Scientific and technological advancements are rapidly transforming underground engineering,shifting from labor-intensive,time-consuming methods to automated,real-time systems.This timely and comprehensive review cover...Scientific and technological advancements are rapidly transforming underground engineering,shifting from labor-intensive,time-consuming methods to automated,real-time systems.This timely and comprehensive review covers in-situ testing,intelligent monitoring,and geophysical testing methods,highlighting fundamental principles,testing apparatuses,data processing techniques,and engineering applications.The state-of-the-art summary emphasizes not only cutting-edge innovations for complex and harsh environments but also the transformative role of artificial intelligence and machine learning in data interpretations.The integration of big data and advanced algorithms is particularly impactful,enabling the identification,prediction,and mitigation of potential risks in underground projects.Key aspects of the discussion include detection capabilities,method integration,and data convergence of intelligent technologies to drive enhanced safety,operational efficiency,and predictive reliability.The review also examines future trends in intelligent technologies,emphasizing unified platforms that combine multiple methods,real-time data,and predictive analytics.These advancements are shaping the evolution of underground construction and maintenance,aiming for risk-free,high-efficiency underground engineering.展开更多
BACKGROUND Research examining the relationships among anxiety,depression,self-perceived burden(SPB),and psychological resilience(PR),along with the determinants of PR,in patients with chronic renal failure(CRF)receivi...BACKGROUND Research examining the relationships among anxiety,depression,self-perceived burden(SPB),and psychological resilience(PR),along with the determinants of PR,in patients with chronic renal failure(CRF)receiving maintenance hemodia-lysis(MHD)is limited.AIM To investigate the correlation between anxiety,depression,SPB,and PR in pati-ents with CRF on MHD.METHODS This study included 225 patients with CRF on MHD who were admitted between June 2021 and June 2024.The anxiety level was evaluated using the Self-Rating Anxiety Scale(SAS);the depression status was assessed using the Self-Rating Depression Scale(SDS);the SPB was measured using the SPB Scale(SPBS);and the PR was determined using the Connor–Davidson Resilience Scale(CD-RISC).The correlations among the SAS,SDS,SPB,and CD-RISC were analyzed using Pearson’s correlation coefficients.Univariate and multivariate analyses were performed to identify the factors that influence the PR of patients with CRF on MHD.RESULTS The SAS,SDS,SPB,and CD-RISC scores of the 225 patients were 45.25±15.36,54.81±14.68,32.31±11.52,and 66.48±9.18,respectively.Significant negative correlations were observed between SAS,SDS,SPB,and CD-RISC.Furthermore,longer dialysis vintage(P=0.015),the absence of religious beliefs(P=0.020),lower monthly income(P=0.008),higher SAS score(P=0.013),and higher SDS score(P=0.006)were all independent factors that adversely affected the PR of patients with CRF on MHD.CONCLUSION Patients with CRF on MHD present with varying degrees of anxiety,depression,and SPB,all of which exhibit a significant negative correlation with their PR.Moreover,longer dialysis vintage,the absence of religious beliefs,lower monthly income,higher SAS score,and higher SDS score were factors that negatively affected the PR of patients with CRF on MHD.展开更多
While Artificial Intelligence (AI) is leading the way in terms of hardware advancements, such as GPUs, memory, and processing power, real-time applications are still catching up. It is inevitable that when one aspect ...While Artificial Intelligence (AI) is leading the way in terms of hardware advancements, such as GPUs, memory, and processing power, real-time applications are still catching up. It is inevitable that when one aspect leads and other trails behind, they coexist in life, as is often the case. The trailing aspect cannot remain far behind because, without application and use, there would be a dead end. Everything, whether an object, software, or tool, must have a practical use for humans. Without this, it will become obsolete. We can see this in many instances, such as blockchain technology, which is superior yet faces challenges in practical implementation, leading to a decline in adoption. This publication aims to bridge the gap between AI advancements and maintenance, specifically focusing on making predictive maintenance a practical application. There are multiple building blocks that make predictive maintenance a practical application. Each block performs a function leading to an output. This output forms an input to the receiving block. There are also foundational parts for all these building blocks to perform a function. Eventually, once the building blocks are connected, they form a loop and start to lead the path to predictive maintenance. Predictive maintenance is indeed practically achievable, but one must comprehend all the building blocks necessary for its implementation. Although detailed explanations will be provided in the upcoming sections, it is important to understand that simply purchasing software and plugging it in might be a far-fetched approach.展开更多
This paper presents a project aimed at developing a trilingual visual dictionary for aircraft maintenance professionals and students.The project addresses the growing demand for accurate communication and technical te...This paper presents a project aimed at developing a trilingual visual dictionary for aircraft maintenance professionals and students.The project addresses the growing demand for accurate communication and technical terminology in the aviation industry,particularly in Brazil and China.The study employs a corpus-driven approach,analyzing a large corpus of aircraft maintenance manuals to extract key technical terms and their collocates.Using specialized subcorpora and a comparative analysis,this paper demonstrates challenges and solutions into the identification of high-frequency keywords and explores their contextual use in aviation documentation,emphasizing the need for clear and accurate technical communication.By incorporating these findings into a trilingual visual dictionary,the project aims to enhance the understanding and usage of aviation terminology.展开更多
Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries.However,traditional predictive maintenance methods often face challenges in adaptin...Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries.However,traditional predictive maintenance methods often face challenges in adapting to diverse industrial environments and ensuring the transparency and fairness of their predictions.This paper presents a novel predictive maintenance framework that integrates deep learning and optimization techniques while addressing key ethical considerations,such as transparency,fairness,and explainability,in artificial intelligence driven decision-making.The framework employs an Autoencoder for feature reduction,a Convolutional Neural Network for pattern recognition,and a Long Short-Term Memory network for temporal analysis.To enhance transparency,the decision-making process of the framework is made interpretable,allowing stakeholders to understand and trust the model’s predictions.Additionally,Particle Swarm Optimization is used to refine hyperparameters for optimal performance and mitigate potential biases in the model.Experiments are conducted on multiple datasets from different industrial scenarios,with performance validated using accuracy,precision,recall,F1-score,and training time metrics.The results demonstrate an impressive accuracy of up to 99.92%and 99.45%across different datasets,highlighting the framework’s effectiveness in enhancing predictive maintenance strategies.Furthermore,the model’s explainability ensures that the decisions can be audited for fairness and accountability,aligning with ethical standards for critical systems.By addressing transparency and reducing potential biases,this framework contributes to the responsible and trustworthy deployment of artificial intelligence in industrial environments,particularly in safety-critical applications.The results underscore its potential for wide application across various industrial contexts,enhancing both performance and ethical decision-making.展开更多
