System Maintenance Research Articles

The toxic effects of exposure to fibrous and fragmented microplastic in juvenile rockfish based on two omics approach

Although the hazards of environmental microplastics (MPs) are well known, it is unclear which of their characteristics have the greatest effects on organism. We investigated the toxic effects of oral administration according to physical properties, including the shape of fragmented polyethylene terephthalate (PET) (FrPET) and fibrous PET (FiPET) MPs. After 72 h of exposure, apoptosis and phagocytic activity varied significantly among juvenile rockfish (Sebastes schlegeli) exposed to both FrPET and FiPET. The levels of immune-related genes and hepatic metabolic activity also increased after exposure to both shapes of MPs, but the variation in responses was greater in fish exposed to FiPET compared with those exposed to FrPET. The transcriptomic and metabolomics analysis results indicated that the maintenance and homeostasis of immune system was affected by oral exposure to FrPET and FiPET. The amino acid metabolic processes were identified in rockfish exposed to FrPET, but the notch signaling pathway were evident in the FiPET exposure group. Metabolomics analysis revealed that oral ingestion of MP fibers led to a stronger inflammatory response and greater oxidative stress in juvenile rockfish. These results can be used to understand environmentally dominant MP toxic effects such as type, size, shapes, as well as to prioritize ecotoxicological management.

Investigation of the Effects of Heavy Metals (Copper, Cobalt, Manganese, Selenium, and Zinc) on Fish Immune Systems – An Overview

Abstract Aquaculture, as a strategic and developmental industry, plays an important role in ensuring food security and economic stability within countries. This crucial sector faces various influences, including infectious and nutritional diseases. Certain minerals function as essential nutrients, playing a main role in processes across all aquatic animals when present in appropriate dietary proportions. Studies showed that some heavy metals serve as a catalyst for various biological functions, including the maintenance of colloidal systems, acid-base balance, bone formation, and the regulation of fundamental natural elements such as vitamins, hormones, and minerals. Fish acquire essential elements crucial for their well-being through both diet and water sources. Adequate levels of essential elements, such as iron, zinc, copper, cobalt, manganese, and selenium, in the diet contribute to enhanced growth, increased survival rates, improved disease resistance, and heightened specific immunity in fish. When incorporated in appropriate quantities, these elements play a pivotal role in disease prevention, fostering the production of high-quality fish, minimizing economic losses, and solidifying aquaculture as a robust and profitable industry. This article aims to delve into the immunological effects induced by metal elements, specifically copper (Cu), cobalt (Co), manganese (Mn), zinc (Zn), and selenium (Se), in diverse fish species. The objective is to underscore the significance of this exploration, paving the way for the development of more potent immune supplements in the future. These supplements have the potential to improve growth, fortify immune responses, and enhance resilience against diseases in various fish species.

Exploring ultrafine particle emission characteristics from in-use light-duty diesel trucks in China using a portable measurement system

Diesel vehicle exhaust is one of the major contributors to ultrafine particles (UFPs) in urban areas in China. However, there is still a lack of knowledge about UFPs emission characteristics from current in-use diesel vehicles. This study has carried out an on-road test of 10 in-use Light-duty Diesel Trucks (LDDTs) with different emission control standards in China using a self-established portable measurement system based on the Electronic Low-pressure Impactor (ELPI) and characterized the ultrafine particle number (PN) concentration, particle size distribution and metal element contents. The results revealed a significant reduction of 93.37% in the average PN0.1 emission factor of LDDTs from China IV to China VI. Notably, LDDTs compliant with the China VI vehicle emission control standard exhibited the lowest PN0.1 and PM0.1 emission factors, measuring 4.991×1014 #/km and 0.627 g/km, respectively. By taking into account emissions under real driving conditions, we found that the PN emission rates grow with the increase of the Vehicle Specific Power (VSP). The cold-start phase had higher PN emissions than the hot-start phase, with 8590, 1890, 477, and 22 times higher than those of the ambient air (1.18×105 #/cm3), respectively. The installation of Diesel Particulate Filter (DPF) can decrease UFPs by more than 99.8%, while the PN emission factor during the DPF regeneration stage (1.85×1016 #/km) increased by 5 orders of magnitude that of the DPF normal works (7.51×1011 #/km). Metal element contents analysis shows that Fe, Ca, Al and Mg are the dominant elements in UFPs of LDDT exhaust gas, but the element of Ni is slightly increasing in a China VI, possibly due to the new automotive engine exhaust manifolds being made of Ni instead of cast iron for the purpose of having more high-temperature resistance. Our study demonstrates the importance of monitoring and routine maintenance of exhaust after-treatment systems.

