Monitoring Maintenance Research Articles

Artificial Intelligence as a tool for enhancing Building Information Modeling (BIM)

Building Information Modeling (BIM) has revolutionized the Architecture, Engineering, and Construction (AEC) industry, transforming how building lifecycle data is managed and visualized. By consolidating physical and functional characteristics into a shared digital model, BIM facilitates collaboration, minimizes rework, and streamlines planning and execution. In sustainability, BIM supports "Green BIM" practices that reduce carbon footprints by enabling energy modeling and lifecycle analysis. However, despite these advances, BIM faces interoperability issues, largely due to proprietary software formats, leading to isolated data silos that impede efficient data exchange across platforms. The integration of Artificial Intelligence (AI) into BIM introduces groundbreaking solutions to these challenges. AI enhances BIM through automation of design validation, clash detection, and real-time data analysis, transforming BIM into an adaptive system capable of proactive decision-making. AI applications, including predictive maintenance, generative design, and real-time construction monitoring, promise higher safety standards, reduced errors, and improved lifecycle management. However, AI-enhanced BIM adoption is hampered by technical, ethical, and financial challenges, such as data quality, privacy concerns, and high implementation costs. Addressing these obstacles with standardized data protocols, workforce upskilling, and collaborative frameworks can maximize the potential of AI-driven BIM, advancing sustainability, efficiency, and resilience within the construction industry.

Group medical visits in cancer survivorship care: a scoping review

Abstract Background More than 60% of cancer survivors report unmet physical, psychosocial, and informational needs. The care of cancer survivors includes surveillance, health maintenance monitoring, referral for long-term adverse effects of cancer treatment, and coordination of care. Group medical visits (GMV) include medical care, education, and peer support and can be used to facilitate the delivery of multidisciplinary survivorship care. We aimed to characterize the current state of related research describing the role of GMV in cancer survivorship care. Methods For this scoping review, we searched for published literature using PubMed, Embase, and other resources. We included intervention studies of multidisciplinary care involving GMVs of adult patients with a history of cancer requiring the presence of medical personnel, physicians, advanced practitioners, or oncology nurses. We included studies that focused on broad cancer survivorship care, rather than those using specific modalities, such as yoga, or limited to specific aspects of cancer survivorship care, such as weight loss. We characterized the studies by cancer type, structure of GMVs, and reported outcomes. Results We identified 2311 studies (2122 from PubMed and 189 from Embase). We excluded 1524 duplicates and screened 787 studies for title and abstract review. Finally, 63 studies were retrieved for full-text review, and six were included in this scoping review. Of the included studies, four were non-randomized, and two were randomized. Breast cancer was the most common site (4); other studies included breast and other cancers (1) and hematopoietic transplant cancer survivors (1). There was heterogeneity in the structure and frequency of sessions and the survivorship domains addressed. The outcomes studied included quality of life, healthcare utilization, and costs. Conclusions Limited high-quality research exists on the role of GMV in cancer survivorship. Though shown to be effective in chronic disease, the evidence for the effectiveness of this important and widely used approach in multidisciplinary survivorship care remains inconsistent and preliminary; the literature to date provides a starting point for larger-scale studies of GMV in cancer survivorship care. Implications for Cancer Survivors While Group medical visits are a promising intervention to provide multidisciplinary care, larger studies are needed to support their benefit in the care of cancer survivors.

Non-conventional feature-based LSTM model for prediction of bearing performance degradation

Abstract Mechanical bearings are critical in electrical machines, such as motors and generators. They provide support and facilitate the movement of rotating components in these machines. They are essential to the operation of electrical machinery cause they provide support, reduce friction, ensure proper alignment, and improve overall reliability and efficiency. The appropriate selection, installation, and maintenance of these bearings allow the longevity of such machines. The maintenance and the extent of usage determine the usefulness of the bearings. In predictive maintenance and condition monitoring, accurately predicting the level of bearing degradation holds significant importance for strategizing maintenance tasks, reducing downtime, and avoiding unforeseen breakdowns. This study centers on predicting the level of bearing degradation through the application of neural networks, focusing mainly on non-conventional features, rather than the usual features like kurtosis, and skewness. The dataset utilized is sourced from NASA and includes diverse time series signals that provide insights into the entire lifespan of the bearing. In this investigation, advanced machine learning techniques, long-short term memory (LSTM) in particular, are employed to harness the intricate patterns within the bearing dataset, aiming to improve the accuracy of the predictions.

