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This paper investigates the problem of event‐triggered fault detection for discrete‐time networked systems subject to packet dropout. The main aim of the proposed approach is to efficiently use the communication network and reduce the signal transmission over the network. Toward this goal, a dynamic parity space event‐triggered fault detection approach is developed to generate a robust residual signal. Moreover, an adaptive threshold is utilized to overcome the limitations of static thresholds. The efficiency of proposed approach is experimentally demonstrated and validated for a laboratory three‐tank system. [ABSTRACT FROM AUTHOR]

The problem of simultaneous fault detection, isolation and tracking (SFDIT) control design for linear systems subject to both bounded energy and bounded peak disturbances is considered in this work. A dynamic observer is proposed and implemented by using theH∞/H−/L1formulation of the SFDIT problem. A single dynamic observer module is designed that generates the residuals as well as the control signals. The objective of the SFDIT module is to ensure that simultaneously the effects of disturbances and control signals on the residual signals are minimised (in order to accomplish the fault detection goal) subject to the constraint that the transfer matrix from the faults to the residuals is equal to a pre-assigned diagonal transfer matrix (in order to accomplish the fault isolation goal), while the effects of disturbances, reference inputs and faults on the specified control outputs are minimised (in order to accomplish the fault-tolerant and tracking control goals). A set of linear matrix inequality (LMI) feasibility conditions are derived to ensure solvability of the problem. In order to illustrate and demonstrate the effectiveness of our proposed design methodology, the developed and proposed schemes are applied to an autonomous unmanned underwater vehicle (AUV). [ABSTRACT FROM AUTHOR]

In this paper the problem of fault diagnosis in an aircraft jet engine is investigated by using an intelligent-based methodology. The proposed fault detection and isolation (FDI) scheme is based on the multiple model approach and utilizes autoassociative neural networks (AANNs). This methodology consists of a bank of AANNs and provides a novel integrated solution to the problem of both sensor and component fault detection and isolation even though possibly both engine and sensor faults may occur concurrently. Moreover, the proposed algorithm can be used for sensor data validation and correction as the first step for health monitoring of jet engines. We have also presented a comparison between our proposed approach and another commonly used neural network scheme known as dynamic neural networks to demonstrate the advantages and capabilities of our approach. Various simulations are carried out to demonstrate the performance capabilities of our proposed fault detection and isolation scheme. [ABSTRACT FROM AUTHOR]

Recent advances in high-throughput technologies for biological data acquisition have spurred a broad interest in the construction of mathematical models for biological phenomena. The development of such mathematical models relies on the estimation of unknown parameters of the system using the time-course profiles of different metabolites in the system. One of the main challenges in the parameter estimation of biological phenomena is the fact that the number of unknown parameters is much more than the number of metabolites in the system. Moreover, the available metabolite measurements are corrupted by noise. In this paper, a new parameter estimation algorithm is developed based on the stochastic estimation framework for nonlinear systems, namely the unscented Kalman filter (UKF). A new iterative UKF algorithm with covariance resetting is developed in which the UKF algorithm is applied iteratively to the available noisy time profiles of the metabolites. The proposed estimation algorithm is applied to noisy time-course data synthetically produced from a generic branched pathway as well as real time-course profile for the Cad system of E. coli. The simulation results demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM PUBLISHER]

In this brief, a novel real-time fault detection and isolation (FDI) scheme that is based on the concept of multiple model is proposed for aircraft jet engines. A modular and a hierarchical architecture is developed which enables the detection and isolation of both single faults as well as multiple concurrent faults in the jet engine. The nonlinear dynamics of a dual spool jet engine is linearized and a set of linear models corresponding to various operating modes of the jet engine (namely healthy and different faulty modes) at each operating point is obtained. Using the multiple model approach the probabilities corresponding to each operating point of the jet engine are generated and the current operating mode of the system is detected based on evaluating the maximum probability criteria. It is shown that the proposed methodology is also robust to the failure of pressure and temperature sensors and extensive levels of noise outliers in the sensor measurements. Simulation results are presented that demonstrate the effectiveness and capabilities of our proposed multiple model FDI algorithm for both structural faults and an actuator fault in the aircraft jet engine. [ABSTRACT FROM AUTHOR]

Abstract: Measured microarray genomic and metabolic data are a rich source of information about the biological systems they represent. For example, time-series biological data can be used to construct dynamic genetic regulatory network models, which can be used to design intervention strategies to cure or manage major diseases. Also, copy number data can be used to determine the locations and extent of aberrations in chromosome sequences. Unfortunately, measured biological data are usually contaminated with errors that mask the important features in the data. Therefore, these noisy measurements need to be filtered to enhance their usefulness in practice. Wavelet-based multiscale filtering has been shown to be a powerful denoising tool. In this work, different batch as well as online multiscale filtering techniques are used to denoise biological data contaminated with white or colored noise. The performances of these techniques are demonstrated and compared to those of some conventional low-pass filters using two case studies. The first case study uses simulated dynamic metabolic data, while the second case study uses real copy number data. Simulation results show that significant improvement can be achieved using multiscale filtering over conventional filtering techniques. [ABSTRACT FROM PUBLISHER]

