• Incorporating asset interdependencies markedly improves the accuracy of infrastructure performance predictions • Fusion of long short-term temporal dependencies with spatial neighbourhood correlations in the proposed hybrid model, BaGTA, which is temporally aware and spatially informed, captures the spatiotemporal relationships between drainage performance and track degradation • Incorporation of a Bayesian inference module enables dynamic and probabilistically grounded spatiotemporal predictions under complex interdependent infrastructure systems • The multi-level modelling framework addresses decision-making needs across both asset and system levels Effective prediction of infrastructure performance is essential for informed asset management. However, traditional approaches often treat different types of assets in isolation, overlooking critical interdependencies (such as those between track and drainage systems) that significantly influence asset degradation and risk. This paper proposes a hybrid model, BaGTA, that is temporally aware, spatially informed and probabilistically grounded to predict railway track performance while accounting for both uncertainty and inter-asset dependencies. The model was trained and validated on a dataset comprising 6,072 track segments and 31,628 drainage assets across four UK railway routes. We demonstrate that incorporating track-drainage interdependencies improves prediction accuracy in both classification and regression tasks. Specifically, the inclusion of interdependencies reduced the prediction error for the Vertical Settlement Standard Deviation (VSD), which is a key indicator of track performance, by 24.65%. The proposed method not only captures complex spatiotemporal relationships but also quantifies uncertainty in predictions, offering a robust decision-support tool for infrastructure operators. This approach has the potential to transform maintenance strategies by enabling proactive, risk-informed, and cost-effective asset management.

Structural health monitoring of underground tunnels increasingly uses advanced sensing and data-driven methods. Laser-scanned 3D point clouds capture spatially rich measurements of segmental tunnel linings and require segmentation as a prerequisite for downstream analysis. Deep learning (DL) is effective for point-cloud segmentation, but scarce datasets and costly annotation limit practical use. This paper presents Tunnel Scanner , a high-fidelity simulator that synthesises realistic tunnel point clouds with automatic annotation. The plug-and-play module Hybrid Position–Normal Local Spatial Encoding embeds geometric priors into DL backbones and combines with transfer learning (TL) to exploit synthetic data for domain adaptation. Models trained only on synthetic data achieved 71.9% mean Intersection-over-Union (mIoU) and 86.6% Overall Accuracy (OA), and TL increased performance to at least 78.8% mIoU and 90.9% OA with limited real data. This paper highlights geometry-informed data synthesis as a viable augmentation approach for digital inspection and asset management of large-scale tunnels. • Develop a high-fidelity simulator to address the scarcity of tunnel point clouds. • Propose a plug-and-play module encoding geometric features into DL backbones. • Investigate transfer learning to enhance 3D Sim-to-Real domain adaptation. • The end-to-end framework yields ＋24% segmentation accuracy with limited real data.

Purpose: This study examined the application of Current Operational Value (COV) in public-sector asset management, specifically focusing on the Fire and Rescue Department of Malaysia (JBPM) under International Public Sector Accounting Standards (IPSAS) 45 and IPSAS 46. The study aimed to develop a framework for implementing COV, which emphasises audit and risk management to improve the decision-making processes. Methodology: Qualitative data were collected through semi-structured interviews with representatives of JBPM, accountants, and auditors from the Ministry of Housing and Local Government (KPKT). Thematic analysis was utilised to identify the impact of COV across financial reporting and risk management. Findings: The findings highlighted six (6) focus areas where COV can strengthen asset management, namely financial reporting, risk management, resource allocation, investment decisions, budget planning, and compliance. The COV enhances the accuracy and transparency of financial information by aligning asset values with actual usage and service potential, further facilitating risk-based auditing and lifecycle monitoring, improving resource distribution and budget forecasting, and supporting compliance with IPSAS standards through standardised training and reporting practices. Limitations: The study focused on operational assets and excluded property and plant. Practical Implications: The proposed framework provides public sector managers and auditors with guidance to improve accountability, optimise resource utilisation, and support evidence-based decision making. Originality/ Value: This study is among the first to integrate COV into audit and risk management in Malaysia, contributing to more transparent and efficient public-sector asset management. Keywords: Current Operational Value (COV), Jabatan Bomba dan Penyelamat Malaysia (JBPM), IPSAS 45, IPSAS 46, public sector accounting

ABSTRACT This paper uses China’s New Asset Management Rules as the policy shock and tests whether passageway business regulation affects stock price crash risk. The results indicate that treated firms experience a marked decline in crash risk after the regulation. Mechanism tests suggest that this effect is associated with better information disclosure, stronger focus on core operations, and reductions in both explicit and hidden debt. Additional tests document positive short-window market reactions, with stronger effects among firms with greater bad-news hoarding incentives, in regions with more developed financial systems, and among firms with higher capital dependence. The results also hold for both legitimate and regulation-evading passageway business. Overall, the findings provide firm-level evidence that targeted financial oversight is linked to lower downside risk and more disciplined use of financial resources.