Objective:To investigate the clinical efficacy and cost-effectiveness of combined hemodialysis(HD)and hemoperfusion(HP)therapy in managing secondary hyperparathyroidism(SHPT)in patients undergoing maintenance hemodial...Objective:To investigate the clinical efficacy and cost-effectiveness of combined hemodialysis(HD)and hemoperfusion(HP)therapy in managing secondary hyperparathyroidism(SHPT)in patients undergoing maintenance hemodialysis(MHD).Methods:A total of 195 patients with MHD and SHPT at Deyang People's Hospital from April 2024 to April 2025 were enrolled.Patients were randomly assigned to a control group receiving standard HD treatment and an experimental group receiving HD combined with HP therapy.The experimental group underwent 1 year of observation(97 cases in the experimental group,98 cases in the control group).During treatment,changes in parathyroid hormone(PTH),serum calcium,serum phosphorus,and inflammatory factors were monitored,along with analysis of treatment-related economic benefits and safety.Results:The experimental group demonstrated significantly better reductions in PTH,serum phosphorus,and inflammatory factors compared to the control group(P<0.05).Although the total treatment cost was slightly higher,the unit cost per therapeutic effect was lower,resulting in a superior cost-effectiveness ratio.Conclusion:Combined HD and HP therapy can significantly improve SHPT-related indicators in MHD patients,demonstrating safety,controllability,and high cost-effectiveness,making it a clinically applicable and recommended treatment option.展开更多
BACKGROUND No clear guidelines for long-term postoperative maintenance therapy have been established for patients with lung oligometastases from colorectal cancer(CRC)who achieve radiological no evidence of disease af...BACKGROUND No clear guidelines for long-term postoperative maintenance therapy have been established for patients with lung oligometastases from colorectal cancer(CRC)who achieve radiological no evidence of disease after radiofrequency ablation(RFA)treatment.We compared the outcomes of patients with lung oligometa-stases from CRC after RFA plus maintenance capecitabine with RFA alone.AIM To determine whether adding capecitabine to RFA improves prognosis compared with RFA alone.METHODS This multicenter retrospective study included consecutive patients from two tertiary cancer centers treated for pulmonary oligometastases from CRC between 2016 and 2023.Subjects were assigned to RFA plus capecitabine(combined)or RFA alone(only RFA)groups.Primary outcomes included overall survival(OS)and progression-free survival(PFS)survival and the secondary outcome was local tumor progression(LTP).The OS,PFS,and LTP rates were compared between the two groups.In addition,prognostic factors were identified using univariate and multivariate analyses.RESULTS Combination therapy(RFA+capecitabine,n=148)and RFA monotherapy(n=99)were compared in patients with CRC and lung metastases.The median OS was 37.8 months(22.4,50.3),the PFS was 18.7 months(13.0,36.5),and the LTP was 31.5 months(20.0,52.4)in the Only RFA group.The OS increased significantly(P=0.011)and the LTP decreased at all time points(P<0.001)in the combined group.The multivariate cox analysis revealed that combined chemotherapy significantly improved OS,with hazard ratios ranging from 0.29 to 0.35(all P<0.015)after adjusting for demographic,tumor,and treatment-related factors.The risk of death was consistently lower in the combination therapy group compared to RFA monotherapy.CONCLUSION RFA prolongs survival and local control in patients with CRC pulmonary oligometastases.Adjuvant capecitabine increases OS and reduces LTP compared to RFA alone,but PFS did not significantly change.展开更多
Objective:To evaluate the effectiveness of digital-intelligent health education for patients undergoing maintenance hemodialysis.Methods:From December 2023 to December 2024,82 patients undergoing maintenance hemodialy...Objective:To evaluate the effectiveness of digital-intelligent health education for patients undergoing maintenance hemodialysis.Methods:From December 2023 to December 2024,82 patients undergoing maintenance hemodialysis in our hospital were selected and randomly divided into an observation group(n=41,receiving routine health education)and a control group(n=41,receiving digital health education).The levels of knowledge,belief,and behavior related to dry weight control,as well as changes in dry weight and complications,were compared before and after intervention.Results:After intervention,the observation group had higher scores for knowledge(40.96±6.43),belief(39.11±6.39),behavior(39.66±5.78),and total score(119.04±13.11)compared to the control group(p<0.05).The observation group also showed better dry weight control than the control group(p<0.05).The total incidence of complications in the observation group(4.88%,2/41)was lower than that in the control group(21.95%,9/41)(p<0.05).Conclusion:The rational application of digital-intelligent health education can effectively maintain dry weight in patients undergoing maintenance hemodialysis,reduce complications,and improve patients’knowledge,belief,and behavior levels.This approach is worthy of promotion.展开更多
BACKGROUND Eosinophilic esophagitis(EoE)is a chronic inflammatory disorder presenting as symptoms of dysphagia,esophageal food impaction,chest pain,and heartburn.After an initial trial of proton pump inhibitor(PPI)the...BACKGROUND Eosinophilic esophagitis(EoE)is a chronic inflammatory disorder presenting as symptoms of dysphagia,esophageal food impaction,chest pain,and heartburn.After an initial trial of proton pump inhibitor(PPI)therapy,swallowed topical corticosteroids(STC)are effective as induction therapy for EoE.However,out-come data for STC as a maintenance strategy is limited.RESULTS Three randomized control trials and one observational study were included,involving 303 patients(189 in the STC group,114 in the placebo-controlled group).Analysis showed that histologic recurrence was significantly lower with STC(OR:0.04,95%CI:0.01-0.28,P<0.00001,I^(2)=78%).Overall symptom recurrence was similar between groups(OR:0.23,95%CI:0.02-3.54,P=0.29,I^(2)=92%).On sensitivity analysis,symptom recurrence was significantly lower in the STC group(OR:0.05,95%CI:0.02-0.17,P=0.00001,I^(2)=39%).Odds of repeat dilation were significantly lower in the STC group(OR:0.14,95%CI:0.02-0.91,P=0.04,I^(2)=0%).Candida infection rates were similar between groups(OR:6.13,95%CI:0.85-44.26,P=0.07,I^(2)=24%).Proportion of concomitant PPI use was similar between groups(OR:1.64,95%CI:0.83-3.21,P=0.15,I^(2)=0%).CONCLUSION For patients who successfully achieved remission of EoE with STC induction therapy,maintaining treatment is effective in sustaining histologic remission,while newer regimens may be effective in preventing symptom recurrence compared to placebo.We found no significant difference for oropharyngeal/esophageal candidiasis with STC maintenance therapy.Future studies with longer follow-up periods are needed.展开更多
This paper discusses multiple aspects of modern landscape greening maintenance technology,including core elements such as smart irrigation,plant physiological monitoring,and ecological restoration,along with their syn...This paper discusses multiple aspects of modern landscape greening maintenance technology,including core elements such as smart irrigation,plant physiological monitoring,and ecological restoration,along with their synergistic effects.It also introduces the evolution characteristics,key maintenance points in different scenarios,intelligent management methods,and relevant case studies and benefit assessments.Additionally,it analyzes the challenges faced in technology promotion and proposes solutions.展开更多
