[Preventive treatment alternatives in pediatric patients with autism spectrum disorder in the dental practice. literature review]. / Alternativas de tratamiento preventivo en pacientes pediátricos con trastorno del espectro autista en la consulta odontológica. revisión de la literatura.

Autism comes from the Greek word auto, which means "self." Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction and communication. Dental treatment in patients with ASD can be challenging due to their behavior. Therefore, this review discusses preventive treatment techniques for pediatric patients with ASD at the dental office, as the prevalence of children with autism is growing. Thus, dentists would face more patients with autism in their daily practice. Regarding treatment protocols, they would require specialized attention in dental management. Information was searched in the following databases: PubMed, SciELO, Redalyc, Elsevier, and the International Association of Paediatric Dentistry (IAPD). The descriptors used were: Pediatric Dentistry, Autism, ASD, Autism Spectrum Disorder, and Management of the autistic patient.

Application of the Nadaraya-Watson estimator based attention mechanism to the field of predictive maintenance.

Attention mechanism has recently gained immense importance in the natural language processing (NLP) world. This technique highlights parts of the input text that the NLP task (such as translation) must pay "attention" to. Inspired by this, some researchers have recently applied the NLP domain, deep-learning based, attention mechanism techniques to predictive maintenance. In contrast to the deep-learning based solutions, Industry 4.0 predictive maintenance solutions that often rely on edge-computing, demand lighter predictive models. With this objective, we have investigated the adaptation of a simpler, incredibly fast and compute-resource friendly, "Nadaraya-Watson estimator based" attention method. We develop a method to predict tool-wear of a milling machine using this attention mechanism and demonstrate, with the help of heat-maps, how the attention mechanism highlights regions that assist in predicting onset of tool-wear. We validate the effectiveness of this adaptation on the benchmark IEEEDataPort PHM Society dataset, by comparing against other comparatively "lighter" machine learning techniques - Bayesian Ridge, Gradient Boosting Regressor, SGD Regressor and Support Vector Regressor. Our experiments indicate that the proposed Nadaraya-Watson attention mechanism performed best with an MAE of 0.069, RMSE of 0.099 and R2 of 83.40 %, when compared to the next best technique Gradient Boosting Regressor with figures of 0.100, 0.138, 66.51 % respectively. Additionally, it produced a lighter and faster model as well.•We propose a Nadaraya-Watson estimator based "attention mechanism", applied to a predictive maintenance problem.•Unlike the deep-learning based attention mechanisms from the NLP domain, our method creates fast, light and high-performance models, suitable for edge computing devices and therefore supports the Industry 4.0 initiative.•Method validated on real tool-wear data of a milling machine.

Rheological Performance Analysis of Different Preventive Maintenance Materials in Porous High-Viscosity Asphalt Pavements.

Porous asphalt pavements are widely used in rainy and wet areas for their skid resistance, noise reduction, runoff minimization and environmental sustainability. Long-term moisture vapor erosion and the destabilization of large pore structures can easily result in pavement problems such as fragmentation, spalling, cracking, and excessive permanent deformation. To this end, four different preventive maintenance materials, including the rejuvenation (RJ), cohesion reinforcement (CEM), polymerization reaction, and emulsified asphalt (EA) types, were selected in this paper to improve the high-viscosity porous asphalt pavement. The effects of the different preventive maintenance materials on the temperature sensitivity, rheological properties and fatigue performance of high-viscosity modified asphalt were evaluated through temperature sweep, frequency sweep, multi-stress creep recovery (MSCR), linear amplitude sweep (LAS), and bending beam rheometer (BBR) tests. The results showed that the four preventive maintenance materials exhibit different enhancement mechanisms and effects. RJ improves the fatigue properties, deformation resistance and low-temperature cracking resistance of aged asphalt by adding elastomeric components; CEM materials are more conducive to increasing the low-temperature crack resistance of aged asphalt; while GL1 and EA improve the viscoelastic behavior of aged asphalt, but the effect of the dosing ratio needs to be considered.

Valorization of recycled wastes in pavement preventive maintenance: A review on reclaimed asphalt pavement and recycled waste tire.

