BackgroundPopulation aging has emerged as a global concern, older adults’ ability to access health knowledge and manage their well-being impacts their health outcomes. In the artificial intelligence era, generative artificial intelligence (GenAI) videos hold promise for enhancing geriatric health management. However, their potential and the needs of older adults in using GenAI videos for health-related purposes deserve a more in-depth investigation.ObjectiveThis study aims to explore the application potential and multifaceted needs of older adults in using GenAI videos for health information acquisition and management, while providing actionable recommendations for future aging-friendly GenAI video tools.MethodsA qualitative approach was adopted. Twenty older adults (aged ≥60 y) with basic digital literacy were recruited from communities for participation in iterative GenAI video workshops. Semistructured interviews were conducted. Thematic analysis, following Braun and Clarke’s 6-phase reflexive framework, was used to identify key themes from interview transcripts.ResultsOur results have identified a 3-layer hierarchical structure of needs when older adults interact with GenAI videos. The first layer is technical accessibility, which suggests the direct barriers preventing them from operating GenAI tools independently. The second layer is age-friendly design, which depicts the needs of age-friendly design adaptations, which can help older adults to better use GenAI features. The final layer is integration with professional resources, highlighting the need for integrating professional medical information resources with GenAI tools, so that the GenAI videos can be more appropriately used for health management purposes. By triangulating the core needs of older adults, the capabilities of GenAI, and their remaining gaps, we have found that the existing gaps limit the extent to which the health needs of older adults are met, and also inversely restrain the acceptance and use of GenAI videos by older adults.ConclusionsThis study explored the potential and gaps of using GenAI videos in older adults’ health management. GenAI videos are prominent for self-management of health, but our triangular model reveals coexisting opportunities and challenges. While the core needs drive the development directions of GenAI video technologies, our study suggests that GenAI health videos can benefit from the improvements in technical capabilities, service innovation, localization, and integration of health resources.

The rapid development of big data is profoundly reshaping the insurance industry, while traditional actuarial methods face obvious limitations in dealing with high-dimensional, nonlinear, and unstructured data. This paper systematically reviews the current application status of machine learning (ML) in the insurance industry under the background of big data, focusing on four key areas: fraud detection, risk assessment and management, claims issues, and customer segmentation. Integrated empirical research shows that ML methods generally outperform traditional statistical models in terms of prediction accuracy, operational efficiency, and decision support. Meanwhile, this paper identifies four types of real-world challenges that restrict the responsible deployment of machine learning: data quality issues, regulatory and ethical constraints, privacy and security concerns, as well as organizational and operational barriers. Based on the literature review, this paper holds that machine learning does not replace actuarial science, but rather substantially enhances its predictive ability, operational efficiency and strategic decision-making level. Responsible technology application must simultaneously promote the construction of transparency, fairness and compliance. This paper constructs an analytical framework that integrates technical capabilities with practical constraints, providing a structured reference for subsequent research and practical guidance for insurance companies and policymakers to promote the sustainable and compliant application of machine learning.

IntroductionWe report on using electronic health records (EHRs) and other health information technology (IT) (eg, REDCap, Excel, and population-health tools) for tracking patients and managing interventions to improve colorectal screening (CRC) among primary care practices who participated in the National Cancer Institute’s Accelerating Colorectal Cancer Screening and Follow-up through Implementation Science (ACCSIS) program.MethodsWe conducted semi-structured, recorded interviews with staff from 7 ACCSIS Research Projects (RPs). Using the interview notes, we conducted content analysis to report on the characteristics of the EHR systems and health IT, and thematic analysis to identify key concepts related to the ability to capture and monitor data for CRC screening.ResultsRPs used different data capture models to support EHRs and health IT: (1) centralized data capture models from projects or third-party services; or (2) direct data capture models, relying on features and functions within commercial EHRs. Respondents reported challenges to using EHRs and health IT, including generating patient reports to track interventions, working across EHR and research platforms because of lack of interoperability, and training for clinic staff on EHR and research platforms.DiscussionRPs would benefit from more streamlined data capture and reporting for managing CRC screening in primary care. Efforts reportedly fell onto staff who could have benefited from training around data handling and EHR-specific navigation.ConclusionsRPs experienced challenges in leveraging data capture models for EHR and health IT data management. Our research calls for technical capabilities that promote more efficient data capture and reporting, as well as greater capacity building among clinic staff.

