Efficient Maintenance Research Articles

Maintenance cost optimisation in critical single-component systems: A technician’s training approach based on a joint learning–forgetting and fuzzy maintenance quality model

Effective maintenance in critical systems is crucial to ensure their operability and safety. Maintenance technicians play an important role in achieving the safety and reliability objectives of critical system components. However, the role of the technical team in maintenance quality is often overlooked. Our study addresses this knowledge gap by developing a methodology to optimise the training scheduling of maintenance technicians in single-component systems, focusing on a learn-and-forget approach. Our main objective is to improve maintenance efficiency by mitigating the negative impact of technician expertise level. To this end, we implemented a simulation-based optimisation, modelling the evolution of technician experience and its influence on maintenance costs. As a case study, we used an urban aerial passenger ropeway system, demonstrating that proper training of technical staff can significantly reduce both corrective and preventive maintenance costs. Our methodology identified three strategic areas for training technical staff throughout the system lifecycle. The results highlight the importance of investing in the training scheduling of technical equipment in critical systems and suggest that an appropriate training strategy can improve operational efficiency and reduce maintenance costs. In conclusion, our research underlines the need to actively consider the human factor in the maintenance of critical systems to ensure their optimal performance and long-term safety.

Efficient opportunistic maintenance strategies via pruning in parallel–series systems with economic dependence

Opportunistic maintenance stands out as an effective strategy for minimizing maintenance costs and enhancing system efficiency. Despite its importance, current opportunistic maintenance models are often tailored to specific system types, particularly those with series structures, or they offer heuristic approaches for multi-component redundant systems. This paper shifts focus to the most common of such systems, the parallel–series system. We approach the optimization of opportunistic maintenance in a parallel–series system through a Markov Decision Process framework, utilizing phase-type approximations of individual component hazard functions. Addressing the challenge of the curse of dimensionality in the action space, this study conducts a thorough structural analysis of the optimal opportunistic maintenance policy, deriving a pruning procedure, which can reduce the extensive combinatorial action space into a more manageable linear complexity without sacrificing the strategy’s effectiveness in parallel–series systems. Building on this more manageable action space, we introduce a more efficient algorithm leveraging the proposed pruning procedure for determining the optimal opportunistic maintenance policy. The effectiveness and practical applicability of this approach are rigorously demonstrated through comprehensive numerical experiments.

Understanding the conversion of nitrogen compounds during ammonia electrooxidation: effect of current density, chloride concentration and pH on nitrate formation

AbstractBACKGROUNDAmmonia removal from wastewater by electrooxidation (EO) is an interesting approach because of its efficiency and easy maintenance and operation. In this process, ammonia is oxidized to nitrate or nitrogen gas. Ammonia conversion to the latter is desirable to remove nitrogen from the liquid; however, the influence of several operating parameters on nitrate generation has not been systematically evaluated. Therefore, this work aimed to investigate the effect of current density (200–800 A m−2), chloride concentration (0–10 000 mg L) and initial pH (5–9) on the electrooxidation of an ammonia‐containing solution and the associated generation of nitrate. For this purpose, a laboratory‐scale electrochemical reactor containing two Ti/RuO2 electrodes was used.RESULTSThe results indicated high ammonia removal efficiency – 98% within 150 min at 800 A m−2 and 97% within 240 min at 500 A m−2 – generating around 70 and 102 mg ‐N L−1 under these conditions, respectively. Increasing the chloride concentration from 5000 to 7500 mg L−1 reduces the electrolysis time needed to remove all ammonia from 180 to 150 min. However, with increasing initial chloride concentration, the amount of nitrate generated rose from 69.5 to 135.9 mg N L−1. On the other hand, in the test without chloride, nitrate generation was considerably lower (0–0.61 mg L−1).CONCLUSIONThe higher the current density applied, the greater the ammonia removal by EO. Although current density influenced the ammonia oxidation rates, it did not directly affect nitrate formation. The lower the concentration of ammonia in the solution, the more significant was the fraction of nitrate generated. Most of the inlet ammonia was oxidized to nitrogen gas, nitrite was rapidly oxidized to nitrate and higher chloride concentrations enhanced ammonia oxidation. © 2024 Society of Chemical Industry (SCI).

