Maintenance Resources Research Articles

A framework of composite factors for assessing ecosystem service supply drivers: A sustainable socio-ecological perspective

Studying the coupled relationship between social-ecological systems (SES) provides a more comprehensive understanding of the impact of human activity on ecosystems, thereby facilitating the formulation of more rational environmental protection policies. A deeper understanding of this relationship can promote the sustainable utilization and management of the environment. With its abundant natural resources, Ganzhou City represents the most typical ecological location in the southern hilly region of China. In this study, we constructed a sustainable framework for composite factors to analyze their effects on the ecosystem services supply (ESS). This framework emphasizes the positive roles assessment indicators such as water, forests, lakes, grasslands, and governance have on ESS, which are directly linked to the objectives of Sustainable Development Goal (SDG) 15, which include forest conservation, biodiversity maintenance, and water resource management. The results indicate that: (1) From 2005 to 2020, the overall ESS index in the central and western regions of Ganzhou showed a trend of being lower in the north and higher in the south, with areas outside the central and western regions showing a yearly increase. (2) Water, forest, lake, grassland, and governance indicators have a positive impact on the total ESS, while indicators such as mountains, sand, society, and actors have a negative impact. (3) From 2005 to 2020, the coupling coordination relationship in the central-northern and southern regions of Ganzhou gradually strengthened, with most areas shifting from a dysfunctional state to a low coupling state. By 2030, the total ESS index in counties such as Ningdu, Yudu, Huichang, Chongyi, and Xunwu is expected to decline. Some northeastern and southern regions are projected to show an upward trend by 2035. This study will significantly contribute to the realization of SDG 15, as it integrates closely related composite factors into a sustainable SES framework, by further delving into the factors influencing ESS and providing practical guidance for achieving SDGs.

Decision-Making Model for Life Cycle Management of Aircraft Components

This paper presents a novel decision-making framework for the life cycle management of aircraft components, integrating advanced data analytics, artificial intelligence, and predictive maintenance strategies. The proposed model addresses the challenges of balancing safety, reliability, and cost-effectiveness in aircraft maintenance. By using real-time health monitoring systems, failure probability models, and economic analysis, the framework enables more informed and dynamic maintenance strategies. The model incorporates a comprehensive approach that combines reliability assessment, economic analysis, and continuous re-evaluation to optimize maintenance, replacement, and life extension decisions. The optimization method on the base of genetic algorithm (GA) is employed to minimize total life cycle costs while maintaining component reliability within acceptable thresholds. The framework’s effectiveness is demonstrated through case studies on three distinct aircraft components: mechanical, avionics, and engine. These studies showcase the model’s versatility in handling different failure patterns and maintenance requirements. This study introduces a data-driven decision-making framework for optimizing the life cycle management of aircraft components, focusing on reliability, cost-effectiveness, and safety. To achieve optimal maintenance scheduling and resource allocation, a GA is employed, allowing for an effective exploration of complex solution spaces and enabling dynamic decision-making based on real-time data inputs. The GA-based optimization approach minimizes total life cycle costs while maintaining component reliability, with the framework’s effectiveness demonstrated through case studies on key aircraft components. Key findings from the case study demonstrate significant cost reductions through optimization, with mechanical components showing a 10% more reduction in total life cycle costs, avionics components achieving a 14% more cost reduction, and engine components demonstrating a 7% more decrease in total costs. The research also presents an optimized dynamic maintenance schedule that adapts to real-time component health data, extending component lifespans and reducing unexpected failures. The framework effectively addresses key industry challenges such as no fault found events while minimizing unexpected failures and enhancing the overall reliability and safety of aircraft maintenance practices. Sensitivity analysis further demonstrates the model’s robustness, showing stable performance under varying failure rates, maintenance costs, and degradation rates. The study contributes a scalable approach to predictive maintenance, balancing safety, cost, and resource allocation in dynamic operational environments.

Automation is Live! Christopher Newport University, OCLC, Project MUSE, and Wiley Partnerships Using KBART Holdings Automation to Streamline Workflows

In this session, presenters from Christopher Newport University, OCLC, Project MUSE, and Wiley discussed knowledge bases and related tools (KBART) holdings automation to streamline electronic resources workflows. KBART Automation reduces manual and tedious tasks performed by electronic resources management personnel, allowing additional time for enhanced management and maintenance of electronic resources. This presentation highlighted library, publisher, and vendor perspectives and emphasized how they all come together to streamline the management of electronic resources.