The impact of artificial intelligence on regulatory compliance in the oil and gas industry

Artificial Intelligence (AI) is increasingly transforming the regulatory compliance landscape in the oil and gas industry. This paper examines the profound impact of AI on ensuring adherence to complex regulatory frameworks governing this sector. Regulatory compliance in the oil and gas industry involves adhering to a myriad of environmental, safety, and operational regulations, often posing significant challenges due to the volume and complexity of data involved. AI technologies, including machine learning, natural language processing, and predictive analytics, offer innovative solutions to these challenges. AI enhances data management and analysis by automating data collection, processing, and reporting, thereby increasing accuracy and efficiency. Predictive maintenance and risk assessment tools powered by AI can identify potential compliance issues before they arise, allowing for proactive measures. Moreover, AI-driven compliance monitoring systems enable real-time tracking of regulatory adherence, reducing the risk of non-compliance and associated penalties. Automated auditing and inspection processes further streamline compliance checks, ensuring thorough and consistent evaluations. Case studies demonstrate successful AI implementations in regulatory compliance, such as automated reporting systems in offshore drilling and predictive maintenance in pipeline management, which have resulted in improved compliance rates and reduced operational risks. However, the adoption of AI is not without challenges. Issues related to data quality and integration, cybersecurity, and regulatory acceptance pose significant hurdles. Additionally, ethical and legal considerations surrounding AI deployment must be addressed to ensure responsible use. AI holds substantial potential to revolutionize regulatory compliance in the oil and gas industry by enhancing efficiency, accuracy, and proactive risk management. As AI technologies continue to evolve, their integration into compliance processes will likely become more sophisticated, offering greater benefits and addressing current limitations. The future of regulatory compliance in the oil and gas sector will be increasingly shaped by the advancements in AI, driving both operational excellence and adherence to stringent regulatory standards.

A Study on the Hybrid Application of Classifiers and Predictors for the Predictive Maintenance of Cooling System

A Study on the Hybrid Application of Classifiers and Predictors for the Predictive Maintenance of Cooling System

AI-enhanced fault-tolerant control and security in transportation and logistics systems: addressing physical and cyber threats

Transportation and logistics systems are becoming increasingly complex and critical to modern infrastructure. This paper proposes a novel AI-enhanced fault-tolerant control framework to address the dual challenges of physical malfunctions and cyber threats. By leveraging advanced machine learning algorithms and real-time data analytics, the proposed methodology aims to enhance the reliability, safety, and security of transportation and logistics systems. This research explores the foundations and practical implementations of AI-driven anomaly detection, predictive maintenance, and autonomous response systems. The findings demonstrate significant improvements in system resilience and robustness, making a substantial contribution to the field of intelligent transportation management.

АВТОМАТИЗАЦИЯ ПРОГНОЗИРОВАНИЯ НЕИСПРАВНОСТЕЙ МЕДТЕХНИКИ

The study identifies the problems faced by modern medical institutions, in particular, the Meditsina clinic. The management, maintenance, and user operation of medical devices cause these difficulties. To overcome them, it is necessary to revise the system of maintenance and repair of medical equipment, as well as to ensure continuous professional development of the employees responsible for the equipment operation. The paper proposes an information model that includes technical tools for forecasting, such as exponential smoothing. The authors create software implementa-tion of the information system and forecasting module based on the research; test these developments in the working conditions of the medical and preventive institution Meditsina. Thanks to the constructed forecast of the service life of the PHoX Plus analyzer parts, it is possible to adjust the purchase of consumables downwards.

Modular and Portable System Design for 3D Imaging of Breast Tumors Using Electrical Impedance Tomography

This paper presents a prototype of a portable and modular electrical impedance tomography (EIT) system for breast tumor detection. The proposed system uses MATLAB to generate three-dimensional representations of breast tissue. The modular architecture of the system allows for flexible customization and scalability. It consists of several interconnected modules. Each module can be easily replaced or upgraded, facilitating system maintenance and future enhancements. Testing of the prototype has shown promising results in preliminary screening based on experimental studies. Agar models were used for the experimental stage of this project. The 3D representations provide clinicians with valuable information for accurate diagnosis and treatment planning. Further research and refinement of the system is warranted to validate its performance in future clinical trials.

