Maintenance Level Research Articles

Dependency-Oriented Versus Autonomy-Oriented Help: Inferred Motivations and Intergroup Perceptions

Intergroup helping is a crucial interaction behavior between groups, which can be classified as either dependency-oriented or autonomy-oriented help. The widely recognized model of intergroup helping relations as status relations proposes that dependency-oriented help enables the helper group to maintain its dominant position. In other words, when a helper group has the motivation to preservation of their intergroup status, it will be more inclined to provide the recipient with dependency-oriented help. However, little research to date has focused on whether members of recipient groups recognize this status maintenance motivation, and how this might influence their inference of altruistic motivations or their perceptions of the helper’s warmth and competence. The results of three experiments involving a total of 677 participants indicated that compared to those receiving autonomy-oriented help, those receiving dependency-oriented help inferred a higher level of status maintenance and a lower level of altruistic motivation of the helper. Furthermore, they also perceived the helpers as having lower levels of warmth and competence. In response, these perceptions led to a reduced willingness to accept offers of help or cooperation. This effect was stronger when the help offered was needed more by the recipients, or when the helpers ignored requests for autonomy-oriented help and provided dependency-oriented help instead. This research complements the existing understandings of autonomy- and dependency-oriented help from the recipient’s perspective, while also outlining potential directions for future research.

Combined HDAC8 and checkpoint kinase inhibition induces tumor-selective synthetic lethality in preclinical models.

The elevated level of replication stress is an intrinsic characteristic of cancer cells. Targeting the mechanisms that maintain genome stability to further increase replication stress and thus induce severe genome instability has become a promising approach for cancer treatment. Here, we identify histone deacetylase 8 (HDAC8) as a drug target whose inactivation synergizes with the inhibition of checkpoint kinases to elicit substantial replication stress and compromise genome integrity selectively in cancer cells. We showed that simultaneous inhibition of HDAC8 and checkpoint kinases led to extensive replication fork collapse, irreversible cell-cycle arrest, and synergistic vulnerability in various cancer cells. The efficacy of the combination treatment was further validated in patient tumor-derived organoid (PDO) and xenograft mouse (PDX) models, providing important insights into patient-specific drug responses. Our data revealed that HDAC8 activity was essential for reducing the acetylation level of structural maintenance of chromosomes protein 3 (SMC3) ahead of replication forks and preventing R loop formation. HDAC8 inactivation resulted in slowed fork progression and checkpoint kinase activation. Our findings indicate that HDAC8 guards the integrity of the replicating genome, and the cancer-specific synthetic lethality between HDAC8 and checkpoint kinases provides a promising replication stress-targeting strategy for treating a broad range of cancers.

Impact of physical activity levels on the association between air pollution exposures and glycemic indicators in older individuals

BackgroundAir pollution may exacerbate diabetes-related indicators; however, the longitudinal associations between air pollutant concentrations and glycemic markers remain unclear. In this prospective cohort study, we examined the longitudinal associations between air pollution and glycemic indicators among older individuals with normoglycemia at baseline and determined whether these associations differed according to changes in physical activity levels.MethodsOverall, 1,856 participants (mean age, 70.9 years) underwent baseline and 4-year follow-up surveys. We used linear mixed-effect models to examine the associations between previous 1-year exposures to air pollutants and glycemic indicators. We further investigated associations between previous 5-year exposures to air pollutants and glycemic indicators after the inverse probability of treatment weighting (IPTW). We explored effect modifications by the level of physical activity maintenance and changes in metabolic equivalent of task (METs) for physical activity.ResultsLevels of particulate matter with aerodynamic diameters ≤ 10 μm (PM10) and ≤ 2.5 μm, and nitrogen dioxide (NO2) were significantly associated with increased fasting blood glucose, Hemoglobin A1c, insulin, and homeostatic model assessment for insulin resistance (HOMA-IR) values. After IPTW, the associations remained significant for PM10 and NO2. The positive associations of NO2 with insulin and HOMA-IR remained significant in the maintained inactive group, but not in the maintained moderate-to-vigorous active group. The positive associations of PM10 or NO2 with insulin and HOMA-IR remained significant in the group with increased METs, but not in those with decreased METs. In the post-hoc analysis of non-linear relationships between an increase in METs and glycemic indicators, insulin and HOMA-IR remarkably increased in the higher PM10 and NO2 exposure group from the point of 12,000 and 13,500 METs-min/week increase, respectively.ConclusionsWe demonstrated longitudinal associations between air pollution exposures and increased insulin resistance in older individuals. Maintaining moderate-to-vigorous physical activity may mitigate the adverse effects of air pollution on insulin resistance. In older individuals dwelling in highly polluted areas, an increase of less than 12,000 METs-min/week may be beneficial for insulin resistance.

