Fault diagnosis using partial syndromes: a modified Hopfield neural network approach

This paper presents a modified Hopfield neural network (HNN) for solving the system-level fault diagnosis problem which aims at identifying the set of faulty nodes. This problem has been extensively studied in the last three decades. Nevertheless, identifying the set of all faulty nodes using only partial syndromes, i.e. when some of the testing or comparison outcomes are missing prior to initiating the diagnosis phase, remains an outstanding research issue. The new HNN-based diagnosis algorithm does not require any prior learning or knowledge about the system, nor about any faulty situation, hence providing a better generalisation performance. Results from a thorough simulation study demonstrate the effectiveness of the HNN-based fault diagnosis algorithm in terms of diagnosis correctness, diagnosis latency and diagnosis scalability, for randomly generated diagnosable systems of different sizes and under various fault scenarios. We have also conducted extensive simulations using partial syndromes. Simulations showed that the HNN-based diagnosis performed efficiently, i.e. diagnosis correctness was around 99% when at most half of the test or comparison outcomes are missing, making it a viable alternative to existing diagnosis algorithms.     

Airway Delivery of Hydrogel-Encapsulated Niclosamide for the Treatment of Inflammatory Airway Disease

Repurposing of the anthelminthic drug niclosamide was proposed as an effective treatment for inflammatory airway diseases such as asthma, cystic fibrosis, and chronic obstructive pulmonary disease. Niclosamide may also be effective for the treatment of viral respiratory infections, such as SARS-CoV-2, respiratory syncytial virus, and influenza. While systemic application of niclosamide may lead to unwanted side effects, local administration via aerosol may circumvent these problems, particularly when the drug is encapsulated into small polyethylene glycol (PEG) hydrospheres. In the present study, we examined whether PEG-encapsulated niclosamide inhibits the production of mucus and affects the pro-inflammatory mediator CLCA1 in mouse airways in vivo, while effects on mucociliary clearance were assessed in excised mouse tracheas. The potential of encapsulated niclosamide to inhibit TMEM16A whole-cell Cl⁻ currents and intracellular Ca²⁺ signalling was assessed in airway epithelial cells in vitro. We achieved encapsulation of niclosamide in PEG-microspheres and PEG-nanospheres (Niclo-spheres). When applied to asthmatic mice via intratracheal instillation, Niclo-spheres strongly attenuated overproduction of mucus, inhibited secretion of the major proinflammatory mediator CLCA1, and improved mucociliary clearance in tracheas ex vivo. These effects were comparable for niclosamide encapsulated in PEG-nanospheres and PEG-microspheres. Niclo-spheres inhibited the Ca²⁺ activated Cl⁻ channel TMEM16A and attenuated mucus production in CFBE and Calu-3 human airway epithelial cells. Both inhibitory effects were explained by a pronounced inhibition of intracellular Ca²⁺ signals. The data indicate that poorly dissolvable compounds such as niclosamide can be encapsulated in PEG-microspheres/nanospheres and deposited locally on the airway epithelium as encapsulated drugs, which may be advantageous over systemic application.     

Effects of almond gum as texture and sensory quality improver in wheat bread

The aim of this work was to investigate the effect of almond gum as dietary fibre source in enhancing the wheat bread quality. Different amounts of almond gum (2%, 5% and 10% (w/w)) were used in bread formulation. The volume, texture, crust and crumb colour, as well as the sensorial properties, were evaluated and compared to control (without almond gum). The obtained results showed that almond gum addition enhanced significantly the volume of bread. The highest volume was obtained using 2% almond gum concentration with 23.6% increase, compared to control. Using almond gum in bread formulation improved considerably its texture with a notable decrease in hardness by 61.7% and 42.5% when using 2% and 5% almond gum, respectively. The sensory analysis scores showed that the better overall acceptability was found for breads supplemented with 2% almond gum, as compared to control and breads supplemented with 5% or 10% almond gum.     

An improved machine learning technique based on downsized KPCA for Alzheimer's disease classification

Alzheimer's disease (AD), a neurodegenerative disorder, is a very serious illness that cannot be cured, but the early diagnosis allows precautionary measures to be taken. The current used methods to detect Alzheimer's disease are based on tests of cognitive impairment, which does not provide an exact diagnosis before the patient passes a moderate stage of AD. In this article, a novel classifier of brain magnetic resonance images (MRI) based on the new downsized kernel principal component analysis (DKPCA) and multiclass support vector machine (SVM) is proposed. The suggested scheme classifies AD MRIs. First, a multiobjective optimization technique is used to determine the optimal parameter of the kernel function in order to ensure good classification results and to minimize the number of retained principle components simultaneously. The optimal parameter is used to build the optimized DKPCA model. Second, DKPCA is applied to normalized features. Downsized features are then fed to the classifier to output the prediction. To validate the effectiveness of the proposed method, DKPCA was tested using synthetic data to demonstrate its efficiency on dimensionality reduction, then the DKPCA based technique was tested on the OASIS MRI database and the results were satisfactory compared to conventional approaches.     

