Search Results (18)

Background/Objectives: Cone-beam computed tomography (CBCT)-guided online adaptive radiotherapy (oART) represents a significant advancement in radiation oncology, enabling on-couch plan adaptation to account for daily anatomical changes. While this automation improves precision and workflow efficiency, it also introduces new failure modes (FMs) and workflow irregularities. This study aimed to systematically evaluate the clinical and technical challenges associated with CBCT-guided oART implementation. Methods: We retrospectively analyzed over 1000 CBCT-guided oART sessions for pelvic malignancies performed at our institution. A multidisciplinary team conducted a comprehensive review to identify and classify FMs, followed by root cause analysis (RCA) to evaluate their impact on treatment safety, efficacy, and workflow robustness. Results: In addition to session-terminating FMs, we identified recurring failure modes across three major domains: (1) system-driven issues, such as rigid target localization and software-driven irregularities; (2) patient-driven challenges, including interfractional and intrafractional anatomical variations; and (3) treatment planning and execution failures, including excessive dose hotspots from field-of-view limitations. The system’s closed-loop automation, while streamlining processes, introduced rigid constraints in plan adaptation and fallback plan execution, occasionally leading to unintended dose discrepancies. Conclusions: This study provides a comprehensive clinical practice-based evaluation of CBCT-guided oART, highlighting system-specific failure modes and their implications. Addressing these challenges requires structured quality assurance processes, multidisciplinary collaboration, and continuous workflow refinement. Our findings contribute to the development of safer and more robust adaptive radiotherapy platforms and clinical workflows. Full article

This paper describes the investigation and findings from the root cause analysis (RCA) of a significant collapse that occurred in a hydropower tunnel at a confidential location. This collapse involved about 12,000 m³ of material being deposited in the tunnel from a narrow 20 m width failure zone encountered in the haunch and crown area of the main power tunnel. This paper describes contributing factors which include the following: (1) degradation of a highly zeolitized (laumontite-rich) zone of rock within a bedding concordant fault zone, termed the fault-damaged zone or FDZ; (2) relatively high in situ rock stresses concentrated in the haunch and crown area of the collapse zone in the tunnel; (3) large transient water pressure differences in the rock above the collapse zone and upstream and downstream of the collapse zone; (4) cyclical repetition of the above-described factors resulted in the propagation of crown and sidewall collapse in and around the FDZ. Lessons learnt on this project and other projects with similar durability problems in volcanic rock are distilled in this paper. It is hoped that advances made in the understanding of the failure mechanism at the unnamed tunnel can be included in future tunnel investigations and design in volcanic rocks. Full article

This paper explores the role of business process optimization in achieving manufacturing excellence in railway manufacturing through Lean principles and Quality Function Deployment (QFD). It identifies key inefficiencies, such as waiting times, overproduction, and document errors, using the DMAIC method, along with Root Cause Analysis (RCA) and Failure Mode and Effect Analysis (FMEA), to prioritize waste reduction. A significant 42.86% of activities were classified as non-value added, pointing to substantial opportunities for improvement. This study proposes key solutions, including the development of a shared database, streamlined procedures, and the alignment of targets with the Master Production Schedule to reduce waste and improve operational efficiency. These recommendations aim to foster manufacturing excellence by enhancing communication, process integration, and employee training. Full article

The effectiveness of a comprehensive maintenance and reliability management process can be assessed through an in-depth analysis of various factors that collectively represent the maintenance contribution to the operational and production processes of an industrial asset. There are no simple formulas for designing an integrated maintenance and reliability model within an asset management framework (in accordance with the ISO 55001 standard), nor are there fixed or universal rules that apply equally to all production assets over time. In light of this, the primary goal of this article is to provide an overview of the implementation project of the AMORMS (Asset Management, Operational Reliability and Maintenance Survey), based on the maintenance management model (MMM) developed by INGEMAN, at the SINEA PERU plant, a leading company in Latin America specializing in the industrial production of recycled plastic containers for commercial beverages (PET preforms—Polyethylene Terephthalate). Lastly, the article outlines the recommendations and high-impact action plans that will support SINEA PERU in strengthening the efficiency of its maintenance and reliability management processes while effectively optimizing the value of its industrial assets throughout its life cycle. Full article