Feeding twin-bearing Merino ewes above the metabolisable energy requirements for maintenance during late gestation increases the duration of parturition of the first-born lamb

Context In Australia, approximately 53% of lamb deaths are caused by dystocia. One of the main welfare concerns in the sheep industry is under- and overfeeding ewes, which may be contributing to cases of dystocia. Aims This pilot study aimed to investigate how increasing energy intake affects the duration of parturition and predictors of lamb survival. Methods On Day 100 of gestation (dG), 20 twin-bearing and 10 singleton Merino ewes were selected and allocated to three treatment groups; (1) singleton ewes fed at 1.0× maintenance levels (n = 10); (2) twin-bearing ewes fed at 1.0× maintenance levels (n = 10) or (3) twin-bearing ewes fed at 1.25× maintenance (n = 10). Ewes were housed indoors in individual pens. Urine and blood were sampled from ewes on dG 130, 140, then daily from dG 145 through to parturition, and blood was sampled at the onset of parturition and 30 min post-partum. Urine was analysed for pH and blood was analysed for metabolic parameters, mineral concentration and acid–base balance. Predictors of lamb survival from birth to 24 h consisted of weight, rectal temperature, blood glucose and lactate, and body morphology. Key results Serum calcium in late gestation and blood base excess pre-parturition were higher in Singletons 1M compared with Twins 1M (P < 0.05). The Twins 1.25M group took longer to give birth to the first-born lamb (104.4 ± 21.1 min) compared with the Twins 1M group (44.1 ± 6.6 min; P = 0.015). There were no differences in the predictors of lamb survival measures between the twin-bearing groups (P > 0.05). Conclusions Ewes from the Twins 1.25M group took significantly longer to give birth to the first-born lamb. There were no other significant findings between the twin-bearing groups, including lamb liveweight, however, lambs born to ewes from the Twins 1.25 group were numerically heavier, which may explain the increase in parturition length. Implications Feeding ewes above maintenance did not provide any production benefits to metabolic health or any other physiological parameters. Producers should avoid overfeeding ewes during late gestation.

Selective maintenance of the complex system considering maintenance time uncertainty for system components with multiple repairpersons

AbstractThis research presents an innovative selected maintenance model for complex systems that considers the uncertainty in maintenance time (MT) for system components with multiple repairpersons. The computational model of uncertain MT for system components is established. An imperfect maintenance model is introduced, which has many imperfect maintenance levels not only considering do nothing, minimal repair, and replacement but also considering multiple intermediate maintenance levels. Furthermore, the system components maintenance assignment algorithm with multiple repairpersons is proposed to addresses the problem of how to assign multiple maintenance tasks to multiple repairpersons in order to minimize system MT. And it is innovatively integrated into the particle swarm optimization (PSO) to solve the proposed selective maintenance model, which enables heuristic algorithm to efficiently assign the multiple maintenance tasks with multiple repairpersons. The effectiveness and advantages of the proposed model and algorithm are verified by numerical experiments.

Family Management of Hypertension in Brazil: A Cross-Sectional Study.