Funding Mechanisms for State Road Maintenance

The deferred maintenance practices for road infrastructure will result in increasing life-cycle costs and deteriorating economic productivity. Governments have addressed the issue by expanding revenue sources, but we don’t know much about the revenue allocation mechanisms. This research reviewed 50 states’ constitutions and statutes to find the similarities and differences in the institutional features of the funding mechanisms for road maintenance. As of 2020, it found that ten states had strict earmarking mechanisms for securing resources for road maintenance; four states had less strict earmarking mechanisms. Furthermore, this research found wide variations in the designs of 50 states’ transportation/highway trust funds and their uses. The research developed the eight propositions to examine the relationship between the funding mechanisms and state highway maintenance spending. Overall, this research contributes to the current literature and policymakers to enhance our understanding of the funding mechanisms of 50 states for road maintenance.

Research on the Application of Intelligent Technologies in Equipment Management of Vocational Logistics Training Rooms

As the importance of vocational logistics training rooms in education continues to grow, issues such as low equipment management efficiency, untimely maintenance, and underutilization of resources have become increasingly apparent, severely impacting teaching effectiveness. The rapid development of intelligent technologies offers new solutions in this field. Technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics are widely applied in the logistics industry and gradually provide efficient and refined management methods for vocational logistics training room equipment management. This study analyzes the shortcomings of existing management models, explores key applications of intelligent technologies in real-time equipment monitoring, maintenance optimization, and resource scheduling, and constructs an intelligent equipment management system aimed at improving management efficiency, reducing maintenance costs, and enhancing teaching effectiveness. The research provides theoretical support and practical references for the intelligent transformation of equipment management in vocational logistics training rooms.

The positive impact of daily well-being practices on individual veterinary professionals' professional quality of life self-assessment scores within an emergency and specialty hospital.

Our study set out to identify the benefits for veterinary workers of structured daily well-being practices on compassion satisfaction, burnout, and secondary traumatic stress. Suggested origins of high rates of psychological stress and burnout are significant occupational concerns for veterinary workers. Many professional helpers experience an extreme state of tension and preoccupation from exposure to the suffering of those being helped. Veterinary workers are further impacted by negative associations and moral distress experienced due to limitations in the provision of quality medical care for veterinary patients. These negative experiences exist despite veterinary professionals' work being worthwhile and highly valued. A randomized controlled study was performed over 6 months. Volunteer participants were members of a team of veterinary workers at a 24-h emergency and specialty hospital. Study participants were asked to incorporate daily well-being practice(s) into existing routines whereas control participants were not. Measures of well-being and the negative impacts of serving as veterinary helpers were assessed by having participants complete the Professional Quality of Life (ProQOL) self-assessment at baseline and at 1, 3 and 6 months. Composite scores for compassion satisfaction, burnout and secondary traumatic stress were calculated. Baseline ProQOL scores were similar between study participants and controls. Baseline compassion satisfaction (CS), burnout scores (BS), and secondary traumatic stress (STS) scores for those instituting well-being practices were 37.6 (+/- 3.6), 26 (+/- 5.3), and 26.6 (+/- 5.2). Study participants had higher compassion satisfaction scores at 6 months with mean CS scores (n = 15) of 40.1 (+/- 6.8) and adjusted scores that were on average 3.0 (95% CI 0-6.1) higher than the control group (p = 0.048). Significant differences in BS or STS scores were not seen. Improvements were seen in CS but not BO/STS for those caregivers who implemented well-being practices into their daily routines. Factors that likely contributed to successful implementation of well-being practices include educational resources, supportive leadership, accessibility, and consistent acknowledgement and positive rewards. Proposed supportive elements and resources for maintenance of well-being practices within a team of veterinary workers include provision of a psychologically safe community and team support (including formal or informal "buddy systems").

Effect of negative emotional stimuli on working memory: Impact of voluntary and automatic attention.

