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
Objective:Triple-negative breast cancer(TNBC)is highly aggressive and lacks an effective targeted therapy.This study aimed to elucidate the functions and possible mechanisms of action of zinc finger miz-type containin...Objective:Triple-negative breast cancer(TNBC)is highly aggressive and lacks an effective targeted therapy.This study aimed to elucidate the functions and possible mechanisms of action of zinc finger miz-type containing 2(ZMIZ2)and minichromosome maintenance complex component 3(MCM3)in TNBC progression.Methods:The relationship between ZMIZ2 expression and clinical characteristics of TNBC was investigated.In vitro and in vivo experiments were performed to investigate the role of ZMIZ2 dysregulation in TNBC cell malignant behaviors.The regulatory relationship between ZMIZ2 and MCM3 was also explored.Transcriptome sequencing was performed to elucidate possible mechanisms underlying the ZMIZ2/MCM3 axis in TNBC.Results:High ZMIZ2 expression levels were associated with the malignant degree of TNBC.ZMIZ2 overexpression promoted TNBC cell proliferation,migration,and invasion;inhibited apoptosis;and induced G1 phase cell cycle arrest,whereas knockdown of ZMIZ2 had the opposite effect.ZMIZ2 directly targeted and positively regulated MCM3 expression.MCM3 knockdown reversed the effect of ZMIZ2 overexpression on TNBC tumor growth both in vitro and in vivo.High MCM3 expression levels were linked to the degree of malignancy and poor prognosis in TNBC.The differentially expressed genes associated with the ZMIZ2/MCM3 axis were significantly enriched in multiple pathways,such as the mitogen-activated protein kinase(MAPK),mechanistic target of rapamycin(mTOR),Wnt,and Ras signaling pathways,as verified by The Cancer Genome Atlas data.Conclusions:ZMIZ2 and MCM3 were highly expressed in TNBC.ZMIZ2 promoted the development by positively regulating MCM3 expression.Key pathways,such as the Ras/MAPK,phosphatidylinositol 3-kinase(PI3K)/protein kinase B(AKT)/mTOR,and Wnt signaling pathways,may be key downstreammechanisms.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
The rapid progress in the construction of heavy-haul and high-speed railways has led to a surge in rail defects and unforeseen failures.Addressing this issue necessitates the implementation of more sophisticated rail ...The rapid progress in the construction of heavy-haul and high-speed railways has led to a surge in rail defects and unforeseen failures.Addressing this issue necessitates the implementation of more sophisticated rail inspection methods,specifically involving real-time,precise detection,and assessment of rail defects.Current applications fail to address the evolving requirements,prompting the need for advancements.This paper provides a summary of various types of rail defects and outlines both traditional and innovative non-destructive inspection techniques,examining their fundamental features,benefits,drawbacks,and practical suitability for railway track inspection.It also explores potential enhancements to equipment and software.The comprehensive review draws upon pertinent international research and review papers.Furthermore,the paper introduces a fusion of inspection methods aimed at enhancing the overall reliability of defect detection.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
Subgrade engineering is a fundamental aspect of infrastructure construction in China.As the primary structural element responsible for bearing and distributing traffic loads,the subgrade must not only withstand the su...Subgrade engineering is a fundamental aspect of infrastructure construction in China.As the primary structural element responsible for bearing and distributing traffic loads,the subgrade must not only withstand the substantial pressures exerted by vehicles,trains,and other forms of transportation,but also efficiently transfer these loads to the underlying foundation,ensuring the stability and longevity of the roadway.In recent years,advancements in subgrade engineering technology have propelled the industry towards smarter,greener,and more sustainable practices,particularly in the areas of intelligent monitoring,disaster management,and innovative construction methods.This paper reviews the application and methodologies of intelligent testing equipment,including cone penetration testing(CPT)devices,soil resistivity testers,and intelligent rebound testers,in subgrade engineering.It examines the operating principles,advantages,limitations,and application ranges of these tools in subgrade testing.Additionally,the paper evaluates the practical use of advanced equipment from both domestic and international perspectives,addressing the challenges encountered by various instruments in realworld applications.These devices enable precise,comprehensive testing and evaluation of subgrade conditions at different stages,providing real-time data analysis and intelligent early warnings.This supports effective subgrade health management and maintenance.As intelligent technologies continue to evolve and integrate,these tools will increasingly enhance the accuracy,efficiency,and sustainability of subgrade monitoring.展开更多
