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PurposeIndia's textile industries play a vital role in the Indian economy. These industries consume the highest thermal energy (steam power). The demand of the steam in process industries is increasing rapidly, and this demand can be met by increasing the capacity utilization of steam boilers. The purpose of this paper is to present a new approach for reliability analysis by expert judgment method.Design/methodology/approachA lack of adequate life data is one of the biggest challenge in the reliability analysis of mechanical systems. This research provides an expert judgment approach for assessing the boiler's reliability characteristics. For this purpose, opinions of experts on time to failure and time to repair data were elicited in the form of statistical distributions. In this work, reliability analysis of the boiler system is carried out by expert judgment method and by using best-fit failure model. The system reliability along with preventive maintenance intervals of all components is also evaluated.FindingsIt is observed that the reliability analysis results obtained by expert judgment method and best-fit failure model method indicate that there are no significant differences. Therefore, in case when insufficient data are available, the expert judgment method can be effectively used. The analysis shows that the feedwater tank, feedwater pump, supply water temperature sensor, strainer, return water temperature sensor, condensate filter, mechanical dust collector, coal crusher and fusible plug are identified as critical components from a reliability perspective, and preventive maintenance strategy is suggested for these components.Originality/valueIn this research paper, a system reliability model by the expert judgment method is developed, and it can be effectively used where insufficient failure data are available. This paper is useful for the comparative evaluation of reliability characteristics of a boiler system by expert judgment method and best-fit failure model method.

To avoid the decrease of system reliability due to insufficient component maintenance and the resource waste caused by excessive component maintenance, identifying the critical components of complex products is an effective way to improve the efficiency of maintenance activities. Existing studies on identifying critical components of complex products are mainly from two aspects i.e., topological properties and functional properties, respectively. In this paper, we combine these two aspects to establish a hybrid intuitionistic fuzzy set to incorporate the different types of attribute values. Considering the mutual correlation between attributes, a combination of AHP (Analytic Hierarchy Process) and Improved Mahalanobis-Taguchi System (MTS) is used to determine the λ -Shapley fuzzy measures for attributes. Then, the λ -Shapley Choquet integral intuitionistic fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is proposed for calculating the closeness degrees of components to generate their ranking order. Finally, a case study which is about the right gear of airbus 320 is taken as an example to verify the feasibility and effectiveness of this method. This novel methodology can effectively solve the critical components identification problem with different types of evaluation information and completely unknown weight information of attributes, which provides the implementation of protection measures for the system reliability of complex products.

Large water distribution systems (WDSs) are networks with both topological and behavioural complexity. Thereby, it is usually difficult to identify the key features of the properties of the system, and subsequently all the critical components within the system for a given purpose of design or control. One way is, however, to more explicitly visualize the network structure and interactions between components by dividing a WDS into a number of clusters (subsystems). Accordingly, this paper introduces a clustering strategy that decomposes WDSs into clusters with stronger internal connections than external connections. The detected cluster layout is very similar to the community structure of the served urban area. As WDSs may expand along with urban development in a community-by-community manner, the correspondingly formed distribution clusters may reveal some crucial configurations of WDSs. For verification, the method is applied to identify all the critical links during firefighting for the vulnerability analysis of a real-world WDS. Moreover, both the most critical pipes and clusters are addressed, given the consequences of pipe failure. Compared with the enumeration method, the method used in this study identifies the same group of the most critical components, and provides similar criticality prioritizations of them in a more computationally efficient time.

The mechanical reliability of a direct potable reuse (DPR) treatment train—consisting of ozone, biological activated carbon, microfiltration/ultrafiltration, reverse osmosis, and ultraviolet light with advanced oxidation—was evaluated using critical component analysis at the Demonstration Pure Water Facility in San Diego, Calif. Operators maintained logs of all mechanical issues that occurred over a yearlong test period; these were used to calculate several reliability metrics for the unit processes. Mechanical issues were also cross‐referenced with treatment performance data to determine whether any “critical” failures—i.e., those that impacted pathogen reduction performance—occurred. The results demonstrated that no critical failures occurred, though the communication systems experienced critical malfunctions. These malfunctions triggered an immediate system shutdown, mitigating the potential risk to public health. While several components experienced failures, malfunctions, and/or maintenance events, the processes maintained a high degree of availability, demonstrating that DPR facilities can be designed with a high degree of mechanical reliability.

Software maintenance is a necessary and frequently occurring activity in software engineering. However, different factors such as inadequate documentation, project size, complex dependencies, and hard to understand architecture cause software maintenance to consume a large part of project resources. Therefore, it is important to assist the newcomers by providing program comprehension facilities that can reveal important information about the software system and can speed up the maintenance tasks. This important information about software includes knowledge about the core part (classes, components, design, etc.) of the system that mainly controls its whole functionality. In literature, different researches attempted to determine core part of the software using various structural, dynamic, and network metrics and termed them as key or critical classes. These approaches have an open scope for modeling coupling relations among different elements of software and most of these approaches need human expertise to identify key classes of the software. Moreover, multimedia software systems are generally interface driven and thus many micro level classes collectively constitute macro level units called as multimedia components. Therefore, this paper focuses to identify key critical units of the multimedia software at component level. The proposed approach in this paper consists of three main phases. In the first phase, different features of a class are identified and assigned a coupling based functional score that represents its significance in the overall functionality of the class. In the second phase, different independent components present in the multimedia software are identified by modeling the system as a dependency graph at the class level. Finally, key critical components of the multimedia software are identified by performing hierarchical agglomerative clustering based on the dependency strength among different identified components. The proposed approach is empirically evaluated on open-source multimedia software of different sizes and the obtained results support the feasibility and usability of the proposed approach of this paper.