In Brazil, research indicates that primary family members are the main source of support for individuals with chronic conditions such as hypertension (HTN). The burden of caregiving not only hinders effective HTN management but can also cause stress and anxiety, potentially leading to HTN in caregivers. Despite this, few studies have explored the impact of caregiving on these family members. Aims of the study were to: (1) Describe the prevalence of blood pressure (BP) control in family members of individuals with HTN; (2) identify family member perspectives on facilitators and barriers to HTN management; and (3) identify influences that help or interfere with family member functioning (levels of stress, quality of life [QOL], and caregiver burden). This descriptive, cross-sectional study included 213 family members randomly selected from 3 Family Health Strategy units. Family members were largely female (n = 143; 67.1%); the mean age was 60.1 years (SD ± 17.02) and 42.6% (n = 96) had less than a high school education. The three most important facilitators and barriers were related to medication, medical visits, healthy eating, physical activity, and stress. The mean systolic BP was 132.7 (SD ± 21.9) mmHg and a diastolic BP of 85.9 (SD ± 18.1) mmHg with 120 (56.3%) of family members classified as having normal BP. In regard to family member contributions to the self-care of the individual with HTN, family members displayed low levels of self-care maintenance (n = 148; 69.4%) and management (n = 47; 71.2%) support, while a slight majority (n = 114; 53.5%) had adequate levels of self-care confidence in supporting the individual with HTN. Family members (n = 189; 88.8%) showed moderate-to-high levels of perceived stress, but good physical (n = 189; 88.7%) and mental QOL (n = 196; 92%) and low levels of caregiver burden (n = 113; 53.1%). A variety of contextual sociocultural influences were associated with the outcomes under study. Family-based interventions are urgently needed to address the inadequate management of HTN.

Flexible curved multi-functional sensors for air flow and vibration signal measurement of train bogie

Abstract The fluid-solid coupling effects with the aerodynamic noise and structural vibration signal have great part in measurement accuracy and fault diagnosis of the train bogie. It is important to collect aerodynamic noise and structural vibration data for safety evaluation and on-line structural health monitoring (SHM) of the bogie, which brings new challenges to the integration and installation of the sensors in the limited space and complicated operation condition of the train bogie. Flexible curved multifunctional sensors (FCMFS) integrated with the hot film and structural vibration sensor units is designed to the air flow and structural vibration measurement of the train bogie. Bogie vibration and wind tunnel test platforms with FCMFS are built to the air flow velocity and vibration data measurement on the bogie surface under different operation conditions. Piezoelectric Polyvinylidene fluoride(PVDF) sensors have significant output in the wind tunnel and vibration experiments, and the hot film sensor is only sensitive to the air flow signal. The effect of vibration signal to the hot film sensor could be ignored, which is applied to decouple the aerodynamic noise and structural vibration signals for improving the measurement accuracy of the train bogie. The dynamic performance of FCMFS is verified in Rail Transmit Base of East China Jiaotong University, which is applied to SHM and rapid fault diagnosis of train bogie. The air flow distribution and vibration data are collected to safety assessment and fault diagnosis of train bogie, which would promote the intelligent maintenance level of the train bogie.

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.

Research on Wind Turbine Fault Detection Based on CNN-LSTM

With the wide application of wind energy as a clean energy source, to cope with the challenge of increasing maintenance difficulty brought about by the development of large-scale wind power equipment, it is crucial to monitor the operating status of wind turbines in real time and accurately identify the specific location of faults. In this study, a CNN-LSTM-based wind motor fault detection model is constructed for four types of typical faults, namely gearbox faults, electrical faults, yaw faults, and pitch faults of wind motors, combining CNN’s advantages of excelling in feature extraction and LSTM’s advantages of dealing with long-time sequence data, to achieve the simultaneous detection of multiple fault types. The accuracy of the CNN-LSTM-based wind turbine fault detection model reaches 90.06%, and optimal results are achieved for the effective discovery of yaw system faults, pitch system faults, and gearbox faults, obtaining 94.09%, 96.46%, and 97.39%, respectively. The CNN-LSTM wind turbine fault detection model proposed in this study improves the fault detection effect, avoids the further deterioration of faults, provides direction for preventive maintenance, reduces downtime loss due to restorative maintenance, and is essential for the sustainable use of wind turbines and maintenance of wind turbine service life, which helps to improve the operation and maintenance level of wind farms.