Emotions are known to influence cognitive performance, particularly working memory (WM) in both its aspects, processing, and maintenance. One explanatory mechanism might be that negative stimuli capture attentional resources, leaving fewer resources for attentional maintenance and processing of information in WM. However, this attentional capture was only investigated using WM tasks in which participants were explicitly asked to process negative items. The aim of this paper was to determine whether explicit processing of emotional stimuli is necessary to impair WM performance, or if their mere presence is enough to capture attention. For this purpose, participants performed a complex span task in which they alternated between memorizing a series of neutral words and processing either emotional images or neutral ones. In Experiment 1, participants were instructed to explicitly process emotional images, whereas in Experiment 2, emotional images were presented without any processing being required. In both experiments, we observed a decrease in memory performance when the images were negative compared to neutral. Whether or not voluntary processing is involved, emotional images seem to capture attentional resources, which in turn leads to a decline in memory performance. These results were discussed in relation to attentional theories and the influence of emotion on the specific mechanisms of WM.

Railways Health Monitoring Employing KSK Approach: A Novel AIIoT based Decision-Making Approach for Railways

Central to this AIIoT approach is the implementation of machine learning algorithms that can process vast amounts of data generated from the sensors. These algorithms can identify patterns and anomalies that might indicate wear and tear or incipient failures in critical systems. For example, vibration sensors on trains can detect irregularities in wheel dynamics, while track-side monitoring systems can check for track integrity. By integrating these insights into a centralized health monitoring platform, railway operators are not only able to understand the current health status of their assets but also make informed decisions about maintenance schedules and resource allocation. Moreover, the innovative use of edge computing in this AIIoT framework allows for localized data processing, reducing latency and enabling immediate responses to critical situations. This is crucial in a railway environment where timely interventions can prevent accidents and improve service reliability. Additionally, the combination of AIIoT with cloud computing creates opportunities for advanced data analytics and machine learning models that can continuously improve their accuracy over time as more data becomes available. In essence, this novel AIIoT approach not only enhances operational efficiency but also aligns with broader initiatives aimed at making rail transport more sustainable by reducing unnecessary maintenance trips and optimizing resource utilization. The system informs the decision based on Track condition, speed of train, train condition to the authority.

Dynamic scheduling of workshop resource in cloud manufacturing environment

In the cloud manufacturing environment, workshop resource scheduling serves as a pivotal component, characterized by increased dynamics and complexities. Nevertheless, existing dynamic scheduling methods are often limited to solving specific dynamic events. Thus, considering the actual workshop resource scheduling in a cloud manufacturing environment, this article examines the methods to address unexpected events including randomly arriving tasks, resource breakdown, as well as resource maintenance. Besides, a dynamic scheduling method based on the Game Theory, considering workshop capacity in cloud manufacturing, was developed. In the first place, the priority of workshop tasks was evaluated by Game Theory, and the optimal task processing sequence in the workshop was determined to maximize benefits. Secondly, to verify the dynamic regulation performance of the method, it was combined with the particle swarm optimization (PSO) algorithm considering multi-objective factors to obtain an ameliorated PSO algorithm addressing the challenge of resource optimization scheduling in a genuinely dynamic workshop environment. Finally, this method was tested through a case study, and the results demonstrate that it can achieve superior dynamic and static performance compared to alternative algorithms.

A Comparative Study of Pavement Roughness Prediction Models under Different Climatic Conditions

Predicting the International Roughness Index (IRI) is crucial for maintaining road quality and ensuring the safety and comfort of road users. Accurate IRI predictions help in the timely identification of road sections that require maintenance, thus preventing further deterioration and reducing overall maintenance costs. This study aims to develop robust predictive models for the IRI using advanced machine learning techniques across different climatic conditions. Data were sourced from the Ministry of Energy and Infrastructure in the UAE for localized conditions coupled with the Long-Term Pavement Performance (LTPP) database for comparison and validation purposes. This study evaluates several machine learning models, including regression trees, support vector machines (SVMs), ensemble trees, Gaussian process regression (GPR), artificial neural networks (ANNs), and kernel-based methods. Among the models tested, GPR, particularly with rational quadratic specifications, consistently demonstrated superior performance with the lowest Root Mean Square Error (RMSE) and highest R-squared values across all datasets. Sensitivity analysis identified age, total pavement thickness, precipitation, temperature, and Annual Average Daily Truck Traffic (AADTT) as key factors influencing the IRI. The results indicate that pavement age and higher traffic loads significantly increase roughness, while thicker pavements contribute to smoother surfaces. Climatic factors such as temperature and precipitation showed varying impacts depending on the regional conditions. The developed models provide a powerful tool for predicting pavement roughness, enabling more accurate maintenance planning and resource allocation. The findings highlight the necessity of tailoring pavement management practices to specific environmental and traffic conditions to enhance road quality and longevity. This research offers a comprehensive framework for understanding and predicting pavement performance, with implications for infrastructure management both locally and worldwide.

