Maintenance Scenarios Research Articles

Interpreting environmental impacts of wooden windows based on existing EPDs: An application in Italy

To meet climate targets, it is necessary to adopt robust and recognized methods, such as life cycle assessment (LCA), to evaluate emissions, particularly from building materials. As shown in the literature, embodied impacts have become as significant as operational impacts. The embodied impacts of a specific product are given in a transparent and quantitative manner in environmental product declarations (EPDs), based on LCA results. Windows play a crucial role in the energy performance of buildings. In fact, they typically account for 10 %–25 % of the exposed area of a building, resulting in more than 60 % of its total energy loss. In Italy, wooden windows make up 28 % of the window market and are the cheapest option for mild climates over a timeline of 60 years. However, an important element is still missing: a simplified interpretation of EPD results to enable straightforward, balanced comparisons of the environmental performance between alternatives. In this context, an analysis based EPDs of wooden windows is proposed, with a focus on production and use stages, for the six Italian climate zones. The three main steps of the study are: 1) evaluation of the influence of window properties on the embedded impacts; 2) assessment of maintenance scenarios; and 3) estimation of operational impacts regarding window properties and exposed climate. The results suggest that the influence of thermal transmittance, wood type and exposed climate is higher for cold climates due to better performing production materials and more frequent maintenance during service life.

Precise Tool to Target Positioning Widgets (TOTTA) in Spatial Environments: A Systematic Review.

TOTTA outlines the spatial position and rotation guidance of a real/virtual tool (TO) towards a real/virtual target (TA), which is a key task in Mixed reality applications. The task error can have critical consequences regarding safety, performance, and quality, such as surgical implantology or industrial maintenance scenarios. The TOTTA problem lacks a dedicated study and it is scattered in different domains with isolated designs. This work contributes to a systematic review of the TOTTA visual widgets, studying 70 unique designs from 24 papers. TOTTA is commonly guided by the visual overlap -an intuitive, pre-attentive "collimation" feedback- of simple shaped widgets: Box, 3D Axes, 3D Model, 2D Crosshair, Globe, Tetrahedron, Line, Plane. Our research discovers that TO and TA are often represented with the same shape. They are distinguished by topological elements (e.g. edges/vertices/faces), colors, transparency levels, and added. shapes, widget quantity, and size. Meanwhile some designs provide continuous "during manipulation feedback" relative to the distance between TO and TA by text, dynamic color, sonification, and amplified graphical visualization. Some approaches trigger discrete "TA reached feedback" such as color alteration, added sound, TA shape change, and added text. We found the lack of golden standards, including in testing procedures, as current ones are limited to partial sets with different and incomparable setups (different target configurations, avatar, background, etc.). We also found a bias in participants: right-handed, young male, non-color impaired.

Insights on the Optimization of Short- and Long-Term Maintenance Decisions for Floating Offshore Wind Using Nested Genetic Algorithms

The present research explores the optimization of maintenance strategies for floating offshore wind (FOW) farms using nested genetic algorithms. The primary goal is to provide insights on the decision-making processes required for both immediate and strategic maintenance planning, crucial for the viability and efficiency of FOW operations. A nested genetic algorithm was coupled with discrete-event simulations in order to simulate and optimize maintenance scenarios influenced by various operational and environmental parameters. The study revealed that short-term maintenance timing is significantly influenced by wind conditions, with higher electricity prices justifying on-site spare parts storage to mitigate operational disruptions, suggesting economic incentives for maintaining on-site inventory of spare parts. Long-term strategic findings emphasized the impact of planned intervals between inspections on financial outcomes, identifying optimal strategies that balance operational costs with energy production efficiency. Ultimately, this study highlights the importance of integrating sophisticated predictive models for failure detection with real-time operational data to enhance maintenance decision-making in the evolving landscape of offshore wind energy, where future farms are likely to operate farther from onshore facilities and under potentially highly varying market conditions in terms of electricity prices.