Children in prehospital emergency care are particularly vulnerable to medication errors, often with serious consequences. A prior study analyzing prehospital pediatric medication dosing errors, conducted after the implementation of a statewide pediatric drug-dosing reference for emergency medical services (EMS), identified an alarmingly high error rate. This significant finding led to the current study, which aims to develop technological interventions to reduce the frequency of medication errors for children during treatment by EMS. The current study focuses on the design and development of a safety strategy to automate medication administration using mixed-reality technology. Simulations were conducted to inform the design process, focusing on three scenarios: cardiac arrest, seizure, and burns. The design team included medical and engineering researchers, paramedics, and emergency medical technicians from multiple emergency medical service agencies. Root cause analysis (RCA) and failure mode and effects analysis (FMEA) were conducted after the simulations were completed. The RCA and FMEA were used to identify and prioritize failure points, which were then addressed in a mixed-reality solution using Microsoft HoloLens 2 to automate and enhance pediatric medication administration in prehospital emergency care. The resulting application will provide real-time assistance to guide paramedics through the complicated medication dosing and administration process using a detailed step-by-step guide, aiming to decrease medication errors and improve medication dosing accuracy. Full article

Abstracting causal knowledge from process measurements has become an appealing topic for decades, especially for fault root cause analysis (RCA) based on signals recorded by multiple sensors in a complex system. Although many causality detection methods have been developed and applied in different fields, some research communities may have an idiosyncratic implementation of their preferred methods, with limited accessibility to the wider community. Targeting interested experimental researchers and engineers, this paper provides a comprehensive comparison of data-based causality detection methods in root cause diagnosis across two distinct domains. We provide a possible taxonomy of those methods followed by descriptions of the main motivations of those concepts. Of the two cases we investigated, one is a root cause diagnosis of plant-wide oscillations in an industrial process, while the other is the localization of the epileptogenic focus in a human brain network where the connectivity pattern is transient and even more complex. Considering the differences in various causality detection methods, we designed several sets of experiments so that for each case, a total of 11 methods could be appropriately compared under a unified and reasonable evaluation framework. In each case, these methods were implemented separately and in a standard way to infer causal interactions among multiple variables to thus establish the causal network for RCA. From the cross-domain investigation, several findings are presented along with insights into them, including an interpretative pitfall that warrants caution. Full article

Microservice architecture has been widely adopted by large-scale applications. Due to the huge amount of data and complex microservice dependency, it also poses new challenges in ensuring reliable performance and maintenance. Existing approaches still suffer from limitations of anomaly data, over-simplification of metric relationships, and lack of diagnosing interpretability. To solve these issues, this paper builds a hierarchy root cause diagnosis framework, named Hi-RCA. We propose a global perspective to characterize different abnormal symptoms, which focuses on changes in metrics’ causation and correlation. We decompose the diagnosis task into two phases: anomalous microservice location and anomalous reason diagnosis. In the first phase, we use Kalman filtering to quantify microservice abnormality based on the estimation error. In the second phase, we use causation analysis to identify anomalous metrics and generate anomaly knowledge graphs; by correlation analysis, we construct an anomaly propagation graph and explain the anomaly symptoms via graph comparison. Our experimental evaluation on an open dataset shows that Hi-RCA can effectively locate root causes with 90% mean average precision, outperforming state-of-the-art methods. Full article

The COVID-19 pandemic has caused many negative socio-economic consequences for seafarers and shipowners of such importance that, on the one hand, it inspired and, on the other hand, it became an impulse to undertake research in this direction. It seems that avoiding at least some consequences would be possible if both shipowners and ship masters operated based on safety management procedures strictly adapted to the pandemic situation. Of course, many crisis management procedures have been developed in maritime practice so far. Still, they mainly relate to such events as maritime incidents, maritime accidents, maritime disasters, oil spills, terrorist attacks, or sea piracy. However, they do not consider the specificity of a crisis situation created for the safety of the ship’s crew by the global pandemic. Its appearance made all maritime transport entities, especially shipowners, aware of the lack of preparation for such an eventuality. Based on the general recommendations of international organizations, such as the WHO (World Health Organization) or the IMO (International Maritime Organization), they began developing and implementing urgent procedures for handling ships under COVID-19 conditions. Since the recommendations were formulated generally and the pandemic spread very quickly, the prevention and response procedures for a ship found to be affected by COVID-19 were developed ad hoc and, therefore, were often flawed. Consequently, it was concluded that it is worth creating a universal model of the procedure for dealing with a sea-going ship in pandemic conditions and reducing the adverse socio-economic consequences for shipowners and seafarers. This became the primary goal of the research undertaken in this direction, and this goal was closely related to the adopted central research hypothesis. The substance of the matter comes down to the fact that knowledge of the factors causing coronavirus infections will allow the development and implementation of effective procedures for handling ships in pandemic conditions. It will also reduce the risk and consequences of coronavirus infections. COVID-19 infections can be caused by many factors that are beyond the control of the shipowner and the ship’s captain. Still, there are also those factors that they can control and thus eliminate or at least reduce the risk of contracting the coronavirus by the crew. Thus, their correct identification, ranking their importance in terms of the risk of infection, and then focusing on the elimination of the most important of them is the basis for building a universal model, in the sense of the possibility of applying to any sea-going ship in pandemic conditions. The work includes RCA (Root Cause Analysis), stratification analysis, weighted Ishikawa diagram, and Lorenz-Pareto chart. The primary sources of information used in the research came from the literature review, the analysis of normative acts, the analysis of documentation and procedures on board ships in pandemic conditions, questionnaire research, direct and focus interviews, and participant observation. Full article