This study evaluates network security vulnerabilities and countermeasures in Tanzania’s banking sector. Using a mixed-methods approach, data were collected from 15 commercial banks through structured interviews with ICT managers, document reviews of regulatory compliance, and vulnerability scanning with Nessus. The assessment focused on five criteria: risk exposure, technical capabilities, regulatory compliance, business impact, and adaptability to evolving threats. Results show that phishing, credential theft, and malware are the most common types of attacks, with only 40% of sampled banks conducting regular penetration tests and 30% utilising multifactor authentication. Compliance with PCI DSS and Bank of Tanzania guidelines was partial, particularly in areas such as access control and vulnerability management. Limited budgets and a shortage of skilled personnel further weakened the security posture. The findings highlight the urgent need for coordinated investment in layered defences, staff training, and more vigorous enforcement of security standards to protect customer data and maintain public trust in Tanzania’s banking system.

Severe extremity trauma represents a critical challenge in reconstructive plastic surgery, requiring decision-making that extends beyond anatomical restoration toward long-term functional and psychosocial recovery. This review analyzes the evidence surrounding limb salvage and amputation, reconstructive timing strategies, microsurgical and perforator-based techniques, and patient-reported outcome measures (PROMs), with emphasis on functional performance, psychological adaptation, and quality of life. Comparative data suggest that long-term global functional outcomes between salvage and amputation may converge under specific conditions, although recovery pathways and treatment burdens differ. Early definitive coverage has been associated with improved local wound control when physiologically feasible, whereas staged approaches aligned with damage control principles remain essential in unstable patients. Advances in free flaps, perforator flaps, and upper-extremity reconstruction have expanded technical capabilities, yet durable recovery depends on rehabilitation access, complication management, and patient-centered evaluation. PROM-based research underscores that physical function, social participation, symptom burden, and emotional well-being are central domains in outcome assessment. Long-term trajectories reveal persistent disability in a subset of patients despite partial quality-of-life adaptation over time. From an international perspective, including Latin American contexts such as Mexico, Colombia, and Ecuador, reconstructive decisions must integrate healthcare system capacity and socioeconomic realities. Reconstructive surgery after trauma, therefore, must be interpreted as a longitudinal restorative process aimed at preserving autonomy, dignity, and meaningful life participation rather than solely achieving anatomical survival.

Brain-computer interfaces (BCIs) are communication and control systems centered on neural signals that incorporate both the user and the brain into a closed-loop interaction framework, and are widely regarded as a transformative paradigm in human-computer interaction. However, despite the existence of broadly accepted definitions within the research community, the rapid acceleration of BCI translation and commercialization has led to increasing ambiguity in scientific definitions, expansion of conceptual scope, and overstatement of technical capabilities. To address these issues, this paper proposed a scientifically grounded definition of BCIs and systematically analyzed their essential system components and fundamental characteristics. On this basis, the major and specific factors that constrain the capability boundaries of current and foreseeable BCI systems were examined. Furthermore, the scope of BCI was explicitly delineated by distinguishing BCIs from adjacent neurotechnologies based on their functional roles and system characteristics. This work aims to promote a more rigorous and coherent understanding of BCI definitions, scope, and capability limits within the academic community, and to provide essential theoretical foundations for responsible translation and long-term development. By clarifying conceptual boundaries and realistic expectations, it seeks to mitigate risks associated with conceptual generalization and distorted projections in both research and industrial practice, thereby fostering a more rational, robust, and sustainable ecosystem for the BCI field.

Artificial Intelligence continues to reshape the ICT sector in Pakistan, yet organizations differ widely in how and why they adopt this technology. This study explores the key drivers of AI adoption by focusing on national ICT professionals who work directly with digital systems and emerging technologies. A total of 110 valid responses were collected through an organized online survey using purposive sampling. The investigation was guided by Technology Organization Environment framework combined with innovation characteristics from Diffusion of Innovation theory. The variables examined include the perceived suitability of AI to current systems, the benefits and complexity of adopting AI, organizational technical capability, and external environmental pressures. Data analysis involved Smart PLS-SEM, which facilitated reliability and validity assessment along with hypothesis evaluation. The outcomes highlight that seamless compatibility with existing infrastructure plays a key role in encouraging AI adoption, offers clear operational value, and is not overly difficult to implement. Technical capability also demonstrates a strong influence, indicating that firms with mature digital systems are better prepared to integrate AI solutions. In contrast, external environmental pressures did not show a significant role in the adoption process. These findings highlight that internal technological perceptions and readiness are stronger predictors of AI adoption than external forces in operating ICT firms in Pakistan. The study provides insights that can help organizations strengthen their technical readiness and make more confident decisions when transitioning toward AI enabled transformation. This study contributes to AI adoption literature by isolating organizational technical capability and providing national level evidence from an emerging ICT economy.

