delay software releases. Contemporary CI/CD systems lack embedded mechanisms for cryptographic provenance tracking, policy automation, and tamper-evident audit trail generation required by federal oversight bodies. The novel compliance-aware pipeline architecture presented in this work integrates containerization technology with distributed version control systems while embedding policy enforcement at each deployment stage, representing a significant advancement over existing approaches that treat compliance as an external validation layer. Cryptographic chains of custody establish verifiable artifact lineage from source commits through production deployment. Multi-tier promotion workflows mirror environment segregation mandates while automated policy gates validate compliance requirements before permitting environment transitions. Implementation strategies address build reproducibility through immutable container images, content-addressable artifact storage, and role-based access controls enforcing segregation of duties. Evaluation across operational biomedical systems demonstrates that properly architected pipelines achieve deployment efficiency improvements while maintaining rigorous audit quality standards. This framework establishes transferable architectural patterns enabling research agencies to modernize software delivery infrastructure without compromising governance structures demanded by regulatory frameworks, bridging a critical gap that has prevented federal institutions from adopting continuous delivery practices while satisfying comprehensive auditability obligations.

Risk management is a cornerstone of patient safety in anesthesia, where complex procedures, physiological variability, and time-critical decisions create inherent risks. Effective risk control requires a structured approach that spans all phases of anesthesia care. The present review systematically examined the five stages of risk management, which are risk awareness, risk identification, risk assessment, risk management, and risk evaluation, within anesthesia practice. A comprehensive literature search was conducted in PubMed, Scopus, and Web of Science for articles published between January 2000 and December 2024, using the keywords “anesthesia”, “risk management”, “patient safety”, and “perioperative”. Inclusion criteria were peer-reviewed studies, guidelines, and systematic reviews addressing any stage of anesthesia risk management. Findings highlight that anesthesia risks arise from human factors, equipment/technical failures, environmental conditions, and hospital system issues. Successful strategies include preoperative briefings, structured checklists, incident reporting, root cause analysis, and implementation of validated risk matrices. Continuous feedback and integration with hospital safety systems enhance sustainability. For anesthesia providers, applying a stage-based model promotes early hazard recognition, prioritization of high-impact risks, and targeted preventive measures. Embedding these processes into daily practice not only reduces adverse events but also fosters a culture of safety, improving both patient outcomes and team performance.

DevOps is a paradigm shift in software development today, aimed at the rapid delivery of software through a task-oriented approach to work and the integration of development and operations teams. Software testing is one of the most important and challenging steps in the DevOps pipeline because it should be effective and stable without being slow. To improve the process of software testing among DevOps professionals, this paper suggests a semi-automatic testing approach as a more viable solution between the extremes. The semi-automated methodology involves automation of processes that are tedious and time-consuming (such as unit testing, regression testing, and continuous integration), but human inspection in processes that require critical thinking, business domain knowledge, and exploratory insight (such as UI validation, business logic, and end-user behavior simulation). The combination creates the ability to do faster feedback loops, reduced test maintaining burden, improved test coverage, and quality assurance (QA) in general. The study utilizes a combination of case study evaluation, the implementation of the tools, and the reaction of the practitioner to the survey to gauge the performance of semi-automation in the DevOps context. The core performance indicators assessment of performance includes reduced test cycle time, defect leakage rate, productivity of the team, and the rate of deployment. The findings indicate that semi-automatic solutions result in radical improvements of test efficiency, scalability, and flexibility, especially in groups with dynamic codebases and limited automation budgets. The proposed study will integrate an affordable yet quality testing model that is dynamic enough to keep up with the DevOps and the advancement of the ideal balance between human judgment and automation. The method described here does not only enhance the effect of testing as an undertaking but also does the more universal DevOps objectives of quicker delivery, quality, and better teamwork.

This research examines the influence of business intelligence on strategic analyzer behavior through an empirical study conducted among high-tech companies. The survey, carried out with 275 firms, was analyzed using SPSS and AMOS 22 software. The results show that the structured and systematic adoption of business intelligence practices constitutes a key lever for developing strategic analyzer behavior. By providing companies with tools for analyzing and interpreting information, business intelligence enables them to anticipate changes and respond effectively to shifts in their competitive environment. The study highlights that the continuous and organized integration of business intelligence promotes a more refined, predictive, and dynamic understanding of market challenges, thereby strengthening companies’ ability to adapt and strategically position themselves in an ever-evolving context.

This article analyzes the historical development of the organ as a complex technological system whose evolution reflects successive transformations in scientific knowledge, materials, and engineering practices from antiquity to the digital age. The study employs a combined methodology of historical textual analysis, examination of archaeological and material evidence, and interpretation through the technological-systems approach, supported by modern acoustical and engineering research. The results demonstrate that each major stage in organ history corresponds to a distinct scientific and technological environment. The Hellenistic hydraulis reveals early applications of pneumatic and hydraulic regulation grounded in the mechanical theories of Ctesibius and described by Vitruvius, while Roman and Byzantine adaptations illustrate how metalworking, woodworking, and empirical acoustics shaped early organ design. The medieval period shows a shift toward large wooden structures, the refinement of tin-lead alloys, and the emergence of elaborate tracker mechanisms suited to the architectural acoustics of Romanesque and Gothic churches. Renaissance and early modern developments link organ building to the rise of mathematical acoustics, with theorists such as Zarlino and Mersenne providing conceptual explanations for pitch, scaling, and harmonic structure that informed workshop practice. During the Industrial Revolution, machine tools, standardized materials, and pneumatic assist devices enabled unprecedented increases in size, reliability, and mechanical complexity, while nineteenth-century acoustical science, particularly the work of Helmholtz, clarified the physical basis of pipe tone. Electrification in the late nineteenth and twentieth centuries reshaped organ control systems, separating console and pipes, introducing electromagnetic actions, and integrating the organ into broader electromechanical networks. The second half of the twentieth century and the early twenty-first century reveal the growing influence of electronics, digital sampling, physical modeling, CNC manufacturing, and hybrid designs that combine traditional pipes with computational sound generation. Taken together, these findings show that the organ evolved not through the replacement of old technologies by new ones but through their continuous accumulation, reinterpretation, and integration within changing scientific paradigms. The article concludes that the organ’s two-millennia history offers a distinctive case study for understanding long-term interactions between scientific knowledge, material innovation, and technological continuity.