Разработка комплексного показателя оценки состояния городского электротранспорта для органов исполнительной власти Российской Федерации

This article analyzes 115 cities on the territory of the Russian Federation in which one or more urban electric transport systems operate. It is determined that in the modern industry there is no single comprehensive indicator of the state of urban electric transport, which would allow an assessment of the state of systems not only in relation to their own indicators of previous years, but also in relation to other, similar systems. A comprehensive indicator is proposed that will allow a comprehensive assessment of the state of urban electric transport and interpret it in numerical terms for its application by state and regional authorities, allowing to quickly draw conclusions and make a decision on further adjustment of the functioning of the electric transport system, characterized by a relatively simple formula for calculation and does not require specific competencies for those conducting the analysis. The key indicators of urban electric transport are systematized, including those related to the operated energy infrastructure, linear infrastructure, park facilities, including technical buildings and structures for system maintenance, indicators related to rolling stock and its key technical characteristics, as well as the availability of urban electric transport for residents of the considered locality and passenger flows within this framework systems. The boundaries of the values of the final indicator have been determined, within which the state of urban electric transport will be assessed by state and regional authorities as satisfactory or unsatisfactory and requiring decisions on revising approaches to the development of the system within the framework of strategic transport planning documents.

Analysis of Plant Watering Efficiency Using IoT Technology Controlled Through Google Assistant

This paper presents a systematic literature review (SLR) of 120 academic documents sourced from the Scopus database, which analyses the efficiency of crop watering systems utilizing Internet of Things (IoT) technology controlled via Google Assistant. The review explores key advances in IoT-based irrigation systems, highlighting how real-time data from sensors and voice-controlled automation improve water efficiency and user experience. The findings reveal that IoT systems can reduce water wastage by 25-30%, optimise plant health, and offer convenience through voice commands. However, challenges such as connectivity issues, high implementation costs, and system maintenance complexity also need to be addressed. This paper discusses potential future research directions, including scalability, AI integration, and cost-effective solutions to expand the adoption of these technologies in agriculture and horticulture.

Enabling Precision Agriculture through a Web-Based Fertilization Management System for Nawungan Selopamioro Fruit Orchards

Precision Agriculture (PA) is an integrated farming system based on information and technology for managing agriculture to identify, analyze, and manage spatial and temporal diversity information in specific locations to obtain optimum and sustainable benefits while minimizing unwanted environmental impacts. Fertilization is one of the crucial phases in agricultural production process considering technical cultivation aspects, costs, and environmental impacts. The current fertilization process at Kebun Buah Nawungan Selopamioro (KBNS) is still conventional, so there is no standard rule in determining the fertilization dose. Therefore, a PA approach is needed to provide suitable fertilizer doses for agricultural production needs. This objective of this study was to develop of a web-based fertilizer management system, integrating with orchard management to enhance accessibility and decision-making. The system calculates fertilizer requirements by analyzing soil nutrient availability (N, P, K), cultivation area, crop type and age, and available fertilizer types. The development followed the waterfall methodology, encompassing stages from requirement analysis to system maintenance. The outcome is a web application that manages land assets, administrative activities, and fertilizer needs tailored to specific land blocks, crop characteristics, and nutrient inventories. Subsequent validation against field conditions ensures the accuracy of its recommendations. Although comprehensive testing confirmed a 100% success rate in functionality, the system currently operates within a limited scope of variables. Future enhancements are planned to incorporate broader agronomic factors, such as soil pH and texture, to augment the system's precision. Despite its limitations, this system represents a significant technological advance in precision agriculture, promising to improve fertilizer application efficiency and support sustainable farming practices.

Research on Maintenance Capability Standards for Drive Motor Systems of New Energy Vehicles

Developing industry recognized industry standards within the qualification framework is the benchmark for learning outcomes transformation. Based on the study of general competency standards, this article takes the maintenance competency requirements of new energy vehicle drive motor systems as a case study, and conducts a detailed analysis of the requirements for this module in vocational education undergraduate education for secondary and higher vocational schools. Finally, the vocational skill level standard for "electric vehicle high-voltage system evaluation and maintenance" is revised and preliminarily verified.