An Overview of the Approaches for Automotive Safety Integrity Levels Allocation

Journal of Failure Analysis and Prevention - ISO 26262, titled Road Vehicles–Functional Safety, is the new automotive functional safety standard for passenger vehicle industry. In order to...     

Ant colony algorithm for automotive safety integrity level allocation

ISO 26262, the new automotive functional safety standard, aims to foster the design and development of safe products by ensuring that the risks posed by hazardous components are reduced to a residual level. Therefore, the standard defines and uses the concept of Automotive Safety Integrity Levels (ASILs) that classify the strictness of safety requirements to be assigned to the failure modes of the system based on the hazard they may cause. ASIL allocation can be described as a hard optimization problem focused on finding the optimal ASIL allocation that maximizes the safety requirements and minimizes cost. However, finding this optimal allocation among a set of possible allocations can represent a difficult task in large systems that contain a large number of components, which subsequently increases the search space. In this paper, we introduce a novel approach that uses the nature-inspired meta-heuristic Ant Colony Optimization (ACO) algorithm to solve the ASIL allocation problem and makes use of strategies that reduce the solution space. The problem was formulated as a construction graph, which the ants use to construct possible ASIL allocations. The search space reduction is accelerated considerably by both the effective performance of the ACO and the convergence of the algorithm on the optimal solution. This approach has been evaluated by applying it to a hybrid braking system and a steer-by-wire system. The results show a significant improvement over genetic-based, penguins search-based and tabu search-based approaches.     

Partial syndrome-based system-level fault diagnosis using game theory

This paper introduces a novel diagnosis approach, using game theory, to solve the comparison-based system-level fault identification problem in distributed and parallel systems based on the asymmetric comparison model. Under this diagnosis model tasks are assigned to pairs of nodes and the results of executing these tasks are compared. Using the agreements and disagreements among the nodes’ outputs, i.e. the input syndrome, the fault diagnosis algorithm identifies the fault status of the system’s nodes, under the assumption that at most t of these nodes can permanently fail simultaneously. Since the introduction of the comparison model, significant progress has been made in both theory and practice associated with the original model and its offshoots. Nevertheless, the problem of efficiently identifying the set of faulty nodes when not all the comparison outcomes are available to the fault identification algorithm prior to initiating the diagnosis phase, i.e. partial syndromes, remains an outstanding research issue. In this paper, we first show how game theory can be adapted to solve the fault diagnosis problem by maximising the payoffs of all players (nodes). We then demonstrate, using results from a thorough simulation, the effectiveness of this approach in solving the fault identification problem using partial syndromes from randomly generated diagnosable systems of different sizes and under various fault scenarios. We have considered large diagnosable systems, and we have experimented extreme faulty situations by simulating all possible fault sets even those that are less likely to occur in practice. Over all the extensive simulations we have conducted, the new game-theory-based diagnosis algorithm performed very well and provided good diagnosis results, in terms of correctness, latency, and scalability, making it a viable addition or alternative to existing diagnosis algorithms.     

Synchronised interrelationship between lipid/protein oxidation analysis and sensory attributes in refrigerated minced beef meat formulated with Punica granatum peel extract

Microbiological, chemical and sensory effects of pomegranate peel ethanol extract (EE) at 0.1, 0.5 and 1% (w w⁻¹) on refrigerated minced beef meat were undertaken. Chemical analyses revealed that the treated meat underwent significant decrease (P < 0.05) of primary and secondary lipid oxidation. By the end of storage, EE at 1% reduced the MetMb, carbonyl group accumulation and the disappearance of sulphydryl proteins by 56.68, 65.71 and 59.69% respectively, compared with control samples. EE at 1% showed also the highest scores for colour, appearance, odour and overall acceptability (OA) attributes using a 9-point hedonic scale. Chemometric approaches showed that lipid/protein oxidation analysis, sensory attributes and storage time allowed the samples discrimination. Oxidative stability parameters were equally used to predict OA by linear regression analysis. The reported data provide an approach to strengthen the interpretation of sensory quality and how they are affected by chemical changes in newly formulated meat products.     

An optimization of a non-volatile latch using memristors for sequential circuit applications

Analog Integrated Circuits and Signal Processing - The next generation of non-volatile memory elements have been attracting significant attention for future emerging memory applications in recent...