Comprehensive Analysis of Major Fault-to-Failure Mechanisms in Harmonic Drives

The present paper proposes a detailed Failure Mode, Effects, and Criticality Analysis (FMECA) on harmonic drives, focusing on their integration within the UR5 cobot. While harmonic drives are crucial for precision and efficiency in robotic manipulators, they are also prone to several failure modes that may affect the overall reliability of a system. This work provides a comprehensive analysis intended as a benchmark for advancements in predictive maintenance and condition-based monitoring. The results not only offer insights into improving the operational lifespan of harmonic drives, but also provide guidance for engineers working with similar systems across various robotic platforms. Robotic systems have advanced significantly; however, maintaining their reliability is essential, especially in industrial applications where even minor faults can lead to costly downtimes. This article examines the impact of harmonic drive degradation on industrial robots, with a focus on collaborative robotic arms. Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) approaches are discussed, highlighting how digital twins and data-driven models can enhance fault detection. A case study using the UR5 collaborative robot illustrates the importance of fault diagnosis in harmonic drives. The analysis of fault-to-failure mechanisms, including wear, pitting, and crack propagation, shows how early detection strategies, such as vibration analysis and proactive maintenance approaches, can improve system reliability. The findings offer insights into failure mode identification, criticality analysis, and recommendations for improving fault tolerance in robotic systems.

Otomatisasi Perhitungan OEE pada Mesin Filling ILAPAK 50g Menggunakan PLC dan HMI

This study addressed the limitations in calculating Overall Equipment Effectiveness (OEE) manually, which frequently led to inaccuracies and inefficiencies in industrial processes. The purpose of this research was to design an automated OEE calculation system for the ILAPAK 50g filling machine to improve data accuracy and reduce dependency on manual input. A Design Science Research (DSR) methodology was employed, involving stages of system analysis, design, implementation, and evaluation. The system utilized key components, including an Omron CP2E PLC, proximity sensors, relays, and a Human-Machine Interface (HMI) for real-time data acquisition. The findings indicated that the automated system significantly improved data accuracy, achieving reliable, continuous OEE calculations across different machine shifts. The system successfully replaced manual logbook methods, reducing human estimation errors, and displayed OEE data locally on the HMI, making it accessible for operational decisions. Furthermore, the implemented automation reduced overall downtime, enhanced machine availability, and minimized errors associated with manual record-keeping. This research has practical implications for industries aiming to optimize production efficiency. The automated OEE system demonstrated potential for scalability to other machines in manufacturing settings, highlighting its adaptability and utility in modern industrial environments. Future research could explore integration with cloud-based databases to enhance data storage and accessibility, providing a foundation for advanced predictive maintenance and performance monitoring across multiple machines.

Digital Transformation in the Maritime Industry; Opportunities and Challenges for Indonesia

Digital transformation is reshaping industries worldwide, including the maritime sector, by integrating advanced technologies to improve efficiency, safety, and sustainability. In the context of Indonesia, a country with a vast archipelago and significant maritime activities, digital transformation offers substantial opportunities but also presents distinct challenges. This paper explores the multifaceted impacts of digital transformation on Indonesia's maritime industry, examining both the opportunities and challenges that arise from this technological shift. Opportunities in the maritime sector include enhanced operational efficiency, improved safety, and better environmental management. Technologies such as the Internet of Things (IoT), big data analytics, artificial intelligence (AI), and blockchain are pivotal in optimizing logistics, predictive maintenance, and real-time monitoring of vessels and cargo. These advancements can lead to reduced operational costs, minimized human errors, and lower environmental footprints. For Indonesia, leveraging these technologies could significantly boost its competitiveness in global shipping, enhance port operations, and support the sustainable management of its extensive marine resources. However, the journey towards digital transformation in Indonesia’s maritime industry is fraught with challenges. Key issues include inadequate digital infrastructure, limited technological expertise, and regulatory hurdles. The uneven distribution of digital infrastructure across Indonesia's archipelago hampers the consistent application of advanced technologies. Additionally, the maritime workforce requires upskilling to handle new digital tools effectively, necessitating substantial investment in education and training. Regulatory frameworks also need to evolve to address data security, privacy concerns, and the integration of international standards, ensuring a seamless transition to digital operations.

