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.

BackgroundDigital health literacy refers to the ability to find, access, understand and evaluate health related information and apply health information obtained from digital sources. Digital health literacy has become an essential competency for health professionals in modern healthcare settings. With the increasing adoption of several digital softwares by the government of Nepal like District Health Information System, telemedicine, mobile health applications, public assets management System, health professionals are expected to effectively use digital platforms to deliver evidence-based care and communicate with patients and other providers. This study aims to assess the digital health literacy and its associated factors among health professionals in Kaski district, Nepal.MethodsA quantitative cross-sectional analytical study was done among 318 health professionals working in public and private health facilities of Kaski district. Data were collected through self-administered questionnaires utilizing e-HEALS tools. Maintaining the health research ethics, multistage sampling technique was used to obtain the study participants. Data were analyzed in SPSS software using descriptive and inferential statistics including chi-square test and binary logistic regression.ResultsAbout 46.2% of the participants had a low level of digital health literacy. Most of the health professionals used digital health technology to gain information about diseases and fewer used it for patient follow-up. Although the majority of participants (73.8%) agreed that digital health technology has enhanced healthcare delivery, many participants expressed low confidence in making health related decisions using online information and had difficulty in understanding the limitations of online health resources. Additionally, factors such as; being a doctor (AOR = 5.176; 95% CI: 1.274–21.031), working in the Pharmacy department (AOR = 3.995; 95% CI: 1.129–14.141) and having a positive attitude towards use of digital technology (AOR = 0.544; 95% CI: 0.326–0.909) were identified as contributors to high digital health literacy.ConclusionNotable, few participants had received training on digital health technology. Hence, training, practical workshops and simulation exercises should be organized to build confidence among health workers in health-related decision making using online information.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12913-026-14720-x.

With the rising level of complexity within the industry, the role of maintenance management became very important for efficient operation. Maintenance management systems are mostly traditional and require a lot of effort because they are mainly manual based. Therefore, the current work focuses on the development of an automated maintenance management system that would make the process easier, more consistent, and reliable. This paper discusses the design and implementation of such Computerized Maintenance Management System that allows to monitor assets and work order information. At the same time, this system would automate the planning process and ensure proper utilization of resources. The design was developed using modern software engineering principles and an intuitive interface was used to enable efficient interaction. The results of testing demonstrated that the suggested Computerized Maintenance Management System has a number of advantages over other types of maintenance management systems. In particular, maintenance is performed faster and cheaper than before while at the same time it remains reliable.

Background/Objectives: The digital transformation of healthcare has become a key component of building resilient and sustainable health systems. However, the long-term sustainability of digital health technologies depends not only on user acceptance but also on the institutional governance conditions that shape how these technologies are implemented and utilised in practice. This study examines how institutional factors shape the sustainable utilisation patterns of Internet of Things (IoT) technologies in regulated healthcare environments, with hospital IoT-based asset management systems, a mature and widely deployed use case in China’s public hospitals, providing the empirical context for the investigation. Methods: Drawing on institutional theory and the Technology Acceptance Model (TAM), we conceptualise user perceptions as behavioural micro-foundations through which institutional conditions influence technology utilisation. A survey of 293 healthcare professionals from two large public hospitals in China was analysed using Structural Equation Modelling (SEM), incorporating mediation and Multi-Group Analysis (MGA). Results: The results demonstrate that technical compatibility (TC) significantly enhances perceived ease of use (PEU) (β = 0.40), while organisational support (OS) positively influences both perceived usefulness (PU) (β = 0.35) and PEU (β = 0.30). Conversely, regulatory compliance (RC) negatively affects PU (β = −0.25) and PEU (β = −0.20), revealing a tension between accountability requirements and operational efficiency. The model explains between 58% and 67% of the variance in key constructs. Conclusions: Overall, the findings indicate that sustainable utilisation patterns depend on alignment between technological capabilities and institutional governance conditions, with user perceptions operating as behavioural micro-foundations through which institutional effects are transmitted. By integrating institutional theory with technology acceptance research, this study contributes a governance perspective for understanding sustainable digital transformation in healthcare systems and provides practical insights for designing interoperable, compliant, and supportive digital health infrastructures to enhance hospital operational efficiency and quality of care.