Patient-derived organoid model of olfactory ensheathing cell tumor.

We developed a culture model of a human olfactory ensheathing cell tumor. Cultured organoids resemble normal ensheathing cells. Assays suggest that this model provides a tool for studying the roles of these glial cells in the maintenance of the peripheral olfactory system.

Muscle-resident mesenchymal progenitors sense and repair peripheral nerve injury via the GDNF-BDNF axis.

Fibro-adipogenic progenitors (FAPs) are muscle-resident mesenchymal progenitors that can contribute to muscle tissue homeostasis and regeneration, as well as postnatal maturation and lifelong maintenance of the neuromuscular system. Recently, traumatic injury to the peripheral nerve was shown to activate FAPs, suggesting that FAPs can respond to nerve injury. However, questions of how FAPs can sense the anatomically distant peripheral nerve injury and whether FAPs can directly contribute to nerve regeneration remained unanswered. Here, utilizing single-cell transcriptomics and mouse models, we discovered that a subset of FAPs expressing GDNF receptors Ret and Gfra1 can respond to peripheral nerve injury by sensing GDNF secreted by Schwann cells. Upon GDNF sensing, this subset becomes activated and expresses Bdnf. FAP-specific inactivation of Bdnf (Prrx1Cre; Bdnffl/fl) resulted in delayed nerve regeneration owing to defective remyelination, indicating that GDNF-sensing FAPs play an important role in the remyelination process during peripheral nerve regeneration. In aged mice, significantly reduced Bdnf expression in FAPs was observed upon nerve injury, suggesting the clinical relevance of FAP-derived BDNF in the age-related delays in nerve regeneration. Collectively, our study revealed the previously unidentified role of FAPs in peripheral nerve regeneration, and the molecular mechanism behind FAPs' response to peripheral nerve injury.

Non-inclusive g-extra diagnosability of interconnection networks under PMC model

System level fault diagnosis theory is of great weight in the design and maintenance of multiprocessor systems. In this paper, we propose a novel kind of diagnosability, called non-inclusive g-extra diagnosability, that requires all faulty sets to be non-inclusive and every connected fault-free subgraph has at least g+1 vertices. Furthermore, we determine the non-inclusive g-extra diagnosability of a class of graphs under PMC model. As applications, the non-inclusive g-extra diagnosability of (n,k)-star graph Sn,k, data center networks DCell (Dk,n), BCDC (Bn) and BCube (BCn,k) under PMC model are determined successively.

Forecasting models analysis for predictive maintenance

IntroductionThis study explores the shift toward predictive maintenance through real-time data analytics to minimize machine downtime and improve machinery insights in industrial environments. Predictive maintenance aims to enable proactive interventions by predicting failures, enhancing operational efficiency.MethodsThe research was conducted in three stages. First, BA Glass equipment was sensorized using OPC Router and PowerStudio SCADA to facilitate real-time data extraction. A predictive maintenance algorithm was then developed in Python to analyze sensor data, predict failures, and trigger alarms. Finally, various forecasting models, including Linear and Polynomial Regression, Simple and Double Exponential Smoothing, ARIMA, and Prophet, were evaluated using a combination of blocked cross-validation and rolling window methodologies. The algorithm calculated performance metrics such as MSE, RMSE, and MAE for different parameter configurations and training sizes.ResultsA comparative analysis between wired and wireless sensors concluded that wireless sensors, although more expensive, were more practical and interchangeable in the factory setting. The results from the evaluation of prediction models showed that the Double Exponential Smoothing (DES) model with an additive damped trend and linear models performed best for datasets with daily seasonality and gradual oscillations. For datasets with stable trends and higher frequency oscillations, ARIMA and Prophet models proved to be more accurate.DiscussionThese findings suggest that the choice of sensors and prediction models can significantly impact the effectiveness of predictive maintenance systems. Wireless sensors offer long-term benefits in terms of flexibility and practicality, while the DES model and ARIMA/Prophet models are optimal depending on the dataset characteristics. This research highlights the value of real-time data analytics and predictive models in industrial environments for reducing downtime and improving decision-making.

