Search Results (1,060)

Bedding is an important component of equine accommodation management. Choosing the right bedding is important for stable management and its selection may include considerations such as the sourcing of the material, the capital investment and ongoing costs, delivery, storage, installation, ongoing labour and maintenance, removal and disposal. Furthermore, it is crucial that the consequences for the health and welfare of horses and humans and the impact on the environment should also be considered. This review aimed to outline the advantages and disadvantages of different horse bedding types, focusing on their effects on the well-being of horses, humans, and the environment. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) technique was used as the methodology for this review. The search was performed in Scopus and Web of Science bibliometric databases and a total of 176 records were screened reading the title and the abstract. After screening, 58 records were retained and another 19 records were identified using their reference lists (i.e., snowballing). Therefore, a total of 77 records were considered. Straw and wood shavings were the most commonly used and studied bedding materials, while research on alternative options remains limited. Straw is identified as horses’ preferred option, while shavings appear to be the easiest to clean, making them the preferred choice for stable workers. The parameters to consider when choosing the bedding most fit for purpose are many and their attributes differ across the various bedding types. This review has compared all the bedding types within the research literature to determine the best overall option using the research-based evidence. Each bedding type offers unique benefits and drawbacks summarised in a user-friendly table. Stable managers must consider and evaluate them to suit their specific needs, including the health and welfare of each horse and the husbandry system involved. Our findings may, therefore, be useful in the decision-making process of equine industry members. Full article

Pavement deterioration is influenced by various factors with degradation rates varying widely depending on the type of pavement, its use, and the environment in which it is located. In Nepal, where the climate varies from alpine to subtropical monsoon, understanding pavement degradation is essential for effective road asset management. This study employs a Markov deterioration hazard model to predict pavement deterioration for the national highways managed by Nepal’s Department of Roads. The model uses Surface Distress Index data from 2021 to 2022, with traffic and cumulative monsoon rainfall as explanatory variables. Monsoon rainfall data from meteorological stations were interpolated using Inverse Distance Weighted and Empirical Bayesian Kriging 3D methods for comparative analysis. To compare the accuracy of interpolated values from the IDW and EBK3D methods, error metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Bias Error (MBE) were employed. Lower values for MAE, RMSE, and MBE indicate that EBK3D, which accounts for spatial correlation in three dimensions, outperforms IDW in terms of interpolation accuracy. The monsoon rainfall interpolated values using the EBK3D method were then used as an explanatory variable in the Markov deterioration hazard model. The Bayesian estimation method was applied to estimate the unknown parameters. The study demonstrates the potential of integrating the Markov deterioration hazard model with monsoon rainfall as an environmental factor to enhance pavement deterioration modeling. This model can be adapted for regions with a similar monsoon climate and pavement types making it a practical framework for supporting decision-makers in strategic road maintenance planning. Full article

Enhancing the effectiveness of aviation engine borescope inspection is critical for flight safety. Statistics indicate that engine defects contribute to 20% of mechanical-related flight accidents, while existing defect detection and segmentation models for borescope images suffer from a low operational efficiency and suboptimal accuracy. To address these challenges, this study proposes a Visual State Space with Multi-directional Feature Fusion Mamba (VMmamba) model and constructs a real-world borescope defect dataset. First, a feature compensation module with symmetrical diagonal feature optimization fusion is developed to enhance the feature representation capabilities, expand the receptive fields, and improve the feature extraction of the model. Second, a content-aware upsampling module is introduced to restructure contextual information for complex scene understanding. Finally, the learning process is optimized by integrating Smooth L1 Loss with Focal Loss to strengthen defect recognition. The experimental results demonstrate that VMmamba achieves a 43.4% detection mAP and 36.4% segmentation mAP on our dataset, outperforming state-of-the-art models by 2.3% and 1.4%, respectively, while maintaining a 29.2 FPS inference speed. This framework provides an efficient and accurate solution for borescope defect analysis, offering significant practical value for aviation maintenance and safety-critical decision making. Full article

Smart cities are confronted with a variety of disaster threats. Among them, natural fires pose a serious threat to human lives, the environment, and asset security. In view of the fact that existing research mostly focuses on the analysis of accident precursors, this paper proposes a dynamic risk-modeling method based on Stochastic Petri Nets (SPN) and Bayesian theory to deeply explore the evolution mechanism of urban natural fires. The SPN model is constructed through natural language processing techniques, which discretize the accident evolution process. Then, the Bayesian theory is introduced to dynamically update the model parameters, enabling the accurate assessment of key event nodes. The research results show that this method can effectively identify high-risk nodes in the evolution of fires. Their dynamic probabilities increase significantly over time, and key transition nodes have a remarkable impact on the emergency response efficiency. This method can increase the fire prevention and control efficiency by approximately 30% and reduce potential losses by more than 20%. The dynamic update mechanism significantly improves the accuracy of risk prediction by integrating real-time observation data and provides quantitative support for emergency decision making. It is recommended that urban management departments focus on strengthening the maintenance of facilities in high-risk areas (such as fire alarm systems and emergency passages), optimize cross-departmental cooperation processes, and build an intelligent monitoring and early-warning system to shorten the emergency response time. This study provides a new theoretical tool for urban fire risk management. In the future, it can be extended to other types of disasters to enhance the universality of the model. Full article