Conveyor belts are critical equipment in mining operations, where continuous and reliable material transport is essential for production efficiency. This systematic review aims to analyze computer vision-based techniques applied to conveyor belt condition monitoring. Following PRISMA guidelines, a search was conducted in the Scopus and Web of Science databases, and 80 studies were selected after applying predefined eligibility criteria. These studies were synthesized through quantitative bibliometric methods and structured qualitative thematic categorization. The findings reveal a significant increase in scientific output after 2020, as well as its geographic distribution and potentially the most influential contributions. The main research lines focus on damage detection, deviation detection, and foreign object detection. A clear transition is also observed from traditional image processing methods-such as filtering, segmentation, and geometric analysis-toward deep learning models, including YOLO, CenterNet, and hybrid architectures, with improvements in precision, speed, and stability. Nevertheless, challenges remain related to datasets representativeness, the heterogeneity of evaluation protocols, and variability in operational conditions. Finally, opportunities for advancement are identified through multimodal datasets, adaptive models, and lightweight solutions that facilitate integration into asset management systems and support scalable industrial adoption.

Organisations are increasingly concerned about their ability t the opportunities that arise in the market. In this context, it is that assets are in top condition so that these opportunities can be taken advantage. With the advance of technology, it is possible to use tools such as artificial intelligence (AI) to support decision making, for asset maintenance or replacement. The asset management system brings together that make room for AI. AI has become a topic of importance today, bringing new challenges for people and organisations. This article aims to compile relevant documents in the field of asset management and analyse them to identify gaps. Considering that maintenance is the fundamental pillars of asset management, it proposes the application of technologies, RNNs, to predict abnormal behaviour. Models were tested, findings are intended to be integrated into the asset management system , helping to optimise processes and mitigate operational anomalies.

This study aims to design and build a website-based laboratory asset management information system at the Information Systems Department, Faculty of Mathematics and Natural Sciences, Tanjungpura University. The main problem with the current system is that the asset management process is still carried out manually, resulting in delays in recording, potential data errors, and limited access to information. In addition, the lack of an integrated system capable of supporting real-time asset management indicates a gap between the need for effective asset management and current system capabilities. The system development method used is the Fountain Model, which is iterative and flexible in supporting the software design and development process. System evalua-tion was conducted using the Black-Box Testing method to assess the suitability of system func-tions, and the System Usability Scale (SUS) to measure system usability from the user perspective. The test results showed that all system features run according to the functional requirements, with no critical errors found during Black Box testing. In addition, the evaluation results using the SUS method yielded a score of 82.5, which falls within the excellent category and indicates that the sys-tem is easy to use and acceptable to users. The implementation of this system also improved the ef-ficiency of the asset management process, sped up data retrieval, and reduced the risk of recording errors compared to the previous manual system. Thus, the developed information system not only addresses asset management problems but also improves service quality and data-driven deci-sion-making in the laboratory environment.

This system aggregates securities data from various global capital markets into a single interface and enables real-time updates and synchronization. It provides significant convenience for investors who frequently need to allocate assets across markets. Investors can freely configure and integrate according to their needs and can understand the changes in all account assets in real-time, providing effective data support for investment decisions.

Energy producers must balance safety, regulatory compliance and efficiency while operating ageing infrastructure in a rapidly changing market. Traditional inspection methods depend heavily on human judgement, require plant shutdowns and can miss subtle faults, leading to failures and costly downtime. Advances in artificial intelligence (AI) provide a pathway to transform these inspection processes. This work describes an applied AI platform for inspections in the upstream energy sector. The platform combines computer vision to detect anomalies in inspection images, natural language processing to transcribe and analyse voice‑captured field notes, and automated comparison of inspection results over time. Drawing on published case studies and industry surveys, the paper discusses how computer vision pipelines automate detection of cracks and corrosion, how drones and robotics are used to collect inspection data and detect defects in distribution equipment, and how AI‑enabled inspection tools are being integrated with asset management systems to support predictive maintenance and risk‑informed decision making (Dastidar 2024; Optelos 2024; Joshi 2026). A proposed system architecture is presented to illustrate the flow from data capture to decision support, demonstrating how AI can provide early warning of risks, reduce downtime and improve regulatory compliance. The paper concludes with discussion on human oversight, data availability, and lessons learned from early adopters. By grounding the discussion in real‑world examples rather than theory, the extended abstract highlights how AI‑powered inspections can help energy producers operate more safely and efficiently while adapting to evolving regulatory and market requirements.