Estimates of speech efficiency in monolingual and bilingual speakers of English.

Introduction The Speech Efficiency Score (SES) serves as an acoustic metric for assessing fluency in conversational speech within the temporal domain. This study leverages SES to investigate conversational speech efficiency among native speakers of American English (AE) compared to speakers of Mandarin-accented English (MAE). Methods SES, speaking rate, articulation rate, and vocabulary diversity were measured and compared between two groups: native AE speakers and MAE speakers. The study utilized conversational speech samples collected from both groups to analyze these metrics. Results Findings indicate a disparity in speaking rate and articulation rate between the AE and MAE groups, with the AE group exhibiting significantly faster speech. However, no significant differences were observed in SES and vocabulary diversity between the two groups. Conclusion The results are discussed in the context of the interplay between speaking rate, speech fluency, and vocabulary diversity. These findings shed light on the maintenance of speech efficiency among bilingual speakers, suggesting that despite differences in speaking rate and articulation rate, SES and vocabulary diversity remain comparable between native AE speakers and MAE speakers. .

Implementation of Linear Regression Method for Calibration and Temperature Measurement on PT100 Temperature Sensor

The PT100 temperature sensor, a type RTD (resistance temperature detector), is widely employed in industries, particularly for steam boiler temperature regulation. Precise temperature measurement is vital for operational efficiency and safety maintenance. Utilizing linear regression for calibrating and measuring temperature with the PT100 sensor integrated with Arduino Uno and LCD I2C is proposed to enhance accuracy. Testing involved assembling the PT100 sensor with Arduino Uno and LCD I2C. Test data were employed for linear regression to derive the calibration equation. Measurements were conducted by comparing PT100 sensor readings with a digital thermometer across a temperature range of 30ºC to 75ºC at 5ºC intervals, with each temperature point tested five times. Results exhibit good accuracy in temperature measurement with the PT100 sensor, featuring low error and percentage error. The standard deviation indicates consistent measurement. Integration of the PT100 temperature sensor with Arduino Uno and LCD I2C, coupled with linear regression for calibration, yields a precise temperature measurement system. Testing against a digital thermometer demonstrates highly accurate outcomes. This developed system finds application in high-precision temperature measurement for steam boiler operations.

Многофакторный анализ эффективности автономных транспортных средств на основе метода анализа среды функционирования

The design of robotic and unmanned vehicles includes a virtual commissioning stage that provides an analysis of the operation of objects using digital twins. Conducting a large number of experiments on models leads to the use of methods for evaluating the results obtained to choose the best design solution. A method of multifactorial anal-ysis of the efficiency of the complex of autonomous vehicles (AV) for agro-industrial purposes is proposed. Multivariate analysis is performed at the virtual commissioning stage in order to plan early maintenance and repair activities. The paper uses the method of analyzing the operating environment for a comparative assessment of various operating scenarios. The method is based on solving a complex of optimization problems of linear programming. A formal description of virtual test scenarios is proposed. The method of multifactorial analysis is implemented in the process of virtual tests using optimal purpose system models and AV simulation. A procedure for virtual AV tests has been developed, including step-by-step modeling and multivariate analysis. The sets of input and output key parameters of the AV and analyzed scenarios are determined. It is proposed to perform two consecutive efficiency assessment tasks. The first task is to compare the effectiveness of individual AV and determine the limits of their effectiveness. The solution of the second task makes it possible to evaluate the effectiveness of AV system operation scenarios. The target values of changes in the key parameters of the AV and operating scenarios are obtained, leading to an increase in the efficiency of operation and maintenance. Conducting a multifactorial analysis of the results of virtual tests allows you to formulate requirements and recommendations for the design of AV maintenance systems.