Architectural excellence has traditionally been associated with creativity, innovation, aesthetic quality, and conceptual strength. While these characteristics remain essential, contemporary project environments increasingly demonstrate that design quality alone is insufficient to guarantee successful outcomes. Many projects begin with ambitious architectural visions yet encounter challenges during technical development, construction execution, operational occupancy, or long-term asset management. As a result, a growing distinction has emerged between design excellence and execution excellence. The most successful architectural projects are not merely those that generate compelling concepts but those that effectively translate conceptual intent into built environments that perform successfully throughout their lifecycle. This article proposes a comprehensive framework for understanding architectural execution excellence as the integration of conceptual design, technical delivery, construction implementation, and operational performance. Rather than treating these stages as independent activities, the paper argues that they form a continuous system in which decisions made during one phase influence outcomes across all others. Architectural success therefore depends on the quality of coordination, communication, and decision-making throughout the entire project lifecycle. The study examines the mechanisms through which design intent is translated into executable architectural systems, explores the role of technical coordination in preserving project quality, and analyzes how construction processes influence the realization of architectural objectives. Particular attention is given to interdisciplinary collaboration, stakeholder management, digital technologies, lifecycle thinking, and operational performance evaluation. The paper further investigates how architects can strengthen project outcomes by expanding their focus beyond design production and engaging more actively with implementation and performance management. The findings suggest that execution excellence represents a strategic capability that bridges the traditional divide between design and delivery. Projects that successfully align architectural vision with technical feasibility, construction realities, and operational objectives are more likely to achieve long-term value, user satisfaction, and organizational success. The article concludes by presenting a holistic model that positions architectural execution excellence as a critical competency for contemporary architectural practice.

Urban pipeline infrastructure plays a vital role in ensuring the operational efficiency and service reliability of modern utility systems, especially in industrial regions. While previous studies have focused primarily on pipe failure prediction, limited research has addressed the forecasting of key pipeline performance indicators such as velocity, pressure, and head loss within the context of infrastructure asset management. This study investigates the performance of advanced machine learning (ML) models, PSO-ANN, Genetic CNN, Quantum SVR, Fuzzy Logic Tree, and Bayesian GPR, in predicting three critical output variables: velocity, head loss, and pressure. A dataset comprising 91 instances with geometric and hydraulic descriptors was employed, and descriptive statistics revealed significant variability in flow-dependent parameters. SHAP-based sensitivity analysis highlighted elevation (0.9287) as the dominant factor for pressure prediction, while flow rate (0.4574) and diameter (0.2273) strongly influenced head loss. For velocity, flow rate (0.1139) emerged as the most influential, though other parameters also contributed, justifying their inclusion in the modeling framework. The models were trained using data from the Gadhra Water Distribution Network (District Metered Area-03) in East Singhbhum, Jamshedpur, India. Model evaluation was conducted using R², RMSE, MAE, and MAPE. Results demonstrated a clear performance hierarchy, with Bayesian GPR and Fuzzy Logic Tree exhibiting superior accuracy and stability (R² ≥ 0.98, RMSE ≤ 0.06, MAPE ≤ 0.13), whereas PSO-ANN and Genetic CNN showed relatively weaker performance. The near-perfect R² observed for Fuzzy Logic Tree reflects the small dataset size and its high capacity, highlighting that generalization may be limited in larger or unseen datasets. The analysis of regressor plots, residual distributions, and normalized accuracy matrices further validated these findings. Overall, the study establishes Bayesian GPR and Fuzzy Logic Tree as robust predictive tools for hydraulic modeling while acknowledging dataset constraints that may affect generalization.