The article reveals the theoretical and methodological foundations of the formation, evaluation, and management of non-current assets of an enterprise in modern economic conditions. It is substantiated that the effective functioning of economic entities directly depends on the availability, structure, and level of use of non-current assets, which form the production potential of an enterprise and affect its financial results, competitiveness, and market value. Scientific approaches to interpreting the essence of non-current assets are summarized, their key characteristics are highlighted, and their role in ensuring the stability of financial and economic activities is determined. The paper systematizes the main functions of non-current assets in the process of production, sales, and enterprise management, and reveals their importance as a source of cash flow and financial stability. Considerable attention is paid to the policy of managing non-current assets, defining its purpose, objectives, and place in the enterprise's financial management system. The classification of non-current asset management tasks is presented and the stages of this process are described, taking into account the strategic and tactical goals of enterprise development. Methodological approaches to determining the enterprise's need for fixed assets are studied, in particular, direct and indirect calculation methods, as well as the peculiarities of planning the volume of production equipment, machinery, structures, and means of managing operational activities. Indicators for assessing the availability, condition, and efficiency of fixed assets are systematized, allowing for a comprehensive assessment of their level of involvement in the economic process. Particular attention is paid to the processes of reproduction and renewal of fixed assets, the essence of simple and expanded reproduction is revealed, as well as the role of depreciation as the main instrument for financing long-term assets. Methods for calculating depreciation in accordance with national accounting regulations (standards) and the requirements of Ukrainian tax legislation have been summarized. The principles of financing non-current assets have been substantiated, in particular, ensuring an optimal volume of financial resources, a rational balance of capital sources, minimizing the cost of borrowed funds, and increasing the efficiency of their use. It is concluded that the formation of an effective system of non-current asset management is a necessary condition for improving the efficiency of an enterprise's economic activity, ensuring its financial stability, and implementing a long-term development strategy in a dynamic external environment.

Optimizing asset utilization is a strategic component of hospital management, as it directly influences operational efficiency and service quality. The effectiveness of asset management is determined not only by the availability of asset management systems but also by the quality of employees involved in planning, maintenance, documentation, and supervision. This study aims to analyze the role of employee quality in the implementation of asset maintenance governance and the optimization of asset utilization at RSI Jakarta Cempaka Putih. The research employs a qualitative approach with a case study design, involving key informants from management, supporting units, technicians, and asset users. Data were collected through in-depth interviews, observations, and document reviews, and analyzed using Braun & Clarke’s (2006) thematic analysis method. The study concludes that employee quality is a dominant factor linking effective asset maintenance to optimized asset utilization. These findings underscore the importance of strengthening human resource competence through technical training, certification, performance evaluation, and digitalization of asset management to support the sustainability of hospital operations. This research is expected to serve as a foundation for developing more comprehensive, employee-based asset management policies and strategies within RSI Jakarta Cempaka Putih. Keywords : Employee Quality, Asset Management, Asset Maintenance, Asset Optimization, Hospital, RSI Jakarta Cempaka Putih.

Reliable assessment of bridge technical condition is a key challenge in infrastructure management due to uncertainty, subjectivity, and heterogeneity inherent in inspection-based data. Traditional deterministic evaluation methods often fail to capture the gradual nature of structural deterioration and the complex interactions between bridge components. This study proposes a hybrid methodology that integrates fuzzy logic and artificial neural networks (ANNs) to quantify the overall technical condition of bridge structures using long-term inspection data. A comprehensive dataset, derived from real bridge inspection reports collected over more than 15 years across various regions of Ukraine, served as the basis for model development. Five key input parameters—substructure condition, superstructure condition, deck condition, overall structural condition, and channel and channel protection condition—were employed to compute an integrated Bridge Condition Assessment indicator using a Mamdani-type fuzzy inference system. The resulting fuzzy-based indicator was subsequently used as the target variable for training ANN models. To ensure optimal predictive performance and training stability, Bayesian Optimization was applied for systematic hyperparameter tuning. Model performance was evaluated using standard regression metrics, including MSE, MAE, MAPE, and the coefficient of determination (R2). The results demonstrate that the proposed approach enables accurate approximation of the fuzzy-based Bridge Condition Assessment indicator, with MAPE values as low as 0.2% and R2 exceeding 0.982 for the best-performing model. The hybrid framework effectively combines interpretability and scalability, providing a decision-support framework based on fuzzy logic and surrogate modeling for automated fuzzy-based bridge condition assessment, maintenance prioritization, and integration into digital asset management systems.