The digital transformation of port infrastructure is a key element in the evolution towards Smart Ports and Industry 4.0. This paper presents an optimized port asset management system based on Digital Twin technology and BIM/GIS integration, aiming to enhance efficiency, sustainability, and decision-making in port operations. The proposed system leverages real-time data acquisition, predictive maintenance, and resource optimization, addressing critical challenges in port asset lifecycle management. By integrating Digital Twin models with Internet of Things (IoT) sensors, cloud computing, and machine learning algorithms, this approach enables data-driven decision-making, which improves operational performance and minimizes costs. The Frankenstein Strategy is introduced as an innovative methodology for port digitalization, allowing incremental integration of digital twins into existing infrastructures. The results demonstrate that this system provides enhanced asset monitoring, optimized maintenance planning, and increased operational resilience, contributing to the automation and optimization of production processes in Industry 4.0. This research highlights the potential of Digital Twin technology to revolutionize port asset management, establishing a framework for smart, data-driven, and sustainable port operations. Full article

The management of public institutions is focused not only on providing and improving public services but also on managing the physical infrastructure that these institutions use—buildings for provision of such services. The focus of this paper is on decision support to the management of individual buildings and the set of such buildings (portfolio) during the planning phase. More precisely, it is directed towards support towards both the decision-maker (DM) and decision-making process (DMP) when planning construction activities/projects such as maintenance, renovation, reconstruction, extension, construction, design/preparation of project-technical documentation, etc. The aforementioned DMP includes the processing of a large amount of diverse data (technical, economic, social, etc.) expressed differently—numerically or descriptively, as well as in different units of measurement, simultaneously taking into account the different wishes and attitudes of stakeholders (consequently meeting their often conflicting goals and criteria). The above indicates that it is a complex and ill-defined multi-criteria problem faced by the DM/planner. On top of that, and knowing that the DM usually does not have all the necessary knowledge and skills, this paper proposes how to overcome these issues by supporting the DM within the DMP during such a planning process. The proposed concept promotes an integral (considering relevant aspects of this management problem) and inclusive (taking into account the views of relevant stakeholders) approach to managing complex construction projects and their portfolios. It is methodologically based on the logic of decision support systems and multi-criteria analysis. The multi-criteria methods used include the Preference Ranking Organization METhod for Enrichment Evaluation (PROMETHEE) for the evaluation and comparison of alternatives in an integral manner, as well as the Analytic Hierarchy Process (AHP) for determining the weights of criteria and achieving an inclusive and consistent approach to relevant stakeholders (based on the goal tree approach). The concept was tested on the planning of infrastructure management at a university in the Republic of Croatia, and it was proven to be useful because it provided the DM with a basis for decision making. The usefulness of the concept was confirmed by the concordance of the plan obtained using the concept and the activities/projects actually realized. Full article

The global adoption of dental implants has significantly reshaped modern dental practices, with the market projected to reach USD 16 billion by 2029. However, despite high success rates, dental implants can still be prone to complications, particularly when underlying causes of tooth loss, such as periodontal disease and bone loss, are not addressed. This paper explores the biological and mechanical considerations in the decision-making process between preserving a tooth through periodontal therapy or opting for extraction and implant placement. It also highlights the importance of a holistic approach that includes assessing the patient’s oral health, periodontal status, and the biomechanical factors influencing tooth retention. Periodontal therapy has been proven to be highly effective, with both non-surgical and surgical therapies showing long-term efficacy in preserving natural teeth, especially in the presence of furcation involvement. Studies show that proper periodontal management, including regular maintenance therapy after the active therapy, significantly enhances tooth survival, even in cases of severe periodontitis. In contrast, dental implants, while effective, are not free of complications, mainly inflammatory peri-implant diseases, but also mechanical complications, which can compromise long-term outcomes. The paper reviews clinical studies on implant survival, demonstrating that periodontal therapy can sometimes offer a more cost-effective and biologically sound alternative to implant therapy, especially for teeth with severe attachment loss or furcation involvement. In conclusion, treatment decisions should be based on a comprehensive evaluation of clinical, biological, and patient-specific factors. By integrating regenerative therapies even in more compromised teeth and addressing the root causes of tooth loss, implant rehabilitation can be postponed for many years and offer a cost-effective and successful long-term treatment plan. This approach underscores the importance of individualized care in the evolving landscape of restorative dentistry and implantology. Full article