This study proposes a novel visual maintenance method for photovoltaic(PV)modules based on a two-stage Wiener degradation model,addressing the limitations of traditional PV maintenance strategies that often result in ...This study proposes a novel visual maintenance method for photovoltaic(PV)modules based on a two-stage Wiener degradation model,addressing the limitations of traditional PV maintenance strategies that often result in insufficient or excessive maintenance.The approach begins by constructing a two-stage Wiener process performance degradation model and a remaining life prediction model under perfect maintenance conditions using historical degradation data of PV modules.This enables accurate determination of the optimal timing for postfailure corrective maintenance.To optimize the maintenance strategy,the study establishes a comprehensive cost model aimed at minimizing the long-term average cost rate.The model considers multiple cost factors,including inspection costs,preventive maintenance costs,restorative maintenance costs,and penalty costs associated with delayed fault detection.Through this optimization framework,the method determines both the optimal maintenance threshold and the ideal timing for predictive maintenance actions.Comparative analysis demonstrates that the twostage Wiener model provides superior fitting performance compared to conventional linear and nonlinear degradation models.When evaluated against traditional maintenance approaches,including Wiener process-based corrective maintenance strategies and static periodic maintenance strategies,the proposed method demonstrates significant advantages in reducing overall operational costs while extending the effective service life of PV components.The method achieves these improvements through effective coordination between reliability optimization and economic benefit maximization,leading to enhanced power generation performance.These results indicate that the proposed approach offers a more balanced and efficient solution for PV system maintenance.展开更多
This study focuses on the management of maintenance hemodialysis(MHD)patients,with a specific emphasis on the practical application effect of the network information management model including its impact on patients’...This study focuses on the management of maintenance hemodialysis(MHD)patients,with a specific emphasis on the practical application effect of the network information management model including its impact on patients’compliance.A network information management model for MHD patients was constructed around three management schemes:“software reminders+follow-up guidance”,“dietary records+self-management reminders”,and“dialysis plan+precise weight management”.These schemes were respectively used to optimize anemia management,control the risk of hyperphosphatemia,and improve toxin clearance efficiency.A controlled experiment was conducted,with an experimental group and a control group set up for comparative practice.The results showed that the network information management model can effectively improve patients’anemia,help alleviate mineral metabolism disorders and the accumulation of small-molecule toxins,and exert a positive impact on patients’treatment compliance.展开更多
Objective:In the Radiology Department of Mzuzu Central Hospital(MCH),daily training for radiographers now includes content on Computed Tomography(CT)image quality control and equipment maintenance to ensure the normal...Objective:In the Radiology Department of Mzuzu Central Hospital(MCH),daily training for radiographers now includes content on Computed Tomography(CT)image quality control and equipment maintenance to ensure the normal,continuous,and stable operation of the 16-slice spiral CT scanner.Methods:Through comprehensive analysis of relevant equipment,we have identified key parameters that significantly impact CT image quality.Innovative optimization strategies and solutions targeting these parameters have been developed and integrated into daily training programs.Furthermore,starting from an examination of prevalent failure modes observed in CT equipment,we delve into essential maintenance and preservation techniques that CT technologists must master to ensure optimal system performance.Results:(1)Crucial factors affecting CT image quality include artifacts,noise,partial volume effects,and surrounding gap phenomena,alongside spatial and density resolutions,CT dose,reconstruction algorithms,and human factors during the scanning process.In the daily training for radiographers,emphasis is placed on strictly implementing image quality control measures at every stage of the CT scanning process and skillfully applying advanced scanning and image processing techniques.By doing so,we can provide clinicians with accurate and reliable imaging references for diagnosis and treatment.(2)Strategies for CT equipment maintenance:①Environmental inspection of the CT room to ensure cleanliness and hygiene.②Rational and accurate operation,including calibration software proficiency.③Regular maintenance and servicing for minimizing machine downtime.④Maintenance of the CT X-ray tube.CT technicians can become proficient in equipment maintenance and upkeep techniques through training,which can significantly extend the service life of CT systems and reduce the occurrence of malfunctions.Conclusion:Through the regular implementation of rigorous CT image quality control training for radiology technicians,coupled with diligent and proactive CT equipment maintenance,we have observed profound and beneficial impacts on improving image quality.The accuracy and fidelity of radiological data ultimately leads to more accurate diagnoses and effective treatments.展开更多
A situation maintenance-based cooperative guidance strategy is proposed to intercept a high-speed and high-maneuverability target via inferior missiles.Reachability and relative motion analyses are conducted to develo...A situation maintenance-based cooperative guidance strategy is proposed to intercept a high-speed and high-maneuverability target via inferior missiles.Reachability and relative motion analyses are conducted to develop and pursue virtual targets,respectively.A two-stage guidance strategy under nonlinear kinematics is developed on the basis of virtual targets.The first stage optimizes the coverage and collision situation by pursuing virtual targets under specific angular constraints.The second stage subsequently intercepts the superior target based on the handover condition optimized by the first stage.Numerical simulation results are provided to compare the effectiveness and superiority of the proposed strategy with those of the reachability-based cooperative strategy(RCS),coverage-based cooperative guidance(CBCG)and augmented proportional navigation(APN)under various maneuvering modes.展开更多
The fractionating tower bottom in fluid catalytic cracking Unit (FCCU) is highly susceptible to coking due to the interplay of complex external operating conditions and internal physical properties. Consequently, quan...The fractionating tower bottom in fluid catalytic cracking Unit (FCCU) is highly susceptible to coking due to the interplay of complex external operating conditions and internal physical properties. Consequently, quantitative risk assessment (QRA) and predictive maintenance (PdM) are essential to effectively manage coking risks influenced by multiple factors. However, the inherent uncertainties of the coking process, combined with the mixed-frequency nature of distributed control systems (DCS) and laboratory information management systems (LIMS) data, present significant challenges for the application of data-driven methods and their practical implementation in industrial environments. This study proposes a hierarchical framework that integrates deep learning and fuzzy logic inference, leveraging data and domain knowledge to monitor the coking condition and inform prescriptive maintenance planning. The framework proposes the multi-layer fuzzy inference system to construct the coking risk index, utilizes multi-label methods to select the optimal feature dataset across the reactor-regenerator and fractionation system using coking risk factors as label space, and designs the parallel encoder-integrated decoder architecture to address mixed-frequency data disparities and enhance adaptation capabilities through extracting the operation state and physical properties information. Additionally, triple attention mechanisms, whether in parallel or temporal modules, adaptively aggregate input information and enhance intrinsic interpretability to support the disposal decision-making. Applied in the 2.8 million tons FCCU under long-period complex operating conditions, enabling precise coking risk management at the fractionating tower bottom.展开更多