Affordable generic digital twins for existing building environment management and an onsite deployment

The energy consumption during the operation and maintenance phase of buildings is huge. As the built-up area in China increases, the demand for energy conservation in existing buildings has become a key focus of its dual carbon policy. Intelligent operation and maintenance based on digital twins is an emerging means to reduce carbon emissions from buildings, but it faces some problems in the process of promotion. Complete digital and intelligent transformation requires significant investment and has certain requirements for project parties and operation and maintenance teams. Small businesses or individual households have relatively simple requirements for intelligent operation and maintenance scenarios and do not require complete digital twins. To address the above issues, this article uses an affordable universal digital twin framework to provide a digital solution for intelligent operation and maintenance of existing buildings. This solution allows networking communication between devices and uses IoT modules to monitor and control the environment. This digital twinning model can reduce the measurement and control of energy-consuming end devices without on-site transformation and has rich scalability. This article uses the solution to deploy an office at a university in Henan Province and specifically measures the power consumption of displays, indoor environment, and air conditioning. According to the needs, it expands the space occupation, fans, air handlers, lights, and other end devices of the digital twin. The digital twin accurately presents the energy consumption of the office during extreme weather conditions, which has an auxiliary role in promoting digital twins in the region and optimizing energy consumption in existing buildings.

Studying the Structure of the Road Pavement and Specifying the Repair Method

Studying the structural condition of road pavement is essential for making effective decisions about the type of maintenance or rehabilitation that will be carried out on a pavement section. Reliable quantitative assessment of the volume of necessary repairs will ensure the performance characteristics and durability throughout the entire service life at minimal cost. This article presents detailed results from Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR) studies. In addition, an analysis of these results and recommendations for strengthening the road pavement for the Sh-176 “Sighnaghi-Saint Nino Monastery” road to develop rehabilitation and maintenance scenarios. FWD results combined with road pavement layer thickness and traffic load information to determine residual service life and necessary treatment type. Thus, using the nonlinear theory of layered halfspace elasticity, pavement structure analyzed in the same way as other civil engineering structures.

Enhanced Wind Farm Maintenance Scheduling Including Wake Effects

With the expansion and dissemination of offshore wind farms, the costs associated with asset maintenance and operation become ever more relevant. Among maintenance activities, preventive interventions represent a significant share of the costs and, as opposed to corrective interventions, may be calendarized according to the farm operator’s strategy. In an offshore wind farm there is often aerodynamic interaction between different machines, namely as a given upstream turbine extracts energy from the wind, it will necessarily reduce the energy available for a turbine that is downstream in the farm. This is broadly referred to as wake effects. The relative proximity of turbines within a farm means such an interaction results in lower energy yield for downstream turbines. Maintenance activities require the shutdown of the turbine, hence the turbine under maintenance effectively produces no wake effect. It follows that, depending on weather conditions, the sequence of wind turbines to be deactivated for large preventive maintenance campaigns may impact the total energy production of an offshore wind farm, especially when the wind direction is considered. The present study investigates the potential of incorporating wake effects in the scheduling of offshore wind farm maintenance activities, considering a reference farm layout and using historical climate data for a real offshore site. Different maintenance scenarios are considered, including distinct maintenance vessels and different intervention criteria. Results show there is a small but relevant potential gain in energy yield when the maintenance scheduling is determined including wake effects, with annual energy production increase of up to 0.18% for the parametric values considered.

Game-based collaborative scientific inquiry learning using realistic context and inquiry process-based multidimensional scaffolding

ABSTRACT Integrating multidimensional scaffolding into digital game-based learning is expected to address scientific inquiry teaching challenges, simulating real-world complexities to ignite learner interest. This study stands out by using realistic scenarios and multidimensional scaffolding to develop an online collaborative scientific inquiry game, designed based on the scientific inquiry process to create corresponding game mechanics and processes. It leverages cloud technology integration as scaffolding, accompanied by a train maintenance scenario with non-player characters (NPC) to enhance electromagnetism learning for learners. This study involved 90 college students divided into three groups: game scaffolding group (remote gaming teaching activities with NPC-based scaffolding), document scaffolding group (remote case study activities with document-based scaffolding), and no scaffolding group (remote case study activities without scaffolding). The results indicate that providing scaffolding in remote scientific inquiry teaching enhances learning outcomes compared to activities without scaffolding. Learners in the game scaffolding group reported a high level of flow experience, perceived that remote gaming teaching was helpful for scientific inquiry learning, and experienced lower anxiety. Furthermore, learners provided highly positive feedback on the usefulness of multidimensional scaffolding in the game. There were no significant differences among the three instructional activities in terms of flow state, activity anxiety, and the usefulness of scientific inquiry.