In mobile networks, crowdsourcing in Quality of Experience (QoE) assessment phase involves collecting data from the user terminals or dedicated collection devices. A mobile operator or a research group may provide applications that can be run in different mobility test modes such as walk or drive tests. Crowdsourcing using users’ terminals (e.g., a smartphone) is a cheap approach for operators or researchers for addressing large scale area and may help to improve the allocated resources of a given service and/or the network provisioning in some segments. In this work, we first collect a dataset for three popular Internet services: on-demand video streaming, web browsing and file downloading at the user terminal level. We consider two user terminals from two different vendors and many mobility test modes. The dataset contains more than 220,000 measures from one of the major French mobile operators in the Île-de-France region. The measurements are effectuated for six months in 2021. Then, we implement different models from the literature for estimating the QoE in terms of user’s Mean Opinion Score (MOS) for every service using features at radio or application levels. After that, we provide an in-depth analysis of the collected dataset for detecting the root cause of poor performance. We show that the radio provisioning issues is not the only cause of detected anomalies. Finally, we discuss the prediction quality of HD video streaming service (i.e., launch time, the bitrate and the MOS) based only on the physical indicators. Our analysis is applied on both plain-text and encrypted traffic within different mobility modes. Full article

The convenience and efficiency of mobile cranes are expanding their applicability in industrial sites, but fatal accidents continue to occur as their use increases. There were 56 cases in South Korea from 2015 to 2019, killing 59 workers. To accurately investigate the cause of a fatal accident, accident investigation reports were used. Since they are used not only as the cause of the accident but also as a result of judicial treatment, only direct causes are mentioned. Thus, indirect causes in this study were separately analyzed to induce a complex cause analysis. The man-made, management, economic, physical, political, and social (MEPS) analysis method, developed by the National Institute of Disaster in South Korea, is a type of root cause analysis (RCA), used to derive the fundamental causes of various types of disasters, mainly social ones. The complex causes of fatal accidents were analyzed by applying a modified MEPS method to mobile cranes. The MEPS method investigated three categories, namely man-made, management, and physical factors, among six categories and a newly established level four, to find the root cause of fatal accidents. The analysis results showed that violations of procedures and regulations were the most frequent causes in the man-made factors. A lack of general and special safety education was the most common cause in the management factor, and the overturning, falling, and jamming of the mobile crane were the most frequent causes in the physical factor. Full article

Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes less damage to the environment. Rapid increases in the population and demand for construction throughout the world lead to a significant deterioration or reduction in natural resources. Meanwhile, construction waste continues to grow at a high rate as older buildings are destroyed and demolished. As a result, the use of recycled materials may contribute to improving the quality of life and preventing environmental damage. Additionally, the application of recycled coarse aggregate (RCA) in concrete is essential for minimizing environmental issues. The compressive strength (CS) and splitting tensile strength (STS) of concrete containing RCA are predicted in this article using decision tree (DT) and AdaBoost machine learning (ML) techniques. A total of 344 data points with nine input variables (water, cement, fine aggregate, natural coarse aggregate, RCA, superplasticizers, water absorption of RCA and maximum size of RCA, density of RCA) were used to run the models. The data was validated using k-fold cross-validation and the coefficient correlation coefficient (R²), mean square error (MSE), mean absolute error (MAE), and root mean square error values (RMSE). However, the model’s performance was assessed using statistical checks. Additionally, sensitivity analysis was used to determine the impact of each variable on the forecasting of mechanical properties. Full article