The detection of fake news is a multidimensional challenge that demands solutions extending beyond purely computational approaches. Although advances in natural language processing, machine learning, deep learning, and multimodal analysis have strengthened technical capabilities, misinformation continues to proliferate. Fake news thrives within environments shaped by complex social interactions, platform-specific advantages, and human judgement. Social factors (such as user profiles, sharing behaviours, engagement metrics, network structures, and crowd-sourced credibility signals) play a critical role in how misinformation is created, propagated, and perceived, yet these contextual nuances are often overlooked in algorithmic models. These social dimensions operate alongside technical elements, including linguistic cues, visual content, temporal dissemination patterns, and hybrid feature integration. Drawing on a review of the recent literature, this work synthesises sociotechnical elements to inform the development of an integrated approach to the detection of fake news. We introduce the SHAPE conceptual framework to guide the development of the Informed Fake News Advisor (IFNA). This conceptual framework will guide the creation of IFNA, which will consist of detection tools that combine technical precision with sensitivity to social context. By framing fake news detection as a sociotechnical problem, IFNA shifts the focus from isolated technical optimisation towards a holistic design philosophy, supporting the development of solutions that are both effective in detection and responsible in deployment within complex information ecosystems.

Abstract Building component reuse (BCR) has a high potential to mitigate the environmental impacts of the construction sector. However, while studies have explored BCR as an alternative to the highly linear practices of construction, a thorough understanding of what is needed for BCR to become feasible remains lacking. To bridge this research gap and accelerate the diffusion of reuse within the construction sector, our study aims to identify the constituents of BCR feasibility and elucidate how such constituents facilitate BCR practice. We conducted an in-depth multiple-case study on precast concrete element reuse in Finland, Germany, and the Netherlands, utilising 23 interviews, extensive ethnographic observation data, and secondary sources. We found that BCR feasibility has six main constituents (regulation, societal aspects, building component characteristics, technological and technical capabilities, organisation of actors, and business models), each with its own configurations and effects. Furthermore, we place the results within a framework that locates the constituents of BCR feasibility in societal, technical, and business domains. Our study contributes to the circular business, sustainability policy, and circular construction literature, providing managers with a comprehensive understanding of BCR.

Purpose This study investigates the factors influencing the adoption of new technologies among event professionals, extending the Unified Theory of Acceptance and Use of Technology (UTAUT) by integrating perceived investment value (PIV). Design/methodology/approach Survey data were collected from 275 event professionals in the United States. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS to examine the hypothesized relationships. Findings Results show that performance expectancy, effort expectancy, social influence, facilitating conditions, and PIV significantly influence professionals' attitudes toward adopting new technologies. Additionally, facilitating conditions and attitudes significantly predict willingness to use such technologies. Experience moderates the effects of facilitating conditions and social influence on attitudes, suggesting nuanced adoption patterns based on experience levels. Practical implications The strong role of performance expectancy in shaping adoption attitudes suggests that technology providers need to shift their focus from talking about features to showing real, tangible value. Instead of emphasizing technical capabilities, vendors should clearly illustrate how their solutions make events better in practice, whether by boosting attendee engagement, increasing sponsor exposure, or simplifying event operations through tools like AI-driven analytics or virtual event platforms. Originality/value By integrating PIV into the UTAUT framework, this study presents the first application of this extended model to event professionals, who are an underexplored experiential sector in technology adoption research. This novel perspective underscores how investment considerations shape adoption decisions, offering theoretical advancement and practical guidance for event organizers and technology providers.

This study explores the integration of large language models (LLMs) into audit workflows as "co-auditors," emphasizing the necessity of embedding them within frameworks that ensure evidence traceability, governance, and human accountability. Despite growing interest in AI-augmented auditing, prior work has not systematically bridged LLM technical capabilities with audit standards and regulatory compliance requirements. Through a narrative literature review synthesizing audit doctrine, AI governance frameworks, and natural language processing research, the study examines how such integration can be achieved.Rather than substituting professional judgment, LLMs offer auditable support that enhances assurance processes. By incorporating hybrid retrieval, policy-constrained generation, and cryptographic provenance, the proposed architecture addresses both factual reliability and regulatory compliance. The findings underscore that effective LLM deployment requires strict alignment with standards. Ultimately, the research confirms that trustworthy AI in auditing depends on robust technical safeguards, governance structures, and sustained human oversight.