This paper investigates the selective maintenance o systems that perform multi-mission in succession. Selective maintenance is performed on systems with limited break time to improve the success of the next mission. I...This paper investigates the selective maintenance o systems that perform multi-mission in succession. Selective maintenance is performed on systems with limited break time to improve the success of the next mission. In general, the duration of the mission is stochastic. However, existing studies rarely take into account system availability and the repairpersons with different skill levels. To solve this problem, a new multi-mission selective maintenance and repairpersons assignment model with stochastic duration of the mission are developed. To maximize the minimum phase-mission reliability while meeting the minimum system availability, the model is transformed into an optimization problem subject to limited maintenance resources. The optimization is then realized using an analytical method based on a self-programming function and a Monte Carlo simulation method, respectively. Finally, the validity of the model and solution method approaches are verified by numerical arithmetic examples. Comparative and sensitivity analyses are made to provide proven recommendations for decision-makers.展开更多
In the context of energy structure transformation,digital and intelligent technologies have been introduced into the field of hydropower,which has accelerated the technological and equipment innovation of hydropower p...In the context of energy structure transformation,digital and intelligent technologies have been introduced into the field of hydropower,which has accelerated the technological and equipment innovation of hydropower plants.However,it has also brought severe challenges to the operation and maintenance of hydropower plants.Traditional hydropower plant operation and maintenance suffer from problems such as low efficiency,equipment aging,and high labor costs,which seriously hinder the innovation and upgrading of hydropower plant operation and maintenance.Therefore,this article focuses on the operation and maintenance of hydropower plants,summarizes a series of innovative strategies,and applies them in practice to effectively improve the operation and maintenance level of hydropower plants.展开更多
As an essential part of the urban infrastructure,underground utility tunnels have a long service life,complex structural performance evolution and dynamic changes both inside and outside the tunnel.These combined fact...As an essential part of the urban infrastructure,underground utility tunnels have a long service life,complex structural performance evolution and dynamic changes both inside and outside the tunnel.These combined factors result in a wide variety of disaster risks during the operation and maintenance phase,which make risk management and control particularly challenging.This work first reviews three common representative disaster factors during the operation and maintenance period:settlement,earthquakes,and explosions.It summarizes the causes of disasters,key technologies,and research methods.Then,it delves into the research on the intelligent operation and maintenance architecture for utility tunnels.Additionally,it explores the data challenges,monitoring technologies,and management platform architectures faced during the operation and maintenance process.This work provides new research perspectives for the long-term,healthy,and sustainable development of utility tunnels,which serve as the underground arteries of cities.展开更多
In the face of data scarcity in the optimization of maintenance strategies for civil aircraft,traditional failure data-driven methods are encountering challenges owing to the increasing reliability of aircraft design....In the face of data scarcity in the optimization of maintenance strategies for civil aircraft,traditional failure data-driven methods are encountering challenges owing to the increasing reliability of aircraft design.This study addresses this issue by presenting a novel combined data fusion algorithm,which serves to enhance the accuracy and reliability of failure rate analysis for a specific aircraft model by integrating historical failure data from similar models as supplementary information.Through a comprehensive analysis of two different maintenance projects,this study illustrates the application process of the algorithm.Building upon the analysis results,this paper introduces the innovative equal integral value method as a replacement for the conventional equal interval method in the context of maintenance schedule optimization.The Monte Carlo simulation example validates that the equivalent essential value method surpasses the traditional method by over 20%in terms of inspection efficiency ratio.This discovery indicates that the equal critical value method not only upholds maintenance efficiency but also substantially decreases workload and maintenance costs.The findings of this study open up novel perspectives for airlines grappling with data scarcity,offer fresh strategies for the optimization of aviation maintenance practices,and chart a new course toward achieving more efficient and cost-effective maintenance schedule optimization through refined data analysis.展开更多
Virtual maintenance,as an important means of industrial training and education,places strict requirements on the accuracy of participant pose perception and assessment of motion standardization.However,existing resear...Virtual maintenance,as an important means of industrial training and education,places strict requirements on the accuracy of participant pose perception and assessment of motion standardization.However,existing research mainly focuses on human pose estimation in general scenarios,lacking specialized solutions for maintenance scenarios.This paper proposes a virtual maintenance human pose estimation method based on multi-scale feature enhancement(VMHPE),which integrates adaptive input feature enhancement,multi-scale feature correction for improved expression of fine movements and complex poses,and multi-scale feature fusion to enhance keypoint localization accuracy.Meanwhile,this study constructs the first virtual maintenance-specific human keypoint dataset(VMHKP),which records standard action sequences of professional maintenance personnel in five typical maintenance tasks and provides a reliable benchmark for evaluating operator motion standardization.The dataset is publicly available at.Using high-precision keypoint prediction results,an action assessment system utilizing topological structure similarity was established.Experiments show that our method achieves significant performance improvements:average precision(AP)reaches 94.4%,an increase of 2.3 percentage points over baseline methods;average recall(AR)reaches 95.6%,an increase of 1.3 percentage points.This research establishes a scientific four-level evaluation standard based on comparative motion analysis and provides a reliable solution for standardizing industrial maintenance training.展开更多
基金supported by Ministry of Education of Singapore,under Academic Research Fund Tier 1(Grant Number RG143/23).