Prediction of maintenance hours based on maintenance scenarios

Maintenance time is one of the important parameters that affect maintenance costs, and is a factor in evaluating whether civil aircraft can be repaired quickly and economically, comprehensively reflecting the level of maintainability design. The study in this article is based on the analysis of maintenance scenarios across the entire system by identifying typical maintenance modes and their decomposition actions. At the same time, the concept of degree division and recognition principles are introduced to establish a maintenance time prediction table. The aim is to scientifically and reasonably predict maintenance time during the aircraft design stage, identify improved designs that can reduce maintenance costs, reduce the consumption of maintenance resources, improve maintenance efficiency, and ultimately achieve the goal of reducing the entire life cycle cost of the aircraft. Finally, a case study of maintenance time for a certain aircraft scheduled maintenance scenario was conducted using the prediction table, the main factors affecting maintenance time are analyzed, and directions for optimizing design are proposed.

Simulating the corrective actions affecting system availability: a system dynamics approach

Purpose This paper aims to simulate vital corrective actions (CAs) affecting system availability through a system dynamics approach based on the results obtained by analyzing the causal relationships among failure modes and effects analysis elements. Design/methodology/approach A stock and flow diagram has been developed to simulate system behaviors during a timeframe. Some improvement scenarios regarding the most necessary CAs according to their strategic priority and the possibility of eliminating root causes of critical failure modes in a roller-transmission system have been simulated and analyzed to choose the most effective one(s) for the system availability. The proposed approach has been examined in a steel-manufacturing company. Findings Results indicated the most effective CAs to remove or diminish critical failure causes that led to the less reliability of the system. It illustrated the impacts of the selected CAs on eliminating or decreasing root causes of the critical failure modes, lessening the system’s failure rate and increasing the system availability more effectively. Research limitations/implications Results allow managers and decision-makers to consider different maintenance scenarios without wasting time and more cost, choosing the most appropriate option according to system conditions. Originality/value This study innovation would be the dynamic analysis of interactions among failure modes, effects and causes over time to predict the system behavior and improve availability by choosing the most effective CAs through improvement scenario simulation via VENSIM software.

Enhancing Transparency and Accountability in Predictive Maintenance with Explainable AI

Predictive maintenance is a critical aspect of industrial operations, enabling proactive identification and mitigation of potential failures in machinery and equipment. However, the widespread adoption of AI-driven predictive maintenance solutions has been hindered by the opaque nature of many machines learning models, raising concerns about transparency, accountability, and trust. This research aims to address these challenges by developing explainable AI techniques for predictive maintenance in industrial systems. By integrating interpretability methods with advanced predictive models, we seek to enhance the transparency and interpretability of AI-driven maintenance decisions. Our proposed methodology combines state-of-the-art machine learning algorithms with local and global explainability techniques, such as LIME, SHAP, and feature importance analysis. Through extensive experiments on real-world industrial data, we evaluate the performance of our explainable AI models and demonstrate their ability to provide insightful explanations, enabling domain experts to understand the underlying reasoning and critical factors contributing to maintenance predictions. Furthermore, we explore the impact of explainable AI on improving trust, accountability, and adoption of AI systems in industrial predictive maintenance scenarios. Keywords— Predictive Maintenance, Explainable AI (XAI), Machine Learning, Interpretability, LIME, SHAP, Feature Importance, Industrial Systems, Trust in AI, Accountability.