BACKGROUND : Diseases, which pathogenesis is based on iron overload - hereditary hemochromatosis, β-thalassemia, porphyria cutanea tarda - are associated with type 2 diabetes mellitus; this suggests the role of excess iron in the formation of carbohydrate metabolism disorders (CMD). The question of the possibility and informative content of using traditional ferrokinetics parameters as predictors and markers for diagnosing various CMD remains debatable. AIM : To establish relationships between ferrokinetics markers and indicators of carbohydrate metabolism in overweight and obese individuals. The scientific hypothesis is that disturbances in ferrokinetics, such as dysmetabolic iron overload, influence the risk of induction and progression of CMD, regardless of body mass index. MATERIALS AND METHODS : Patients underwent anthropometry, blood sampling with the determination of a detailed biochemical analysis, lipid spectrum analysis, a detailed general blood test and biochemical indicators of iron metabolism. Taking into account the technical capabilities of the device, a number of patients included in the study underwent T2*-magnetic resonance relaxometry of the liver. RESULTS : The study included 108 patients, stratified into groups depending on the presence of CMD (without CMD, with impaired glucose tolerance (IGT) and with T2DM), as well as depending on the iron metabolism (with relatively high and relatively low ferritin levels). Ferritin levels were significantly higher in patients with T2DM than in patients with IGT (298.10 [145.80–336.95] and 124.00 [58.30–170.55] ng/ml, respectively, p=0.029) and persons without CMD (59.80 [24.10–108.85] ng/ml, p=0.002), and significantly higher in persons with IGT compared to patients without CMD (p=0.035). Patients with ferritin levels above the 75th percentile had higher glycated hemoglobin levels (HbA1c) (5.8 [5.3–6.6] and 5.4 [5.2–5.7]%, respectively, p=0.016). Ferritin was highly informative in the diagnosis of T2DM: sensitivity 77.8%, specificity 91% with a diagnostic threshold of 208.1 ng/ml (area under the curve = 0.813; p=0.002). In diagnosing IGT ferritin had a high sensitivity of 75% and specificity of 84.4%, but with a lower diagnostic threshold of 126.65 ng/ml (area under the curve = 0.738; p=0.016). CONCLUSION : The level of hyperferritinemia increases as dysglycemia progresses. Ferritin is a promising marker that is highly informative in the diagnosis of various carbohydrate metabolism disorders.

Data engineering serves as the cornerstone of contemporary analytics infrastructures, enabling organizations to efficiently collect, process, and deliver data throughout enterprise ecosystems. This technical article explores how data engineering has evolved from traditional batch processing paradigms to sophisticated real-time architectures that support mission-critical business operations across industries. The global datasphere continues expanding at unprecedented rates, with creation and replication significantly outpacing available storage capacity, creating both challenges and opportunities for data professionals. As organizations increasingly depend on timely, accurate insights for competitive advantage, robust data engineering practices have become essential strategic assets rather than merely technical capabilities. The article examines implementation methodologies, including pipeline automation, ETL/ELT processes, integration frameworks, orchestration platforms, and scalability considerations that form the architectural foundation of modern data ecosystems. Further sections explore cloud-based data engineering's transformational impact on operational economics, artificial intelligence's dependence on well-structured data infrastructure, and the quantifiable business impacts of mature versus underdeveloped data engineering capabilities. For organizations navigating digital transformation initiatives, understanding these fundamental principles and applications provides the foundation for leveraging data as a strategic asset.

This exploratory qualitative study investigates how K–5 public school teachers in Tennessee, United States perceived and used artificial intelligence (AI) tools to support literacy instruction. Framed by the Technology Acceptance Model (TAM) and the Technological Pedagogical Content Knowledge (TPACK) framework, the study examined teacher motivations, constraints, and pedagogical reasoning related to AI integration. Ten teachers were recruited via word-of-mouth and snowball sampling. Data were collected through semi-structured interviews and analyzed using both inductive thematic analysis and deductive coding guided by TAM and TPACK constructs. Although exploratory and limited to K–5 teachers in Tennessee, findings revealed a cautious but growing curiosity toward AI, with teachers valuing its planning support while voicing strong concerns about ethics, identity, and instructional alignment. Most use occurred outside of direct instruction and was shaped by time, access, and institutional support. The findings point toward a future in which AI’s role in literacy instruction will be determined not solely by its technical capabilities, but by the systems, policies, and cultures that govern its use. Implications highlight the need for professional learning tied to HQIM-aligned models, policy clarity, and AI tool features that reinforce, not replace, teacher expertise.