The national grid and other life-sustaining critical infrastructures face an unprecedented threat from prolonged blackouts,which could last over a year and pose a severe risk to national security.Whether caused by phy...The national grid and other life-sustaining critical infrastructures face an unprecedented threat from prolonged blackouts,which could last over a year and pose a severe risk to national security.Whether caused by physical attacks,EMP(electromagnetic pulse)events,or cyberattacks,such disruptions could cripple essential services like water supply,healthcare,communication,and transportation.Research indicates that an attack on just nine key substations could result in a coast-to-coast blackout lasting up to 18 months,leading to economic collapse,civil unrest,and a breakdown of public order.This paper explores the key vulnerabilities of the grid,the potential impacts of prolonged blackouts,and the role of AI(artificial intelligence)and ML(machine learning)in mitigating these threats.AI-driven cybersecurity measures,predictive maintenance,automated threat response,and EMP resilience strategies are discussed as essential solutions to bolster grid security.Policy recommendations emphasize the need for hardened infrastructure,enhanced cybersecurity,redundant power systems,and AI-based grid management to ensure national resilience.Without proactive measures,the nation remains exposed to a catastrophic power grid failure that could have dire consequences for society and the economy.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
Tunnel facility management(FM)is crucial for ensuring safety,efficiency,and resilience of tunnel infrastructure.Current FM practices,such as reactive and preventive maintenance,have limitations-reactive maintenance ca...Tunnel facility management(FM)is crucial for ensuring safety,efficiency,and resilience of tunnel infrastructure.Current FM practices,such as reactive and preventive maintenance,have limitations-reactive maintenance can't prevent failures,and preventive maintenance can't predict asset maintenance needs,leading to costly and inefficient processes.In addition,existing computerized tunnel FM systems face various challenges,including the lack of integrated real-time monitoring information,visualization of assets in a three-dimensional(3D)environment,supporting predictive maintenance,and scaling into long infrastructure with complicated spatiotemporal relationships.This study addresses these limitations by proposing a data-driven digital twin(DT)-based framework that supports predictive maintenance to improve tunnel FM processes and enhance resilience.The proposed framework consists of six layers,allowing the integration of data from monitoring system,FM system,and building information modeling(BIM)models.The framework proposes a flexible tunnel data model and classification system that hierarchically divides the tunnel models,ensuring an efficient data connection from the physical twin to the DT.The system was implemented in a tunnel case study that generates maintenance plans and work orders using historical and current condition monitoring data,and the 3D visualization technology suggests maintenance and repair processes,making the FM decision process more effective.The proposed system detected and predicted the twin state based on a data-driven analysis,and the prediction accuracy of the machine learning models was sufficiently high for use in real scenarios to make FM plans in advance and prevent asset failures.The proposed framework is contributing to the infrastructure resilience by enhancing the tunnel system ability to predict the maintenance tasks and prevent failures using data-driven DT technology.展开更多
AI’s(artificial intelligence)groundbreaking impact on energy optimization and efficiency across various fields is growing,minimizing costs,increasing environmental sustainability,and improving energy resource managem...AI’s(artificial intelligence)groundbreaking impact on energy optimization and efficiency across various fields is growing,minimizing costs,increasing environmental sustainability,and improving energy resource management.As the global energy demand is predicted to rise,traditional energy management methods are proved to be inefficient,calling for new,innovative AI-driven solutions.This research unfolds the revolutionary impact of AI in energy optimization,focusing on its modern approaches,most significantly,predictive maintenance and analytics.A notable achievement is reflected by Stem Inc.,whose AI-powered energy storage system reduced its electricity costs by 60%,through predictive analytics of demand-based battery charging and discharging.Additionally,the study also investigates the logic behind AI’s energy optimization methods and AI’s role in crucial sectors like oil extraction,solar energy maintenance,and smart buildings,showcasing its flexibility across various fields.Finally,the study also uncovers a groundbreaking solution to improve AI’s role in energy optimization.Ultimately,this paper highlights the significance of AI in energy optimization and efficiency in the 21st century,the current methods used,and its projected growth and potential in the future.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
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
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
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
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