As the technology is emerging, the process industries are actively migrating to Industry 4.0 to optimize energy, production, profit, and the quality of products. It should be noted that real-time process monitoring is the area where most of the energies are being placed for the sake of optimization and safety. Big data and knowledge-based platforms are receiving much attention to provide a comprehensive decision support system. In this study, the Advanced Advisory system for Anomalies (AAA) is developed to predict and detect the abnormal operation in fired heaters for real-time process safety and optimization in a petrochemical plant. This system predicts and raises an alarm for future problems and detects and diagnoses abnormal conditions using root cause analysis (RCA), using the combination of FMEA (failure mode and effects analysis) and FTA (fault tree analysis) techniques. The developed AAA system has been integrated with databases in a petrochemical plant, and the results have been validated well by testing the application over an extensive period. This AAA online system provides a flexible architecture, and it can also be integrated into other systems or databases available at different levels in a plant. This automated AAA platform continuously monitors the operation, checks the dynamic conditions configured in it, and raises an alarm if the statistics exceed their control thresholds. Moreover, the effect of heaters’ abnormal conditions on efficiency and other KPIs (key performance indicators) is studied to explore the scope of improvement in heaters’ operation. Full article

To minimize the environmental risks and for sustainable development, the utilization of recycled aggregate (RA) is gaining popularity all over the world. The use of recycled coarse aggregate (RCA) in concrete is an effective way to minimize environmental pollution. RCA does not gain more attraction because of the availability of adhered mortar on its surface, which poses a harmful effect on the properties of concrete. However, a suitable mix design for RCA enables it to reach the targeted strength and be applicable for a wide range of construction projects. The targeted strength achievement from the proposed mix design at a laboratory is also a time-consuming task, which may cause a delay in the construction work. To overcome this flaw, the application of supervised machine learning (ML) algorithms, gene expression programming (GEP), and artificial neural network (ANN) was employed in this study to predict the compressive strength of RCA-based concrete. The linear coefficient correlation (R²), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were evaluated to investigate the performance of the models. The k-fold cross-validation method was also adopted for the confirmation of the model’s performance. In comparison, the GEP model was more effective in terms of prediction by giving a higher correlation (R²) value of 0.95 as compared to ANN, which gave a value of R² equal to 0.92. In addition, a sensitivity analysis was conducted to know about the contribution level of each parameter used to run the models. Moreover, the increment in data points and the use of other supervised ML approaches like boosting, gradient boosting, and bagging to forecast the compressive strength, would give a better response. Full article

Hydraulic systems are widely used in the aeronautic, machinery, and energy industries. The functions that these systems perform require high reliability, which can be achieved by examining the causes of possible defects and failures and by taking appropriate preventative measures. One of the most popular methods used to achieve this goal is FMEA (Failure Modes and Effects Analysis), the foundations of which were developed and implemented in the early 1950s. It was systematized in the following years and practically implemented. It has also been standardized and implemented as one of the methods of the International Organization for Standardization (ISO) 9000 series standards on quality assurance and management. Apart from wide application, FMEA has a number of weaknesses, which undoubtedly include risk analysis based on the RPN (Risk Priority Number), which is evaluated as a product of severity, occurrence, and detection. In recent years, the risk analysis has been very often replaced by fuzzy logic. This study proposes the use of matrix analysis and statistical methods for performing simplified RCA (Root Cause Analysis) and for classification potential failures for a variable delivery vane pump. The presented methodology is an extension of matrix FMEA and allows for prioritizing potential failures and their causes in relation to functions performed by pump components, the end effects, and the defined symptoms of failure of the vane pump. Full article

Improving nursing students’ attitudes towards patient safety is a current and relevant topic. This study aims to evaluate the effectiveness of an educational intervention based on critical incident and root cause analysis (RCA) techniques regarding attitudes towards patient safety in nursing students. A quasi-experimental before and after study was developed between January 2018 and December 2019 in a sample of 100 nursing students at Universitat Jaume I (Spain). The intervention was developed in two phases. Phase I was at university, where students applied the RCA technique in a real case. Phase II took place during clinical practice. Students used critical incidents to identify a risk situation for the patients and applied RCA to detect its root causes. The measurement of attitudes was performed with the Attitudes to Patient Safety Questionnaire (APSQ-III). The global score of the questionnaire in the baseline measurement was 3.911 (±0.335), in the intermediate measurement it was 4.031 (±0.337) and in the final measurement it was 4.052 (±0.335), with significant differences (p = 0.03). However, intra-group differences were observed in the final measurement (p = 0.021). The teamwork dimension had the highest mean score on all three measures and the notification dimension had the lowest mean scores. An educational intervention combining critical incident and RCA techniques can improves nursing students’ attitudes toward patient safety. Full article