The Application of Pavement Performance Models in Pavement Asset Management System for the Purpose of Improving the Structural Pavement Condition

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The management of Regional Government-Owned Assets (BMD) is an essential element in supporting the effectiveness of government administration and realizing accountable governance. This study aims to analyze the implementation of Regional Government Owned Asset management at the Regional Development Planning Agency (BAPPEDA) of Buton Regency based on Government Regulation Number 28 of 2020 and the Regulation of the Minister of Home Affairs Number 19 of 2016. This research employed a qualitative descriptive approach, with data collected through interviews, observations, and documentation. Data analysis was conducted using the interactive analysis model of Miles and Huberman, which includes data reduction, data display, and conclusion drawing. The results indicate that administratively, BMD management at BAPPEDA of Buton Regency has been implemented in accordance with applicable regulations, particularly in the aspects of planning, procurement, and asset administration. However, several obstacles remain in its implementation, especially in the aspects of asset use, utilization, security, maintenance, and supervision. Prominent issues include suboptimal needs analysis, weak recording of asset utilization, reactive maintenance practices, and the absence of clear Standard Operating Procedures and sanction mechanisms. This study concludes that although BMD management at BAPPEDA of Buton Regency has fulfilled administrative requirements, it has not fully reflected the principles of good governance, particularly accountability, effectiveness, and internal control. Therefore, strengthening internal regulations, improving employee discipline, and optimizing regional asset management systems are necessary.

This study examines how public sector reforms and anti-corruption policies and agencies were implemented and established in Nigeria from colonial times to the present. Using the hybrid theoretical framework that incorporates institutional, principal-agent and social norms theories, the paper closely studies the policies and institutions that different governments used to fight corruption. These include early actions like the Foster-Sutton Tribunal (1956), the Recovery of Public Property Decree, 1984, the establishment of the Code of Conduct Bureau, 1979, the Independent Corrupt Practices and Other Related Offenses Commission, (2000), the Economic and Financial Crimes Commission, (2003) and recent reforms like the Proceeds of Crime (Recovery and Management) Act, (2022) and the Digital Asset Management Systems started by President Tinubu (2024). While these initiatives reflect a consistent policy commitment to transparency and accountability, the study points out ongoing challenges, such as elite impunity, political interference, weak enforcement, lack of institutional continuity, overlapping roles and limited public confidence. Despite some good efforts, Nigeria's fight against corruption has often been weakened by societal norms and structural contradictions. The study concludes by proposing a holistic approach to reform and strengthen anti-corruption agencies to ensure judicial independence and foster a culture of accountability across all the levels of government. This study adds to the broader discourse on governance in Nigeria by offering a theory-based analysis of reform paths and their different results based on political history. Keywords: Corruption, Public Sector Reform, Anti-Corruption Policies and Anti-Corruption Agencies

The IT Inventory Management System provides a centralized platform for efficient IT asset tracking to improve resource allocation accuracy by optimizing operational efficiency, supporting informed strategies within organizations, and enhancing the overall management of IT resources. Current IT inventory systems exhibit significant deficiencies, including inadequate integration, methodological accuracy, reporting efficiency, security, and recovery mechanisms. To address these issues, this study examines a real-world system implemented for an oil and gas company in Iraq. This research aims to enhance asset management by integrating and improving the existing inventory system through an advanced database and data management platform. The proposed solution demonstrates scalability, enabling adding new fields and departments while supporting seamless database migration, table creation, and relational structuring within the IT inventory system. Generic tools like Excel or other systems need more specialized features for an effective inventory system, leading to incomplete tracking, limited automation, and inadequate support for complex IT requirements. The methodology involves developing a system using the programming language PHP and HTML, enhancing functionality through phased expansions. Key steps include implementing centralized access control and refining system usability and security. The system enhances asset management by tracking details, providing monitoring tools, generating accurate reports, and supporting management and renewal decisions.