Optimal preventive maintenance: Balancing reliability and costs in the electricity market

This study addresses the challenge of achieving optimal preventive maintenance within power systems, aiming to balance reliability and costs effectively. The primary focus is on understanding the relationship between preventive maintenance expenditures and the failure rate of essential transmission components, particularly transformers in substations. We contribute to the existing literature by providing two key insights. Firstly, we establish a theoretical framework for determining the optimal level of preventive maintenance, which can be extended across various electricity facilities. By analyzing the relationship between preventive maintenance costs and failure rates, our goal is to identify the investment level that ensures a reliable power system while minimizing financial burdens. Secondly, we explore the functional form that accurately characterizes the link between preventive maintenance costs and failure rates. The diminishing marginal rate of failure, dependent on different functional forms, highlights the variability of optimal preventive maintenance expenses. Utilizing Lasso regression, we identify the functional form that optimally characterizes the relationship between outage occurrences and maintenance costs. These insights extend well beyond the confines of the Korean power industry, offering a sustainable approach to managing transmission facilities and enhancing the overall stability and efficiency of global electricity networks.

Research on monitoring and energy management systems for energy storage stations on the power generation side

Abstract Addressing the challenges of limited operational efficiency, inadequate battery safety, inflexible system scalability, inadequate AGC/AVC functionality, and low levels of intelligent operation and maintenance encountered by energy storage power stations integrated with new energy generation sources, this article delves into the pivotal role of these power stations on the generation side. It meticulously examines the network architecture, functional framework, and pivotal technologies underpinning the system. Furthermore, the article proposes a comprehensive monitoring and energy management system tailored specifically for energy storage on the generation side. The system uses micro-service architecture and container technology to realize the processing and storage of massive data. Through complete isolation of the information network and control network and millisecond communication with GOOSE, the charging and discharging response speed of the energy storage station is greatly improved. The power allocation algorithm based on device status recognition completes the system’s high-precision power control of PCS, and this system uses big data analysis and data mining technology to conduct a comprehensive statistical analysis of battery health status, power station operation efficiency, and equipment operation status and energy consumption. The design of the system has favorable technical and application guidance for the subsequent energy management design and engineering application of the energy storage power station.

Implementation of decarbonisation actions in general practice: a systematic review and narrative synthesis protocol

IntroductionThere is growing recognition of the importance of primary care in addressing climate change. The World Organisation of Family Doctors has urged general practitioners worldwide to commit to tackling climate...

Digital Twin-driven Health Operation and Maintenance Strategies for Complex Equipment with Self-healing Capabilities

With the development of the industrial level, the service-oriented transformation of the manufacturing industry has become a new growth point for the interests of the manufacturing industry recognized by all countries, and in this transformation process, the health operation and maintenance (HOM) of equipment has become a crucial link, especially for complex equipment, and its health operation and maintenance level directly affects the overall efficiency of the manufacturing industry. However, with the improvement of intelligence and complexity, a more general and complete set of methods and theories is needed for the healthy operation and maintenance of complex devices with self-healing states. Based on an in-depth analysis of the key issues of complex equipment health operation and maintenance, this paper introduces digital twin technology as a breakthrough. Digital twins provide new ideas for the healthy operation and maintenance of complex equipment. By defining a digital twin-driven PHM (Prediction and Health Management) framework, this paper not only solves the problem of mobility (i.e., virtual and real connection) of the cross-platform health operation and maintenance model, but also focuses on improving the accuracy and evaluation indicators of the model (the consistency between the virtual model and the entity). Addressing issues of strategy development and effectiveness evaluation. Based on the digital twin technology, a general model was established, and the self-healing phenomenon and different maintenance strategies were introduced to explore its impact on reliability. Based on the game idea, the reliability model is used to evaluate different maintenance strategies. So as to develop the optimal operation and maintenance strategy. This not only improves the accuracy and efficiency of O&M, but also provides a solid theoretical foundation and technical support for the intelligent and autonomous health O&M of complex equipment.