文摘Scientific and technological advancements are rapidly transforming underground engineering,shifting from labor-intensive,time-consuming methods to automated,real-time systems.This timely and comprehensive review covers in-situ testing,intelligent monitoring,and geophysical testing methods,highlighting fundamental principles,testing apparatuses,data processing techniques,and engineering applications.The state-of-the-art summary emphasizes not only cutting-edge innovations for complex and harsh environments but also the transformative role of artificial intelligence and machine learning in data interpretations.The integration of big data and advanced algorithms is particularly impactful,enabling the identification,prediction,and mitigation of potential risks in underground projects.Key aspects of the discussion include detection capabilities,method integration,and data convergence of intelligent technologies to drive enhanced safety,operational efficiency,and predictive reliability.The review also examines future trends in intelligent technologies,emphasizing unified platforms that combine multiple methods,real-time data,and predictive analytics.These advancements are shaping the evolution of underground construction and maintenance,aiming for risk-free,high-efficiency underground engineering.
基金Supported by Key Research Fund of Wannan Medical College,No.WK2021ZF15Research Foundation for Advanced Talents of Wannan Medical College,No.YR202213+3 种基金Foundation of Anhui Educational Committee,No.2023AH051759Excellent Youth Research Project of Anhui UniversitiesNo.2023AH030107Horizontal Project of Wannan Medical College,No.622202504003 and No.662202404013.
文摘BACKGROUND Research examining the relationships among anxiety,depression,self-perceived burden(SPB),and psychological resilience(PR),along with the determinants of PR,in patients with chronic renal failure(CRF)receiving maintenance hemodia-lysis(MHD)is limited.AIM To investigate the correlation between anxiety,depression,SPB,and PR in pati-ents with CRF on MHD.METHODS This study included 225 patients with CRF on MHD who were admitted between June 2021 and June 2024.The anxiety level was evaluated using the Self-Rating Anxiety Scale(SAS);the depression status was assessed using the Self-Rating Depression Scale(SDS);the SPB was measured using the SPB Scale(SPBS);and the PR was determined using the Connor–Davidson Resilience Scale(CD-RISC).The correlations among the SAS,SDS,SPB,and CD-RISC were analyzed using Pearson’s correlation coefficients.Univariate and multivariate analyses were performed to identify the factors that influence the PR of patients with CRF on MHD.RESULTS The SAS,SDS,SPB,and CD-RISC scores of the 225 patients were 45.25±15.36,54.81±14.68,32.31±11.52,and 66.48±9.18,respectively.Significant negative correlations were observed between SAS,SDS,SPB,and CD-RISC.Furthermore,longer dialysis vintage(P=0.015),the absence of religious beliefs(P=0.020),lower monthly income(P=0.008),higher SAS score(P=0.013),and higher SDS score(P=0.006)were all independent factors that adversely affected the PR of patients with CRF on MHD.CONCLUSION Patients with CRF on MHD present with varying degrees of anxiety,depression,and SPB,all of which exhibit a significant negative correlation with their PR.Moreover,longer dialysis vintage,the absence of religious beliefs,lower monthly income,higher SAS score,and higher SDS score were factors that negatively affected the PR of patients with CRF on MHD.
文摘While Artificial Intelligence (AI) is leading the way in terms of hardware advancements, such as GPUs, memory, and processing power, real-time applications are still catching up. It is inevitable that when one aspect leads and other trails behind, they coexist in life, as is often the case. The trailing aspect cannot remain far behind because, without application and use, there would be a dead end. Everything, whether an object, software, or tool, must have a practical use for humans. Without this, it will become obsolete. We can see this in many instances, such as blockchain technology, which is superior yet faces challenges in practical implementation, leading to a decline in adoption. This publication aims to bridge the gap between AI advancements and maintenance, specifically focusing on making predictive maintenance a practical application. There are multiple building blocks that make predictive maintenance a practical application. Each block performs a function leading to an output. This output forms an input to the receiving block. There are also foundational parts for all these building blocks to perform a function. Eventually, once the building blocks are connected, they form a loop and start to lead the path to predictive maintenance. Predictive maintenance is indeed practically achievable, but one must comprehend all the building blocks necessary for its implementation. Although detailed explanations will be provided in the upcoming sections, it is important to understand that simply purchasing software and plugging it in might be a far-fetched approach.
文摘This paper presents a project aimed at developing a trilingual visual dictionary for aircraft maintenance professionals and students.The project addresses the growing demand for accurate communication and technical terminology in the aviation industry,particularly in Brazil and China.The study employs a corpus-driven approach,analyzing a large corpus of aircraft maintenance manuals to extract key technical terms and their collocates.Using specialized subcorpora and a comparative analysis,this paper demonstrates challenges and solutions into the identification of high-frequency keywords and explores their contextual use in aviation documentation,emphasizing the need for clear and accurate technical communication.By incorporating these findings into a trilingual visual dictionary,the project aims to enhance the understanding and usage of aviation terminology.