Scratch geometry effects on lubrication and performance of two-lobe journal bearings: A detailed study

Scratches on bearing surfaces can significantly affect the performance and lifespan of journal bearings. This study investigates the impact of scratches on two-lobe journal bearings through both theoretical and numerical analyses. The theoretical approach provides insights into how scratches influence lubrication, while the numerical simulations investigate how the three key scratch parameters, namely width, position, and depth, affect bearing performance. Key metrics analyzed include pressure, velocity distributions, temperature profiles, eccentricity, attitude angle, minimum film thickness, and maximum temperature. The findings offer valuable guidance for optimizing bearing design, maintenance, and condition monitoring, contributing to enhanced reliability and operational efficiency.

To Integrate and Optimize the Use of Programmable Logic Controller in Development of Horizontal Flow Wrap Packaging Machinery for Industry Needs

This paper focuses on the use of PLC and HMI in the automation industry for a highly effective and versatile horizontal flow wrap plastic film packaging solution. The machine's functioning and efficiency are thoroughly evaluated through extensive testing and validation procedures across multiple criteria. The main aim of this paper is to do the automatic plastic film packaging of any product with packaging dimensions of 4cm X 5cm. For the process automation the FX5U 32MT PLC of Mitsubishi is used for controlling and automating the system using ladder logic. The result shows that the developed system provides fully automatic wrap packaging of products and able to do automatic packaging of 24 packets in one minute. Additionally, the results show the increased efficiency of product rate in less time for automatic packaging system. Finally, it has been observed that the system is very beneficial for the automatic packaging process using PLC and the process can be monitored and visualized on a display screen using HMI. Future advancements are required in the system to meet industry4.0 needs. By incorporating some smart technologies, deeper integration with Industry 4.0 principles could boost capabilities in data analytics, connectivity, and remote monitoring for predictive maintenance.

Toward a Smart Suрervision Systems in the Рetroleum Industry Using Artificial Intelligence

In this study, we aim to enhance the oрerational efficiency and safety of liԛuid рetroleum storage facilities by integrating advanced analytics and artificial intelligence (AI) for рredictive maintenance and real-time monitoring. Liԛuid рetroleum storage facilities are critical nodes in the oil and gas suррly chain, and their efficient oрeration is рaramount to ensuring continuous suррly and safety [1]. Traditional suрervision systems, such as Distributed Control Systems (DCS) and Suрervisory Control and Data Acԛuisition (SCADA), have been widely adoрted to monitor and control these facilities[2]. However, these systems can be significantly augmented with modern AI techniԛues to рredict eԛuiрment failures, oрtimize maintenance schedules, and рrovide real-time oрerational insights [3]. This рaрer builds uрon рrevious research by incorрorating advanced machine learning algorithms and real-time data analytics into the existing suрervisory framework. We will deрloy sensors to collect real-time data on eԛuiрment рerformance, environmental conditions, and oрerational рarameters, which will be integrated into a centralized data рlatform [4]. Using this data, we will develoр and train AI models caрable of рredicting eԛuiрment malfunctions and maintenance needs with high accuracy [5]. The results of this integration are exрected to demonstrate a significant reduction in eԛuiрment downtime, oрtimized maintenance schedules, enhanced safety, and overall cost savings [6]. Real-time monitoring and рredictive insights will enable more рroactive maintenance рractices, reducing the risk of unexрected failures and imрroving oрerational continuity [7]. This study will рrovide a robust framework for other рetroleum storage facilities to adoрt similar enhancements, leveraging AI to drive oрerational excellence in the industry.

Artificial intelligence for enhancing resilience

In an increasingly complex and unpredictable world, resilience-the ability to withstand and recover from adverse conditions is essential across various sectors. This research paper investigates the transformative potential of artificial intelligence (AI) in enhancing resilience across multiple domains. We explore how AI technology can be utilized to develop resilient infrastructure, providing advanced predictive maintenance and real-time monitoring capabilities that ensure robustness and longevity. The study examines the role of AI in improving disaster response, offering rapid data analysis and decision-making support to enhance emergency management outcomes. In climate change, AI-driven strategies are assessed for their effectiveness in fostering climate resilience, including predictive modeling of extreme weather events and optimizing resource allocation. The paper also discusses AI applications in healthcare resilience, such as enhancing diagnostics, patient care, and operational efficiency during crises. Business continuity and crisis management are examined, highlighting AI's capability to anticipate disruptions and maintain operational stability. The paper emphasizes the importance of strengthening cybersecurity resilience using AI to detect and mitigate threats proactively. AI's role in enhancing community and social resilience is analysed, particularly in supporting vulnerable populations and fostering social cohesion. Additionally, we explored AI-powered solutions for urban resilience, focusing on smart cities and sustainable development. The study also covers AI's contributions to environmental and ecological resilience, resilient supply chain management, and resilience in the hospitality and tourism industry. Finally, we investigated AI's potential in fostering psychological resilience, providing personalized mental health support and stress management tools. Through these diverse applications, the paper underscores AI's critical role in building a resilient future.