Utilizing spontaneous plants for sustainable development in residential green spaces: Insights from environmental drivers and niche analysis in Fuzhou City, China

This study, aimed at exploring low-maintenance, high-diversity, and sustainable greening strategies for residential areas, conducted a comprehensive survey and analysis of spontaneous plants in residential green spaces in Fuzhou City, documenting 361 species. Employing methods such as variance partitioning, Canonical Correspondence Analysis (CCA), and ecological niche analysis, we investigated the environmental factors influencing the distribution and composition of these plants, as well as their interrelationships. The study found that the composition of spontaneous plants in residential green spaces differs from other urban environments, with a high proportion of alien species (43.77%) due to influences such as resident activities, including a large number of ornamental and edible plants. Maintenance level, urbanization gradient, and green space ratio are common factors affecting the composition and distribution of spontaneous plants in urban environments, while unique residential socio-economic factors like building age, housing prices, and population density significantly affect the spontaneous plants in residential green spaces. The overall dominant plant community shows a significant positive association, indicating a relatively stable stage of succession. Although competition among most species is not significant and interspecific connectivity is weak, the presence of seven dominant invasive species intensifies competition. Based on these findings, the study proposes several specific sustainable management measures: adopting the concept of New Naturalistic Ecological Planting Design, selecting native spontaneous plants with strong adaptability, and constructing plant communities that are ecologically stable and have ornamental value by mimicking natural ecosystems. Additionally, specific methods for managing specific invasive species in residential green spaces using competitive replacement control methods are proposed. These measures aim to promote the health and sustainable development of urban residential green spaces.

The application of the Bayesian linear regression model to optimize the maintenance of a programmable logic controller

In this paper, an optimization approach, which is based on the Bayesian Linear Inference (BLI) model, has been proposed for the maintenance of Programmable Logic Controllers (PLCs). The BLI model, which is implemented using historical data, incorporates maintenance indicators like the number of failures (NF), total downtime (TD), total unexpected intervals (TUI), mean time to repair (MTTR) and mean time between failures (MTBF). It offers a probabilistic framework for determining the influence of each predictor variable on PLC maintenance. The model produces posterior means, credible intervals, and standard deviations, which provide insights into the magnitude and uncertainty of these relationships. The results from the study show that factors like NF and TD are influenced by the magnitude and direction of the maintenance levels. Also, the R-squared score (0.85) also indicates how much of the variability in maintenance in the system. From the results obtained, the study can conclude that the BLI model can optimize PLC maintenance procedures by identifying essential components and their contributions. Also, it is able to estimate future maintenance requirements and helps with resource allocation and process optimization decisions.

Quantitative Monitoring Method for Conveyor Belt Deviation Status Based on Attention Guidance

The efficient monitoring of the belt deviation state will help to reduce unnecessary abnormal wear and the risk of belt tear. This paper proposes a coupling characterization method involving the prediction box features of the target detection network and the linear features of the conveyor belt edge to achieve the quantitative monitoring of conveyor belt deviations. The impacts of the type, location, and number of attention mechanisms on the detection effect are fully discussed. Compared with traditional image-processing-based methods, the proposed method is more efficient, eliminating the tedious process of threshold setting and improving the detection efficiency. In detail, the improved practice and tests are carried out based on the Yolov5 network, and the Grad-CAM technique is also used to explore the effect of attention mechanisms in improving the detection accuracy. The experiments show that the detection accuracy of the proposed method can reach 99%, with a detection speed of 67.7 FPS on a self-made dataset. It is also proven to have a good anti-interference ability and can effectively resist the influence of the conveying material flow, lighting conditions, and other factors on the detection accuracy. This research is of great significance in improving the intelligent operation and maintenance level of belt conveyors and ensuring their safe operation.