This systematic literature review examines teachers' pedagogical competence in designing deep learning and Higher-Order Thinking Skills (HOTS) assessment in elementary schools. The study analyzed 27 peer-reviewed articles published between 2020 and 2025, focusing on three primary dimensions: pedagogical competence in HOTS-based learning design, assessment development, and impact on student learning outcomes. The findings reveal that elementary school teachers demonstrate moderate to good pedagogical competence in designing HOTS-based learning, with strengths in lesson planning and implementation but challenges in assessment development and consistent application. Key factors influencing successful HOTS implementation include professional learning communities, continuous training programs, technology integration, and institutional readiness. The review identifies significant gaps in teachers' ability to develop creative and evaluative assessment instruments, with most teachers relying on analytical-level questions. Furthermore, the integration of HOTS assessment with deep learning approaches shows positive impacts on students' critical thinking skills and learning outcomes. The study recommends establishing systematic professional development programs, strengthening teacher learning communities, developing comprehensive HOTS assessment instruments, and fostering collaboration between stakeholders. This review contributes to understanding the current state of pedagogical competence in HOTS implementation and provides evidence-based recommendations for improving elementary education quality through enhanced teacher competence in deep learning and assessment practices.

Digital Resilience and Public Safety: Why Secure Automation Matters Beyond IT

The convergence of Artificial Intelligence (AI) with DevOps, DataOps, and MLOps has transformed the software development lifecycle, enabling scalable, automated, and intelligent systems. This paper explores the transition from traditional DevOps to MLOps, emphasizing the integration of machine learning workflows into continuous integration, deployment, and training pipelines. We present a practical framework for implementing MLOps using tools such as MLflow, Airflow, and Kubernetes, and address challenges like overfitting, underfitting, and model drift. The proposed architecture leverages Docker and ONNX for model packaging and deployment, ensuring reproducibility and cross-platform compatibility. Through real-world examples and pipeline automation strategies, we demonstrate how MLOps enhances model reliability, governance, and performance monitoring in dynamic environments. This study contributes to the growing body of knowledge on AI-driven DevOps by offering actionable insights for researchers and practitioners aiming to build robust ML systems. Build an Apache Airflow pipeline to load, train, and evaluate a ML model, store it, and use it for inferencing by deploying the model with a sleek Streamlit UI, Docker, and auto-scale it with Kubernetes as container orchestration tool. Techniques for implementing and automating continuous integration (CI), continuous delivery (CD), and continuous training (CT) for machine learning (ML) systems. This document applies primarily to predictive AI systems.

The increasing demand for software systems that are continuously updated requires deployment processes that are both automated and reliable. Manual deployment often causes delays, configuration inconsistencies, and downtime, especially in dynamic environments where changes occur frequently. To address these challenges, this study focuses on the implementation of a Continuous Integration and Continuous Deployment (CI/CD) pipeline that automates the entire build and release process for the KUPI System Information. The main objective of this research is to design and evaluate a self-hosted CI/CD architecture that integrates Docker, GitHub Actions, and Traefik to achieve a consistent, efficient, and secure deployment workflow. Unlike most previous studies that automate existing systems, this research applies CI/CD from the initial stage of system development, making automation a core architectural principle. The study employs an experimental method combined with iterative validation to test the performance, reliability, and resource utilization of the implemented pipeline. The results show that the automated pipeline successfully reduced the average deployment time to less than ten minutes, eliminated manual configuration errors, and maintained stable operation under continuous deployment cycles. Resource monitoring confirmed that CPU and memory consumption remained within optimal thresholds, ensuring system stability. In conclusion, this research demonstrates that implementing CI/CD from the early stages of system development can significantly enhance deployment speed, reliability, and operational consistency. This novelty provides a practical and reproducible model for small to medium-scale systems adopting DevOps practices with open-source tools while maintaining full control over infrastructure and security.

Regression testing is necessary in modern software development with continuous integration and delivery, but running it in full after every change is often too expensive. Test case prioritisation (TCP) can aid this process by prioritising test cases that reveal faults earliest. Still, current TCP approaches focus on single information sources (coverage, change history, and/or past faults) and do not model semantic relationships across software artefacts. Meanwhile, current deep learning-based methods still suffer from cross-project generalisation and misinterpretation. These gaps can be alleviated with BugPrioritizeAI, an explainable, multimodal TCP framework we propose that jointly uses bug reports, source code changes, and test metadata to rank test cases. At the core of BugTestRankNet is a component responsible for generating a priority score that allows for quicker fault detection. BugPrioritizeAI is an AI-enhanced approach to bug triage that operates at the bug report level and ranks potentially buggy files in the bug repo using textual features. This framework reduces testing overhead and provides SHAP-based explanations, giving developers insight into the reasons for prioritising individual test cases.