As a new type of rail transit vehicle, maglev trains have extremely high requirements for safety and reliability. With the gradual commercial operation of maglev trains, how to scientifically and effectively assess the safety and analyze the risks of train equipment has become an urgent issue to be addressed. Against the backdrop of the practical application of maglev train projects, this paper integrates domestic and international risk analysis models, proposes the steps for conducting the risk analysis of maglev rail transit, and establishes a risk analysis system for the entire lifecycle of maglev rail transit. Based on the results of fault analysis, a risk analysis of the levitation system is carried out. The theory of multi-attribute decision-making is studied, new risk evaluation indicators are established using triangular fuzzy numbers, the risk levels of the levitation system are determined, and the weak links within the system and the relationships between the pieces of equipment are identified. These efforts provide guidance for enhancing the safety and reliability of train equipment and for carrying out train maintenance work. Full article

The vulnerability of existing buildings to recent earthquakes underscores the critical need to explore effective retrofit solutions thoroughly. This study presents a comprehensive methodology for ranking seismic retrofit alternatives for reinforced concrete beams with shear deficiencies. It evaluates five alternatives to ensure a 50-year service life, meeting current seismic standards and incorporating specific preventive maintenance measures for each option. A cradle-to-grave life cycle assessment was used to analyze the impacts associated with the sustainability of each alternative. Hybridization of emerging multi-criteria decision-making methods was applied for criteria weighting and final ranking, and a hierarchical model including economic, environmental, social, and functional criteria was developed. The results highlight carbon fiber reinforcements and steel plates with epoxy adhesives as optimal solutions due to their lower environmental and social impact, along with improvements in execution time and minimal architectural impact. This study underscores the necessity of a comprehensive approach to identifying optimal retrofitting alternatives, demonstrating the imperative to complement the conventional structural engineering objective of ensuring safety while minimizing investment. Full article

Many modern engineering systems, such as offshore wind turbines, rely on k-out-of-n configurations to ensure reliability. These systems are exposed to both internal deterioration and external shocks, which can significantly impact operational efficiency and maintenance costs, necessitating optimal maintenance policies. This study investigates an optimal condition-based maintenance policy for a k-out-of-n system, where each unit deteriorates independently following a gamma process and is subject to random external shocks that cause sudden jumps in deterioration. This study considers (1) stochastic dependencies among units, where shock-induced cumulative deterioration in one unit affects others, and (2) interdependencies between external shocks and internal deterioration, where internal deterioration influences external factors and vice versa. Using a Markov decision process framework, we derive an optimal maintenance policy that minimizes expected maintenance costs while incorporating these interdependencies. Under reasonable assumptions, we establish key structural properties of the optimal policy, enabling its efficient identification. A case study on offshore wind turbines demonstrates the effectiveness of the proposed approach, achieving up to a 9.9% reduction in maintenance costs compared to alternative policies. This cost reduction is achieved by optimizing the timing of preventive maintenance while incorporating the two aforementioned types of dependence into the decision-making process. Sensitivity analyses further explore the effects of cost parameters, deterioration rates, and shock characteristics, offering valuable insights into designing maintenance strategies for systems influenced by shocks and interdependent deterioration. Full article

The civil engineering sector operates within a complex ecosystem of stakeholders, requiring efficient management and maintenance of structural and infrastructural assets. In this context, there is an increasing need for robust tools to track critical events (e.g., alerts, unusual behaviors) and support decision-making processes related to maintenance and interventions. At the same time, ensuring secure and prompt payments is essential for timely and effective responses. This paper investigated the potential of smart contracts, integrated with blockchain technology, to automate and optimize asset management and maintenance processes. The proposed framework examines how these technologies can enhance operational efficiency, security, and event traceability, providing a structured approach for both routine operations and emergency interventions. Although smart contracts have been widely applied in the construction phase of infrastructure projects, their use in long-term asset management remains largely unexplored. As a conceptual study, this work does not present a quantitative analysis but instead lays the groundwork for future research and real-world applications of blockchain-based smart contracts in infrastructure management and safety procedures. Full article