Maintenance strategy for urban micro wind farm considering maintenance route and resource allocation

Emerging urban wind farms (UMWF) are becoming increasingly prevalent, and optimizing maintenance strategy for UMWF has become crucial for reducing costs and improving wind power efficiency. This research formulates an optimization problem of UMWF by considering the maintenance route and resources. To solve this problem, firstly, the Efficient Maintenance Value (EMF) is proposed as an objective function to measure the maintenance value, based on which a novel Random Variable Neighborhood Descent and Cuckoo Search-based Hybrid Discretized Artificial Fish Swarm Algorithm (RVNDCS-HDAFSA) is proposed to search the optimal maintenance strategy. Experiments are conducted to verify the local search capabilities of the hybrid algorithm, and extensive comparison experiments with the other algorithms are conducted at different scales to validate the effectiveness of the RVNDCS-HDAFSA algorithm; the result of the comparison experiments and the ANOVA shows that the HDAFSA demonstrates a superior capability in solving the medium-scale and large-scale problems compared to the existing algorithm. In conclusion, the practical application experiments validate the effectiveness of our proposed approach.

Hybrid group opportunistic maintenance strategy for offshore wind turbines considering maintenance resource allocation in harsh working environments

The intelligent maintenance of offshore wind turbines (OWTs) is vital for sustainable energy generation and utilization, as harsh working environments generally limit their application. As offshore wind farms expand into deeper waters, operational and maintenance costs continue to increase. Therefore, devising targeted maintenance strategies for offshore wind farms is crucial. Existing strategies often overlook the role of maintenance resources in controlling the operational and maintenance costs. To address this problem, this study proposes a hybrid group opportunistic maintenance strategy to implement resource allocation-based maintenance for OWTs under dynamic conditions. First, a failure rate model based on the life decline and system coupling is proposed to capture the evolution of failures in OWTs in harsh working environments, thereby enhancing the accuracy of fault maintenance. Subsequently, a hybrid linear and nonlinear deterioration model is proposed to identify the different deterioration trends owing to the various sensitivities of diverse components to harsh working environments, which enhances the precision of degradation maintenance. In addition, a maintenance resource allocation model is proposed to allocate resources in advance, thereby increasing resource utilization and reducing maintenance costs. A multi-objective optimization approach is proposed to maximize the mean maintenance level (MML) and minimize the per unit cycle expected maintenance cost rate (PECR) by considering harsh working environments. Finally, the nutcracker optimization algorithm is introduced into the maintenance model to generate the optimal solution in a multi-objective manner. The results show that the maintenance model is feasible and effective for practical adaptive maintenance applications of OWTs in harsh working environments in practical applications.

A deep reinforcement learning ensemble for maintenance scheduling in offshore wind farms

Offshore wind energy, a cornerstone of sustainable power generation, faces escalating operational challenges as farms expand to harness cost efficiencies, including the imperative to counteract power fluctuations caused by wake effects and weather volatility. This study introduces a domain-informed Deep Q-Network (DQN) framework, engineered to optimize the allocation of maintenance resources and the strategic selection of maintenance tasks, resulting in an 11.1% increase in power generation compared to default wind conditions. By incorporating multiple wake model for enhanced decision-making accuracy, the scheduling dilemma is formulated as Markov Decision Processes (MDPs) to navigate the complexities of maintenance scheduling. A notable innovation is the integration of convolutional layers, which expedite algorithmic convergence. These results underscore the significant potential of our model to improve operational productivity in large-scale offshore wind farms.