USE OF INNOVATIVE TECHNOLOGIES IN THE CONSTRUCTION OF ROAD PAVEMENTS

Problem statement. Ukraine, on its way to European integration, faces the challenge not only of joining the European Union, but also of adapting its infrastructure to high European quality standards. After the end of the war, it is necessary not only to rebuild the destroyed roads, aerodromes and buildings, but also to guarantee the sustainability and durability of infrastructure facilities. One of the key industries where innovations can make a significant difference is road construction. Asphalt and concrete are the most common building materials in the construction of flexible and rigid pavements. Innovative technologies for repairing cracks in asphalt and concrete road pavements are constantly evolving. New methods and materials are being sought for more efficient and long-term maintenance of the road surface. The use of advanced technologies allows us to improve the quality of repairs, extend the service life of roads and reduce their environmental impact. Innovative approaches to crack repair help to ensure safety and comfort on the road for all road users. The purpose of the article is to analyze foreign experience in the application of effective innovative technologies in the construction of road pavements and to substantiate the feasibility of using a self-healing top layer of flexible and rigid asphalt and concrete pavements in Ukraine based on the analysis of their properties. Conclusions. The inclusion of self-healing properties in asphalt concrete pavements is an effective method for increasing their service life and achieving environmental friendliness of pavements. Existing and advanced self-healing technologies suitable for use in asphalt pavements, namely, the inclusion of healing agents, induction heating, and hybrid technologies, have been investigated. The mechanisms of self-healing of concrete have been studied: autogenous, based on autonomous bacteria, and based on autonomous capsules. Research on self-healing pavements is aimed at developing a smart and efficient pavement system capable of self-assessment and automated crack repair. The choice of technology should be based on its ability to effectively repair damage and cracks in asphalt and concrete, providing long-term and reliable protection.

Development of a novel full-scaled self-centering brace with notched steel tubes

A novel self-centering brace with notched steel tubes (SCB-NST) was developed and experimentally studied. The proposed SCB-NST consists of a novel notched steel tube (NST) for energy dissipation and disc springs for self-centering, respectively. Three full-scaled specimens were tested to investigate the hysteresis behavior under cyclic loading. These specimens include a brace with NST only, a brace with disc spring only, and an SCB-NST. The first two tests were specifically conducted to obtain the contributions of the NST and disc springs alone, respectively. The test results illustrate that the first proposed NST displays excellent energy dissipation and deformation capabilities. The proposed SCB-NST exhibits a stable flag-shaped hysteresis behavior, distinguished self-centering, and significant energy dissipation capacity without residual displacement. Furthermore, experimental findings reveal that the disc spring functions as a secondary defense line of the SCB-NST system upon sudden failure of the NST. The cyclic test of the brace with disc spring only shows that the friction between the disc springs should not be neglected, especially when quantities of large-diameter disc springs are stacked. The modular design of the proposed SCB-NST facilitates the replacement of energy-dissipating components, allowing for efficient maintenance and ultimately achieving the design objective of promoting reusability and resource conservation. The SCB-NST, equipped with two NSTs featuring different ultimate displacements, has been confirmed through finite element analysis to have three defense lines, which improves the reliability and redundancy of the SCB-NST system. The parametric finite element analysis results reveal that the thickness of the energy-dissipation ring has the most significant influence on the load-bearing capacity and energy-dissipation capability of the SCB-NST. Concurrently, the pre-pressed force of the disc spring is crucial for eliminating the residual displacement of the SCB-NST.