The global public HPC(high-power charging)network for EVs(electric vehicles)is rapidly expanding.This growth is crucial for supporting the increasing adoption of EVs but highlights the industry’s early stage.Regional...The global public HPC(high-power charging)network for EVs(electric vehicles)is rapidly expanding.This growth is crucial for supporting the increasing adoption of EVs but highlights the industry’s early stage.Regional maturity varies,with China leading due to strong government support,followed by Europe and the United States.A significant challenge is the lack of industry standards,causing inconsistencies in charger types and payment systems.Efforts are underway,to ensure interoperability and reliability.Interoperability is crucial for the success of EV HPC infrastructure,ensuring seamless integration among charge points,management systems,and service providers.Despite the use of protocols like the OCPP(Open Charge Point Protocol),variations in implementation create complexities.Ensuring uniform standards across the ecosystem is essential for reliability and efficiency.Vendor-specific error codes,which are more detailed than standardized codes,are vital for diagnosing issues but lack standardization,adding complexity.Addressing these challenges is key to supporting widespread EV adoption and enhancing user experience.To provide a compelling driver value proposition,EV charging services must be reliable and seamless.The operations and maintenance of the HPC network must be cost-effective and leverage the intelligence of the integrated ecosystem.The technical complexity of managing high-power DC charging,combined with diverse authentication and payment systems,results in numerous potential issues.Moving from reactive to predictive maintenance is essential for undisrupted operations and a smooth driver experience.Shell’s Intelligent Operations Technology Strategy incorporates GenAI elements in its advanced analytics and operational performance management tools.By ingesting big data from multiple sources across the EV ecosystem,Shell engineers can perform detailed pattern recognition and targeted troubleshooting.Monitoring,configurable alerting,and remote fixing based on auto-healing and targeted auto-allocation enhance charger availability and reduce downtime.This automation has evolved Shell’s maintenance and operations strategy from reactive to predictive,improving overall charger performance and user satisfaction.Key achievements include transitioning to prescriptive and preventive asset management approaches,significantly improving uptime and charging experience,and increasing commercial value through cost reduction and enhanced revenue.Future challenges include evolving OCPP,integrating data from non-OCPP systems,and ensuring interoperability across diverse systems.Standardization and cross-collaboration within the industry are essential for smooth interoperability,higher uptime,and increased CSR(charging success rate).Technological innovations will further shape the industry,promoting stabilization and efficiency as it matures.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
Preventing the recurrence of lung oligometastases after local therapy in patients with colorectal cancer is an area requiring investigation.A recent article demonstrated that adding capecitabine maintenance therapy af...Preventing the recurrence of lung oligometastases after local therapy in patients with colorectal cancer is an area requiring investigation.A recent article demonstrated that adding capecitabine maintenance therapy after radiofrequency ablation improved the 5-year overall survival(88.7%vs 69.1%)and reduced local tumor progression(22.7%vs 49.0%)compared with radiofrequency ablation alone.Although progression-free survival did not differ significantly between the two treatments,multivariate analysis confirmed a robust survival benefit.These findings support the use of systemic maintenance to eradicate micrometastases after locoregional control and warrant validation in prospective randomized trials.展开更多
The integration of 5G technology with cloud-based control systems in industrial robots holds significant promise for the future of industrial automation.With its ultra-low latency,high data transfer speeds,and massive...The integration of 5G technology with cloud-based control systems in industrial robots holds significant promise for the future of industrial automation.With its ultra-low latency,high data transfer speeds,and massive connectivity,5G is poised to revolutionize real-time communication and coordination in manufacturing environments.This paper explores the prospects and challenges of applying 5G technology in industrial robots,focusing on cloud-based control systems that enable scalable,flexible,and efficient operations.Key advantages of 5G,including improved communication speed,enhanced real-time control,scalability,and predictive maintenance capabilities,are discussed.However,the transition to 5G also presents challenges,such as network reliability,security concerns,integration with legacy systems,and high implementation costs.The paper also examines case studies in the automotive,electronics,and aerospace industries,providing real-world examples of 5G adoption in industrial automation.The conclusion highlights key insights and outlines potential research directions for overcoming existing barriers and fully realizing the potential of 5G technology in industrial robot control.展开更多
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.