ABSTRACT The rise of US asset management and the expansion of assetisation into essential sectors such as healthcare or housing have intensified political scrutiny and public backlash. Amid counter-mobilisations and calls for social values in capitalism, this study examines asset managers’ networks that serve as an infrastructure of interest defence. Empirically, we map executive and board members’ affiliations with policy organisations and philanthropic institutions, highlighting divisions within the industry. Our findings reveal that alternative asset managers rely less on traditional policy groups and think tanks than conventional asset managers, instead cultivating ties with more conservative organisations. Additionally, we explore the role of gender in leveraging interest defence networks. Our findings suggest that conventional asset managers may rely on women’s engagement with philanthropic causes to enhance their public legitimacy. By integrating perspectives on instrumental and symbolic power, this study contributes to debates on struggles within asset management, interest defence and the role of gender in political economy.

Road Maintenance Expenditure (RME) forecasting is essential for strategic budgeting, proactive asset management, and long-term infrastructure resilience. However, existing approaches often treat expenditure drivers in isolation and inadequately account for socio-economic conditions, pavement condition metrics, economic disruptions, natural shocks, and their delayed or compounded impacts. This study addresses this gap by developing a domain-integrated forecasting framework to model nonlinear, lagged, and shock-driven RME dynamics using a New Zealand case study. The framework combines Variational Autoencoder (VAE)-based latent feature extraction and nonlinear dimensionality reduction with a fine-tuned Random Forest regressor. Bayesian optimisation is used to identify optimal latent representations, while lagged variables and interaction terms explicitly represent delayed and compounded effects of external shocks. Comparative benchmarking across Local, Total, and New Zealand Transport Agency expenditure categories demonstrates strong in-sample modelling performance, with R2 values of 0.9818, 0.9842, and 0.9800, respectively, outperforming conventional linear, ensemble, and neural network-based models. A supplementary forward-rolling validation under chronological forecasting conditions showed reduced predictive performance, highlighting the challenge of temporal generalisation amid residual uncertainty, decentralised decision-making, and evolving external conditions. The study contributes a robust methodological framework for uncertainty-aware maintenance expenditure forecasting and demonstrates the importance of distinguishing structural relationship learning from realistic temporal prediction.

This paper examines the financialization of hospitality assets in Australia through the analytical lens of asset manager capitalism. Using Hotel Property Investments (HPI), a pub-focused stapled Australian Real Estate Investment Trust (A-REIT), as a longitudinal case study (2014–2024), the research documents how institutional investors and asset managers increasingly govern hospitality assets through financial mechanisms embedded within evolving institutional governance structures. Drawing on corporate disclosures, investor presentations, and governance documents, the study traces how mechanisms such as long weighted average lease expiry (WALE), triple‑net lease (NNN) arrangements, Consumer Price Index (CPI)‑linked rent escalations, rentalized capital expenditure, debt‑platform engineering, and active portfolio trading contribute to the restructuring of hospitality venues into contractualized income streams designed to reduce sensitivity to local tourism demand cycles. Rather than directly evidencing community-level outcomes, the analysis demonstrates how governance mechanisms reshape the conditions under which hospitality assets are managed and valued. Spatially, the paper shows how financialized governance arrangements operate selectively across regional and suburban hospitality landscapes, contributing to portfolio concentration in jurisdictions aligned with regulatory scarcity, yield stability, and institutional investment preferences. The study contributes to tourism geographies by foregrounding spatial selectivity, regional concentration, and the transformation of tourism-adjacent hospitality infrastructure under asset manager capitalism.

The article provides an in-depth study of the functioning, investment policy, and efficiency of Georgia's funded pension system. The paper analyzes performance dynamics from 2019 to the present, the structure of the investment portfolio, and risk management mechanisms. Using empirical data, the Sharpe ratio, and international comparative analysis, the research identifies key challenges, such as low diversification and the underdeveloped local capital market. Finally, the study develops recommendations to ensure long-term financial sustainability. Keywords: pension reform, investment portfolio, diversification, financial margin, asset management.