Application of Medical Implants for Public Health Monitoring and Treatment

The World Health Organisation (WHO) reports that diabetes affects roughly 180 million people worldwide and that cardiovascular illnesses account for nearly 30% of all fatalities worldwide. Applications involving radio frequency (RF) or microwave technology are crucial to medical diagnosis and illness prevention. The most recent developments in the field of implantable medical devices (IMDs), such as biomedical telemetry, allow biosignals to be monitored remotely via wireless communication technology. Providing correct information to the external monitoring station is the primary function of the health maintenance monitoring scheme. Because wireless communication lessens the invasiveness of electromagnetic (EM) medical equipment, it is extremely helpful in improving patient comfort during treatment. The first swallowable pills with sensing capabilities and the introduction of pacemakers in the early 1960s both demonstrated the significance of implantable medical devices (IMDs), which enable disease monitoring and therapy. Using IMDs with wireless connection has allowed for the monitoring of both system performance and patient status. IMDs have been widely employed in healthcare systems to gather real-time signals from biosensors or preprocessed physiological bio-signals for early disease identification, improving treatment quality and promoting healthy living.

Wood discrimination of six commonly traded Phoebe and Machilus species using high-resolution plastid and nuclear DNA barcodes

Abstract Phoebe and Machilus trees, whose wood is known as Nanmu in China, face endangerment owing to long-term manmade destruction and climate change. Therefore, effective and accurate forensic tools to identify Nanmu species are required. This study developed a method for DNA identification of woods from six commonly traded Phoebe and Machilus species. DNA was efficiently extracted from oil cell-rich wood tissues, DNA barcodes were screened, species were compared, and a Nanmu DNA database was constructed. Comparison and analysis involved 26 sets of plastid genomes of Phoebe and Machilus. The five highest variable regions (Pi>0.009; psbA-trnH, psbJ, psbJ-petA, rpl32-trnL, and ycf 1) were identified, and three nuclear regions (ITS, LEAFY, and RPB2) were compared. Among single DNA fragments, the nuclear DNA region LEAFY proved optimal for identifying Nanmu species, while psbA-trnH+psbJ+ycf1 was the optimal plastid combination locus. Compared with distance-based (TaxonDNA) and machine-learning analyses, the tree-based method had a lower success rate for species identification. Four labelled Nanmu samples were selected to validate the method and clustered with P. zhennan and P. sheareri data based on the LEAFY region and plastid combination locus. These findings will contribute to the conservation, classification, architecture maintenance, and trade monitoring of Nanmu natural resources.

Genetic polymorphisms and their association with methotrexate polyglutamates during maintenance treatment in Korean children and young adults with acute lymphoblastic leukemia

The aim of this study was to investigate the impact of genetic polymorphisms on methotrexate (MTX) metabolism in Korean children and young adults with acute lymphoblastic leukemia, specifically focusing on MTX polyglutamates (MTX-PGs) in erythrocytes, which have been rarely studied. Korean children and young adult patients undergoing maintenance therapy for acute lymphoblastic leukemia, who were receiving weekly oral MTX doses of 20 mg/m²/week, were prospectively included. We investigated erythrocyte MTX-PG (PG1 to PG5) levels, MTX-PG/MTX dose ratios, and 222 genetic polymorphisms spanning 78 genes and three intergenic areas related to MTX transport, folate cycle metabolism, purine/pyrimidine pathways, and non-pathway genes (including TPMT and NUDT15 genotypes) to explore their association with MTX metabolism. MTX-PG levels were associated with MTX dose (p < 0.05), and MTX-PG3 comprised the majority of the total MTX-PGs, with a median of 39.3 %. Various polymorphisms within the same gene demonstrated differing associations with each type of MTX-PG, underscoring the complexity of MTX pharmacogenetics. Among the polymorphisms examined, 14 across 13 genes showed significant associations with MTX-PG2–5 levels, even after adjusting for the false discovery rate (ABCC5, ATG16L1, CEP72, FSTL5, GMPS, HTR3A, IMPDH1, NT5C2, SLC28A3, SLCO1B3, SUCLA2, TPMT, and TYMS). This study enhances our understanding of genetic polymorphisms in MTX metabolism and therapeutic monitoring for MTX maintenance, promoting personalized medicine in acute lymphoblastic leukemia patients.

