A smart process controller framework for Industry 4.0 settings

This paper presents a smart supervisory framework for a single process controller, designed for Industry 4.0 shop floors. This digitization of a full supervisory suite for a single process controller enables self-awareness, self-diagnosis, self-prognosis, and self-healing (by definition, these "self" elements are missing from other supervisory frameworks diagnosing numerous controllers in parallel). The proposed framework is aligned with the concept of a Cyber Physical System (CPS), since its implementation generates a rich cyber physical entity of the controlled process. This CPS entity can either be considered as the process digital twin, or can provide a solid basis for generating it. Finally, the framework includes the main characteristics of Industry 4.0, such as advanced use of Artificial Intelligence (AI) and big data analysis. The framework is based on four modules: (1) Control and Awareness module—performing both continuous process control and adjustments, as well as machine learning (ML) and statistical process control (SPC) for identifying abnormalities that require further diagnosis; (2) Process -diagnosis module—performing continual (recurrent) analysis of the process state and trends; (3) Prognosis and Healing module—performing prognosis and automated intervention via parameter changes, re-configurations, and automated maintenance; (4) External Interaction Platform—an interactive module for interfacing with experts, presenting them with the process analysis information and obtaining feedback from them as part of a learning process. Using an implementation showcase to illustrate the methodological framework’s applicability, we demonstrate its real-world potential. The proposed framework could serve as a guide for implementing smart process control and maintenance systems in Industry 4.0 shop floors. It could also provide a firm basis for comparison with future suggested frameworks. Future research directions could include pursuing improvements to the proposed process control framework and validating the framework by case studies of its implementation.

     

Dominant inheritance in two families with familial Mediterranean fever (FMF)

Familial Mediterranean fever (FMF) is an autosomal-recessive disease which affects almost exclusively people of Mediterranean and Middle Eastern origin. We examined the possibility of a dominant inheritance of FMF among our 3,000 patients in Israel. Two hundred forty FMF patients were members of 77 families in which the disease affected more than one generation. In 75 of these families the occurrence of FMF in more than one generation was found to be consistent with a recessive mode of inheritance due to a high gene frequency (q) and consanguinity among parents of the patients. In 2 families, one of Ashkenazi and the other of Georgian Iraqi origin, in which FMF occurred in 4 consecutive generations, the mode of inheritance could be explained only by autosomal-dominant inheritance.     

The Prominin-1-Derived Peptide Improves Cardiac Function Following Ischemia

Myocardial infarction (MI) remains the leading cause of death in the western world. Despite advancements in interventional revascularization technologies, many patients are not candidates for them due to comorbidities or lack of local resources. Non-invasive approaches to accelerate revascularization within ischemic tissues through angiogenesis by providing Vascular Endothelial Growth Factor (VEGF) in protein or gene form has been effective in animal models but not in humans likely due to its short half-life and systemic toxicity. Here, we tested the hypothesis that PR1P, a small VEGF binding peptide that we developed, which stabilizes and upregulates endogenous VEGF, could be used to improve outcome from MI in rodents. To test this hypothesis, we induced MI in mice and rats via left coronary artery ligation and then treated animals with every other day intraperitoneal PR1P or scrambled peptide for 14 days. Hemodynamic monitoring and echocardiography in mice and echocardiography in rats at 14 days showed PR1P significantly improved multiple functional markers of heart function, including stroke volume and cardiac output. Furthermore, molecular biology and histological analyses of tissue samples showed that systemic PR1P targeted, stabilized and upregulated endogenous VEGF within ischemic myocardium. We conclude that PR1P is a potential non-invasive candidate therapeutic for MI.     

Displaying a boundary in graphic and symbolic “wait” displays: Duration estimates and users’ preferences

Two experiments assessed the effect of displaying a boundary on duration estimates and preference ratings for dynamic displays that were shown while users waited for the system's response. Displays were either symbolic (changing numbers) or graphic (increasing rectangles) and could contain a boundary that indicated when the interval was expected to be over. Duration estimates were similar for symbolic and graphic displays and for displays with and without a boundary. However, when the displays were encountered successively, participants assessed the graphic displays as having shorter durations than the symbolic displays. Faster rates of change in both types of displays led to increased duration estimates. Although displaying a boundary did not affect duration estimates, participants preferred displays in which a boundary was shown and preferred the graphic displays over the symbolic displays. Hence, bounded graphic displays are recommended as “wait” displays for computerized applications.     

Incremental application of knowledge to continuously arriving time-oriented data

In our previous work, we introduced a computational architecture that effectively supports the tasks of continuous monitoring and of aggregation querying of complex domain meaningful time-oriented concepts and patterns (temporal abstractions), in environments featuring large volumes of continuously arriving and accumulating time-oriented raw data. Examples include provision of decision support in clinical medicine, making financial decisions, detecting anomalies and potential threats in communication networks, integrating intelligence information from multiple sources, etc. In this paper, we describe the general, domain-independent but task-specific problem-solving method underling our computational architecture, which we refer to as incremental knowledge-based temporal abstraction (IKBTA). The IKBTA method incrementally computes temporal abstractions by maintaining persistence and validity of continuously computed temporal abstractions from arriving time-stamped data. We focus on the computational framework underlying our reasoning method, provide well-defined semantic and knowledge requirements for incremental inference, which utilizes a logical model of time, data, and high-level abstract concepts, and provide a detailed analysis of the computational complexity of our approach.     

On Locally Decodable Codes, Self-Correctable Codes, and t-Private PIR

A k-query locally decodable code (LDC) allows to probabilistically decode any bit of an encoded message by probing only k bits of its corrupted encoding. A stronger and desirable property is that of self-correction, allowing to efficiently recover not only bits of the message but also arbitrary bits of its encoding. In contrast to the initial constructions of LDCs, the recent and most efficient constructions are not known to be self-correctable. The existence of self-correctable codes of comparable efficiency remains open.     

CoRAL: A transparent fault-tolerant web service

The Web is increasingly used for critical applications and services. We present a client-transparent mechanism, called CoRAL, that provides high reliability and availability for Web service. CoRAL provides fault tolerance even for requests being processed at the time of server failure. The scheme does not require deterministic servers and can thus handle dynamic content. CoRAL actively replicates the TCP connection state while maintaining logs of HTTP requests and replies. In the event of a primary server failure, active client connections fail over to a spare, where their processing continues seamlessly. We describe key aspects of the design and implementation as well as several performance optimizations. Measurements of system overhead, failover performance, and preliminary validation using fault injection are presented.     

Using artificial neural networks to detect unknown computer worms

Detecting computer worms is a highly challenging task. We present a new approach that uses artificial neural networks (ANN) to detect the presence of computer worms based on measurements of computer behavior. We compare ANN to three other classification methods and show the advantages of ANN for detection of known worms. We then proceed to evaluate ANN’s ability to detect the presence of an unknown worm. As the measurement of a large number of system features may require significant computational resources, we evaluate three feature selection techniques. We show that, using only five features, one can detect an unknown worm with an average accuracy of 90%. We use a causal index analysis of our trained ANN to identify rules that explain the relationships between the selected features and the identity of each worm. Finally, we discuss the possible application of our approach to host-based intrusion detection systems.