A geostatistical algorithm to reproduce lateral gradual facies transitions: Description and implementation

Valid representations of geological heterogeneity are fundamental inputs for quantitative models used in managing subsurface activities. Consequently, the simulation of realistic facies distributions is a significant aim. Realistic facies distributions are typically obtained by pixel-based, object-based or process-based methods. This work presents a pixel-based geostatistical algorithm suitable for reproducing lateral gradual facies transitions (LGFT) between two adjacent sedimentary bodies. Lateral contact (i.e. interfingering) between distinct depositional facies is a widespread geometric relationship that occurs at different scales in any depositional system. The algorithm is based on the truncation of the sum of a linear expectation trend and a random Gaussian field, and can be conditioned to well data. The implementation introduced herein also includes subroutines to clean and geometrically characterize the obtained LGFT. The cleaned sedimentary body transition provides a more appropriate and realistic facies distribution for some depositional settings. The geometric measures of the LGFT yield an intuitive measure of the morphology of the sedimentary body boundary, which can be compared to analogue data. An example of a LGFT obtained by the algorithm presented herein is also flow simulated, quantitatively demonstrating the importance of realistically reproducing them in subsurface models, if further flow-related accurate predictions are to be made.     

Medical application of information gain based artificial immune recognition system (AIRS): Diagnosis of thyroid disease

In this paper, we have made medical application of a new artificial immune system named the information gain based artificial immune recognition system (IG-AIRS) which minimizes the negative effects of taking into account all attributes in calculating Euclidean distance in shape–space representation which is used in many artificial immune systems. For medical data, thyroid disease data set was applied in the performance analysis of our proposed system. Our proposed system reached 95.90% classification accuracy with 10-fold CV method. This result ensured that IG-AIRS would be helpful in diagnosing thyroid function based on laboratory tests, and would open the way to various ill diagnoses support by using the recent clinical examination data, and we are actually in progress.     

A decision support system for fisheries management using operations research and systems science approach

In this paper, we present a general framework, using a systems science approach, for developing a decision support system (DSS) for fisheries management. Decision support systems are quantitative tools for managers to evaluate outcomes of their policies prior to implementation. Our fishery model considers multiple stocks and fisheries simultaneously in balancing catch among targeted and protected fish abundances. Since in the Northeastern US multispecies fishery the distribution of abundance, catch-per-unit-effort and bycatch vary geographically, we focus on a spatial management approach to address their spatial variability. The core component of this DSS applies operations research techniques of simulation and optimization to determine the optimal inter-annual and intra-annual fishing plans in terms of fishing efforts in each sub-area and the time period. The result is the recommended amount to catch from each fish species at each sub-area at any time period so that while management objectives for sustainability of fish stocks are satisfied, the value of landings is maximized. The graphical user interface of the proposed DSS helps users to define inputs, to set constraints and sub-area boundaries, and to visualize the outcomes.     

Shell Finite Cell Method: A high order fictitious domain approach for thin-walled structures

This article presents a generalization of the recently proposed Finite Cell Method to thin-walled structures. This approach uses a combination of well known Fictitious Domain Methods with high order hierarchical Ansatz spaces known from the p-version of the Finite Element Method. Whereas the original concept embeds a three-dimensional structure in a simple domain being meshed into a grid of cube shaped cells, the extension presented in this paper applies the fictitious domain approach to a two-dimensional master domain defined in the parameter plane of the geometric model. Implementation details are discussed and numerical benchmark problems show the high accuracy and computational efficiency of the new approach. It is also remarked, that the present approach can easily be carried over to isogeometric analysis, opening an attractive possibility to simulate trimmed NURBS-surfaces.     

Packet forwarding with source verification

Routers in the Internet do not perform any verification of the source IP address contained in the packets, leading to the possibility of IP spoofing. The lack of such verification opens the door for a variety of vulnerabilities, including denial-of-service (DoS) and man-in-the-middle attacks. Currently proposed spoofing prevention approaches either focus on protecting only the target of such attacks and not the routing fabric used to forward spoofed packets, or fail under commonly occurring situations like path asymmetry. With incremental deployability in mind, this paper presents two complementary hop-wise packet tagging approaches that equip the routers to drop spoofed packets close to their point of origin. Our simulations show that these approaches dramatically reduce the amount of spoofing possible even under partial deployment.     

An approach based on neural networks for estimation and generalization of crossflow filtration processes

The crossflow filtration process differs of the conventional filtration by presenting the circulation flow tangentially to the filtration surface. The conventional mathematical models used to represent the process have some limitations in relation to the identification and generalization of the system behaviour. In this paper, a system based on artificial neural networks is developed to overcome the problems usually found in the conventional mathematical models. More specifically, the developed system uses an artificial neural network that simulates the behaviour of the crossflow filtration process in a robust way. Imprecisions and uncertainties associated with the measurements made on the system are automatically incorporated in the neural approach. Simulation results are presented to justify the validity of the proposed approach.     

Adaptive course generation through learning styles representation

This paper presents an approach to automatic course generation and student modeling. The method has been developed during the European funded projects Diogene and Intraserv, focused on the construction of an adaptive e-learning platform. The aim of the platform is the automatic generation and personalization of courses, taking into account pedagogical knowledge on the didactic domain as well as statistic information on both the student’s knowledge degree and learning preferences. Pedagogical information is described by means of an innovative methodology suitable for effective and efficient course generation and personalization. Moreover, statistic information can be collected and exploited by the system in order to better describe the student’s preferences and learning performances. Learning material is chosen by the system matching the student’s learning preferences with the learning material type, following a pedagogical approach suggested by Felder and Silverman. The paper discusses how automatic learning material personalization makes it possible to facilitate distance learning access to both able-bodied and disabled people. Results from the Diogene and Intraserv evaluation are reported and discussed.     

Avoidance of bird repellents by mice (Mus musculus)

It is believed that mammalian chemosensory irritants are not aversive to birds and vice versa. Nevertheless, few avian repellents have been tested against mammals. For that reason, we evaluated the efficacy of 1.0% w/v methyl anthranilate, orthoaminoacetophenone, 2-amino-4,5-methoxyacetophenone, 2-methoxyacetophenone, and veratryl amine as mouse repellents in 3-hr no-choice drinking tests. Relative to ingestion of plain water, all test substances significantly reduced (P < 0.05) intake. Orthoaminoaceto-phenone was the most effective repellent, with intake reduced to levels statistically indistinguishable from zero.