Application of Automated Pavement Inspection Technology in Provincial Highway Pavement Maintenance Decision-Making

Taiwan’s provincial highways span approximately 5000 km and are crucial for connecting cities and towns. As pavement deteriorates over time and maintenance funds are limited, efficient pavement inspection and maintenance decision-making are challenging. Traditional inspections rely on manual visual assessments, consuming significant human resources and time without providing quantitative results. This study addresses current maintenance practices by introducing automated pavement damage detection technology to replace manual surveys. This technology significantly improves inspection efficiency and reduces costs. For example, traditional methods inspect 1 km per day, while automated survey vehicles cover 4 km per day, increasing efficiency fourfold. Additionally, automated surveys reduce inspection costs per kilometer by about 1.7 times, lowering long-term operational costs. Inspection results include the crack rate, rut depth, and roughness (IRI). Using K-means clustering analysis, maintenance thresholds for these indicators are established for decision-making. This method is applied to real cases and validated against actual maintenance decisions, showing that the introduced detection technology efficiently and objectively guides maintenance decisions and meets the needs of maintenance units. Finally, the inspection results are integrated into a pavement management platform, allowing direct maintenance decision-making and significantly enhancing management efficiency.

Impactability and susceptibility assessment based on D-S evidence theory for analyzing the risk of fault propagation among catenary components

As an important part of the traction power supply system, the research on fault prevention of the catenary system has become a crucial issue for efficient operation and maintenance. In this paper, we propose a data-driven approach to investigate the underlying correlations among catenary components from the historical fault data, so that the fault propagation mechanisms among components can be revealed. Initially, based on the different roles played by components in the fault propagation process, we define fault impactability and susceptibility of components under different mechanical coupling relationships to capture the fault propagation mechanisms. Then, we propose a risk trust function model based on the D-S evidence theory to assess the fault impactability and susceptibility. Meanwhile, a belief and disbelief-based risk coefficient is proposed in the risk trust function model to construct the evidence source. Finally, the case study, based on the fault database of the Chengdu Railway Bureau, demonstrates that the proposed method can effectively assess the fault impactability and susceptibility of components to reveal the fault propagation mechanisms, which provides valuable references for formulating fault prevention strategies.

Stratified structure of membrane clogs observed in full scale cross-flow tubular ultrafiltration (UF) system: Fouling characterization and a proposed two-stage formation mechanism

Membrane fouling remains a significant challenge in wastewater treatment, hindering both efficiency and lifespan. This study reports a distinct phenomenon of stratified membrane clogging observed in a full-scale cross-flow tubular ultrafiltration (UF) system treating sludge anaerobic digestion (AD) reject water. The distinct stratified structure, comprising inner and outer layers within the cake layer, has not been previously described. This research involved characterizing the filtration performance, analyzing membrane clog composition, and proposing a two-stage formation mechanism for the stratified clogs. It was revealed that higher inorganic and lower organic content in the outer layer compared to the inner layer. Acid and alkali treatments demonstrated the effectiveness of combined cleaning strategies. A mathematical model was developed to determine the critical conditions for stratified clog formation, influenced by membrane flux and cross-flow velocity (CFV). It is proposed that outer layer forms through long-term selective deposition, while the inner layer results from short-term dewatering within limited tubular space. High CFV (>2.5 m/s) prevents inner layer formation. Critical conditions for stratification occur at a flux of 18 L/m2/h with a CFV of 0.1 m/s or 65 L/m2/h with a CFV of 0.35 m/s. This study contributes a novel understanding of stratified membrane clogging, proposing a two-stage formation mechanism and identifying critical conditions, which provides insights for effective fouling control strategies and maintenance of operational efficiency for membrane systems.

Transformer-based novel framework for remaining useful life prediction of lubricant in operational rolling bearings

Accurate prediction of the remaining useful life (RUL) of lubricants in rolling bearings is crucial for maintaining efficient operation of rotating machinery and ensuring timely lubricant replacement. We propose a comprehensive framework that integrates the temporal variation transfer function (TVTF), harmonic-sideband Matrix, and the harmonic frequency transformer (HarFT), a transformer-based model. This approach effectively utilizes vibration characteristics to enhance the accuracy of lubricant degradation prediction in rolling bearings. Validation with rolling bearings experiencing lubrication failure confirms that our framework significantly outperforms alternative methods in RUL prediction. The proposed framework excels in extracting and analyzing harmonic components from vibration responses, enabling detection of minute status variations due to lubricant degradation. By applying explainable artificial intelligence (XAI), it is possible to ascertain the rationale behind the RUL predicted by the HarFT model, facilitating evidence-based decisions. Our research provides a novel strategy for lubricant RUL assessment in rolling bearings, thereby improving reliability and maintenance efficiency in industrial applications.