Mega-facility management has long been inefficient due to manual, reactive approaches. Current facility management systems face challenges such as fragmented data integration, limited predictive systems, use of traditional methods, and lack of knowledge of new technologies, such as Building Information Modeling and Artificial Intelligence. This study examines the transformative integration of Artificial Intelligence and Digital Twin technologies into Building Information Modeling (BIM) frameworks using IoT sensors for real-time data collection and predictive analytics. Unlike previous research, this study uses case studies and simulation models for dynamic data integration and scenario-based analyses. Key findings show a significant reduction in maintenance costs (25%) and energy consumption (20%), as well as increased asset utilization and operational efficiency. With an F1-score of more than 90%, the system shows excellent predictive accuracy for equipment failures and energy forecasting. Practical applications in hospitals and airports demonstrate the developed ability of the platform to integrate the Internet of Things and Building Information Modeling technologies, shifting facilities management from being reactive to proactive. This paper presents a demo platform that integrates BIM with Digital Twins to improve the predictive maintenance of HVAC systems, equipment, security systems, etc., by recording data from different assets, which helps streamline asset management, enhance energy efficiency, and support decision-making for the buildings’ critical systems. Full article

The structural health monitoring (SHM) of bridge infrastructure has become essential for ensuring safety, serviceability, and long-term functionality amid aging structures and increasing load demands. SHM leverages sensor networks to enable real-time data acquisition, damage detection, and predictive maintenance, offering a more reliable alternative to traditional visual inspection methods. A key challenge in SHM is optimal sensor placement (OSP), which directly impacts monitoring accuracy, cost-efficiency, and overall system performance. This review explores recent advancements in SHM techniques, sensor technologies, and OSP methodologies, with a primary focus on bridge infrastructure. It evaluates sensor configuration strategies based on criteria such as the modal assurance criterion (MAC) and mean square error (MSE) while examining optimisation approaches like the Effective Independence (EI) method, Kinetic Energy Optimisation (KEO), and their advanced variants. Despite these advancements, several research gaps remain. Future studies should focus on scalable OSP strategies for large-scale bridge networks, integrating machine learning (ML) and artificial intelligence (AI) for adaptive sensor deployment. The implementation of digital twin (DT) technology in SHM can enhance predictive maintenance and real-time decision-making, improving long-term infrastructure resilience. Additionally, research on sensor robustness against environmental noise and external disturbances, as well as the integration of edge computing and wireless sensor networks (WSNs) for efficient data transmission, will be critical in advancing SHM applications. This review provides critical insights and recommendations to bridge the gap between theoretical innovations and real-world implementation, ensuring the effective monitoring and maintenance of bridge infrastructure in modern civil engineering. Full article

Relocatable modular buildings (RMBs) offer significant advantages, including flexibility, mobility, and scalability, making them ideal for temporary or rapidly changing scenarios. However, as the scale and quantity of RMB modules increase, their allocation across projects poses complex logistical challenges. Inefficiencies in traditional manual allocation methods, such as suboptimal module selection, increased transportation costs, and project delays, underscore the need for innovative solutions. This study develops a Digital Twin (DT)-based decision support system to optimize the allocation and management of RMB modules. The proposed framework integrates Building Information Modeling (BIM), Internet of Things (IoT), and Geographic Information Systems (GISs), enabling the real-time synchronization of physical assets with their digital counterparts. The DT framework incorporates real-time data acquisition, dynamic module condition assessments, and an algorithm-driven allocation process to streamline resource utilization and logistics planning. The system is validated through a case study of South Korea’s first relocatable modular school system project, demonstrating its capability to optimize module allocation, reduce costs, and enhance lifecycle management. This study advances RMB management by offering a practical, data-driven approach, empowering facility managers to leverage real-time data for preventive maintenance, asset optimization, and sustainable resource utilization. Full article

Allogeneic hematopoietic cell transplantation (HCT) remains an important curative-intent treatment for many patients with acute myeloid leukemia (AML), but AML recurrence after allografting is common. Many factors associated with relapse after allogeneic HCT have been identified over the years. Central among these is measurable (“minimal”) residual disease (MRD) as detected by multiparameter flow cytometry, quantitative polymerase chain reaction, and/or next-generation sequencing. Demonstration of a strong, independent prognostic role of pre- and early post-HCT MRD has raised hopes MRD could also serve as a predictive biomarker to inform treatment decision-making, with emerging data indicating the potential value to guide candidacy assessment for allografting as a post-remission treatment strategy, the selection of conditioning intensity, use of small molecule inhibitors as post-HCT maintenance therapy, and preemptive infusion of donor lymphocytes. Monitoring for leukemia recurrence after HCT and surrogacy for treatment response are other considerations for the clinical use of MRD data. In this review, we will outline the current landscape of MRD as a biomarker for patients with AML undergoing HCT and discuss areas of uncertainty and ongoing research. Full article