The scale and complexity of U.S. federal financial management is growing to a level that is increasingly testing the conventional oversight, audit, and accountability mechanisms. Even though artificial intelligence and sophisticated business analytics have become an appealing tool to deal with fraud, improper payments, and performance monitoring, their implementation in federal programs exceeded the creation of governance frameworks to guarantee transparency, auditability, and democratic accountability. The current literature on AI in government finance is disconnected, as technical literature focuses on analytical performance and policy literature on governance principles, but with limited integration between public finance accounting and audit systems. This study addresses this gap through an integrative review of peer-reviewed research, federal oversight reports, and policy frameworks published between 2020 and 2025. The review integrates insights from public finance, audit practice, and AI governance to examine how AI-driven business analytics are currently applied in U.S. federal financial management and where accountability failures persist. The literature indicates that AI initiatives strengthen oversight only when embedded within existing financial control systems and supported by robust data governance, explainability mechanisms, auditable model outputs, and clearly defined human oversight. Analytics deployed as standalone technical solutions frequently increase opacity and weaken audit assurance. Based on these insights, the paper proposes a multi-layered AI-driven business analytics framework that integrates technical capability with federal accounting standards, audit requirements, and governance structures, repositioning AI as a governance instrument rather than a purely technological tool. Keywords: Artificial Intelligence, Public Financial Management, Federal Audit, Accountability, Transparency, Governance

The purpose of the study is to analyse the results of the world’s first ‘Farinelli’ countertenor competition within the framework of the F. G. Handel International Festival, to outline the requirements for the vocal technique of modern countertenors, and promising ways of training Ukrainian singers to perform works of the countertenor repertoire in the musical content of world opera art. The research methodology is based on a comprehensive approach that includes the use of empirical approaches: observation and generalisation of the requirements for modern performers of baroque vocal music and modern repertoire, an analytical method used to highlight the history of countertenor singing and the results of the Farinelli countertenor competition, the importance of both baroque and modern repertoire in the process of educating a countertenor vocalist, his further creative activity. The methods used reveal the practical performing aspect of the research. The scientific novelty of the article lies in highlighting and summarizing the main requirements for the vocal technique of singing of modern countertenor performers, which affects the singer’s career growth and the content of his repertoire. For the first time in Ukrainian musicology, the first ‘Farinelli’ 2025 countertenor competition and the Ukrainian countertenor Vladyslav Shkarupilo, the youngest participant in the competition, are presented. Conclusions As a result of the research conducted the results of the first international competition of countertenors ‘Farinelli’ were analysed. The analysis of the works involved in the performance practice of the finalists of the competition makes it possible to identify a number of important requirements corresponding to the traditions of the performance of the musical aesthetics of the Baroque and the modern countertenor repertoire. Mastering the coloratura technique occupies one of the main places and should be considered not only as an element of the historical context of the performed works, but also as a necessary skill that reveals the technical capabilities of the voice and creates conditions for expanding the stylistic diversity of the repertoire. In addition to the rather complex vocal and technical training, an important role is played by the performer’s intellectual abilities, ability for analytical thinking, erudition and musical taste, knowledge of languages, musical styles and genres, general musical culture, and sociability. The Farinelli Vocal Competition is undoubtedly significant in world musical culture and has an impact on the development of performing vocal art. The competition provides an opportunity for young singers from all over the world, regardless of origin, education, or vocal schools, to demonstrate their talents to an international audience, and receive not only awards and engagements, but also well-deserved recognition from leading opera experts.

ABSTRACT Artificial intelligence is no longer an emerging technology. It is a governance challenge. During 2025, organizations across sectors began to discover that responsible AI could not be achieved through high level principles or isolated policies alone. Regulatory developments, heightened board scrutiny, and rapid advances in general purpose AI are converging to create new expectations for transparency, accountability, and evidence-based oversight. Yet many organizations remain hesitant, with AI initiatives fragmented and governance approaches struggling to keep pace. This article examines how recent AI governance trends are reshaping board and executive responsibilities and argues that Objective Centric Risk and Uncertainty Management (OCRUM) provides a practical and effective framework for governing AI as a strategic, socio technical capability. By anchoring AI oversight to organizational objectives, OCRUM enables clearer decision making, stronger assurance, and more confident adoption of AI in complex and regulated environments.