文摘Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries.However,traditional predictive maintenance methods often face challenges in adapting to diverse industrial environments and ensuring the transparency and fairness of their predictions.This paper presents a novel predictive maintenance framework that integrates deep learning and optimization techniques while addressing key ethical considerations,such as transparency,fairness,and explainability,in artificial intelligence driven decision-making.The framework employs an Autoencoder for feature reduction,a Convolutional Neural Network for pattern recognition,and a Long Short-Term Memory network for temporal analysis.To enhance transparency,the decision-making process of the framework is made interpretable,allowing stakeholders to understand and trust the model’s predictions.Additionally,Particle Swarm Optimization is used to refine hyperparameters for optimal performance and mitigate potential biases in the model.Experiments are conducted on multiple datasets from different industrial scenarios,with performance validated using accuracy,precision,recall,F1-score,and training time metrics.The results demonstrate an impressive accuracy of up to 99.92%and 99.45%across different datasets,highlighting the framework’s effectiveness in enhancing predictive maintenance strategies.Furthermore,the model’s explainability ensures that the decisions can be audited for fairness and accountability,aligning with ethical standards for critical systems.By addressing transparency and reducing potential biases,this framework contributes to the responsible and trustworthy deployment of artificial intelligence in industrial environments,particularly in safety-critical applications.The results underscore its potential for wide application across various industrial contexts,enhancing both performance and ethical decision-making.
基金supported by the Deyang City Science and Technology Planning Project[Grant Number 2023SZZ010].
文摘Objective:To investigate the clinical efficacy and cost-effectiveness of combined hemodialysis(HD)and hemoperfusion(HP)therapy in managing secondary hyperparathyroidism(SHPT)in patients undergoing maintenance hemodialysis(MHD).Methods:A total of 195 patients with MHD and SHPT at Deyang People's Hospital from April 2024 to April 2025 were enrolled.Patients were randomly assigned to a control group receiving standard HD treatment and an experimental group receiving HD combined with HP therapy.The experimental group underwent 1 year of observation(97 cases in the experimental group,98 cases in the control group).During treatment,changes in parathyroid hormone(PTH),serum calcium,serum phosphorus,and inflammatory factors were monitored,along with analysis of treatment-related economic benefits and safety.Results:The experimental group demonstrated significantly better reductions in PTH,serum phosphorus,and inflammatory factors compared to the control group(P<0.05).Although the total treatment cost was slightly higher,the unit cost per therapeutic effect was lower,resulting in a superior cost-effectiveness ratio.Conclusion:Combined HD and HP therapy can significantly improve SHPT-related indicators in MHD patients,demonstrating safety,controllability,and high cost-effectiveness,making it a clinically applicable and recommended treatment option.
基金Supported by the National Natural Science Foundation of China,No.82072034。
文摘BACKGROUND No clear guidelines for long-term postoperative maintenance therapy have been established for patients with lung oligometastases from colorectal cancer(CRC)who achieve radiological no evidence of disease after radiofrequency ablation(RFA)treatment.We compared the outcomes of patients with lung oligometa-stases from CRC after RFA plus maintenance capecitabine with RFA alone.AIM To determine whether adding capecitabine to RFA improves prognosis compared with RFA alone.METHODS This multicenter retrospective study included consecutive patients from two tertiary cancer centers treated for pulmonary oligometastases from CRC between 2016 and 2023.Subjects were assigned to RFA plus capecitabine(combined)or RFA alone(only RFA)groups.Primary outcomes included overall survival(OS)and progression-free survival(PFS)survival and the secondary outcome was local tumor progression(LTP).The OS,PFS,and LTP rates were compared between the two groups.In addition,prognostic factors were identified using univariate and multivariate analyses.RESULTS Combination therapy(RFA+capecitabine,n=148)and RFA monotherapy(n=99)were compared in patients with CRC and lung metastases.The median OS was 37.8 months(22.4,50.3),the PFS was 18.7 months(13.0,36.5),and the LTP was 31.5 months(20.0,52.4)in the Only RFA group.The OS increased significantly(P=0.011)and the LTP decreased at all time points(P<0.001)in the combined group.The multivariate cox analysis revealed that combined chemotherapy significantly improved OS,with hazard ratios ranging from 0.29 to 0.35(all P<0.015)after adjusting for demographic,tumor,and treatment-related factors.The risk of death was consistently lower in the combination therapy group compared to RFA monotherapy.CONCLUSION RFA prolongs survival and local control in patients with CRC pulmonary oligometastases.Adjuvant capecitabine increases OS and reduces LTP compared to RFA alone,but PFS did not significantly change.
文摘Objective:To evaluate the effectiveness of digital-intelligent health education for patients undergoing maintenance hemodialysis.Methods:From December 2023 to December 2024,82 patients undergoing maintenance hemodialysis in our hospital were selected and randomly divided into an observation group(n=41,receiving routine health education)and a control group(n=41,receiving digital health education).The levels of knowledge,belief,and behavior related to dry weight control,as well as changes in dry weight and complications,were compared before and after intervention.Results:After intervention,the observation group had higher scores for knowledge(40.96±6.43),belief(39.11±6.39),behavior(39.66±5.78),and total score(119.04±13.11)compared to the control group(p<0.05).The observation group also showed better dry weight control than the control group(p<0.05).The total incidence of complications in the observation group(4.88%,2/41)was lower than that in the control group(21.95%,9/41)(p<0.05).Conclusion:The rational application of digital-intelligent health education can effectively maintain dry weight in patients undergoing maintenance hemodialysis,reduce complications,and improve patients’knowledge,belief,and behavior levels.This approach is worthy of promotion.
文摘BACKGROUND Eosinophilic esophagitis(EoE)is a chronic inflammatory disorder presenting as symptoms of dysphagia,esophageal food impaction,chest pain,and heartburn.After an initial trial of proton pump inhibitor(PPI)therapy,swallowed topical corticosteroids(STC)are effective as induction therapy for EoE.However,out-come data for STC as a maintenance strategy is limited.RESULTS Three randomized control trials and one observational study were included,involving 303 patients(189 in the STC group,114 in the placebo-controlled group).Analysis showed that histologic recurrence was significantly lower with STC(OR:0.04,95%CI:0.01-0.28,P<0.00001,I^(2)=78%).Overall symptom recurrence was similar between groups(OR:0.23,95%CI:0.02-3.54,P=0.29,I^(2)=92%).On sensitivity analysis,symptom recurrence was significantly lower in the STC group(OR:0.05,95%CI:0.02-0.17,P=0.00001,I^(2)=39%).Odds of repeat dilation were significantly lower in the STC group(OR:0.14,95%CI:0.02-0.91,P=0.04,I^(2)=0%).Candida infection rates were similar between groups(OR:6.13,95%CI:0.85-44.26,P=0.07,I^(2)=24%).Proportion of concomitant PPI use was similar between groups(OR:1.64,95%CI:0.83-3.21,P=0.15,I^(2)=0%).CONCLUSION For patients who successfully achieved remission of EoE with STC induction therapy,maintaining treatment is effective in sustaining histologic remission,while newer regimens may be effective in preventing symptom recurrence compared to placebo.We found no significant difference for oropharyngeal/esophageal candidiasis with STC maintenance therapy.Future studies with longer follow-up periods are needed.