Purpose: This study aims to examine the determinants of civil servant performance by evaluating the impact of the digital attendance system, employee competencies, and work discipline—both individually and collectively—at the Dompu Regency Finance and Asset Management Agency (BPKAD). Research Method: Using a quantitative causal design, primary data were collected through a questionnaire distributed to the entire population of 77 employees (saturation sampling). The data were then analyzed using the Structural Equation Modeling-Partial Least Squares (SEM-PLS) approach. Results and Discussion: Digital attendance systems and work discipline have been shown to have a positive and significant impact on performance. Conversely, employee competence has been shown to have no significant impact. Collectively, this model explains 52.2% of the variation in performance. These findings confirm that the power of technological oversight and rule compliance can compensate for weaknesses in individual technical capacity. Implications: In practical terms, agency leaders are advised to prioritize E-HRM investments to ensure reliable infrastructure for monitoring and enforcing disciplinary regulations. Future research is recommended to expand the sample scope to include other Regional Government Work Units (SKPDs). Originality: This study integrates the Information System Success Model and the 21st-century digital competency framework into a single holistic Socio-Technical System analytical model for the public financial management sector.

The rapid advancement of web technologies and digital health platforms has significantly transformed the medical ecosystem, particularly in the areas of appointment scheduling, automated patient communication, and clinic administration. Patients seeking medical care often face difficulties tracking doctor availability, booking reliable appointments, and securing diagnostic services due to fragmented traditional management systems. Conventional hospital consultancy relies on physical queues, manual front-desk operations, and disjointed payment tracking, which become highly inefficient during peak operational hours. This research presents the design, development, and implemen-tation of a Full-Stack Hospital Management System leveraging the modern MERN stack (MongoDB, Express.js, React.js, and Node.js). The proposed platform provides centralized healthcare administration, distinct rolebased access for Major Admins, Doctors, and Patients, dynamic appointment scheduling, auto-mated welcome email onboarding, and secure online payment workflows. A critical advancement in this architecture is the implementation of robust Stripe payment synchronization, engi-neered to ensure database consistency even if a user immediately closes their browser post-transaction. Additionally, the platform enhances medical documentation by integrating a custom React RichTextEditor utilizing AIChatSession to generate automated, intelligent medical work summaries. Developed utilizing Vite, Tailwind CSS, Clerk for authentica-tion, Cloudinary for asset management, and secure JWT proto-cols, the system guarantees a highly responsive user interface and a scalable backend. Experimental analysis demonstrates that the platform eliminates scheduling overlaps, reduces administrative overhead, ensures seamless financial transactions, and provides an accessible digital healthcare environment.

Extended reality (XR) and interactive 3D reconstructions are increasingly used in forensic and legal medicine for crime-scene work, autopsy workflows, training, remote collaboration, and courtroom communication. However, published reports often lack the evidence-grade details needed for independent technical and medico-legal scrutiny. We present FORCE-XR, a Minimum Reporting Set spanning system description, data provenance and transformations, auditability and reproducibility, integrity and chain-of-custody, validation and outcomes, human factors and safeguards, and security, privacy and governance. Using a multi-database scoping search and full-text screening, we audited 51 included studies and coded reporting completeness (Y/N/NR) across all FORCE-XR items. The audit reveals systematic reporting gaps that limit falsifiability, counter-expertise, and courtroom defensibility, particularly for reproducibility packs, logging, asset management, and integrity controls. FORCE-XR provides an operational 'demo-to-evidence' pathway to improve transparency, cumulative science, and trust in forensic XR artefacts. We provide pragmatic implementation guidance and a research agenda to support benchmarking, editorial uptake, and standard evolution.

• Enriched infrastructure BIM model integration with GPR- and MR-derived data. • Development of an immersive mixed reality tool for bridge asset management that captures hidden reinforcements and visible structural pathologies. • Real-time MR visualization with high accuracy geospatial data. The adoption of Building Information Modelling (BIM) has grown significantly, transforming design, construction, and operational workflows through enhanced data transparency, long-term accessibility, and multidisciplinary collaboration. This study presents new methodology to enrich BIM model of a pedestrian bridge by integrating Ground Penetrating Radar (GPR) and Mixed Reality (MR) data. Subsurface reinforcements and visible defects were precisely mapped, providing an integrating representation of both hidden and exposed structural elements. Subsequent real-time MR visualization enables interactive inspection and alignment of digital datasets with real-world counterparts. The integration of MR within BIM workflows enhances spatial understanding, contextual analysis, and in-field validation. This MR-BIM fusion provides a robust, immersive tool for bridge inspection, maintenance, and asset management, advancing data-driven, interactive construction practices.