This study analyzes the impact of 360° panoramic imaging on the documentation of heritage sites, using a SWOT analysis. A multi-method approach was adopted, combining a review of scholarly literature and institutional reports, expert interviews, user surveys, and hands-on testing of three widely used platforms: Kuula, 3DVista, and Pano2VR. The findings demonstrate that 360° imaging significantly improves visual engagement and spatial understanding, particularly in educational and public outreach contexts. However, challenges remain in terms of data integration, navigation, and long-term digital preservation. Platforms such as 3DVista and Pano2VR offer extensive features and advanced media integration, but their complexity and cost make them less accessible to smaller institutions. Conversely, Kuula was found to be more accessible and user-friendly, though it offers fewer customization options. This study adds to the growing body of literature by applying a SWOT lens to evaluate not just the technical capabilities but also the strategic usability of 360° tools in heritage documentation. It highlights key gaps in data management and cross-platform functionality, calling attention to the need for standardization and training. Future research should explore hybrid models that integrate panoramic imaging with high-precision technologies such as LiDAR and immersive media (VR/AR), aiming to enhance both accuracy and public engagement in digital heritage preservation.

Rice fields are a critical source of CH 4 and N 2 O, necessitating accurate, field-level measurements to inform effective mitigation programs. This review (Volume 1) offers a novel perspective on this topic by focusing on the measurement systems themselves, driven by one core question: To what extent have the technical capabilities and limitations of field measurement systems shaped the current scientific understanding and knowledge gaps on rice greenhouse gas (GHG) emissions? We provide a comprehensive assessment of three major field approaches: Manually Sampled Chambers (MSC), Fully Automated Chambers (FAC), and Eddy Covariance (EC). Reversing the narrative of typical literature reviews that focus primarily on scientific findings, this paper starts with the technical evolution of each method, followed by a comparative assessment based on frameworks for method selection and scientific key contributions. The current scientific consensus and global estimates are overwhelmingly derived from the highly versatile MSC approach, which has generated a vast database across different rice-growing regions and management treatments, enabling statistically robust meta-analyses. Despite limitations—such as altering the microclimate in the headspace, possibly missing diurnal or seasonal peaks, and limited spatial scalability—the MSC remains a cornerstone of rice GHG research and will continue to play a central role. The FAC system was developed as an alternative, overcoming limitations in sampling frequency and providing robust data on diurnal and seasonal emission patterns, which proved especially valuable in comparative studies on crop management impacts. Finally, we discuss the use of EC, which provides high-resolution, integrative datasets that allow for a greatly improved process-based understanding of GHG fluxes. The established FluxNet collaboration of EC researchers could serve as a blueprint to coordinate chamber-based studies, thereby building the comprehensive dataset necessary to support data-driven modeling and Machine Learning (ML) development. This retrospective assessment in Volume 1 establishes a critical framework for evaluating and selecting rice GHG measurement methods. Volume 2 of this paper will supplement this work by addressing emerging technical innovations and prospects against the backdrop of diversified research objectives.

Focusing on the digital preservation and management of Lingnan modern historical gardens, this study proposes and practices a full-process framework of landscape information modeling (LIM), integrating multi-source data collection, information integration and business collaboration in view of the three major challenges of insufficient overall records, regional information integration difficulties, and disconnection between digitalization and management practice. Its innovation lies in the fusion of ground/handheld laser scanning and 3D Gaussian splash technology to cope with the complex environment of buildings, vegetation and topography, and achieve high-precision interpretation of modern historical garden elements in Lingnan for the first time. On this basis, The study established the first regional heritage information platform integrating a cloud-based information management system with a game engine, incorporating local protection rules. In this study, application modules such as preventive preservation, emergency response, and assessment and repair for daily management are further developed, and the synergy between technical capabilities and management needs is initially realized. On the practical surface, the framework achievements realize the analysis of complex historical garden elements and control the accuracy within 4 mm, and the platform effectively integrates 5 types of multi-source data and connects the link from data to management. This study provides a set of reusable digital preservation and management methodologies for the sustainable protection and refined management of Lingnan and even similar historical gardens.