Socio-spatial features of neighbourhoods supporting social interaction between locals and migrants in peri-urban China

Western literature shows evidence of a positive relationship between socio-spatial features of neighbourhoods and social interaction. However, there is little research exploring this relationship in the Chinese context, particularly between locals and migrants in peri-urban China where significant housing is being created. This paper studies the socio-spatial features of neighbourhoods in supporting social interaction between locals and migrants across different neighbourhood types in the peri-urban areas in Guangzhou. In this research, data were collected using door-to-door questionnaires and site surveys in 9 peri-urban neighbourhoods in Guangzhou. The nature and strength of relationships between socio-spatial features of neighbourhoods and social interaction were examined through statistical analysis. The results of this study suggest that the level of maintenance and accessibility can help improve social interaction for locals and migrants living together in villages, redeveloped villages, and commodity housing. Neighbourhood boundaries and quality of neighbourhoods were found to be positively related to social interaction for locals and migrants in redeveloped villages only. In addition, the perceived character of the neighbourhood can positively impact social interaction for locals in villages only. This study demonstrates that high-quality neighbourhoods can improve social interaction between migrants and locals in peri-urban villages, commodity housing, and redeveloped villages in China. The study provides a guide for neighbourhood designers, urban planners, and property managers in peri-urban China regarding how to create a neighbourhood supporting social interaction between locals and migrants in villages, redeveloped villages and commodity housing respectively.

Bringing the Value Chain for High-Impact Weather Warnings into the Classroom

Abstract University students can learn about weather warnings and contribute to a database for the World Meteorological Organization (WMO) project on value chain approaches to evaluate the end-to-end warning chain. The project offers students a way to understand how information about high-impact weather is created, shared, and used within a complete warning system for a selected event. Their contributions are intended to inform researchers and practitioners on what has and what has not worked well in the warning process. The students use a structured questionnaire designed to collect information on observations, forecasting, hazards, impacts, warning communications, and responses.

Response of spontaneous plant communities to microhabitats in a riparian corridor in Beijing, China

With the increasing urban environmental problems, spontaneous vegetation has been gradually emphasized for high ecological value, whose distribution has been reported as strongly influenced by fine-scale environmental factors. As one of the major zones for urban spontaneous vegetation distribution, urban riparian corridors are well suited for research on the response of spontaneous plants to microhabitats. Taking the Wenyu River-North Canal in Beijing as the study site, we measured six microhabitat factors, including the level of maintenance and visitor activity, canopy density, litter thickness, and distance to water and road, to investigate their effects on spontaneous plant assemblages. The results showed that spontaneous plants respond significantly to fine-scale habitat variation, and were more responsive to human disturbance than other factors. Compared with diversity indicators, the functional trait compositions were more significantly correlated with microhabitat factors. Under lower maintenance of plants, the spontaneous communities had a higher invasion risk for plants. Thick litter can impede the occurrence of invasive species while favoring the growth of native ones. Our findings are important for furthering understanding of the spontaneous plant community establishment, and can serve as a good reference for the maintenance and management of spontaneous plants in urban riparian corridors.

An Early Warning Model for Turbine Intermediate-Stage Flux Failure Based on an Improved HEOA Algorithm Optimizing DMSE-GRU Model

As renewable energy sources such as wind and photovoltaics continue to enter the grid, their intermittency and instability leads to an increasing demand for peaking and frequency regulation. An efficient dynamic monitoring method is necessary to improve the safety level of intelligent operation and maintenance of power stations. To overcome the insufficient detection accuracy and poor adaptability of traditional methods, a novel fault early warning method with careful consideration of dynamic characteristics and model optimization is proposed. A combined loss function is proposed based on the dynamic time warping and the mean square error from the perspective of both shape similarity and time similarity. A prediction model of steam turbine intermediate-stage extraction temperature based on the gate recurrent unit is then proposed, and the change in prediction residuals is utilized as a fault warning criterion. In order to further improve the diagnostic accuracy, a human evolutionary optimization algorithm with lens opposition-based learning is proposed for model parameter adaptive optimization. Experiments on real-world normal and faulty operational data demonstrate that the proposed method can improve the detection accuracy by an average of 1.31% and 1.03% compared to the long short-term memory network, convolutional neural network, back propagation network, extreme learning machines, gradient boosting decision tree, and LightGBM models.