The problem of designing and developing a hybrid fault detection and isolation (FDI) scheme for a non-linear system that is subject to large environmental disturbances is investigated. In the proposed FDI scheme, a hybrid architecture is introduced, which is composed of a bank of residual generators and a discrete-event system (DES) fault diagnoser. A novel set of residuals is generated so that a DES fault diagnoser will robustly detect and isolate actuator faults in the system by incorporating an appropriate combination of residuals and their sequential features. Necessary and sufficient conditions for the existence of a set of residuals that are used by the DES fault diagnoser are derived based on the non-linear geometric FDI approach. The proposed hybrid FDI scheme is then applied to actuator fault detection and isolation of an almost-lighter-than-air-vehicle. Simulation results presented demonstrate and validate the performance capabilities of the proposed hybrid FDI scheme. [ABSTRACT FROM AUTHOR]

In this brief, the problem of designing and developing a hybrid fault detection and isolation (FDI) scheme for a network of unmanned vehicles (NUVs) that is subject to large environmental disturbances is investigated. The proposed FDI algorithm is a hybrid architecture that is composed of a bank of continuous-time residual generators and a discrete-event system (DES) fault diagnoser. A novel set of residuals is generated so that the DES fault diagnoser empowered by incorporating appropriate combinations of the residuals and their sequential features will robustly detect and isolate faults in the NUVs. Our proposed hybrid FDI algorithm is then applied to actuator fault detection and isolation in a network of quad-rotors. Simulation results demonstrate and validate the performance capabilities of our proposed hybrid FDI algorithm. [ABSTRACT FROM PUBLISHER]

Background: Current medical simulators for extracorporeal membrane oxygenation (ECMO) are expensive and rely on low-fidelity methodologies. This creates a challenge that demands a new approach to eliminate high costs and integrate with critical care environments, especially in light of the scarce resources and supplies available after the COVID-19 pandemic., Methods: To address this challenge, we examined the current state-of-the-art medical simulators and collaborated closely with Hamad Medical Corporation (HMC), the primary healthcare provider in Qatar, to establish criteria for advancing the cutting-edge ECMO simulation. This article presents a comprehensive ambulatory high-realism and cost-effective ECMO simulator., Results: Over the past 3 years, we have surveyed relevant literature, gathered data, and continuously developed a prototype of the system modules and the accompanying tablet application. By doing so, we have successfully addressed the issue of cost and fidelity in ECMO simulation, providing an effective tool for medical professionals to improve their understanding and treatment of patients requiring ECMO support., Conclusions: This paper will focus on presenting an overall ambulatory ECMO simulator, detailing the various sub-systems and emphasizing the modular casing of the physical components and the simulated patient monitor., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

In response to the urgent requirement for rapid, precise, and cost-effective detection in intensive care units (ICUs) for ventilated patients, as well as the need to overcome the limitations of traditional detection methods, researchers have turned their attention towards advancing novel technologies. Among these, biosensors have emerged as a reliable platform for achieving accurate and early diagnoses. In this study, we explore the possibility of using Pyocyanin analysis for early detection of pathogens in ventilator-associated pneumonia (VAP) and lower respiratory tract infections in ventilated patients. To achieve this, we developed an electrochemical sensor utilizing a graphene oxide-copper oxide-doped MgO ( GO - Cu - Mgo ) (GCM) catalyst for Pyocyanin detection. Pyocyanin is a virulence factor in the phenazine group that is produced by Pseudomonas aeruginosa strains, leading to infections such as pneumonia, urinary tract infections, and cystic fibrosis. We additionally investigated the use of DNA aptamers for detecting Pyocyanin as a biomarker of Pseudomonas aeruginosa , a common causative agent of VAP. The results of this study indicated that electrochemical detection of Pyocyanin using a GCM catalyst shows promising potential for various applications, including clinical diagnostics and drug discovery.