Pavement preventive maintenance (PPM) is critical to ensuring traffic efficiency, road user experience, and safety. However, it imposes significant costs in annual road infrastructure budgets because it requires high-quality and natural material resources. This study provides a systematic and comprehensive review on the use of recycled wastes as an alternative for the natural materials used in PPM mixes. Specifically, the use of recycled waste tires (RWT) and reclaimed asphalt pavement (RAP) in chip seals, microsurfacing, slurry seals, and thin asphalt overlays were discussed. The current state-of-practice in terms of material specification and mix design were comprehensively investigated for PPM mixes containing RAP (RAP-PPM) and PPM with RWT (RWT-PPM). Laboratory and field performances of waste-treated PPM mixes were elaborated and compared with conventional PPM treatments to determine the feasibility of the RAP-PPM and RWT-PPM technologies. Furthermore, current research gaps were identified, and prospects for future investigations were discussed. It is envisaged that this study can provide a sufficient theoretical basis for the widespread practical application and beneficial use of this valuable technology, towards promoting sustainability in pavement maintenance practice.

Research on the value of refined centralized management combined with scientific preventive maintenance in the standardized management of clinical ventilators / 中国医学装备

Objective:To study the application value of refined centralized management combined with scientific preventive maintenance in the standardized management of clinical ventilators.Methods:The ventilator failure information wad collected,a ventilator information management database was established,the refined and centralized management and scientific preventive maintenance measures for ventilators was developed in terms of planning,implementing,and updating the ventilator management system as well as training and assessment.119 ventilators in clinical use in People's Hospital of the Xinjiang Uygur Autonomous Region from 2021 to 2022 were selected,the conventional management mode(referred to as conventional mode,50 units)and the refined centralized management combined with scientific preventive maintenance mode(referred to as joint mode,69 units)were adopted respectively for management.The maintenance status of ventilators in the two modes and the total amount of work in 2021 and 2022 were compared.Results:There were 85 ventilator failures in 2021 and 58 ventilator failures in 2022 in conventional mode.34 ventilator failures in 2021 and 44 ventilator failures in 2022 in joint mode.The number of failures,maintenance time and satisfaction of clinical department with the ventilators of the joint mode in 2021 were(3.25±1.06)times/month,(12.57±10.31)days and(91.50±1.73)% respectively,the number of failures and maintenance time were less than those of conventional mode,satisfaction of clinical department was higher than that of the conventional mode,the difference was statistically significant(t=14.458,2.501,2.563,P<0.05).The number of failures of ventilators of joint mode in 2022 was(3.08±2.02)times/month,which was less than that of conventional mode,the difference was statistically significant(t=4.655,P<0.05).The number of failures,number of times to contact the equipment department,consumable costs,adverse events,and number of terminal disinfections for 119 ventilators of the two modes in 2021 were(10.25±2.34)times/month,(16.75±6.54)times/month,and CNY(44,000±49,300)/month,(2.49±0.92)pieces/month and(382.58±83.67)times/month,which were higher than those in 2022,the equipment completeness rate and frequency of clinical training were(88.27±1.29)% and(3.42±1.51)times/month,respectively,which were lower than those of 2022,the difference was statistically significant(t=5.124,6.103,4.099,7.884,1.980,5.607,3.564,P<0.05).Conclusion:Refined centralized management combined with scientific preventive maintenance can effectively reduce the number of ventilator failures,maintenance time and costs,etc.,and improve the quality of standardized management of clinical ventilators.

Research on optimization of preventive maintenance path for electric medical equipment based on multi-criteria decision analysis / 中国医学装备