基金supported by the Jilin Province Health Science and Technology Ability Improvement Project(2023JL057).
文摘Objective:Triple-negative breast cancer(TNBC)is highly aggressive and lacks an effective targeted therapy.This study aimed to elucidate the functions and possible mechanisms of action of zinc finger miz-type containing 2(ZMIZ2)and minichromosome maintenance complex component 3(MCM3)in TNBC progression.Methods:The relationship between ZMIZ2 expression and clinical characteristics of TNBC was investigated.In vitro and in vivo experiments were performed to investigate the role of ZMIZ2 dysregulation in TNBC cell malignant behaviors.The regulatory relationship between ZMIZ2 and MCM3 was also explored.Transcriptome sequencing was performed to elucidate possible mechanisms underlying the ZMIZ2/MCM3 axis in TNBC.Results:High ZMIZ2 expression levels were associated with the malignant degree of TNBC.ZMIZ2 overexpression promoted TNBC cell proliferation,migration,and invasion;inhibited apoptosis;and induced G1 phase cell cycle arrest,whereas knockdown of ZMIZ2 had the opposite effect.ZMIZ2 directly targeted and positively regulated MCM3 expression.MCM3 knockdown reversed the effect of ZMIZ2 overexpression on TNBC tumor growth both in vitro and in vivo.High MCM3 expression levels were linked to the degree of malignancy and poor prognosis in TNBC.The differentially expressed genes associated with the ZMIZ2/MCM3 axis were significantly enriched in multiple pathways,such as the mitogen-activated protein kinase(MAPK),mechanistic target of rapamycin(mTOR),Wnt,and Ras signaling pathways,as verified by The Cancer Genome Atlas data.Conclusions:ZMIZ2 and MCM3 were highly expressed in TNBC.ZMIZ2 promoted the development by positively regulating MCM3 expression.Key pathways,such as the Ras/MAPK,phosphatidylinositol 3-kinase(PI3K)/protein kinase B(AKT)/mTOR,and Wnt signaling pathways,may be key downstreammechanisms.
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.
文摘The rapid progress in the construction of heavy-haul and high-speed railways has led to a surge in rail defects and unforeseen failures.Addressing this issue necessitates the implementation of more sophisticated rail inspection methods,specifically involving real-time,precise detection,and assessment of rail defects.Current applications fail to address the evolving requirements,prompting the need for advancements.This paper provides a summary of various types of rail defects and outlines both traditional and innovative non-destructive inspection techniques,examining their fundamental features,benefits,drawbacks,and practical suitability for railway track inspection.It also explores potential enhancements to equipment and software.The comprehensive review draws upon pertinent international research and review papers.Furthermore,the paper introduces a fusion of inspection methods aimed at enhancing the overall reliability of defect detection.
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.
基金supported by the National Natural Science Foundation of China for Distinguished Young Scholars(Grant No.42225206)National Natural Science Foundation of China(42207180,42477209,42302320).
