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We present a system for technical diagnostics (TD) that can recognize the actual state of marine equipment. A Bayesian classifier is trained to identify the different classes of a piece of equipment, monitored through multiple pseudo-discrete features. Data learning samples can be acquired with direct experiments for each class. The system is capable of merging subjective expert knowledge and data learning samples using pseudo-Bayesian estimates when the parameters of the conditional likelihood for the classes are identified. In the training process, correction is applied to solve numerical problems arising from zero probabilities. The pseudo-discrete features have hybrid nature and combine probabilistic and fuzzy approaches. They combine the ease of extracting subjective expert knowledge typical for the discrete features with the high precision of using the measured data during recognition typical for the continuous features. The domain of each pseudo discrete feature is divided into several main categories of non-overlapping intervals which are described as words by the expert. If a measured feature falls between two consecutive categories it is treated as a linear combination of those categories. The resubstitution performance of the classifier is assessed using an error matrix. A numerical example of a marine diesel generator demonstrates the proposed algorithm in a classification problem with nine different state classes of the generator, monitored through 23 pseudo-discrete features. Data learning samples are acquired with direct experiments for each class. The created TD system has potential applications in other complex engineering systems and may support improvements in marine engineering education and training.

This paper observes different alignment conditions in a journal-bearing-supported machine (i.e. propeller bearings within the stern tube of a marine vessel). A test rig is built so that the driven machine has fixed supports, roller bearings in an electrical motor. An alignment tolerance is met at the coupling of the journal, which is positioned within three journal bearings. This research can be applied to any industry with machines of the same characteristic. The journal bearings are designed to simulate the large tolerance seen in a vessel's stern tube only for lubrication control oil is used for the bush bearings. Various sensors are placed on the test rig to record data for later machine analysis. Using proximity probes and accelerometers the vibration of the journals and ball bearings can be visualised using orbital plotting and Fast Fourier Transform (FFT) through both LabVIEW and MATLAB respectively. Four alignment procedures are applied to the coupling using a laser unit. The first two will be so that the offset and angular misalignment is produced in the FFT spectrums as per ISO10816 Mechanical vibration so that a reference point is formed for the test rig. The other two alignment procedures represent current industry practice. These procedures are then compared to see which is the preferred reference point that will optimise the reliability value of a whole system. Finding the procedure with minimal vibration will increase the design life of housing structures for such machines, which will increase the overall profit of the shipping industry. Our developments and procedures will be further utilized in practical classes to improve maritime engineering education.

Stepping is the strategy used in standing to prevent fall. Reactive stepping is made when perturbed to fall. Reactive stepping is less assessed in clinical setting, instead, proactive stepping is assessed to measure the risk of fall. Reactive stepping is commonly tested in research settings. This study was done to find relationship between proactive stepping and reactive stepping in healthy adults. We found that proactive stepping ability did not reflect reactive stepping ability. The study suggests that outcomes of proactive stepping measures must be used with caution to understand individual's ability to do reactive stepping and prevent falling.

Das International Social Survey Programme (ISSP) ist ein länderübergreifendes, fortlaufendes Umfrageprogramm, das jährlich Erhebungen zu Themen durchführt, die für die Sozialwissenschaften wichtig sind. Das Programm begann 1984 mit vier Gründungsmitgliedern - Australien, Deutschland, Großbritannien und den Vereinigten Staaten - und ist inzwischen auf fast 50 Mitgliedsländer aus aller Welt angewachsen. Da die Umfragen auf Replikationen ausgelegt sind, können die Daten sowohl für länder- als auch für zeitübergreifende Vergleiche genutzt werden. Jedes ISSP-Modul konzentriert sich auf ein bestimmtes Thema, das in regelmäßigen Zeitabständen wiederholt wird. Details zur Durchführung der nationalen ISSP-Umfragen entnehmen Sie bitte der Dokumentation. Die vorliegende Studie konzentriert sich auf Fragen zu Freizeitaktivitäten und Sport.