Objective:To optimize the preventive maintenance path of electric medical equipment by adopting multi-criteria decision analysis(MCDA),and to verify its optimization effect.Methods:The preventive maintenance paths for electric medical equipment were determined through literature research and management group discussions.An expert meeting was organized to discuss,determine,and demonstrate the preventive maintenance path evaluation criteria,and the weight of the criteria was set.Each preventive maintenance path was scored by experts based on criteria,the score of each maintenance path was calculated using the mean and sorted to form the optimal path.767 electric medical equipment of 7 categories in clinical use in the 960th Hospital of the PLA Joint Logistics Support Force from 2021 to 2022 were selected,conventional preventive maintenance management(referred to as conventional management mode)and preventive maintenance path management optimized by MCDA method(referred to as MCDA management mode)were adopted respectively according to different management modes.The changes in indicators such as failure rate,maintenance time,quality inspection pass rate and maintenance cost of electric medical equipment were compared between the two management models.Results:The failure occurrence rate of electric medical equipment of the MCDA management mode was 8.71%(67/767),which was lower than that of the conventional management mode,the difference was statistically significant(x2=3.960,P<0.05).The equipment maintenance time of the MCDA management mode was(2.24±1.17)days,which was lower than that of the conventional management mode,the difference was statistically significant(t=2.360,P<0.05).The quality inspection qualification rate of the MCDA management model was(96.57±2.74)%,which was higher than that of the conventional management mode,the difference was statistically significant(t=4.342,P<0.05).The average maintenance cost of equipment of the MCDA management model accounted for 2.37%of its assets,which was lower than that of the conventional management mode,the difference was statistically significant(x2=4.261,P<0.05).Conclusion:The MCDA method can provide a quantitative structural model for the optimization of preventive maintenance paths for electric medical equipment,and the optimized preventive maintenance paths can achieve efficient management of electric medical equipment,and focusing on the training of maintenance personnel's technical level can increase the self-repair rate and reduce the failure rate of medical equipment.

Towards partial autonomy of operation and maintenance of unreliable equipment.

This study considers the case of unreliable equipment subjected to random failures that induce high maintenance and environmental costs. We consider situations where the equipment is located in remote areas, which are difficult to access, and situations where there could be confinement linked to a pandemic, making it impossible to perform the replacement of the failed equipment. In such situations, the objective is to explore the possibility of providing a system with self-maintenance capabilities to a certain extent by adding redundant (stand-by) identical modules. Both the designs (with and without passive redundancy) are considered. A mathematical cost model is developed for each alternative to help decide whether to adopt redundancy and determine the optimal number of redundant modules, which minimises the total expected cost. The latter includes the costs related to the acquisition, maintenance, and recycling of failed modules. A numerical example is presented, and a sensitivity study is performed to investigate the effect of variations in relevant input parameters on the optimal design.

Smart Preventive Maintenance of Hybrid Networks and IoT Systems Using Software Sensing and Future State Prediction.

At present, IoT and intelligent applications are developed on a large scale. However, these types of new applications require stable wireless connectivity with sensors, based on several standards of communication, such as ZigBee, LoRA, nRF, Bluetooth, or cellular (LTE, 5G, etc.). The continuous expansion of these networks and services also comes with the requirement of a stable level of service, which makes the task of maintenance operators more difficult. Therefore, in this research, an integrated solution for the management of preventive maintenance is proposed, employing software-defined sensing for hardware components, applications, and client satisfaction. A specific algorithm for monitoring the levels of services was developed, and an integrated instrument to assist the management of preventive maintenance was proposed, which are based on the network of future states prediction. A case study was also investigated for smart city applications to verify the expandability and flexibility of the approach. The purpose of this research is to improve the efficiency and response time of the preventive maintenance, helping to rapidly recover the required levels of service, thus increasing the resilience of complex systems.

Diagnosis, management, and prevention of malfunctions in anesthesia machines.

BACKGROUND: The anesthesia machine serves as a vital piece of lifesaving equipment. OBJECTIVE: To analyze incidents of failures in the Primus anesthesia machine and address these malfunctions to reduce recurrence of failure, save maintenance costs, enhance safety, and improve overall efficiency. METHODS: We conducted an analysis on the records pertaining to the maintenance and parts replacement of the Primus anesthesia machines used in the Department of Anaesthesiology at Shanghai Chest Hospital over the past two years to identify the most common causes of failure. This included an assessment of the damaged parts and degree of damage, as well as a review of factors that caused the fault. RESULTS: The main cause of the faults in the anesthesia machine was found to be air leakage and excessive humidity in the central air supply of the medical crane. The logistics department was instructed to increase inspections to check and ensure the quality of the central gas supply and ensure gas safety. CONCLUSION: Summarizing the methods for dealing with anesthesia machine faults can save hospitals a lot of money, ensure normal hospital and department maintenance, and provide a reference to repair such faults. The use of Internet of Things platform technology can continuously develop the direction of digitalization, automation, and intelligent management in each stage of the "whole life cycle" of anesthesia machine equipment.

Maintenance Cost Minimization for an Agricultural Harvesting Gripper.