文摘Subgrade engineering is a fundamental aspect of infrastructure construction in China.As the primary structural element responsible for bearing and distributing traffic loads,the subgrade must not only withstand the substantial pressures exerted by vehicles,trains,and other forms of transportation,but also efficiently transfer these loads to the underlying foundation,ensuring the stability and longevity of the roadway.In recent years,advancements in subgrade engineering technology have propelled the industry towards smarter,greener,and more sustainable practices,particularly in the areas of intelligent monitoring,disaster management,and innovative construction methods.This paper reviews the application and methodologies of intelligent testing equipment,including cone penetration testing(CPT)devices,soil resistivity testers,and intelligent rebound testers,in subgrade engineering.It examines the operating principles,advantages,limitations,and application ranges of these tools in subgrade testing.Additionally,the paper evaluates the practical use of advanced equipment from both domestic and international perspectives,addressing the challenges encountered by various instruments in realworld applications.These devices enable precise,comprehensive testing and evaluation of subgrade conditions at different stages,providing real-time data analysis and intelligent early warnings.This supports effective subgrade health management and maintenance.As intelligent technologies continue to evolve and integrate,these tools will increasingly enhance the accuracy,efficiency,and sustainability of subgrade monitoring.
文摘The national grid and other life-sustaining critical infrastructures face an unprecedented threat from prolonged blackouts,which could last over a year and pose a severe risk to national security.Whether caused by physical attacks,EMP(electromagnetic pulse)events,or cyberattacks,such disruptions could cripple essential services like water supply,healthcare,communication,and transportation.Research indicates that an attack on just nine key substations could result in a coast-to-coast blackout lasting up to 18 months,leading to economic collapse,civil unrest,and a breakdown of public order.This paper explores the key vulnerabilities of the grid,the potential impacts of prolonged blackouts,and the role of AI(artificial intelligence)and ML(machine learning)in mitigating these threats.AI-driven cybersecurity measures,predictive maintenance,automated threat response,and EMP resilience strategies are discussed as essential solutions to bolster grid security.Policy recommendations emphasize the need for hardened infrastructure,enhanced cybersecurity,redundant power systems,and AI-based grid management to ensure national resilience.Without proactive measures,the nation remains exposed to a catastrophic power grid failure that could have dire consequences for society and the economy.
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Tunnel facility management(FM)is crucial for ensuring safety,efficiency,and resilience of tunnel infrastructure.Current FM practices,such as reactive and preventive maintenance,have limitations-reactive maintenance can't prevent failures,and preventive maintenance can't predict asset maintenance needs,leading to costly and inefficient processes.In addition,existing computerized tunnel FM systems face various challenges,including the lack of integrated real-time monitoring information,visualization of assets in a three-dimensional(3D)environment,supporting predictive maintenance,and scaling into long infrastructure with complicated spatiotemporal relationships.This study addresses these limitations by proposing a data-driven digital twin(DT)-based framework that supports predictive maintenance to improve tunnel FM processes and enhance resilience.The proposed framework consists of six layers,allowing the integration of data from monitoring system,FM system,and building information modeling(BIM)models.The framework proposes a flexible tunnel data model and classification system that hierarchically divides the tunnel models,ensuring an efficient data connection from the physical twin to the DT.The system was implemented in a tunnel case study that generates maintenance plans and work orders using historical and current condition monitoring data,and the 3D visualization technology suggests maintenance and repair processes,making the FM decision process more effective.The proposed system detected and predicted the twin state based on a data-driven analysis,and the prediction accuracy of the machine learning models was sufficiently high for use in real scenarios to make FM plans in advance and prevent asset failures.The proposed framework is contributing to the infrastructure resilience by enhancing the tunnel system ability to predict the maintenance tasks and prevent failures using data-driven DT technology.
文摘AI’s(artificial intelligence)groundbreaking impact on energy optimization and efficiency across various fields is growing,minimizing costs,increasing environmental sustainability,and improving energy resource management.As the global energy demand is predicted to rise,traditional energy management methods are proved to be inefficient,calling for new,innovative AI-driven solutions.This research unfolds the revolutionary impact of AI in energy optimization,focusing on its modern approaches,most significantly,predictive maintenance and analytics.A notable achievement is reflected by Stem Inc.,whose AI-powered energy storage system reduced its electricity costs by 60%,through predictive analytics of demand-based battery charging and discharging.Additionally,the study also investigates the logic behind AI’s energy optimization methods and AI’s role in crucial sectors like oil extraction,solar energy maintenance,and smart buildings,showcasing its flexibility across various fields.Finally,the study also uncovers a groundbreaking solution to improve AI’s role in energy optimization.Ultimately,this paper highlights the significance of AI in energy optimization and efficiency in the 21st century,the current methods used,and its projected growth and potential in the future.