文摘This paper discusses multiple aspects of modern landscape greening maintenance technology,including core elements such as smart irrigation,plant physiological monitoring,and ecological restoration,along with their synergistic effects.It also introduces the evolution characteristics,key maintenance points in different scenarios,intelligent management methods,and relevant case studies and benefit assessments.Additionally,it analyzes the challenges faced in technology promotion and proposes solutions.
基金supported by the National Natural Science Foundation of China(51767017)the Basic Research Innovation Group Project of Gansu Province(18JR3RA133)the Industrial Support and Guidance Project of Universities in Gansu Province(2022CYZC-22).
文摘This study proposes a novel visual maintenance method for photovoltaic(PV)modules based on a two-stage Wiener degradation model,addressing the limitations of traditional PV maintenance strategies that often result in insufficient or excessive maintenance.The approach begins by constructing a two-stage Wiener process performance degradation model and a remaining life prediction model under perfect maintenance conditions using historical degradation data of PV modules.This enables accurate determination of the optimal timing for postfailure corrective maintenance.To optimize the maintenance strategy,the study establishes a comprehensive cost model aimed at minimizing the long-term average cost rate.The model considers multiple cost factors,including inspection costs,preventive maintenance costs,restorative maintenance costs,and penalty costs associated with delayed fault detection.Through this optimization framework,the method determines both the optimal maintenance threshold and the ideal timing for predictive maintenance actions.Comparative analysis demonstrates that the twostage Wiener model provides superior fitting performance compared to conventional linear and nonlinear degradation models.When evaluated against traditional maintenance approaches,including Wiener process-based corrective maintenance strategies and static periodic maintenance strategies,the proposed method demonstrates significant advantages in reducing overall operational costs while extending the effective service life of PV components.The method achieves these improvements through effective coordination between reliability optimization and economic benefit maximization,leading to enhanced power generation performance.These results indicate that the proposed approach offers a more balanced and efficient solution for PV system maintenance.
文摘This study focuses on the management of maintenance hemodialysis(MHD)patients,with a specific emphasis on the practical application effect of the network information management model including its impact on patients’compliance.A network information management model for MHD patients was constructed around three management schemes:“software reminders+follow-up guidance”,“dietary records+self-management reminders”,and“dialysis plan+precise weight management”.These schemes were respectively used to optimize anemia management,control the risk of hyperphosphatemia,and improve toxin clearance efficiency.A controlled experiment was conducted,with an experimental group and a control group set up for comparative practice.The results showed that the network information management model can effectively improve patients’anemia,help alleviate mineral metabolism disorders and the accumulation of small-molecule toxins,and exert a positive impact on patients’treatment compliance.
基金supported by the First Affiliated Hospital of Xi’an Jiaotong University Teaching Reform Project(Grant No.JG2023-0206 and JG2022-0324).
文摘Objective:In the Radiology Department of Mzuzu Central Hospital(MCH),daily training for radiographers now includes content on Computed Tomography(CT)image quality control and equipment maintenance to ensure the normal,continuous,and stable operation of the 16-slice spiral CT scanner.Methods:Through comprehensive analysis of relevant equipment,we have identified key parameters that significantly impact CT image quality.Innovative optimization strategies and solutions targeting these parameters have been developed and integrated into daily training programs.Furthermore,starting from an examination of prevalent failure modes observed in CT equipment,we delve into essential maintenance and preservation techniques that CT technologists must master to ensure optimal system performance.Results:(1)Crucial factors affecting CT image quality include artifacts,noise,partial volume effects,and surrounding gap phenomena,alongside spatial and density resolutions,CT dose,reconstruction algorithms,and human factors during the scanning process.In the daily training for radiographers,emphasis is placed on strictly implementing image quality control measures at every stage of the CT scanning process and skillfully applying advanced scanning and image processing techniques.By doing so,we can provide clinicians with accurate and reliable imaging references for diagnosis and treatment.(2)Strategies for CT equipment maintenance:①Environmental inspection of the CT room to ensure cleanliness and hygiene.②Rational and accurate operation,including calibration software proficiency.③Regular maintenance and servicing for minimizing machine downtime.④Maintenance of the CT X-ray tube.CT technicians can become proficient in equipment maintenance and upkeep techniques through training,which can significantly extend the service life of CT systems and reduce the occurrence of malfunctions.Conclusion:Through the regular implementation of rigorous CT image quality control training for radiology technicians,coupled with diligent and proactive CT equipment maintenance,we have observed profound and beneficial impacts on improving image quality.The accuracy and fidelity of radiological data ultimately leads to more accurate diagnoses and effective treatments.
基金supported by the National Natural Science Foundation of China(Grant No.62203362)the Natural Science Basic Research Program of Shaanxi(Grant No.2023-JC-QN-0569)。
文摘A situation maintenance-based cooperative guidance strategy is proposed to intercept a high-speed and high-maneuverability target via inferior missiles.Reachability and relative motion analyses are conducted to develop and pursue virtual targets,respectively.A two-stage guidance strategy under nonlinear kinematics is developed on the basis of virtual targets.The first stage optimizes the coverage and collision situation by pursuing virtual targets under specific angular constraints.The second stage subsequently intercepts the superior target based on the handover condition optimized by the first stage.Numerical simulation results are provided to compare the effectiveness and superiority of the proposed strategy with those of the reachability-based cooperative strategy(RCS),coverage-based cooperative guidance(CBCG)and augmented proportional navigation(APN)under various maneuvering modes.