A crucial aspect that has to be considered in all fields and, especially, in smart farming, a rapidly developing industry, is maintenance. Due to the costs generated by both under-maintaining and over-maintaining the components of a system, a balance has to be achieved. The paper is focused on presenting an optimal maintenance policy used to ensure cost minimization by determining the optimal time to make a preventive replacement of the actuators of a harvesting robotic system. First, a brief presentation of the gripper with Festo fluidic muscles used in a novel way instead of fingers is given. Then, the nature-inspired optimization algorithm, as well as the maintenance policy are described. The paper also includes the steps and the obtained results of the developed optimal maintenance policy applied for the Festo fluidic muscles. The outcome of the optimization shows that a significant reduction in the costs is obtained if one performs a preventive replacement of the actuators a few days before the lifetime provided by the manufacturer and the lifetime estimated using a Weibull distribution.

Maintenance of GE Lunar Prodigy Dual-Energy X-Ray Bone Densitometer:3 case reports / 医疗卫生装备

The working principle of GE Lunar Prodigy Dual-Energy X-ray Bone Densitometer was introduced.The causes and elimination of three typical failures of GE Lunar Prodigy Dual-Energy X-ray Bone Densitometer were analyzed,and some preventive maintenance suggestions were put forward accordingly.References were provided for clinical engineers to treat similar failures.

Maximizing the Application of RAP in Asphalt Concrete Pavements and Its Long-Term Performance: A Review.

The use of recycled asphalt pavement (RAP) materials in asphalt concrete pavements (ACP) brings significant cost and environmental benefits. In practice, however, the amount of RAP readily available far exceeds the amount being utilized in ACPs, which still leaves the problem of excess RAP in the environment partially solved. Additionally, ACPs containing RAP materials (i.e., RAP-ACPs) can still be landfilled after they have reached the end of their useful life, which may restore the original environmental waste problem. To address these, researchers have demonstrated different ways to maximize the application of RAP in ACPs. Among them, the use of RAP in pavement preventive maintenance (PPM) treatments and the repeated recycling of RAP-ACPs (i.e., RnAP) are specifically discussed in this review. It is envisaged that, by promoting these two practices, the application and benefits of RAP can be further maximized to improve sustainability. This review also discusses the long-term behavior of RAP-ACP, which is crucial to inspire confidence in the wider application of RAP in ACP. Studies on RAP-PPM have shown that virgin PPM treatments can successfully accommodate RAP materials by adjusting their mix design. So far, research on RnAP has been limited to how multiple-recycling affects the performance properties of the blends, showing improvements in rutting resistance and moisture susceptibility but little effect on linear viscoelasticity and cracking. Overall, the lack of sufficient research is considered to be the biggest challenge in facilitating the implementation of these two sustainable RAP technologies. Little or nothing is known about the bonding mechanisms between RAP and fresh PPM binders, the molecular and chemical changes in RnAP binders, or the functional performance characteristics, actual pavement performance, and long-term performance of both RAP-PPM and RnAP blends. An understanding of these aspects is very relevant to maximize and continue the beneficial reuse of RAP in ACPs while safeguarding human and environmental health.

Switchgear Digitalization-Research Path, Status, and Future Work.

To keep pace with global energy efficiency trends and, in particular, emission reduction targets in the maritime sector, both onshore and maritime power distribution systems need to be adapted to the relevant new technologies and concepts. As an important link in the distribution chain, medium-voltage switchgear (MV) is expected to be stable and reliable while operating as efficiently as possible. Failures of MV equipment, while rare because the equipment must be safe to handle and use, have far-reaching consequences. The consequences of such failures, whether to the shore or marine power system, present risks to the entire power plant, so an accurate assessment of equipment condition is required to identify potential failures early. The solution is an emerging concept of digital switchgear, where the implementation of sensor technology and communication protocols enables effective condition monitoring, and the creation of a database that, when combined with machine learning algorithms, enables predictive maintenance and/or fault detection. This paper presents, step by step, the previous challenges, the current research (divided into predictive maintenance, condition monitoring, and fault detection categories), and the future directions in this field. The use of artificial intelligence is discussed to eliminate the disadvantage of manually interpreting operational data, and recommendations for future work are formulated, such as the need to standardize test procedures and data sets to train and compare different algorithms before they are used in practice.