Development of a routing method for ground-air Ad-Hoc network of special purpose

The object of the study is the process of forming control decisions to ensure the operation of the ground-air communication network routing subsystem based on neural network algorithms. The carried-out research is based on the application of the numerical-analytical approach to the selection of modern scientific and applied solutions for building management models for promising Ad-Hoc communication networks. In the Google Collab simulation environment, using the Python programming language, it was possible: firstly, to simulate the operation of a ground-to-air communication network based on previously obtained models and a routing process management system based on the FA-OSELM algorithm. Secondly, in accordance with the scenario of route construction and maintenance described in the article, to experimentally determine the communication metrics of the proposed method of intelligent routing of the ground-air Ad-Hoc special-purpose network, in order to assess its efficiency, adequacy and reliability of the results obtained. Thus, in order to evaluate the effectiveness of the proposed solutions, a comparative analysis of the application of three existing routing methods (FLCA, Q-Routing, Neuro Routing) used in Ad-Hoc networks relative to the developed method was conducted. The result of the experiment showed that the proposed routing method MAODV-FA-OSELM provides significant advantages over analogs. Thus, the method exhibits the best network throughput (2.12e+06), the lowest average network latency (0.12), the lowest packet loss (6.32), the lowest bit error rate (2.41), and the lowest overhead (0.10e+06). However, it should be noted that a promising direction of further research may be the study of the computational complexity of the routing management process and the determination of the minimum allowable representative sample of initial data to ensure online decision-making.

Population Pharmacokinetics and Dosing Simulations for Aripiprazole 2-Month Ready-to-Use Long-Acting Injectable in Adult Patients With Schizophrenia or Bipolar I Disorder.

A ready-to-use (RTU) long-acting injectable (LAI) formulation of aripiprazole monohydrate for administration once every 2 months, available in 960 mg (Ari 2MRTU 960) or 720 mg doses, has been developed for the treatment of schizophrenia or bipolar I disorder. A previously developed and validated population pharmacokinetic model for characterizing aripiprazole plasma concentrations following administration of oral aripiprazole or aripiprazole once-monthly (AOM) intramuscular injection was expanded to include the RTU LAI formulation of aripiprazole (Ari RTU LAI). Overall, 8899aripiprazole pharmacokinetic samples from 1191 adults from 10 clinical trials were included in the final combined analysis data set. Aripiprazole plasma concentration-time profiles were simulated for various Ari RTU LAI initiation and maintenance scenarios in 1000 virtual patients. Diagnostic plots demonstrated that the final population pharmacokinetic model, which incorporated data for oral aripiprazole, AOM, and Ari RTU LAI, adequately described aripiprazole concentrations following Ari RTU LAI administration. Absorption of Ari RTU LAI was modeled by a parallel zero-order and lagged first-order process. Simulations across multiple scenarios were performed to inform dosing recommendations, including various treatment initiation regimens for a 2-monthly formulation of Ari RTU LAI in patients with or without prior stabilization on oral aripiprazole, and for patients switching from AOM. Additional simulations accounted for missed/delayed doses, cytochrome (CYP) 2D6 metabolizer status, and concomitant use of CYP2D6 or CYP3A4 inhibitors. Overall, simulations across a variety of scenarios demonstrated an Ari RTU LAI pharmacokinetic exposure profile that was comparable to AOM, with a longer dosing interval.

Concept of operation of the Agile Robot Transporter for ITER in-vessel maintenance

ITER machine availability is crucial for fusion reactors in order to be the main source of electricity, which is affected by many aspects. This paper focuses on maintenance aspects of the machine. As the plasma facing components inside the fusion reactors become highly activated caused by the neutrons generated by fusion reactions, maintenance of the fusion reactors needs to be done by remote means. In order to increase machine availability, it is required to have efficient maintenance scenario in terms of time and effort, which can be achieved through effective in-vessel remote handling (RH) operations, simplified maintenance of the RH system itself, well-coordinated logistics, and trained personnel, etc. All these aspects need to be investigated during the design phases, which requires a well-defined concept of operation of the RH systems. This paper describes the concept of operation of the Agile Robot Transporter (ART), an ITER in-vessel RH system, that is under development to carry out neutron and optical diagnostic calibrations, and removal/replacement of the blanket first wall samples. Based on the functional analysis of the required in-vessel tasks, system requirements are defined. Among them, key requirements to improve RH operation efficiency are identified and the ART is designed accordingly. Efficient deployment and removal operations of the ART are crucial so that it requires less vacuum vessel (VV) port opening and less cask transportation, which reduces needs for hands-on operations resulting in less human radiation exposure. Various and flexible tool exchange and storage concepts are proposed that allow parallel operations between the in-vessel and ex-vessel RH systems. Modular and re-configurable design is proposed for easy RH system maintenance, and effective rescue operations to rescue the RH system when it fails.