Rapid haemostasis to achieve dressing longevity: evaluation trial results using StatSeal catheter exit site protection.

Peripherally inserted central catheters (PICCs) are vital in delivering intravenous therapy. Despite their advantages, PICCs can lead to complications such as catheter exit site bleeding, which can cause patient distress and increase infection risk. This study evaluated the efficacy of StatSeal, a topical haemostatic device, in managing PICC exit site bleeding. StatSeal uses a hydrophilic polymer and potassium ferrate to form a seal, reducing access site bleeding and minimising dressing changes. For this study, Patients were recruited at Frimley Health NHS Foundation Trust; the trial involved 177 patients with StatSeal, and shows that 99% did not require additional dressing changes within the standard 7-day period. The findings demonstrate StatSeal's effectiveness in improving patient outcomes by reducing exit site bleeding and associated complications, enhancing the efficiency of vascular access maintenance and potentially lowering associated healthcare costs. The trial emphasises the importance of innovative solutions such as StatSeal to advance PICC care and improve patient experience.

Thermal-energy-efficient-secured-link reliable and delay-aware routing protocol (TESLDAR) for wireless body area networks

Wireless Body Area Networks (WBANs) have emerged as a transformative technology for health monitoring, leveraging sensor nodes placed on or within the human body. This paradigm offers real-time insights into physiological parameters, enabling remote healthcare applications and enhancing patient well-being. However, the WBANs bring challenges that demand innovative solutions to ensure their seamless integration into healthcare ecosystems. Secondly, trustworthiness becomes paramount in healthcare applications, where the sensitivity of health-related data necessitates secure and reliable communication. Lastly, congestion issues, arising from bandwidth constraints and concurrent data transmissions, affect the performance of WBANs, impacting data integrity, energy consumption, and quality of service. Therefore, this paper proposes a TESLDAR protocol that incorporates the proposed Jellyfish Updated Aquila Narrow ExploExpand (JUANE) method aims to address issues such as hotspot-induced thermal stress, trustworthiness concerns, and congestion challenges. The proposed algorithm incorporates Jellyfish Optimization (JFO), inspired by the adaptable movement of jellyfish, with characteristics inspired by the Aquila bird. The method's innovative integration of jellyfish and bird-inspired behaviors positions it as a promising solution for efficient route maintenance in WBANs addressing thermal, trust, and congestion challenges for enhanced reliability in healthcare monitoring applications.

Road surface crack detection and classification based on YOLOv5

The burgeoning need for sustainable urban transportation has magnified the importance of effective road infrastructure maintenance, particularly road crack detection. This study addresses the challenge of accurately detecting road surface cracks through automated methods. To accurately identify road surface cracks, we used the YOLOv5 image recognition algorithm, enhanced by comprehensive data augmentation and meticulous training strategies. The YOLOv5 proved superior to traditional methods in accuracy and recall while maintaining high computational speed and a smaller model size in experimental outcomes. The findings affirm that YOLOv5-based detection technologies significantly improve maintenance efficiency and traffic safety, with future work poised to further refine these approaches for greater robustness and integration into intelligent transportation systems.

Predicting Rail Corrugation Based on Convolutional Neural Networks Using Vehicle's Acceleration Measurements.