Robust optimization of uncertainty-based preventive maintenance model for scheduling series-parallel production systems (real case: disposable appliances production).

Industrial companies would attempt to keep themselves agile in the dynamic market. Given the competition among industries, lean methods are employed to reduce costs in the system. Among them, maintenance is significant to have a system available for manufacturing tasks. Maintenance is identified as the largest cost control tools in the equipment-driven industry. Implementing an effectual plan for preventive maintenance helps to be much more flexible and create an innovative solution for planning in the production. In this vein, this paper introduces an optimization model related to flexible flow-shop system scheduling in a series-parallel production system of disposable appliances by considering the preventive maintenance (PM) policy. By planning preventive maintenance, extra operation time is incurred to the system which may influence the cycle time and lead to lost sales and back orders. Therefore, the paper proposes a mathematical model to consider both operations times and availability of the whole production system to minimize the delays to reach an optimal sequence of processing. Since uncertainty exists in real industrial systems, the processing times are uncertain here. To handle uncertainty, robust optimization has been applied to solve the problem. In addition, a scenario-based genetic algorithm (SBGA) and Particle Swarm Optimization (PSO) algorithm have been developed to solve the proposed model. The results indicate the appropriate performance of the proposed approach in terms of time-saving leads to saving the cost of PM.

An effective method for remaining useful life estimation of bearings with elbow point detection and adaptive regression models.

Bearing is one of the critical components in rotating equipment. Therefore, accurate estimation of the remaining useful life (RUL) of bearings plays a vital role in reducing the costly unplanned maintenance and increasing the reliability of machines. This paper proposes a method for bearing prognostics that uses iteratively updated degradation regression models to capture the degradation trend in bearing's health indicator (HI), and the models are utilized to predict the degradation trajectory of HI and to estimate the RUL of bearings. The importance of determining the time to start prediction by elbow point is explained, which is often overlooked in prognostics. To improve the prognostic performance, an adaptive approach for elbow point detection is designed based on the gradient change of HIs, and a new smooth approach is applied to reduce spurious fluctuations in degradation trajectory. The effectiveness of the proposed method is validated on two publicly available data sets, i.e., IMS and FEMTO bearing prognostics data set, and its prognostic performance is compared with that of three state-of-the-art methods. The obtained results demonstrate that the proposed method can effectively detect elbow point and determine the time to start prediction, and can calibrate the degradation regression model dynamically according to the evolving degradation trend in the HI, which validates its superior prognostic performance.

Partial Discharge and Internet of Things: A Switchgear Cell Maintenance Application Using Microclimate Sensors.

This paper proposes a solution for the development of microclimate monitoring for Low Voltage/High Voltage switchgear using the PRTG Internet of Things (IoT) platform. This IoT-based real time monitoring system can enable predictive maintenance to reduce the risk of electrical station malfunctions due to unfavorable environmental conditions. The combination of humidity and dust can lead to unplanned electrical discharges along the isolators inside a low or medium voltage electric table. If no predictive measures are taken, the situation may deteriorate and lead to significant damage inside and outside the switchgear cell. Thus, the mentioned situation can lead to unprogrammed maintenance interventions that can conduct to the change of the entire affected switchgear cell. Using a low-cost and efficient system, the climate conditions inside and outside the switchgear are monitored and transmitted remotely to a monitoring center. From the results obtained using a 365-day time interval, we can conclude that the proposed system is integrated successfully in the switchgear maintaining process, having as result the reduction of maintenance costs.

Preventive maintenance for the flexible flowshop scheduling under uncertainty: a waste-to-energy system.

Nowadays, an efficient and robust plan for maintenance activities can reduce the total cost significantly in the equipment-driven industry. Maintenance activities are directly associated with the impact on the plant output, production quality, production cost, safety, and the environmental performance. To address this challenge more broadly, this paper presents an optimization model for the problem of flexible flowshop scheduling in a series-parallel waste-to-energy (WTE) system. To this end, a preventive maintenance (PM) policy is proposed to find an optimal sequence for processing tasks and minimize the delays. To deal with the uncertainty of the flexible flowshop scheduling of waste-to-energy in practice, the work processing time is modeled to be uncertain in this study. Therefore, a robust optimization model is applied to address the proposed problem. Due to the computational complexity of this model, a novel scenario-based genetic algorithm is proposed to solve it. The applicability of this research is shown by a real-life case study for a WTE system in Iran. The proposed algorithm is compared against an exact optimization method and a canonical genetic algorithm. The findings confirm a competitive performance of the proposed method in terms of time savings that will ultimately save the cost of the proposed PM policy.