基金financially supported by the Innovative Research Group Project of the National Natural Science Foundation of China (22021004)Sinopec Major Science and Technology Projects (321123-1)
文摘The fractionating tower bottom in fluid catalytic cracking Unit (FCCU) is highly susceptible to coking due to the interplay of complex external operating conditions and internal physical properties. Consequently, quantitative risk assessment (QRA) and predictive maintenance (PdM) are essential to effectively manage coking risks influenced by multiple factors. However, the inherent uncertainties of the coking process, combined with the mixed-frequency nature of distributed control systems (DCS) and laboratory information management systems (LIMS) data, present significant challenges for the application of data-driven methods and their practical implementation in industrial environments. This study proposes a hierarchical framework that integrates deep learning and fuzzy logic inference, leveraging data and domain knowledge to monitor the coking condition and inform prescriptive maintenance planning. The framework proposes the multi-layer fuzzy inference system to construct the coking risk index, utilizes multi-label methods to select the optimal feature dataset across the reactor-regenerator and fractionation system using coking risk factors as label space, and designs the parallel encoder-integrated decoder architecture to address mixed-frequency data disparities and enhance adaptation capabilities through extracting the operation state and physical properties information. Additionally, triple attention mechanisms, whether in parallel or temporal modules, adaptively aggregate input information and enhance intrinsic interpretability to support the disposal decision-making. Applied in the 2.8 million tons FCCU under long-period complex operating conditions, enabling precise coking risk management at the fractionating tower bottom.
文摘This paper investigates the selective maintenance o systems that perform multi-mission in succession. Selective maintenance is performed on systems with limited break time to improve the success of the next mission. In general, the duration of the mission is stochastic. However, existing studies rarely take into account system availability and the repairpersons with different skill levels. To solve this problem, a new multi-mission selective maintenance and repairpersons assignment model with stochastic duration of the mission are developed. To maximize the minimum phase-mission reliability while meeting the minimum system availability, the model is transformed into an optimization problem subject to limited maintenance resources. The optimization is then realized using an analytical method based on a self-programming function and a Monte Carlo simulation method, respectively. Finally, the validity of the model and solution method approaches are verified by numerical arithmetic examples. Comparative and sensitivity analyses are made to provide proven recommendations for decision-makers.
文摘In the context of energy structure transformation,digital and intelligent technologies have been introduced into the field of hydropower,which has accelerated the technological and equipment innovation of hydropower plants.However,it has also brought severe challenges to the operation and maintenance of hydropower plants.Traditional hydropower plant operation and maintenance suffer from problems such as low efficiency,equipment aging,and high labor costs,which seriously hinder the innovation and upgrading of hydropower plant operation and maintenance.Therefore,this article focuses on the operation and maintenance of hydropower plants,summarizes a series of innovative strategies,and applies them in practice to effectively improve the operation and maintenance level of hydropower plants.
基金financially supported by the Scientific Research Projects of the Education Department of Zhejiang Province(Grant No.Y202454744)the Ningbo Public Welfare Science and Technology Project(Grant Nos.2023S007 and 2023S165)the Key Research and Development Program of Zhejiang(Grant No.2023C03183).
文摘As an essential part of the urban infrastructure,underground utility tunnels have a long service life,complex structural performance evolution and dynamic changes both inside and outside the tunnel.These combined factors result in a wide variety of disaster risks during the operation and maintenance phase,which make risk management and control particularly challenging.This work first reviews three common representative disaster factors during the operation and maintenance period:settlement,earthquakes,and explosions.It summarizes the causes of disasters,key technologies,and research methods.Then,it delves into the research on the intelligent operation and maintenance architecture for utility tunnels.Additionally,it explores the data challenges,monitoring technologies,and management platform architectures faced during the operation and maintenance process.This work provides new research perspectives for the long-term,healthy,and sustainable development of utility tunnels,which serve as the underground arteries of cities.
文摘In the face of data scarcity in the optimization of maintenance strategies for civil aircraft,traditional failure data-driven methods are encountering challenges owing to the increasing reliability of aircraft design.This study addresses this issue by presenting a novel combined data fusion algorithm,which serves to enhance the accuracy and reliability of failure rate analysis for a specific aircraft model by integrating historical failure data from similar models as supplementary information.Through a comprehensive analysis of two different maintenance projects,this study illustrates the application process of the algorithm.Building upon the analysis results,this paper introduces the innovative equal integral value method as a replacement for the conventional equal interval method in the context of maintenance schedule optimization.The Monte Carlo simulation example validates that the equivalent essential value method surpasses the traditional method by over 20%in terms of inspection efficiency ratio.This discovery indicates that the equal critical value method not only upholds maintenance efficiency but also substantially decreases workload and maintenance costs.The findings of this study open up novel perspectives for airlines grappling with data scarcity,offer fresh strategies for the optimization of aviation maintenance practices,and chart a new course toward achieving more efficient and cost-effective maintenance schedule optimization through refined data analysis.
基金funded by the Joint Development Project with Pharmapack Technologies Corporation:Open Multi-Person Collaborative Virtual Assembly/Disassembly Training and Virtual Engineering Visualization Platform,Grant Number 23HK0101.
文摘Virtual maintenance,as an important means of industrial training and education,places strict requirements on the accuracy of participant pose perception and assessment of motion standardization.However,existing research mainly focuses on human pose estimation in general scenarios,lacking specialized solutions for maintenance scenarios.This paper proposes a virtual maintenance human pose estimation method based on multi-scale feature enhancement(VMHPE),which integrates adaptive input feature enhancement,multi-scale feature correction for improved expression of fine movements and complex poses,and multi-scale feature fusion to enhance keypoint localization accuracy.Meanwhile,this study constructs the first virtual maintenance-specific human keypoint dataset(VMHKP),which records standard action sequences of professional maintenance personnel in five typical maintenance tasks and provides a reliable benchmark for evaluating operator motion standardization.The dataset is publicly available at.Using high-precision keypoint prediction results,an action assessment system utilizing topological structure similarity was established.Experiments show that our method achieves significant performance improvements:average precision(AP)reaches 94.4%,an increase of 2.3 percentage points over baseline methods;average recall(AR)reaches 95.6%,an increase of 1.3 percentage points.This research establishes a scientific four-level evaluation standard based on comparative motion analysis and provides a reliable solution for standardizing industrial maintenance training.