This paper presents a deep learning approach for predicting rail corrugation based on on-board rolling-stock vertical acceleration and forward velocity measurements using One-Dimensional Convolutional Neural Networks (CNN-1D). The model's performance is examined in a 1:10 scale railway system at two different forward velocities. During both the training and test stages, the CNN-1D produced results with mean absolute percentage errors of less than 5% for both forward velocities, confirming its ability to reproduce the corrugation profile based on real-time acceleration and forward velocity measurements. Moreover, by using a Gradient-weighted Class Activation Mapping (Grad-CAM) technique, it is shown that the CNN-1D can distinguish various regions, including the transition from damaged to undamaged regions and one-sided or two-sided corrugated regions, while predicting corrugation. In summary, the results of this study reveal the potential of data-driven techniques such as CNN-1D in predicting rails' corrugation using online data from the dynamics of the rolling-stock, which can lead to more reliable and efficient maintenance and repair of railways.

Time-Dependent Shifts in Intestinal Bacteriome, Klebsiella Colonization and Incidence of Antibiotic-Resistance Genes after Allogeneic Hematopoietic Stem Cell Transplantation.

Dose-intensive cytostatic therapy and antibiotic treatment in allogeneic hematopoietic stem cell transplantation (allo-HSCT) cause severe abnormalities in a composition of gut microbiota as well as the emergence of antibiotic resistance. The data on the longitudinal recovery of major bacterial phyla and the expansion of genes associated with antibiotic resistance are limited. We collected regular stool samples during the first year after allo-HSCT from 12 adult patients with oncohematological disorders after allo-HSCT and performed 16SrRNA sequencing, multiplex PCR, conventional bacteriology and CHROMagar testing. We observed a decline in Shannon microbiota diversity index as early as day 0 of allo-HSCT (p = 0.034) before any administration of antibiotics, which persisted up to 1 year after transplantation, when the Shannon index returned to pre-transplant levels (p = 0.91). The study confirmed the previously shown decline in Bacillota (Firmicutes) genera and the expansion of E. coli/Shigella, Klebsiella and Enterococci. The recovery of Firmicutes was slower than that of other phyla and occurred only a year post-transplant. A positive correlation was observed between the expansion of E. coli/Shigella genera and blaKPC, blaCTX-M-1 and blaTEM (p < 0.001), Klebsiella spp. and blaOXA-48-like, blaNDM, blaCTX-M-1, blaTEM, and blaSHV (p < 0.001), Pseudomonas spp. and blaNDM (p = 0.002), Enterococcus spp. and blaOXA-48-like, blaNDM, blaCTX-M-1, blaSHV (p < 0.01). The correlation was observed between the expansion of Enterobacterales and and carbapenemase-positive CHROMagar samples (p < 0.001). Samples positive for carbapenem-resitant bacteria were at their maximum levels on day +30, and were gradually diminishing one year after allo-HSCT. From day +30 to +60, all isolated K. pneumoniae strains in fecal samples proved to be resistant to the main antibiotic groups (carbapenems, aminoglycosides, fluoroquinolones, third-generation cephalosporins). One year after HSCT, we documented the spontaneous decolonization of K. pneumoniae. The sensitivity of molecular biology techniques in the search for total and antibiotic-resistant Klebsiella seems to be superior to common bacteriological cultures. Future studies should be focused on searching for novel approaches to the efficient reconstitution and/or maintenance of strictly anaerobic microbiota in oncological patients.

Optimal design of parabolic through solar collector networks: A design approach for year-round operation