Comparison of fuzzy semi-Markov models for one unit with mixed standby units with and without preventive maintenance using regenerative point method.

This paper introduced a study for a new system that consists of one unit with mixed standby units. The mathematical model for the system is constructed using semi-Markov model with regenerative point technique in two cases: the first case when there is preventive maintenance provided to the main unit and the second case when there is no preventive maintenance in the system. Life and repair times of the units in the system are assumed to be generally distributed with fuzzy parameters defined by the bell-shaped membership function. A Numerical application is introduced to compare the performance of the system in the two cases.

Dataset of metaheuristics for the flow shop scheduling problem with maintenance activities integrated.

This data article presents a flow shop scheduling problem in which machines are not available during the whole planning horizon and the periods of unavailability are due to random faults. The experimental dataset consists of two problems with different sizes. In the largest one, about 2400 problems were analysed and compared with two diffuse metaheuristics: Genetic Algorithm (GA) and Harmony Search (HS). In the smallest, about 600 problems were analysed comparing the solution obtained with an exhaustive algorithm with those obtained by means of GA and HS. This dataset represents a test-bed for further works, allowing a comparison between the solution quality and the computation time obtained with different optimization methods. The substantial computational effort spent to generate the dataset undoubtedly represents a significant asset for the scientific community.

La capacitación del electromédico en gerencia para el mantenimiento a equipos médicos / The electromedicine technologist's training in management of medical equipment maintenance

El mantenimiento preventivo en las instituciones hospitalarias se ejecuta para minimizar las acciones correctivas. Esto trae consigo una disminución de los gastos, una reducción de los riesgos y un aumento de la disponibilidad técnica de los equipos médicos, con la disminución de los tiempos de parada de los servicios, lo cual redunda en la excelencia de los servicios médicos prestados. En el campo de las tecnologías médicas ocurre un gran avance cada día, por lo que los equipos son más sofisticados y costosos. Este artículo tiene el objetivo de exponer la importancia de la capacitación ineludible del personal de Electromedicina en la actividad de gestión de mantenimiento a equipos médicos, a partir de las necesidades que ofrece la interpretación de su inventario. Se brindan pautas básicas sobre la preparación permanente y continuada del electromédico en mantenimiento a equipos médicos y se demuestra lo útil que resulta un inventario para gestionar la capacitación de los recursos humanos, lo cual se debe a la consulta, el análisis-síntesis y la sistematización de múltiples fuentes bibliográficas impresas e insertadas en la web: bases de datos Medline, Scielo, Redalyc y Google Académico. De ellas se revisó el material de 62 y, finalmente, se seleccionaron 24 artículos. Se concluyó que la capacitación en esta temática es fundamental para que las tareas de mantenimiento constituyan en verdad herramientas eficaces, que beneficien no solo a la institución prestadora de servicios de salud, sino al paciente y al personal médico(AU)

Preventive maintenance in hospital institutions is carried out to minimize corrective actions. This brings about a decrease in expenses, a reduction of risks and an increase in the technical availability of medical equipment, with a reduction in the times that services stop, which results in the excellence of the medical services provided. In the field of medical technologies, great advances occur every day, the reason why pieces of equipment are more sophisticated and expensive. This article aims to expose the importance of the unavoidable training of electromedicine personnel in the activity of maintenance management for medical equipment, based on the needs offered by the interpretation of its stock. Basic guidelines are provided about the permanent and continuous training of the electromedicine technologist in maintenance of medical equipment, while it is demonstrated how useful an equipment stock is to manage the training of human resources, which responds to the work developed after consultation, analysis-synthesis and systematization from multiple bibliographic sources printed and inserted on the databases of Medline, Scielo, Redalyc and Google Scholar. Of these sources, the material of 62 was reviewed and, finally, 24 articles were selected. It was concluded that training on this subject is essential for maintenance tasks, which truly constitute effective tools, benefiting not only the institution that provides health services, but also the patient and the medical personnel(AU)