Research on image compression technology based on improved SPIHT compression algorithm for power grid data

Abstract At present, the amount of information on power grid operation and maintenance monitoring image data is increasing, and the requirements for data compression are higher and higher. Based on the improved SPIHT image compression algorithm, this study presents the research of power grid data compression. First, the basic theory of image compression, and the principle of one-dimensional wavelet transform and two-dimensional wavelet transform are introduced. The development process, characteristics, and advantages of image coding are discussed. Then, the shortcomings of the SPIHT algorithm are analyzed, and the SPIHT coding is improved by parallel computation. The parallel wavelet transform algorithm based on the block idea and the parallel SPIHT coding algorithm based on the code tree are proposed in the data parallelism of the compression algorithm. At the same time, the data dependence between tasks in the process of SPIHT image compression coding is analyzed, and the task parallelism in the compression algorithm is realized by using the relative independence of tasks in different threshold coding. Finally, the application and simulation analysis of power grid data based on the SPIHT compression algorithm, the construction of power grid data model simulation, and the composition of two-dimensional power grid data images are carried out. Secondly, the obtained 2D power grid data image is compressed by the SPIHT algorithm and improved SPIHT algorithm, respectively, and the compression effect of the two algorithms on the power grid data image is compared. When the bit rate is 0.5, the compression effect of the improved SPIHT algorithm is 13.6506. When the bit rate is 1, the compression effect of the improved SPIHT algorithm is 18.9287. The results show that the improved SPIHT algorithm can compress the grid data to obtain better grid image quality.

A Novel Out-of-Control Action Plan (OCAP) for Optimizing Efficiency and Quality in the Wafer Probing Process for Semiconductor Manufacturing.

The out-of-control action plan (OCAP) is crucial in the wafer probing process of semiconductor manufacturing as it systematically addresses and corrects deviations, ensuring the high quality and reliability of semiconductor devices. However, the traditional OCAP involves many redundant and complicated processes after failures occur on production lines, which can delay production and escalate costs. To overcome the traditional OCAP's limitations, this paper proposes a novel OCAP aimed at enhancing the wafer probing process in semiconductor manufacturing. The proposed OCAP integrates proactive measures such as preventive maintenance and advanced monitoring technologies, which are tested and verified through a comprehensive experimental setup. Implementing the novel OCAP in a case company's production line reduced machine downtime by over 24 h per week and increased wafer production by about 23 wafers per week. Additionally, probe test yield improved by an average of 1.1%, demonstrating the effectiveness of the proposed method. This paper not only explores the implementation of the novel OCAP but also compares it with the traditional OCAP, highlighting significant improvements in efficiency and production output. The results underscore the potential of advanced OCAP to enhance manufacturing processes by reducing dependency on human judgment, thus lowering the likelihood of errors and improving overall equipment effectiveness (OEE).

Navigating the Nuclear Renaissance Economic Viability, Zero Emissions, and the Future of Nuclear Energy with Generation IV Reactors and SMRs with Artificial Intelligence Integration

As global efforts intensify to combat climate change and achieve sustainable energy goals, nuclear power is re-emerging as a vital component of the energy mix. This article explores modern nuclear technologies' economic and environmental potential, focusing on the nuclear fuel cycle, Generation IV reactors, Small Modular Reactors (SMRs), and the transformative role of Artificial Intelligence (AI). The nuclear fuel cycle, encompassing fuel production, utilization, and disposal, is evolving to become more efficient and sustainable, reducing costs and environmental impacts. Generation IV reactors and SMRs represent significant advancements, offering enhanced safety, efficiency, and flexibility, making nuclear power more accessible and adaptable. AI integration revolutionizes nuclear operations by optimizing performance, predictive maintenance, and safety monitoring. These technologies collectively enhance nuclear power's economic viability and environmental benefits, positioning it as a cornerstone of global zero-emission strategies. The return on involvement in nuclear energy is substantial, driven by economic growth, energy security, and technological advancements. Continued investment and innovation in these areas are essential for realizing the full potential of nuclear energy in a sustainable and carbon-neutral future. This article underscores the importance of nuclear power in achieving global climate objectives and fostering a sustainable energy landscape.

