Production of H2 and C2 hydrocarbons from methane in a proton conducting solid electrolyte cell using a Au–5Ce–5Na2WO4/SiO2 anode

A novel approach of upgrading methane towards the simultaneous production/separation of H₂ and C₂ hydrocarbons (ethane and ethylene), is developed. The reaction system was studied in a solid state proton (H⁺) conducting cell. Mixtures of methane, steam (and oxygen) were introduced over the anode, while an inert gas flowed over the cathode. Under open-circuit, the reacting mixture produced H₂, C₂H₆, C₂H₄, CO and CO₂. Under closed-circuit and when protons (H⁺) were electrochemically “pumped” from the anode to the cathode, a considerable increase in the production of H₂ was observed while the production of C₂ compounds remained essentially unaffected.     

An extended nonparametric homogeneously weighted moving average sign control chart

Parametric (or traditional) control charts are based on the assumption that the quality characteristic of interest follows a specific distribution. However, in many applications, there is a lack of knowledge about the underlying distribution. To this end, nonparametric (or distribution-free) control charts have been developed in recent years. In this article, a nonparametric double homogeneously weighted moving average (DHWMA) control chart based on the sign statistic is proposed for monitoring the location parameter of an unknown and continuous distribution. The performance of the proposed chart is measured through the run-length distribution and its associated characteristics by performing Monte Carlo simulations. The DHWMA sign chart is compared with other nonparametric sign charts, such as the homogeneously weighted moving average, generally weighted moving average (GWMA), double GWMA, and triple exponentially weighted moving average sign charts, as well as the traditional DHWMA chart. The results indicate that the proposed chart performs just as well as and in some cases better than its competitors, especially for small shifts. Finally, two examples are provided to show the application and implementation of the proposed chart.     

A performance evaluation framework for association mining in spatial data

The evaluation of the process of mining associations is an important and challenging problem in database systems and especially those that store critical data and are used for making critical decisions. Within the context of spatial databases we present an evaluation framework in which we use probability distributions to model spatial regions, and Bayesian networks to model the joint probability distribution and the structural relationships among spatial and non-spatial predicates. We demonstrate the applicability of the proposed framework by evaluating representatives from two well-known approaches that are used for learning associations, i.e., dependency analysis (using statistical tests of independence) and Bayesian methods. By controlling the parameters of the framework we provide extensive comparative results of the performance of the two approaches. We obtain measures of recovery of known associations as a function of the number of samples used, the strength, number and type of associations in the model, the number of spatial predicates associated with a particular non-spatial predicate, the prior probabilities of spatial predicates, the conditional probabilities of the non-spatial predicates, the image registration error, and the parameters that control the sensitivity of the methods. In addition to performance we investigate the processing efficiency of the two approaches.     

Carbohydrate moieties on the in vitro immunoreactivity of soy β-conglycinin

β-Conglycinin is a functional glycoprotein and one of the most important soybean allergens. The aim of the present research was to investigate the role of the N-glycans moieties of β-conglycinin on its in vitro immunoreactivity. The soy allergen was obtained by isoelectric precipitation from commercial soy protein isolate and was enzymatically deglycosylated by PNGase F (Peptide N-Glycosidase F EC 3.5.1.52). In order to optimize deglycosylation conditions different reaction times and allergen concentrations were tested. The extent of deglycosylation was estimated by SDS–PAGE, CZE, RP-HPLC, and MALDI-TOF MS analyses, which provided information related to changes in protein structure. The antigenicity of both native β-conglycinin and its deglycosylated form was evaluated by western-blotting and indirect ELISA employing polyclonal rabbit anti-soybean sera and horseradish peroxidase-labeled goat anti-rabbit IgG while the in vitro allergenicity was assessed by means of indirect competitive inhibition ELISA employing human sera (IgE) of soy allergics. β-Conglycinin was effectively deglycosylated by PNGase F. Data on immunological tests suggested that glycosyl moieties forming this glycoprotein might be involved in its immunoreactivity.     

Novel Approaches to Probabilistic Neural Networks Through Bagging and Evolutionary Estimating of Prior Probabilities

In this contribution, novel approaches are proposed for the improvement of the performance of Probabilistic Neural Networks as well as the recently proposed Evolutionary Probabilistic Neural Networks. The Evolutionary Probabilistic Neural Network’s matrix of spread parameters is allowed to have different values in each class of neurons, resulting in a more flexible model that fits the data better and Particle Swarm Optimization is also employed for the estimation of the Probabilistic Neural Networks’s prior probabilities of each class. Moreover, the bagging technique is used to create an ensemble of Evolutionary Probabilistic Neural Networks in order to further improve the model’s performance. The above approaches have been applied to several well-known and widely used benchmark problems with promising results.      

