A COLLABORATIVE MODEL FOR VIRTUAL ENTERPRISE

Collaborative process characteristics have three dimensions: actors, activities and action’s logic. The aim of this paper is to present a virtual portal’s model that helps managing consortiums. Our model based on dynamic e-collaboration and it has a modular structure, multilayer approach. System’s functionality of virtual enterprise is collaborative model is concern on users’ login, based on role and access control, searching and providing distributed resources, accessibility, metadata management and improved information’s management. Our proposal for developing solution offers a functional architecture of a virtual enterprise using dynamic e-collaboration and shared space.dynamic e-collaboration, multilayer solution, modular approach

Towards the optimal Pixel size of dem for automatic mapping of landslide areas

Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1m, 2m, 5m and 10m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5m DEM-resolution for FFNN and 1m DEM resolution for results. The best performance was found to be using 5m DEM-resolution for FFNN and 1m DEM resolution for ML classification

Insurability Challenges Under Uncertainty: An Attempt to Use the Artificial Neural Network for the Prediction of Losses from Natural Disasters

The main difficulty for natural disaster insurance derives from the uncertainty of an event’s damages. Insurers cannot precisely appreciate the weight of natural hazards because of risk dependences. Insurability under uncertainty first requires an accurate assessment of entire damages. Insured and insurers both win when premiums calculate risk properly. In such cases, coverage will be available and affordable. Using the artificial neural network – a technique rooted in artificial intelligence - insurers can predict annual natural disaster losses. There are many types of artificial neural network models. In this paper we use the multilayer perceptron neural network, the most accommodated to the prediction task. In fact, if we provide the natural disaster explanatory variables to the developed neural network, it calculates perfectly the potential annual losses for the studied country.Natural disaster losses, Insurability, Uncertainty, Multilayer perceptron neural network, Prediction.

Multi-Attribute SCADA-Specific Intrusion Detection System for Power Networks

The increased interconnectivity and complexity of supervisory control and data acquisition (SCADA) systems in power system networks has exposed the systems to a multitude of potential vulnerabilities. In this paper, we present a novel approach for a next-generation SCADA-specific intrusion detection system (IDS). The proposed system analyzes multiple attributes in order to provide a comprehensive solution that is able to mitigate varied cyber-attack threats. The multiattribute IDS comprises a heterogeneous white list and behavior-based concept in order to make SCADA cybersystems more secure. This paper also proposes a multilayer cyber-security framework based on IDS for protecting SCADA cybersecurity in smart grids without compromising the availability of normal data. In addition, this paper presents a SCADA-specific cybersecurity testbed to investigate simulated attacks, which has been used in this paper to validate the proposed approach

A comparison of neural and non-neural machine learning models for food safety risk prediction with European Union RASFF data.

European Union launched the RASFF portal in 1977 to ensure cross-border monitoring and a quick reaction when public health risks are detected in the food chain. There are not enough resources available to guarantee a comprehensive inspection policy, but RASFF data has enormous potential as a preventive tool. However, there are few studies of food and feed risk issues prediction and none with RASFF data. Although deep learning models are good prediction systems, it must be confirmed whether in this field they behave better than other machine learning techniques. The importance of categorical variables encoding as input for numerical models should be specially studied. Results in this paper show that deep learning with entity embedding is the best combination, with accuracies of 86.81%, 82.31%, and 88.94% in each of the three stages of the simplified RASFF process in which the tests were carried out. However, the random forest models with one hot encoding offer only slightly worse results, so it seems that in the quality of the results the coding has more weight than the prediction technique. Our work also demonstrates that the use of probabilistic predictions (an advantage of neural models) can also be used to optimize the number of inspections that can be carried out.pre-print301 K

Ultrathin Amorphous Silica Membrane Enhances Proton Transfer across Solid-to-Solid Interfaces of Stacked Metal Oxide Nanolayers while Blocking Oxygen

A large jump of proton transfer rates across solid-to-solid interfaces by inserting an ultrathin amorphous silica layer into stacked metal oxide nanolayers is discovered using electrochemical impedance spectroscopy and Fourier-transform infrared reflection absorption spectroscopy (FT-IRRAS). The triple stacked nanolayers of Co3O4, SiO2, and TiO2 prepared by atomic layer deposition (ALD) enable a proton flux of 2400 ± 60 s−1 nm−2 (pH 4, room temperature), while a single TiO2 (5 nm) layer exhibits a threefold lower flux of 830 s−1 nm−2. Based on FT-IRRAS measurements, this remarkable enhancement is proposed to originate from the sandwiched silica layer forming interfacial SiOTi and SiOCo linkages to TiO2 and Co3O4 nanolayers, respectively, with the O bridges providing fast H+ hopping pathways across the solid-to-solid interfaces. Together with the complete O2 impermeability of a 2 nm ALD-grown SiO2 layer, the high flux for proton transport across multi-stack metal oxide layers opens up the integration of incompatible catalytic environments to form functional nanoscale assemblies such as artificial photosystems for CO2 reduction by H2O