Background and Objective: Advancement in the treatment of cancer, as a leading cause of death worldwide, has promoted several research activities in various related fields. The development of effective treatment regimens with optimal drug dose administration using a mathematical modeling framework has received extensive research attention during the last decades. However, most of the control techniques presented for cancer chemotherapy are mainly model-based approaches. The available model-free techniques based on Reinforcement Learning (RL), commonly discretize the problem states and variables, which other than demanding expert supervision, cannot model the real-world conditions accurately. The more recent Deep Reinforcement Learning (DRL) methods, which enable modeling the problem in its original continuous space, are rarely applied in cancer chemotherapy., Methods: In this paper, we propose an effective and robust DRL-based, model-free method for the closed-loop control of cancer chemotherapy drug dosing. A nonlinear pharmacological cancer model is used for simulating the patient and capturing the cancer dynamics. In contrast to previous work, the state variables and control action are modeled in their original infinite spaces to avoid expert-guided discretization and provide a more realistic solution. The DRL network is trained to automatically adjust the drug dose based on the monitored states of the patient. The proposed method provides an adaptive control technique to respond to the special conditions and diagnosis measurements of different categories of patients., Results and Conclusions: The performance of the proposed DRL-based controller is evaluated by numerical analysis of different diverse simulated patients. Comparison to the state-of-the-art RL-based method, which uses discretized state and action spaces, shows the superiority of the approach in the process and duration of cancer chemotherapy treatment. In the majority of the studied cases, the proposed model decreases the medication period and the total amount of administrated drug, while increasing the rate of reduction in tumor cells., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflicts of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

This paper proposes a data-driven actuator fault detection and isolation approach for the general class of nonlinear systems. The proposed method uses a deep neural network architecture to obtain an invariant set of basis functions for the Koopman operator to form a linear Koopman predictor for a nonlinear system. Then, the obtained linear model is used for fault detection and isolation purposes without relying on prior knowledge about the underlying dynamics. Moreover, a recursive method is proposed for fault detection and isolation that is entirely data-driven with the key feature of global validity for the system's whole operating region due to the Koopman operator's global characteristic. Finally, the approach's efficacy is demonstrated using two simulations on a coupled nonlinear system and a two-link manipulator benchmark., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 ISA. Published by Elsevier Ltd. All rights reserved.)

In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings' performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings' management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings' performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings., (© The Author(s) 2022.)

Introduction: Immune checkpoint blockade (ICB)-based therapy is revolutionizing cancer treatment by fostering successful immune surveillance and effector cell responses against various types of cancers. However, patients with HER2+ cancers are yet to benefit from this therapeutic strategy. Precisely, several questions regarding the right combination of drugs, drug modality, and effective dose recommendations pertaining to the use of ICB-based therapy for HER2+ patients remain unanswered., Methods: In this study, we use a mathematical modeling-based approach to quantify the growth inhibition of HER2+ breast cancer (BC) cell colonies (ZR75) when treated with anti-HER2; trastuzumab (TZ) and anti-PD-1/PD-L1 (BMS-202) agents., Results and Discussion: Our data show that a combination therapy of TZ and BMS-202 can significantly reduce the viability of ZR75 cells and trigger several morphological changes. The combination decreased the cell's invasiveness along with altering several key pathways, such as Akt/mTor and ErbB2 compared to monotherapy. In addition, BMS-202 causes dose-dependent growth inhibition of HER2+ BC cell colonies alone, while this effect is significantly improved when used in combination with TZ. Based on the in-vitro monoculture experiments conducted, we argue that BMS-202 can cause tumor growth suppression not only by mediating immune response but also by interfering with the growth signaling pathways of HER2+BC. Nevertheless, further studies are imperative to substantiate this argument and to uncover the potential crosstalk between PD-1/PD-L1 inhibitors and HER2 growth signaling pathways in breast cancer., Competing Interests: Author AH was employed by Hamad Medical Corporation. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Padmanabhan, Kheraldine, Gupta, Meskin, Hamad, Vranic and Al Moustafa.)

Background: Training is an essential aspect of providing high-quality treatment and ensuring patient safety in any medical practice. Because extracorporeal membrane oxygenation (ECMO) is a complicated operation with various elements, variables, and irregular situations, doctors must be experienced and knowledgeable about all conventional protocols and emergency procedures. The conventional simulation approach has a number of limitations. The approach is intrinsically costly since it relies on disposable medical equipment (i.e., oxygenators, heat exchangers, and pumps) that must be replaced regularly due to the damage caused by the liquid used to simulate blood. The oxygenator, which oxygenates the blood through a tailored membrane in ECMO, acts as a replacement for the patient's natural lung. For the context of simulation-based training (SBT) oxygenators are often expensive and cannot be recycled owing to contamination issues., Methods: Consequently, it is advised that the training process include a simulated version of oxygenators to optimize reusability and decrease training expenses. Toward this goal, this article demonstrates a mock oxygenator for ECMO SBT, designed to precisely replicate the real machine structure and operation., Results: The initial model was reproduced using 3D modeling and printing. Additionally, the mock oxygenator could mimic frequent events such as pump noise and clotting. Furthermore, the oxygenator is integrated with the modular ECMO simulator using cloud-based communication technology that goes in hand with the internet of things technology to provide remote control via an instructor tablet application., Conclusions: The final 3D modeled oxygenator body was tested and integrated with the other simulation modules at Hamad Medical Corporation with several participants to evaluate the effectiveness of the training session., (© 2022 The Authors. Artificial Organs published by International Center for Artificial Organ and Transplantation (ICAOT) and Wiley Periodicals LLC.)