Design of On-line Monitoring and Fault Diagnosis Platform for Oil-immersed Shunt Reactor

Abstract With the development of society and technology, the demand for power supply and reliability is increasing. In the power system, the reactor plays an important role as a key equipment. However, due to long-term operation, the reactor is affected by factors such as loss, aging and hidden faults, and its reliability gradually decreases. The operation state of the reactor is directly related to the operation effect of the power grid and the quality of the user’s electricity. Therefore, the application of reactor online monitoring and fault diagnosis in real life is very important. This paper considers the actual operation of the coastal power grid. Based on Qt software development platform, C + + language and MySQL database, an online monitoring and fault diagnosis platform for oil-immersed shunt reactors is developed. The application of the platform takes the online monitoring and fault diagnosis method of dissolved gas in oil as the core. The platform realizes the basic information management, online monitoring, fault diagnosis and suggestions for operation and maintenance of the reactor.

Spatiotemporal characteristics and potential pollution factors of water quality in the eastern route of the South-to-North Water Diversion Project in China

The eastern route of the South-to-North Water Diversion Project (SNWDP-ER) in China, an open-channel cross-basin project, is exposed to multiple potential pollution sources and has changing hydrodynamic conditions between diversion and non-diversion periods. After a decade of pollution control efforts, it is imperative to perceive the water quality conditions and identify potential pollution factors for further maintenance. Combining the water quality identification index (WQII) and multivariate statistical techniques (MSTs), this study conducted an exploratory analysis in SNWDP-ER, based on the monitoring data in 2020–2021. The monitoring and WQII results show that the overall water quality in SNWDP-ER is satisfactory and meets the Class III requirements in China Environmental Quality Standard for Surface Water, except for total nitrogen (TN) which falls into Class V or even worse. The periodic alternations of flow direction between diversion and non-diversion periods tend to deteriorate water quality, particularly in the initial stage of alternations. Water quality appears more stable during the diversion period, and impounding lakes along the project are crucial to water environment restoration in this period. Due to the positive influence brought by the self-purification and environmental carrying in the Luoma Lake, Nansi Lake, and Dongping Lake, water quality exhibits an enhancing trend from upstream to downstream in the Lianghu and Jiaodong reaches. Besides, the monitoring stations in Shandong reach exhibit significant divisional heterogeneity under hierarchical cluster analysis (HCA) with the Nansi Lake and Dongping Lake as the demarcation points, and the results of factor analysis/principal component analysis (FA/PCA) further identified the contributions of mining, shipping, and land use to each section. Based on the findings, the study recommends implementing wastewater discharge control, abandoned mines remediation, and continued water quality monitoring for further maintenance and improvement in SNWDP-ER.

Effectiveness of a 12-week telerehabilitation training in people with long COVID: A randomized controlled trial

BackgroundTelerehabilitation has been developed and applied for years for cardiac and pulmonary diseases with good clinical outcomes. However, its application to participants with long COVID remains limited. ObjectivesTo investigate the effectiveness of a 12-week telerehabilitation training program in participants with long COVID. The primary outcome was cardiorespiratory fitness (CRF), and secondary outcomes were physical activity (PA) amounts, exercise self-efficacy, sleep quality, and health-related quality of life (HRQOL). MethodsA parallel-group, randomized controlled trial was conducted. Eligible participants with long COVID (n = 182) were randomly assigned in a 1:1 ratio to either the experimental group (EG) or the control group (CG). The EG received 12 weeks of telerehabilitation training with weekly remote monitoring for exercise maintenance and support. The CG received PA counseling only. CRF, PA amounts, exercise self-efficacy, sleep quality, and HRQOL were assessed at baseline and 12 weeks. Generalized estimating equations were used to analyze the intervention effects by examining the interaction between time and group. ResultsOne hundred twenty-two participants (67 %) completed the study, and 182 were included in the intention-to-treat analysis. The EG had greater walking behavior (β = –763.3, p < 0.001), total amount of PA (β= –711, p = 0.003), exercise self-efficacy (β = –1.19, p < 0.001), and better sleep quality (β = 1.69, p = 0.012) after the 12-weeks of telerehabilitation training than the CG. However, there were no significant differences in any CRF parameters or HRQOL at 12 weeks between the EG and CG. ConclusionTelerehabilitation training offers a personalized and convenient approach that can increase exercise willingness and PA amounts and improve sleep quality. These findings underscore the potential benefits of telerehabilitation training for promoting healthier lifestyles and improving overall health outcomes. Clinical Trial RegistrationThe study was registered at ClinicalTrials.gov (NCT05205460) on January 25, 2022.

An Availability-Constrained Integrated Maintenance–Monitoring Model for a System With Failures Following an NHPP

An Availability-Constrained Integrated Maintenance–Monitoring Model for a System With Failures Following an NHPP