An automatic moment tensor is a significant product of regional seismic networks and an essential piece of information for real‐time seismological applications, like shake maps or tsunami warning. In this article, we present scisola, a new software for automatic moment tensor (MT) retrieval, based on the ISOLated Asperities (ISOLA) MT inversion code and SeisComP3 real‐time processing system. Scisola connects SeisComP3 with the ISOLA FORTRAN codes by retrieving event, station, waveform, and instrumental response data from SeisComP3 and passing the information to ISOLA. The Green’s function calculation and centroid spatiotemporal grid search are done in parallel mode in scisola, thus the computational time is significantly reduced. The user has full control of all calculation aspects, for example, frequency range of inversion and station selection through the creation of magnitude‐based rules. Scisola is programmed in Python and provides a complete graphical user interface (GUI) and a database for storing the results. The automatic solution is stored in the database, and the user is able to revise it through a GUI. The software provides a complete logging of processing steps, extended graphical output, a text file useful for e‐mail dissemination, and a handful of quality indexes of the solutions. The code’s performance was tested against manual MT solutions and proved to be efficient.

SCADA systems monitor critical industrial, energy and other physical infrastructures in order to detect malfunctions, issue alerts and, in many cases, propose or even take remedial actions. However, due to their attachment to the Internet, SCADA systems are, today, vulnerable to attacks such as, among several others, interception of data traffic, malicious modifications of settings and control operations data, malicious modification of measurements and infrastructure data and Denial-of-Service attacks. Our research focuses on strengthening SCADA systems with cryptographic methods and protection mechanisms with emphasis on data and messaging encryption and device identification and authentication. The limited availability of computing power and memory in sensors and embedded devices deployed in SCADA systems make render cryptographic methods with higher resource requirements, such as the use of conventional public key cryptography such as RSA, unsuitable. We, thus, propose Elliptic Curve Cryptography as an alternative cryptographic mechanism, where smaller key sizes are required, with lower resource requirements for cryptographic operations. Accordingly, our approach integrates Modbus, a commonly used SCADA communication protocol, with Elliptic Curve Cryptography. We have, also, developed an experimental set-up in order to demonstrate the performance of our approach and draw conclusions regarding its effectiveness in real SCADA installations.

Farmed fish disease diagnosis is an important problem in the fish farming industry, affecting quality of production and financial losses. In this paper, we present a web-based intelligent system that tackles the problem of fish disease diagnosis. To this end, it uses multiple knowledge representation and reasoning methods: rule-based, case-based, weight-based, and voting. Knowledge, which concerns the diagnosis of sea bass diseases, was acquired from experts in the field and represented in the form of decision trees. The diagnostic process is performed in two stages: a general one and a specialized one. In the general stage, a level-based diagnosis is performed, where environmental parameters, external signs, and internal signs are successively examined, and the three most probable diseases are identified. In the specialized stage, which is optional, a specialized expert system is used for each of the resulting diseases, where additional parameters concerning laboratory tests (microbiological, microscopic, molecular, and chemical) are considered. The general stage is the most useful, given that it can be performed on-site in real-time, whereas the specialized one requires time-consuming lab tests. The system also provides explanations for its decisions. Evaluation of the general-stage diagnostic process showed a top-3 accuracy of 78.79% on expert test cases and 94% on an artificial dataset.