Monitoring reliability for a gamma distribution with a double progressive mean control chart

Control charting technique for time between events (TBE) is very important in high-yield processes for monitoring reliability. For a regularly maintained system, the interfailure times can be modeled by a gamma distribution. This article proposes a new control chart based on the double progressive mean statistic for monitoring the time between k (≥1 ) failures of a maintained gamma distributed system (referred as DPM-TBE chart). The performance of the proposed scheme is measured in terms of the average run-length (ARL) for the case when the scale parameter is known as well as when it is unknown and is estimated from an in-control (IC) reference sample. A comparison study with other TBE charts shows that the DPM-TBE chart is more effective. In addition, the proposed chart is shown to be very robust for large shifts when the true distribution of time between failures is a Weibull or a lognormal. Finally, an illustrative example is given to demonstrate the implementation of the proposed chart.     

A double exponentially weighted moving average control chart for monitoring COM‐Poisson attributes

The Conway‐Maxwell‐Poisson (COM‐Poisson) distribution is a two‐parameter generalization of the Poisson distribution, which can be used for overdispersed or underdispersed count data and also contains the geometric and Bernoulli distributions as special cases. This article presents a double exponentially weighted moving average control chart with steady‐state control limits to monitor COM‐Poisson attributes (regarded as CMP‐DEWMA chart). The performance of the proposed control chart has been evaluated in terms of the average, the median, and the standard deviation of the run‐length distribution. The CMP‐DEWMA control chart is studied not only to detect shifts in each parameter individually but also in both parameters simultaneously. The design parameters of the proposed chart are provided, and through a simulation study, it is shown that the CMP‐DEWMA chart is more effective than the EWMA chart at detecting downward shifts of the process mean. Finally, a real data set is presented to demonstrate the application of the proposed chart.     

Distributional-based texture classification using non-parametric statistics

Texture classification is an important problem in image analysis. In the present study, an efficient strategy for classifying texture images is introduced and examined within a distributional-statistical framework. Our approach incorporates the multivariate Wald–Wolfowitz test (WW-test), a non-parametric statistical test that measures the similarity between two different sets of multivariate data, which is utilized here for comparing texture distributions. By summarizing the texture information using standard feature extraction methodologies, the similarity measure provides a comprehensive estimate of the match between different images based on graph theory. The proposed “distributional metric” is shown to handle efficiently the texture-space dimensionality and the limited sample size drawn from a given image. The experimental results, from the application on a typical texture database, clearly demonstrate the effectiveness of our approach and its superiority over other well-established texture distribution (dis)similarity metrics. In addition, its performance is used to evaluate several approaches for texture representation. Even though the classification results are obtained on grayscale images, a direct extension to color-based ones can be straightforward.

Cyber risk management for autonomous passenger ships using threat-informed defense-in-depth

Recent innovations in the smart city domain have led to the proposition of a new mode of transportation utilizing Autonomous Passenger Ships (APS) or ferries in inland waterways. The novelty of the APS concept influenced the cyber risk paradigm and led to different considerations regarding attack objectives, techniques as well as risk management approaches. The main factor that has led to this is the autoremote operational mode, which refers to autonomous operations and remote supervision and control in case of emergency. The autoremote operational mode influences the risk of cyber attacks due to the increased connectivity and reliance on technology for automating navigational functions. On the other hand, the presence of passengers without crew members imposes a safety risk factor in cyber attacks. In this paper, we propose a new cyber risk management approach for managing the cyber risks against cyber physical systems in general and Autonomous Passenger Ships in particular. Our proposed approach aims to improve the Defense-in-Depth risk management strategy with additional components from the Threat-Informed Defense strategy allowing for more evolved cyber risk management capabilities. Moreover, we have utilized the proposed cyber risk management approach for the proposition of a cybersecurity architecture for managing the cyber risks against an APS use case named milliAmpere2. Additionally, we present our results after conducting a Systematic Literature Review (SLR) in cybersecurity evaluation in the maritime domain. Then, the findings of the SLR were utilized for a suitable evaluation of the proposed risk management approach. Our findings suggest that our proposed risk management approach named Threat-Informed Defense-in-Depth is capable of enriching several risk management activities across different stages in the system development life cycle. Additionally, a comprehensive evaluation of the cybersecurity posture of milliAmpere2 has been conducted using several approaches including risk evaluation, simulation, checklist, and adversary emulation. Our evaluation has uncovered several limitations in the current cybersecurity posture and proposed actions for improvement.