Operation, Maintenance, and Decommissioning Cost in Life-Cycle Cost Analysis of Floating Wind Turbines

Offshore wind farms are great options for addressing the world’s energy and climate change challenges, as well as meeting rising energy demand while taking environmental and economic impacts into account. Floating wind turbines, in specific, depict the next horizon in the sustainable renewable energy industry. In this study, a life-cycle cost analysis for floating offshore wind turbines is developed by combining the most recent data and parametric formulas from databases and relevant papers. The cost analysis models focused on cost minimization with special emphasis on Operation and Maintenance Cost (OPEX), Decommissioning Cost (DECOM), and Levelized Cost of Energy (LCOE), which are important factors in wind power economy. Given that floating wind energy is still developing, the presented scenarios should be beneficial in making future decisions. The cost analysis scenarios include on-site and off-site maintenance scenarios for OPEX. In addition, four alternative scenarios for DECOM have been examined: mechanical recycling, mechanical-incineration, incineration processes, and landfill. According to the findings of these scenarios, OPEX varies from 16.89 to 19.93 £/MWh and DECOM between 3.47 and 3.65 £/MWh, whilst the total LCOE varied from 50.67 to 66.73 £/MWh.

An Index Model for the Evaluation of the Performance of Lock Navigation Scheduling Rules Considering the Perspective of Stakeholders

The lock navigation scheduling problem involves multiple stakeholder groups. The game relationship between stakeholders directly affects the sustainable development of regional society and economy. Considering the objectivity and accuracy of social network analysis (SNA) for relationship identification, indicator screening, and system construction, it completes the stakeholder extraction and evaluation index system construction. Considering that the conditional probability of nodes in Bayesian networks (BN) can flexibly and intuitively characterize the direction and strength of the factors’ roles in a complex scenario, this study proposes an index model for the evaluation of the performance of lock navigation scheduling rules. Firstly, build the BN topology under the efficiency, safety, and fairness criteria. Subsequently, an improved composite index method is combined with the BN to determine the evaluation index from the stakeholder perspective. This study takes the Three Gorges locks maintenance scenario as an example. The index evaluation model can accurately identify the direction, trend, and magnitude of the performance of the scheduling rules on the efficiency, safety, and fairness criteria under navigational constraints, realizing the dynamic and quantitative evaluation of the performance of lock navigation scheduling rules during the observation period.

Designing tailored maintenance strategies for systematic reviews and clinical practice guidelines using the Portfolio Maintenance by Test-Treatment (POMBYTT) framework

BackgroundOrganizations face diverse contexts and requirements when updating and maintaining their portfolio, or pool, of systematic reviews or clinical practice guidelines they need to manage. We aimed to develop a comprehensive, theoretical framework that might enable the design and tailoring of maintenance strategies for portfolios containing systematic reviews and guidelines.MethodsWe employed a conceptual approach combined with a literature review. Components of the diagnostic test-treatment pathway used in clinical healthcare were transferred to develop a framework specifically for systematic review and guideline portfolio maintenance strategies.ResultsWe developed the Portfolio Maintenance by Test-Treatment (POMBYTT) framework comprising diagnosis, staging, management, and monitoring components. To illustrate the framework’s components and their elements, we provided examples from both a clinical healthcare test-treatment pathway and a clinical practice guideline maintenance scenario. Additionally, our literature review provided possible examples for the elements in the framework, such as detection variables, detection tests, and detection thresholds. We furthermore provide three example strategies using the framework, of which one was based on living recommendations strategies.ConclusionsThe developed framework might support the design of maintenance strategies that could contain multiple options besides updating to manage a portfolio (e.g. withdrawing and archiving), even in the absence of the target condition. By making different choices for variables, tests, test protocols, indications, management options, and monitoring, organizations might tailor their maintenance strategy to suit specific contexts and needs. The framework’s elements could potentially aid in the design by being explicit about the operational aspects of maintenance strategies. This might also be helpful for end-users and other stakeholders of systematic reviews and clinical practice guidelines.