Condition-based switching, loading, and age-based maintenance policies for standby systems

Standby techniques are widely incorporated in structural design to enhance the inherent reliability of systems. To further leverage the system performance during operation, decision-makers can adopt operational policies to manage system degradation. Specifically, at the system level, unit switching that dynamically determines the online unit contributes to avoiding unexpected shutdowns. At the unit level, adjusting load levels to manage the trade-off between condition degradation and revenue accumulation is crucial for maximizing profit. Additionally, adopting age-based maintenance as a tactical decision, which effectively facilitates the integration of maintenance resources, can be implemented to restore a degraded system. For instance, maintenance is typically scheduled at fixed moments for multi-generator power systems located in remote areas. In between maintenance moments, the proactive switching of generators can ensure uninterrupted output, and the adjustment of load levels for online generators helps to maximize output. Motivated by such engineering practices, this paper investigates condition-based switching, loading, and age-based maintenance policies for standby systems to maximize the expected profit rate in the long-run horizon. The problem is formulated as a Markov decision process. The structural properties of the control-limit switching and monotone loading policies are analyzed for easy policy implementation and efficient problem solutions. For comparative purposes, several heuristic policies are proposed and evaluated. Finally, numerical examples are presented to validate theoretical results and illustrate the superiority of the proposed risk control policy.

Maintenance Resources Optimization Using Pareto Analysis

Managing resource allocation for optimum effectiveness at various levels of maintenance activities is always a challenging task. Optimizing maintenance resources enables an organization to set priorities towards achieving certain goals which are availability and reliability of the equipment for operational excellence. The purpose of this analysis is to determine the optimum resources allocation proportions among the failure modes and to identify the failure modes that have the greatest cumulative effect on the equipment's downtime. This paper presents a methodology using the Pareto analysis in conjunction with failure mode effect and criticality Analysis in maintenance resources optimization. The approach is based on ensuring all failure mode criticality number are considered to obtain the significant failures mode that you should focus on as a priority. The analysis shows that failure mode; FM5, FM 3, FM 2, FM 12, FM 7 and FM 13 are confirmation to the Pareto principle, identifying that most of the downtime of the Instrumentation Air Compressors originated from these failure modes.

Wood discrimination of six commonly traded Phoebe and Machilus species using high-resolution plastid and nuclear DNA barcodes

Abstract Phoebe and Machilus trees, whose wood is known as Nanmu in China, face endangerment owing to long-term manmade destruction and climate change. Therefore, effective and accurate forensic tools to identify Nanmu species are required. This study developed a method for DNA identification of woods from six commonly traded Phoebe and Machilus species. DNA was efficiently extracted from oil cell-rich wood tissues, DNA barcodes were screened, species were compared, and a Nanmu DNA database was constructed. Comparison and analysis involved 26 sets of plastid genomes of Phoebe and Machilus. The five highest variable regions (Pi>0.009; psbA-trnH, psbJ, psbJ-petA, rpl32-trnL, and ycf 1) were identified, and three nuclear regions (ITS, LEAFY, and RPB2) were compared. Among single DNA fragments, the nuclear DNA region LEAFY proved optimal for identifying Nanmu species, while psbA-trnH+psbJ+ycf1 was the optimal plastid combination locus. Compared with distance-based (TaxonDNA) and machine-learning analyses, the tree-based method had a lower success rate for species identification. Four labelled Nanmu samples were selected to validate the method and clustered with P. zhennan and P. sheareri data based on the LEAFY region and plastid combination locus. These findings will contribute to the conservation, classification, architecture maintenance, and trade monitoring of Nanmu natural resources.