Revisiting Sentiment Analysis for Software Engineering in the Era of Large Language Models

Software development involves collaborative interactions where stakeholders express opinions across various platforms. Recognizing the sentiments conveyed in these interactions is crucial for the effective development and ongoing maintenance of software systems. For software products, analyzing the sentiment of user feedback, e.g., reviews, comments, and forum posts can provide valuable insights into user satisfaction and areas for improvement. This can guide the development of future updates and features. However, accurately identifying sentiments in software engineering datasets remains challenging. This study investigates bigger large language models (bLLMs) in addressing the labeled data shortage that hampers fine-tuned smaller large language models (sLLMs) in software engineering tasks. We conduct a comprehensive empirical study using five established datasets to assess three open-source bLLMs in zero-shot and few-shot scenarios. Additionally, we compare them with fine-tuned sLLMs, using sLLMs to learn contextual embeddings of text from software platforms. Our experimental findings demonstrate that bLLMs exhibit state-of-the-art performance on datasets marked by limited training data and imbalanced distributions. bLLMs can also achieve excellent performance under a zero-shot setting. However, when ample training data is available or the dataset exhibits a more balanced distribution, fine-tuned sLLMs can still achieve superior results.

Research on Convergence Media Ecological Model Based on Blockchain

Currently, the media industry is in the rapid development stage of media integration, which has brought about great changes in content production mode, presentation form, communication mechanism, operation and maintenance management, etc. At the same time, it is also faced with problems such as difficult information traceability, declining industry credibility, low data circulation quality and efficiency, difficult data security and user privacy protection, etc. Utilizing blockchain’s characteristics can solve these problems that the media industry is currently facing. This paper designs a convergence media ecology model based on blockchain (CMEM-BC), focusing on the basic elements of the model, node operation and maintenance system, node management mechanism, value circulation mechanism, and storage mechanism, trying to establish a decentralized, traceable, and immutable convergence media ecosystem. On this basis, this paper summarizes the ecological framework and ecological model of CMEM-BC. Finally, the paper describes the verification of the effectiveness of CMEM-BC in key links through simulation experiments, verifying that CMEM-BC has high originality and is more suitable for the application of convergence media ecology through model analysis and comparison.

Fault stands out in contrast: Zero-shot diagnosis method based on dual-level contrastive fusion network for control moment gyroscopes predictive maintenance

Control moment gyroscopes (CMGs) are the most common control actuators in spacecraft. Their predictive maintenance is crucial for on-orbit operations. However, due to the scarcity of CMG fault data, constructing a diagnosis system for predictive maintenance with CMGs poses significant challenges. Therefore, a zero-shot fault diagnosis method based on a dual-level contrastive learning fusion network was proposed. First, to address the difficulty in training CMG fault diagnosis models without fault data, a contrastive learning method based on CMG clusters was proposed to extract invariant features from healthy CMGs and achieve zero-shot diagnosis for predictive maintenance. Second, considering the limitations of information from a single sensor, a cross-sensor contrastive learning method was proposed to fuse features from different sensors. Third, to tackle the challenges of extracting weak potential fault features, a dual-level joint training method was introduced to enhance the model’s feature extraction capability. Finally, the proposed method was validated using real dataset collected from CMGs serviced on an in-orbit spacecraft. The results demonstrate that the method can achieve zero-shot fault diagnosis for control moment gyroscopes predictive maintenance. The code is available at https://github.com/IceLRiver/DCF.

Optimizing the Utilization of Online Public Access Catalog as a Means of Information Retrieval in Libraries

This study aims to explore the optimization of the Online Public Access Catalogue (OPAC) as an information retrieval tool in the Library of SMA Negeri 3 Medan, focusing on identifying the challenges and barriers to its effective use. A qualitative descriptive approach was employed, utilizing interviews, observations, and documentation as data collection methods. Interviews were conducted with a librarian and three students, while observations focused on the practical issues related to OPAC usage. The findings reveal that the use of OPAC in the library is not optimal, with challenges including system integration difficulties, insufficient librarian training, and limited user familiarity. Efforts to enhance OPAC usage involve promoting OPAC awareness, increasing computer access points, and ongoing system maintenance. However, the effectiveness of these measures is hindered by technical and educational constraints, highlighting the need for targeted interventions to fully leverage OPAC's potential in improving library services.