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Today, a high volume of operation interval data can be efficiently captured by a diverse range of measurements including sensors, control signals and meters, which are deployed in building automation systems (BAS). Hence, advanced data analytics tools such as fault detection and diagnostic (FDD) can be developed to analyze the operational performance of heating, ventilation and air conditioning (HVAC) systems. In the past, enormous efforts have been made to develop various FDD approaches, assuming interval data contains essential information for identifying fault signatures. However, a "data rich, but information poor" phenomenon exists due to the fact that not all measurements are sensitive to faults in HVAC systems. This highlights a significant research gap, the lack of systematic analysis of measurement sensitivity to different HVAC system faults, which is vital for FDD development, measurement deployment optimization, and control system design. To address this gap, this study introduces a novel approach to assess the sensitivity of BAS measurements in relation to various HVAC fault types. We propose two sensitivity indices (SI), the SI of fault (SI_fault) and the global measurement SI (SI_measurement_global) to quantify measurement sensitivities. The SI_fault quantifies the measurement's sensitivity to a particular fault, while the SI_measurement_global assesses its sensitivity across all fault types. These indices integrate probability distributions, enhancing the interpretability and scalability. Utilizing the HVACSIM+ fault simulation dataset, which includes 15 common faults at varying severity levels and 89 different measurements within an HVAC system, we conducted an extensive analysis of measurement sensitivities by looking at the proposed SIs. [Display omitted] • A novel method was developed to evaluate measurement sensitivities to faults in an HVAC system. • Two new sensitivity indices (SI) were proposed to quantify the measurement sensitivity under faults. • Data representing 15 common faults with different severity levels was used to analyze measurement sensitivities. • A use case is illustrated to indicate that the developed SI can be used to identify fault signatures. [ABSTRACT FROM AUTHOR]

Background/Aim: The aim of this study was to investigate the diagnosability of root fractures by cone‐beam computed tomography (CBCT) compared to intraoral radiography and panoramic radiography for finding the first choice of radiography to diagnose root fracture. Methods: We reviewed the CBCT images of 60 patients with root fracture and 60 patients without root fracture. Diagnosability of root fractures by intraoral radiography, panoramic radiography, and CBCT was examined. The influence of metal materials to the diagnosability was also investigated. Sensitivity and specificity of CBCT were calculated. In the group that root fracture was diagnosable; the detection ability of root fractures by inspection was examined. Diagnosability according to vertical root fracture direction was also investigated. Statistical analyses were performed using χ2 test or cross‐tabulation. Results: Diagnosability was statistically significantly different by radiography (P <.001), and most of root fracture was diagnosable by CBCT but it was impossible to diagnose by intraoral radiography and panoramic radiography (P <.05). Root fracture tended to be impossible to diagnose by CBCT because of the presence of metal materials around root fracture teeth (P <.05). The number of the root fractures that was diagnosable by intraoral radiography or panoramic radiography was more than root fracture detectable by inspection (P <.01) and bucco‐lingual vertical fracture (P <.05). Conclusions: These results made it clear that the first choice of radiography to diagnose the root fracture was CBCT especially in the case of suspected of mesio‐distal vertical root fracture. [ABSTRACT FROM AUTHOR]

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The class of ad hoc networks is of great applicability for pervasive and mobile computing. As an ad hoc network has highly dynamic, variable underlying topologies, its mobility management has long been a challenging issue in the area of wireless networking. The localized fault diagnosis can be an effective strategy of the periodic topology control for mobility management applications by identifying fault status of each node. The main contributions of this paper are highlighted as follows: (1) We first establish a topological condition for recognizing whether or not a node is restrictively one-step f -identifiable in an ad hoc network, where f denotes the amount of faulty nodes that are tolerable in our condition. (2) Then we propose a robust comparison-based fault identification algorithm that is applicable to topology formation taken for the maintenance of ad hoc networks. (3) Numerical robustness evaluation is presented to figure out the scalability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The article discusses a study which examined the degree of differentiation of the Taiwanese population of subspecies of Indo-Pacific humpback dolphin to determine if subspecies recognition is warranted. Topics covered include the results of the analysis of the degree of differentiation in pigmentation patterns, evidence of geographical isolation and behavioural differences of the species, and the differentiation of the Taiwanese humpback dolphin population at the subspecies level.

Abstract: In this paper, we propose an approach for achieving detection and identification of faults, and provide fault tolerant control for systems that are modeled using timed hybrid Petri nets. For this purpose, an observer based technique is adopted which is useful in detection of faults, such as sensor faults, actuator faults, signal conditioning faults, etc. The concepts of estimation, reachability and diagnosability have been considered for analyzing faulty behaviors, and based on the detected faults, different schemes are proposed for achieving fault tolerant control using optimization techniques. These concepts are applied to a typical three tank system and numerical results are obtained. [Copyright &y& Elsevier]