Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy,replacement-based maintenance practices that devi...Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy,replacement-based maintenance practices that deviate from actual operational conditions,and static maintenance strategies that fail to adapt to accelerated deterioration trends leading to suboptimal remaining useful life utilization,this study proposes a Time-Based Incomplete Maintenance(TBIM)strategy incorporating reliability constraints through stochastic differential equations(SDE).By quantifying stochastic interference via Brownian motion terms and characterizing nonlinear degradation features through state influence rate functions,a high-precision SDE degradation model is constructed,achieving 16%residual reduction compared to conventional ordinary differential equation(ODE)methods.The introduction of age reduction factors and failure rate growth factors establishes an incomplete maintenance mechanism that transcends traditional“as-good-as-new”assumptions,with the TBIM model demonstrating an additional 8.5%residual reduction relative to baseline SDE approaches.A dynamic maintenance interval optimization model driven by dual parameters—preventive maintenance threshold R_(p) and replacement threshold R_(r)—is designed to achieve synergistic optimization of equipment reliability and maintenance economics.Experimental validation demonstrates that the optimized TBIM extends equipment lifespan by 4.4%and reducesmaintenance costs by 4.16%at R_(p)=0.80,while achieving 17.2%lifespan enhancement and 14.6%cost reduction at R_(p)=0.90.This methodology provides a solution for wind turbine preventive maintenance that integrates condition sensitivity with strategic foresight.展开更多
In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic(PV)modules,this study proposes a predictiv...In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic(PV)modules,this study proposes a predictive maintenance(PdM)strategy based on Remaining Useful Life(RUL)estimation.First,a RUL prediction model is established using the Transformer architecture,which enables the effective processing of sequential degradation data.By employing the historical degradation data of PV modules,the proposed model provides accurate forecasts of the remaining useful life,thereby supplying essential inputs for maintenance decision-making.Subsequently,the RUL information obtained from the prediction process is integrated into the optimization of maintenance policies.An opposition-based learning Harris Hawks Optimization(OHHO)algorithm is introduced to jointly optimize two critical parameters:the maintenance threshold L,which specifies the degradation level at which maintenance should be performed,and the recovery factor r,which reflects the extent to which the system performance is restored after maintenance.The objective of this joint optimization is to minimize the overall operation and maintenance cost while maintaining system availability.Finally,simulation experiments are conducted to evaluate the performance of the proposed PdM strategy.The results indicate that,compared with conventional corrective maintenance(CM)and periodic maintenance(PM)strategies,the RUL-driven PdM approach achieves a reduction in the average cost rate by approximately 20.7%and 17.9%,respectively,thereby demonstrating its potential effectiveness for practical PV maintenance applications.展开更多
With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance s...With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.展开更多
Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e....Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.展开更多
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
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
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 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
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
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
Electromechanical engineering in building facility management is crucial.It involves detecting and maintaining building lighting circuits,central-air-conditioning units,and handling customer maintenance requirements.A...Electromechanical engineering in building facility management is crucial.It involves detecting and maintaining building lighting circuits,central-air-conditioning units,and handling customer maintenance requirements.Approaches like real-time monitoring,scheduled maintenance,non-invasive testing,energy-efficient retrofitting,and client-centric services enhance building operations,reliability,and tenant satisfaction,laying a foundation for sustainable development.展开更多
基金supported in part by the National Natural Science Foundation of China(No.52467008)Gansu Provincial Depatment of Education Youth Doctoral Suppo Project(2024QB-051).
文摘Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy,replacement-based maintenance practices that deviate from actual operational conditions,and static maintenance strategies that fail to adapt to accelerated deterioration trends leading to suboptimal remaining useful life utilization,this study proposes a Time-Based Incomplete Maintenance(TBIM)strategy incorporating reliability constraints through stochastic differential equations(SDE).By quantifying stochastic interference via Brownian motion terms and characterizing nonlinear degradation features through state influence rate functions,a high-precision SDE degradation model is constructed,achieving 16%residual reduction compared to conventional ordinary differential equation(ODE)methods.The introduction of age reduction factors and failure rate growth factors establishes an incomplete maintenance mechanism that transcends traditional“as-good-as-new”assumptions,with the TBIM model demonstrating an additional 8.5%residual reduction relative to baseline SDE approaches.A dynamic maintenance interval optimization model driven by dual parameters—preventive maintenance threshold R_(p) and replacement threshold R_(r)—is designed to achieve synergistic optimization of equipment reliability and maintenance economics.Experimental validation demonstrates that the optimized TBIM extends equipment lifespan by 4.4%and reducesmaintenance costs by 4.16%at R_(p)=0.80,while achieving 17.2%lifespan enhancement and 14.6%cost reduction at R_(p)=0.90.This methodology provides a solution for wind turbine preventive maintenance that integrates condition sensitivity with strategic foresight.