Environmental sustainability from the perspective of assets diversification: A comparative empirical analysis for developed and developing countries

Assets have an important role in sustainability discussions. The substitutability aspects of assets deal with weak and strong sustainability criteria. Weak sustainability enables substitutability between natural and man-made assets while strong sustainability requires the maintenance of natural resources and individual assets. In this respect, this study conducts a comparative empirical analysis of 126 developed and developing countries over the period 1995 to 2018 to investigate the significance of diverse assets such as natural capital, produced capital, human capital, and financial capital on environmental sustainability. Ecological footprint and biological capacity are used as detailed environmental sustainability indicators. The results based on the Fixed Effects Model and Generalized Method of Moments suggest that natural capital has a great potential for the environment and produced capital is environmentally unsustainable while human and financial capital have heterogeneous effects on the environment in developed and developing countries. The study also employs adjusted net saving (as an environmental sustainability indicator) and income group analysis for robustness checks. The findings of the study suggest that the protection of renewable natural capital, including its ability to regenerate is a core aspect of sustainability and the diversification of assets can reduce pressure on natural capital. Cooperative climate policies can enhance the benefits of investing in a diverse portfolio of assets and can help in achieving environmental sustainability.

Application of Deep Convolutional Generative Adversarial Network to Identification of Bridge Structural Damage

In recent years, the increase in the number of vehicles on the road has impacted the service life of bridges, and thus, their performance. In China, bridge inspections must be performed and documented at least once per year using labor-intensive and subjective approaches. In this study, a computationally efficient damage identification methodology for bridges was developed based on a deep convolutional generative adversarial network (DCGAN). To predict the damage to bridges, the vibration data of the structural damage are extracted from sensor measurements on the bridges and applied to the DCGAN model. The proposed damage identification method was validated on a simple supported beam bridge and a continuous girder bridge. The effects of sample size and noise on the identification accuracy were also evaluated. The performance of the DCGAN in the damage identification process is robust and reliable. This method for structural health monitoring offers a more objective evaluation of the overall performance and condition of a bridge, thus enabling bridge owners to efficiently allocate finite maintenance resources.

X-Stor: A Cloud-Native NoSQL Database Service with Multi-Model Support

In recent years at Tencent, we have observed that the use of multiple NoSQL databases for storing business data with diverse models has led to increased programming and deployment costs, as well as inefficient maintenance and underutilized resources. In this paper, we report X-Stor, a cloud-native NoSQL database system that supports multiple data models by extending different storage engines and efficiently managing them through a unified control plane. This design significantly reduces expenses and enables rapid expansion of new models, while seamlessly supporting their complete functionality through storage engine extensions. By consolidating multi-tenant services and data models on the same physical machines, X-Stor significantly enhances the utilization of cluster resources. Additionally, X-Stor introduces a standardized metric called Request Unit (RU) to measure tenant resource consumption for consumption-based pricing purposes. Leveraging this metric, we design RU-based resource management strategies and achieve efficient multi-tenant resource isolation and system load balancing. Currently, X-Stor manages a storage capacity of over 12PB for online operational data, including more than 100,000 tables with multiple data models. It handles 700 billion requests per day with a peak of 30 million requests per second. We evaluate the performance of X-Stor on popular benchmarks and production workloads. The results show that X-Stor performs well under diverse data models.

Optimizing Maintenance Resource Scheduling and Site Selection for Urban Metro Systems: A Multi-Objective Approach to Enhance System Resilience

This study developed an optimization model for the strategic location of maintenance resource supply sites and the scheduling of multiple resources following failures in urban metro systems, with the objective of enhancing system resilience. The model employs a multi-objective optimization framework, focusing primarily on minimizing resource scheduling time and reducing costs. It incorporates critical factors such as spatial location, network topology, station size, and passenger flow. A hybrid method, combining the non-dominated sorting genetic algorithm III and the technique for order of preference by similarity to ideal solution, is used to solve the model, with its effectiveness confirmed through a case study of the Nanjing Metro system. The simulation results yielded an optimal number of 21 maintenance resource supply stations and provided their placement. In the event of large-scale failures, the optimal resource scheduling strategy ensures demand satisfaction rates exceed 90% at critical stations, maintaining an overall rate of 87.09%, therefore significantly improving resource scheduling efficiency and the system’s emergency response capabilities and enhancing the physical resilience and recovery capabilities of the urban metro system. Moreover, the model accounts for economic factors, striving to balance emergency response capabilities with production continuity and cost efficiency through effective maintenance strategies and resource utilization. This approach provides a systematic framework for urban metro systems to manage sudden failures, ensuring rapid recovery to normal operations and minimizing operational disruptions in scenarios of limited resources.