With the skyrocketing development of technologies, there are many issues in information security quantitative evaluation (ISQE) of complex heterogeneous information systems (CHISs). The development of CHIS calls for an ISQE model based on security-critical components to improve the efficiency of system security evaluation urgently. In this paper, we summarize the implication of critical components in different filed and propose a recognition algorithm of security-critical components based on threat attack tree to support the ISQE process. The evaluation model establishes a framework for ISQE of CHISs that are updated iteratively. Firstly, with the support of asset identification and topology data, we sort the security importance of each asset based on the threat attack tree and obtain the security-critical components (set) of the CHIS. Then, we build the evaluation indicator tree of the evaluation target and propose an ISQE algorithm based on the coefficient of variation to calculate the security quality value of the CHIS. Moreover, we present a novel indicator measurement uncertainty aiming to better supervise the performance of the proposed model. Simulation results show the advantages of the proposed algorithm in the evaluation of CHISs.

Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. However, existing methods, often tailored to specific modalities or disease types, lack generalizability across the diverse spectrum of medical image segmentation tasks. Here we present MedSAM, a foundation model designed for bridging this gap by enabling universal medical image segmentation. The model is developed on a large-scale medical image dataset with 1,570,263 image-mask pairs, covering 10 imaging modalities and over 30 cancer types. We conduct a comprehensive evaluation on 86 internal validation tasks and 60 external validation tasks, demonstrating better accuracy and robustness than modality-wise specialist models. By delivering accurate and efficient segmentation across a wide spectrum of tasks, MedSAM holds significant potential to expedite the evolution of diagnostic tools and the personalization of treatment plans. Segmentation is an important fundamental task in medical image analysis. Here the authors show a deep learning model for efficient and accurate segmentation across a wide range of medical image modalities and anatomies.

A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era. With the increasing obtainability of multi-OMICs data comes the need for easy to use data analysis tools. Here, the authors introduce Metascape, a biologist-oriented portal that provides a gene list annotation, enrichment and interactome resource and enables integrated analysis of multi-OMICs datasets.

Background: Active case finding (ACF) is a critical component in the control of tuberculosis (TB), particularly for early detection of cases in communities with limited healthcare access. In 2023, Sleman District in Yogyakarta Province initiated the SIKAT TB program, a community-based screening effort supported by local government commitment to achieving TB elimination by 2030. Aims: This study aims to describe the implementation and outcomes of the SIKAT TB program in Sleman District by utilizing secondary data from July to September 2024. Methods: This descriptive study analysed secondary data, including the number of individuals screened and those identified with drug-sensitive tuberculosis (DSTB) and latent tuberculosis infection (LTBI). Results: Over three-month period, 1,035 individuals were screened. Among these, 33 individuals (3.2%) were diagnosed with DSTB, and 113 individuals (10.9%) were identified with LTBI. These findings suggest that the program effectively identified both active and latent TB cases at the community level. Conclusion: The SIKAT TB program highlights the critical role of government-led, community-based strategies in enhancing tuberculosis case detection. Continuous support, coupled with comprehensive follow-up and treatment systems, is crucial for sustaining long-term impact. The program serves as a practical model for other regions seeking to improve TB control efforts and contributes to the national and global objectives of eradicating TB by 2030.

Noise and imperfections in a quantum system can result in the presence and propagation of errors through the system. A reliable quantum processor will need to be able to correct for these errors and error syndromes. Rosenblum et al. used higher quantum states of a superconducting-based quantum circuit to demonstrate a method for the fault-tolerant measurement of an error-correctable logical qubit. Such fault-tolerant measurements will allow more frequent interrogations of the state of the logical qubit, ultimately leading to the implementation of more quantum operations and more complex entangled quantum circuits. Science , this issue p. 266 A fault-tolerant measurement protocol is demonstrated with an error-correctable logical qubit. A critical component of any quantum error–correcting scheme is detection of errors by using an ancilla system. However, errors occurring in the ancilla can propagate onto the logical qubit, irreversibly corrupting the encoded information. We demonstrate a fault-tolerant error-detection scheme that suppresses spreading of ancilla errors by a factor of 5, while maintaining the assignment fidelity. The same method is used to prevent propagation of ancilla excitations, increasing the logical qubit dephasing time by an order of magnitude. Our approach is hardware-efficient, as it uses a single multilevel transmon ancilla and a cavity-encoded logical qubit, whose interaction is engineered in situ by using an off-resonant sideband drive. The results demonstrate that hardware-efficient approaches that exploit system-specific error models can yield advances toward fault-tolerant quantum computation.