This research aims to analyze strategies for securing and managing problematic land assets belonging to Kodam XIV/Hasanuddin to support the main tasks of the Indonesian National Army. The main problem is the large number of land assets that have not been certified and are involved in problems with other parties which hinder the operational use of Kodam XIV/Hasanuddin. This research uses a qualitative approach method with a descriptive design and data collection techniques through interviews, observation and literature study. Integration of digitalization of management through the Geographic Information System (GIS) and State Asset Management and Information System (SIMAN) is important to facilitate the asset inventory process. The formation of an Integrated Task Force is can to improve coordination between agencies. The main strategy proposed is the application of the Stakes, Fences, Talking Boards, Patrols, Certification and Supervision (P6 methode) to secure and speed up asset certification.The research results show that this strategy can increase the effectiveness of management and utilization of state assets.

Modern practices of planning road maintenance activities are increasingly based on forecasting changes in pavement condition over time. Under conditions of limited funding and growing traffic loads, scientifically substantiated determination of the timing and scope of maintenance and repair actions based on pavement deterioration models becomes particularly important. The key parameter that integrally reflects the influence of structural, climatic, and traffic-related factors is the structural capacity of the pavement structure. This paper examines the patterns of changes in the structural capacity of road pavement structures during operation. The stages of deterioration of flexible pavements are analyzed, and the relationship between the reduction in structural capacity, the development of pavement distress, deterioration of ride quality, and traffic safety conditions is established. The obtained dependencies can be used to substantiate maintenance strategies and to plan repair works. Existing approaches to pavement condition forecasting are often based on simplified empirical models or do not adequately account for the stage-wise nature of pavement structural capacity degradation. In addition, universal models developed for other countries do not always adequately reflect the real operating conditions of Ukrainian highways, which are characterized by excessive axle loads, significant irregularity of funding, and additional destructive impacts caused by military actions and changes in traffic flow structure. The purpose of this study is to generalize and further develop scientific approaches to modeling pavement deterioration based on the stage-wise change in pavement structural capacity, as well as to substantiate the applicability of such models for predicting the serviceability and operational condition of automobile roads under modern operating conditions. This study is of a review nature. A systems approach was applied, which represents a set of general scientific methodological principles based on considering the objects of study as complex systems. It was established that pavement deterioration has a clearly defined stage-wise character, in which the reduction of pavement structural capacity is the determining factor governing the development of pavement distress, the increase in the area of potholes, and the deterioration of surface evenness. The interrelation between structural capacity indicators, ride quality, and traffic safety was demonstrated. The feasibility of using combined deterioration models that integrate mechanistic–empirical and probabilistic approaches was substantiated. It was shown that the application of locally calibrated models improves the reliability of residual service life prediction for pavements. The proposed approach to modeling pavement deterioration based on the stage-wise change in pavement structural capacity can serve as a scientific basis for decision-making in road asset management systems. Adapting deterioration models to the actual operating conditions of Ukrainian highways is a necessary prerequisite for improving the efficiency of road maintenance and the rational use of limited financial resources.

Integrated and reliable occupational safety. This study aims to evaluate the implementation of Asset Management Integrity in offshore oil drilling projects using the Failure Mode Effects and Criticality Analysis (FMECA) method. The research was conducted through a quantitative approach with surveys and secondary data analysis to identify asset failure modes, evaluate the severity and frequency of failures, as well as detection capability. The analysis results were used to calculate the Risk Priority Number (RPN) and to map risk priorities based on a criticality matrix. The findings indicate that the application of FMECA can accurately identify weak points in mechanical systems, support risk-based decision-making, and provide strategic recommendations for improving occupational safety (K3) and operational reliability. This research provides practical contributions in building a resilient asset management system oriented towards sustainability and risk mitigation in extreme offshore environments.