In this article, area optimization in parabolic trough solar collector (PTC) networks used in industrial processes is explored. The Particle Swarm Optimization (PSO) technique to maximise economic benefits while minimising total operating costs is employed. The objective also considers flexibility across wide temperature ranges and process heat loads. To achieve this the relationship between solar collection area and associated costs, including initial investment, maintenance, and energy efficiency is meticulously analysed. This work proposes strategies for the optimal adjustment of PTC collector area, accounting for thermal variations and specific demands of industrial processes. The results indicate that the optimised geometry of a PTC, which maximizes the present value of annual energy savings, has the following dimensions: length: 12.5 m; aperture: 5.8 m; tube diameter: 0.014 m; focal distance: 1.449 m; glass envelope inner diameter: 0.111 m. For a process thermal load ranging from 0.4 MW to 4 MW, the optimal network structure that maximizes energy capture in both summer and winter consists of 12 collectors in series (125 m long), with the number of parallel lines proportional to the heat duty. In this article, area optimization in parabolic trough solar collector (PTC) networks used in industrial processes is explored. The Particle Swarm Optimization (PSO) technique to maximise economic benefits while minimising total operating costs is employed. The objective also considers flexibility across wide temperature ranges and process heat loads. To achieve this the relationship between solar collection area and associated costs, including initial investment, maintenance, and energy efficiency is meticulously analysed. This work proposes strategies for the optimal adjustment of PTC collector area, accounting for thermal variations and specific demands of industrial processes. The results indicate that the optimised geometry of a PTC, which maximizes the present value of annual energy savings, has the following dimensions: length: 12.5 m; aperture: 5.8 m; tube diameter: 0.014 m; focal distance: 1.449 m; glass envelope inner diameter: 0.111 m. For a process thermal load ranging from 0.4 MW to 4 MW, the optimal network structure that maximizes energy capture in both summer and winter consists of 12 collectors in series (125 m long), with the number of parallel lines proportional to the heat duty. The network size is determined by the required heat load more than the inlet and required process temperature.

Reducing oxygen vacancies of MoO3 by polyaniline functionalization for stable and efficient inorganic tri-brominated perovskite solar cells

The photovoltaic performance of perovskite solar cells (PSCs) is closely dependent on the efficient carrier extraction and transport at the interface. Here, a polyaniline (PANI) functionalized MoO3 (PANI/MoO3) hole transport material (HTM) is exploited to perfect the interface between the perovskite layer and carbon electrode in all-inorganic CsPbBr3 PSCs. After functionalization with PANI, the p-type behavior and the hole mobility and conductivity of MoO3 are improved by reducing the oxygen vacancies, which boosts the hole extraction and transport, energy level arrangement at the interface of CsPbBr3 perovskite/(PANI/MoO3) HTM. Meanwhile, the PANI/MoO3 with rich C–N and N–H groups introduced by PANI passivates the ions trap states of perovskite films by the C–N⋯Pb2+ (Cs+) Lewis acid-base coordination and the N–H⋯Br− hydrogen bonding, leading to an effective suppression of non-radiative recombination for improved carrier extraction. As a result, the PANI/MoO3 HTMs-based CsPbBr3 PSCs obtain a remarkably increased power conversion efficiency of 10.41 %, in comparison with the efficiency of the original device (6.55 %). In addition, the unencapsulated device with PANI/MoO3 HTMs shows excellent long-term stability with 93.9 % maintenance of the initial efficiency after storing in air with 85 % relative humidity and at 85 °C for 30 days.

A decision framework for improving the service quality of charging stations based on online reviews and evolutionary game theory

With “charging anxiety” gradually replacing “range anxiety” as the major obstacle for consumers to purchase electric vehicles (EVs), the quantity-driven rather than quality-driven charging station development model barely meets the development of the EV market. It is of great practical significance that a comprehensive and in-depth insight into charging stations’ customer requirements (CRs) to uncover their pain points and improvement measures. Therefore, this study develops a decision framework for improving EV charging service quality based on quality function deployment (QFD). First, CRs are extracted based on charging station online reviews and classified by the revised importance performance analysis (IPA) method to identify the charging service’s main highlights and pain points. Then, the process of achieving consensus among experts on evaluating the correlation between CRs and service design requirements (DRs) is considered as an evolutionary game of coalition, with a meritocratic Fermi rule considering affinities (MFA) being introduced. Finally, the importance of DRs is determined by combining the Shapley value and risk aversion theory. The results indicate that regulation and convenience are the main highlights of charging stations to satisfy EV users, while reliability and assurance are primary pain points. The charging station operators should prioritize their limited resources on providing efficient customer and maintenance services, installing charging stations with international standards, and regularly inspecting and updating charging infrastructures. This study is beneficial for charging station operators to understand what customers want and how to satisfy them.