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.
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.
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.
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The global public HPC(high-power charging)network for EVs(electric vehicles)is rapidly expanding.This growth is crucial for supporting the increasing adoption of EVs but highlights the industry’s early stage.Regional maturity varies,with China leading due to strong government support,followed by Europe and the United States.A significant challenge is the lack of industry standards,causing inconsistencies in charger types and payment systems.Efforts are underway,to ensure interoperability and reliability.Interoperability is crucial for the success of EV HPC infrastructure,ensuring seamless integration among charge points,management systems,and service providers.Despite the use of protocols like the OCPP(Open Charge Point Protocol),variations in implementation create complexities.Ensuring uniform standards across the ecosystem is essential for reliability and efficiency.Vendor-specific error codes,which are more detailed than standardized codes,are vital for diagnosing issues but lack standardization,adding complexity.Addressing these challenges is key to supporting widespread EV adoption and enhancing user experience.To provide a compelling driver value proposition,EV charging services must be reliable and seamless.The operations and maintenance of the HPC network must be cost-effective and leverage the intelligence of the integrated ecosystem.The technical complexity of managing high-power DC charging,combined with diverse authentication and payment systems,results in numerous potential issues.Moving from reactive to predictive maintenance is essential for undisrupted operations and a smooth driver experience.Shell’s Intelligent Operations Technology Strategy incorporates GenAI elements in its advanced analytics and operational performance management tools.By ingesting big data from multiple sources across the EV ecosystem,Shell engineers can perform detailed pattern recognition and targeted troubleshooting.Monitoring,configurable alerting,and remote fixing based on auto-healing and targeted auto-allocation enhance charger availability and reduce downtime.This automation has evolved Shell’s maintenance and operations strategy from reactive to predictive,improving overall charger performance and user satisfaction.Key achievements include transitioning to prescriptive and preventive asset management approaches,significantly improving uptime and charging experience,and increasing commercial value through cost reduction and enhanced revenue.Future challenges include evolving OCPP,integrating data from non-OCPP systems,and ensuring interoperability across diverse systems.Standardization and cross-collaboration within the industry are essential for smooth interoperability,higher uptime,and increased CSR(charging success rate).Technological innovations will further shape the industry,promoting stabilization and efficiency as it matures.
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.
文摘Preventing the recurrence of lung oligometastases after local therapy in patients with colorectal cancer is an area requiring investigation.A recent article demonstrated that adding capecitabine maintenance therapy after radiofrequency ablation improved the 5-year overall survival(88.7%vs 69.1%)and reduced local tumor progression(22.7%vs 49.0%)compared with radiofrequency ablation alone.Although progression-free survival did not differ significantly between the two treatments,multivariate analysis confirmed a robust survival benefit.These findings support the use of systemic maintenance to eradicate micrometastases after locoregional control and warrant validation in prospective randomized trials.
文摘The integration of 5G technology with cloud-based control systems in industrial robots holds significant promise for the future of industrial automation.With its ultra-low latency,high data transfer speeds,and massive connectivity,5G is poised to revolutionize real-time communication and coordination in manufacturing environments.This paper explores the prospects and challenges of applying 5G technology in industrial robots,focusing on cloud-based control systems that enable scalable,flexible,and efficient operations.Key advantages of 5G,including improved communication speed,enhanced real-time control,scalability,and predictive maintenance capabilities,are discussed.However,the transition to 5G also presents challenges,such as network reliability,security concerns,integration with legacy systems,and high implementation costs.The paper also examines case studies in the automotive,electronics,and aerospace industries,providing real-world examples of 5G adoption in industrial automation.The conclusion highlights key insights and outlines potential research directions for overcoming existing barriers and fully realizing the potential of 5G technology in industrial robot control.