Psychosocial stressors related to extreme weather events and multiple resource insecurities: qualitative insights from refugee youth in an Ugandan humanitarian setting

Road asset management agencies, including transportation units in a municipal setting, must ensure that the roads are safe for the public. To do so, one must translate this safety requirement to a set of minimum pavement condition thresholds. Roads that fall below these thresholds should be regarded as failed and must be immediately repaired. So far, determining safety-based thresholds has primarily relied on extensive accident data analysis, which presents challenges for municipal infrastructure agencies lacking such data. To bridge this gap, the study proposes a practical method for determining safety-significant pavement performance thresholds. These thresholds consider road class owing to different design speeds. It begins by identifying safety-sensitive distresses and then reviews their critical thresholds at different speeds. The proposed method is illustrated using the City of Calgary’s pavement assessment practices. Results indicate that low-speed roads should prioritize addressing potholes and rutting, while high-speed roads should focus on mitigating roughness-related safety risks. Additionally, there is no one-to-one relationship between the overall pavement performance index and individual distress, and the overall performance thresholds established in this study should be considered conservative. The findings provide valuable references and insights for municipal road asset management, and the proposed approach can be applicable to other municipalities and provincial transportation agencies or state departments of transportation.

Government agencies' ownership of intangible assets increases each year in line with the expansion of digitization programs; however, this is not always accompanied by optimal management. Many intangible assets eventually fall into disuse—either because they are no longer relevant to the agency's needs or have been replaced by new systems—yet they remain recorded as assets in government financial statements. This issue has ultimately drawn the attention of the Supreme Audit Agency of the Republic of Indonesia (BPK RI). During the 2014–2023 period, the BPK RI identified intangible assets that were no longer in use but were still recorded as current assets in the financial reports of ministries and agencies. Using content analysis, this article aims to describe issues in the management of intangible assets within the Indonesian government, with a focus on those no longer in use. The analysis results show that the BPK RI recommended that ministries and agencies write off these intangible assets because they are considered non-compliant with existing regulations, which stipulate that only intangible assets that are still in use may be recorded.

This study examines the development of collaborative AI models in the asset management industry in China, Canada, the U.S., and the Netherlands and analyzes how technology integration is reshaping decision-making models, resource allocation efficiency, and socioeconomic dynamics. The research aims to offer practical advice on optimizing regulatory frameworks and to guide practitioners in finding a balance between technological innovation and ethical and strategic imperatives, as well as transition needs toward greener and impactful investment models.

Organizations that want to be able to manage all their assets are now required to use Asset Management Systems (AMS).operational efficiency, cost optimisation and revenue sustainability. The increasing Leverage digital technologies like Artificial Intelligence (AI), Internet of Things (IoT), and other innovations.The advent of cloud computing, and Enterprise Resource Planning (ERP) systems has revolutionized the way businesses operate. Integrated the asset management practices with the strategic business functions. The present study examines the effect The impact of Asset Management Systems on revenue generation and organizational performance. The research investigates the efficiency of asset tracking and maintenance, technology, and the management of inventory and capital. The research focuses on asset tracking efficiency, maintenance management, technology, and inventory and capital management. Operational optimization and adoption help to increase profitability and financial sustainability. A quantitative research methodology was followed with the help of structured questionnaires which were distributed Amongst workers and supervisors in separate sectors. Statistical tools including The percentage analysis, correlation analysis, regression analysis and reliability testing were used To assess how AMS relates to organizational growth. The results show that organizations with high-level AMS technologies have reported that they have achieved the following: Augmented working efficiency, lower maintenance expenses, greater asset use, and higher revenue generation. Predictive maintenance and real-time asset monitoring have a significant impact Minimize equipment downtime and increase productivity. The study concludes that effective asset management systems offer long term competitive benefits and increase financial outcomes. Performance.

IMPACT This article will be of value to senior and middle-level public sector accountants, auditors, standard setters, asset managers, and to managers in museums and cultural institutions who are involved in heritage asset reporting. Its contribution is to consolidate and systematize the fragmented academic literature on heritage accounting, showing how the debate has evolved over three decades and where significant issues remain. By mapping dominant themes, methods, geographical patterns, and contrasting views on recognition, definition, and measurement, the article helps practitioners understand the arguments for and against balance sheet recognition, the limits of monetary valuation, and the role of disclosure in conservation, maintenance, use, and cultural significance. The authors’ findings support informed reporting policies, stronger dialogue between accountants and heritage professionals, and balanced decisions on financial valuation, narrative disclosure, or combined approaches that better reflect public value.