Safety and effectiveness of CIMAvax-EGF administered in community polyclinics.

https://rpcec.sld.cu/trials/RPCEC00000205-En, identifier RPCEC00000205.

Fragmentation and biodiversity change in urban vegetation: A case study of tram lines

AbstractThis study explores the vegetation composition along various tram line sections in Bydgoszcz, Poland, with a focus on understanding the impact of usage, maintenance, and historical development on urban vegetation dynamics. This study addresses this knowledge gap by investigating how these factors influence plant species composition, leading to variations in the prevalence of native and non‐native species. The aim of this study is to contribute to urban ecological knowledge and inform vegetation management strategies. The investigation centers on five tram line sites representing different usage and maintenance scenarios: Unused line, New line, Loop rec, Loop old, and Old line. Through phytocoenological relevés conducted in 2020 and 2021, we measured the plant taxa coverage. Detrended Correspondence Analysis (DCA) and Canonical Correspondence Analysis (CCA) revealed relationships between plant species and tram line sites. The results indicated the presence of 107 plant taxa, with graphical representations highlighting the prevalent species on each tram line section. Older sections showed a higher representation of non‐native plant species, whereas new sections exhibited native species dominance. Moisture‐indicative values suggest an affinity for soils with average moisture content. CCA provides insights into the relationships between plant taxa and site variants and offers valuable implications for urban vegetation management and conservation strategies. The novelty of this study lies in its holistic approach for understanding how multiple factors interact to shape urban vegetation. By illuminating urban ecological dynamics and informing decision‐making processes, this study contributes to urban planning, ecology, and biodiversity conservation. Additionally, this study fills a knowledge gap by providing insights into the unique ecological dynamics and species composition of tram lines in urban environments. Unlike previous research in Central European countries focusing on planted vegetation, this study explored the spontaneous spread of plants and successional patterns along tram lines, enhancing our understanding of the environmental conditions created by tram lines that influence vegetation composition and development.

Impact Mitigation of Tunnels Maintenance Works on Highway Performance

Major maintenance interventions on highways (e.g., for the safety improvement of tunnels and viaducts) involve the presence of work zones for extended periods of time and along wide sections of the infrastructure. These interventions lead to supply changes, especially a reduction in capacity, causing a decrease in the performance of the transportation system, which users experience with increased traffic congestion and economic losses in terms of productivity. This paper aims to demonstrate that proper traffic management and work zone planning allow for a significant reduction of such impacts. In relation to traffic management, possible solutions consist of diverting traffic flows onto alternative routes (if possible, using primary roads) or keeping them in the remaining lanes along the work zone. In terms of work zone planning, leverage can be applied, on the one hand, through the design of the work zone layout and maintenance scheme and, on the other, through the duration and scheduling of the interventions. The methodology proposed in this paper is based on traffic simulation models in order to assess different maintenance scenarios under varying demand levels (i.e., during the rush hour and off-peak hours, weekdays and weekends, summer and winter months) and vehicular compositions (i.e., percentage of heavy vehicles), with the goal of identifying the scenario that minimizes the impacts on users. Modelling evidence on a real case study of tunnel maintenance interventions on an Italian highway is reported, discussing the policy implications on the basis of quantitative indicators, such as level of service, duration of delays, length of queues, and economic impacts.

A Knowledge-Assisted Variable Neighborhood Search for Two-Sided Assembly Line Balancing Considering Preventive Maintenance Scenarios

A Knowledge-Assisted Variable Neighborhood Search for Two-Sided Assembly Line Balancing Considering Preventive Maintenance Scenarios