基金supported by the National Natural Science Foundation of China(No.51767017)the Key Research and Development Program of Gansu Province(No.25YFGA032)the Industry Support and Guidance Project for Higher Education Institutions of Gansu Province(No.2022CYZC-22).
文摘In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic(PV)modules,this study proposes a predictive maintenance(PdM)strategy based on Remaining Useful Life(RUL)estimation.First,a RUL prediction model is established using the Transformer architecture,which enables the effective processing of sequential degradation data.By employing the historical degradation data of PV modules,the proposed model provides accurate forecasts of the remaining useful life,thereby supplying essential inputs for maintenance decision-making.Subsequently,the RUL information obtained from the prediction process is integrated into the optimization of maintenance policies.An opposition-based learning Harris Hawks Optimization(OHHO)algorithm is introduced to jointly optimize two critical parameters:the maintenance threshold L,which specifies the degradation level at which maintenance should be performed,and the recovery factor r,which reflects the extent to which the system performance is restored after maintenance.The objective of this joint optimization is to minimize the overall operation and maintenance cost while maintaining system availability.Finally,simulation experiments are conducted to evaluate the performance of the proposed PdM strategy.The results indicate that,compared with conventional corrective maintenance(CM)and periodic maintenance(PM)strategies,the RUL-driven PdM approach achieves a reduction in the average cost rate by approximately 20.7%and 17.9%,respectively,thereby demonstrating its potential effectiveness for practical PV maintenance applications.
基金funded by scientific research projects under Grant JY2024B011.
文摘With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.
文摘Modern industrial environments require uninterrupted machinery operation to maintain productivity standards while ensuring safety and minimizing costs.Conventional maintenance methods,such as reactive maintenance(i.e.,run to failure)or time-based preventive maintenance(i.e.,scheduled servicing),prove ineffective for complex systems with many Internet of Things(IoT)devices and sensors because they fall short in detecting faults at early stages when it is most crucial.This paper presents a predictive maintenance framework based on a hybrid deep learning model that integrates the capabilities of Long Short-Term Memory(LSTM)Networks and Convolutional Neural Networks(CNNs).The framework integrates spatial feature extraction and temporal sequence modeling to accurately classify the health state of industrial equipment into three categories,including Normal,Require Maintenance,and Failed.The framework uses a modular pipeline that includes IoT-enabled data collection along with secure transmission methods to manage cloud storage and provide real-time fault classification.The FD004 subset of the NASA C-MAPSS dataset,containing multivariate sensor readings from aircraft engines,serves as the training and evaluation data for the model.Experimental results show that the LSTM-CNN model outperforms baseline models such as LSTM-SVM and LSTM-RNN,achieving an overall average accuracy of 86.66%,precision of 86.00%,recall of 86.33%,and F1-score of 86.33%.Contrary to the previous LSTM-CNN-based predictive maintenance models that either provide a binary classification or rely on synthetically balanced data,our paper provides a three-class maintenance state(i.e.,Normal,Require Maintenance,and Failed)along with threshold-based labeling that retains the true nature of the degradation.In addition,our work also provides an IoT-to-cloud-based modular architecture for deployment.It offers Computerized Maintenance Management System(CMMS)integration,making our proposed solution not only technically sound but also practical and innovative.The solution achieves real-world industrial deployment readiness through its reliable performance alongside its scalable system design.
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.
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.
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.
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.
文摘Electromechanical engineering in building facility management is crucial.It involves detecting and maintaining building lighting circuits,central-air-conditioning units,and handling customer maintenance requirements.Approaches like real-time monitoring,scheduled maintenance,non-invasive testing,energy-efficient retrofitting,and client-centric services enhance building operations,reliability,and tenant satisfaction,laying a foundation for sustainable development.
