Impact Of Engine Room Simulator As A Tool For Training And Assessing Bsmare Students’ Performance In Engine Watchkeeping

Ship engine room simulator is a tool used by maritime academies that offer the Marine Engineering Program. According to the Standards of Training Certification and Watchkeeping for Seafarers (STCW), to provide physical realism in training and assessment, simulators are employed. Assessment programs have the intent of providing results that educators will utilize to improve their teaching strategies and improve learner performance (Klinger et al. 2008). This study aimed to (1) Determine the level of competencies of the Bachelor of Science in Marine Engineering cadets in Engine Watchkeeping with Resource Management before and after their exposure to the training on the use of the simulator as a tool for learning, and (2) To find out if there is a significant difference in the level of competencies of the cadets in Engine Watchkeeping before and after the training on the use of the simulator as a tool for learning. Mean and Wilcoxon tests were utilized to analyze the data. It was found that a significant difference in the level of competencies of the cadets in Engine Watchkeeping before and after the training, which implies that the Engine Room Simulator is a tool for learning and assessing the competencies of students in Engine Watchkeeping is effective. The study recommends that instructors should maximize the use of the available simulators in teaching the course. Students shall have a hands-on experience as supplementary to the theories that they learn.

Kinesthetic Metaphors for Precise Spatial Manipulation: A Study of Object Rotation

In this article, we report on our investigation of kinesthetic feedback as a means to provide precision, accuracy, and mitigation of arm fatigue in spatial manipulation tasks. Most works on spatial manipulation discuss the use of haptics (kinesthetic/force and tactile) primarily as a means to offer physical realism in spatial user interfaces (SUIs). Our work offers a new perspective in terms of how force-feedback can promote precise manipulations in spatial interactions to aid manual labor, controllability, and precision. To demonstrate this, we develop, implement, and evaluate three new haptics-enabled interaction techniques (kinesthetic metaphors) for precise rotation of 3D objects. The quantitative and qualitative analyses of experiments reveal that the addition of force-feedback improves precision for each of the rotation techniques. Self-reported user feedback further exposes a novel aspect of kinesthetic manipulation in its ability to mitigate arm fatigue for close-range spatial manipulation tasks.

Explanation of Photon Navigation in the Mach-Zehnder Interferometer

Photons in interferometers manifest the functional ability to simultaneously navigate both paths through the device, but eventually appear at only one outlet. How this relates to the physical behaviour of the particle is still ambiguous, even though mathematical representation of the problem is adequate. This paper applies a non-local hidden-variable (NLHV) solution, in the form of the Cordus theory, to explain photon path dilemmas in the Mach–Zehnder (MZ) interferometer. The findings suggest that the partial mirrors direct the two reactive ends of the Cordus photon structures to different legs of the apparatus, depending on the energisation state of the photon. Explanations are provided for a single photon in the interferometer in the default, open-path, and sample modes. The apparent intelligence in the system is not because the photon knows which path to take, but rather because the MZ interferometer is a finely-tuned photon-sorting device that auto-corrects for randomness in the frequency phase to direct the photon to a specific detector. The principles also explain other tunnelling phenomena involving barriers. Thus, navigation dilemmas in the MZ interferometer may be explained in terms of physical realism after all.

Towards Geostatistical Learning for the Geosciences: A Case Study in Improving the Spatial Awareness of Spectral Clustering

The particularities of geosystems and geoscience data must be understood before any development or implementation of statistical learning algorithms. Without such knowledge, the predictions and inferences may not be accurate and physically consistent. Accuracy, transparency and interpretability, credibility, and physical realism are minimum criteria for statistical learning algorithms when applied to the geosciences. This study briefly reviews several characteristics of geoscience data and challenges for novel statistical learning algorithms. A novel spatial spectral clustering approach is introduced to illustrate how statistical learners can be adapted for modelling geoscience data. The spatial awareness and physical realism of the spectral clustering are improved by utilising a dissimilarity matrix based on nonparametric higher-order spatial statistics. The proposed model-free technique can identify meaningful spatial clusters (i.e. meaningful geographical subregions) from multivariate spatial data at different scales without the need to define a model of co-dependence. Several mixed (e.g. continuous and categorical) variables can be used as inputs to the proposed clustering technique. The proposed technique is illustrated using synthetic and real mining datasets. The results of the case studies confirm the usefulness of the proposed method for modelling spatial data.

Hydrodynamics of core-collapse supernovae and their progenitors

Multi-dimensional fluid flow plays a paramount role in the explosions of massive stars as core-collapse supernovae. In recent years, three-dimensional (3D) simulations of these phenomena have matured significantly. Considerable progress has been made towards identifying the ingredients for shock revival by the neutrino-driven mechanism, and successful explosions have already been obtained in a number of self-consistent 3D models. These advances also bring new challenges, however. Prompted by a need for increased physical realism and meaningful model validation, supernova theory is now moving towards a more integrated view that connects multi-dimensional phenomena in the late convective burning stages prior to collapse, the explosion engine, and mixing instabilities in the supernova envelope. Here we review our current understanding of multi-D fluid flow in core-collapse supernovae and their progenitors. We start by outlining specific challenges faced by hydrodynamic simulations of core-collapse supernovae and of the late convective burning stages. We then discuss recent advances and open questions in theory and simulations.

Review on Modeling of Vapor Compression Chillers: District Cooling Perspective

Energy consumption and its associated consequences can be reduced by implementing district cooling strategies that supply low temperature water to a wide range of end users through chillers and distribution networks. Adequate understanding, performance prediction and further optimization of vapor compression chillers used widely in district cooling plants have been a subject of intense research through model-based approaches. In this context, we perform an extensive review of different modeling techniques used for predicting steady-state or dynamic performance of vapor compression liquid chillers. The explored modeling techniques include physical and empirical models. Different physical models used for vapor compression chillers, based on physics laws, are discussed in detail. Furthermore, empirical models (based on artificial neural networks, regression analysis) are elaborated along with their advantages and drawbacks. The physical models can depict both steady- and unsteady-state performance of the vapor compression chiller; however, their accuracy and physical realism can be enhanced by considering the geometrical arrangement of the condenser and evaporator and validating them for various ecofriendly refrigerants and large system size (i.e., cooling capacity). Apparently, empirical models are easy to develop but do not provide the necessary physical realism of the process of vapor compression chiller. It is further observed that DC plants/networks have been modeled from the point of view of optimization or integration but no efforts have been made to model the chillers with multiple VCR cycles. The development of such models will facilitate to optimize the DC plant and provide improved control strategies for effective and efficient operation.

Development and Preliminary Validation of a Rabbit Model of Duodenal Atresia for Training in Pediatric Surgical Skills

Duodenal atresia is a congenital defect that requires advanced surgical skills. The objective of this study is to present an anatomical defect of duodenal atresia using a rabbit model and evaluate the preliminary experience for the training of surgical skills with pediatric surgeons. Adult white New Zealand male rabbits weighing 3.0 to 4.5 kg were used to create the defect. To simulate the bottom of the dilated blind pouch, the gastric antrum of the rabbit was obliterated using a 2-0 Prolene suture, and the cecal appendix was dissected to simulate the continuation of the duodenum. Participants performed laparoscopic duodenal atresia repair in this animal model using the iPhone trainer. Thirteen pediatric surgeons with experience in laparoscopic duodenal atresia repair assessed this model with a questionnaire on 5-point Likert-type scale. Overall, the simulated model of duodenal atresia obtained a general average score of 4.39. The highest observed average was for its physical realism, whereas the lowest score was in surgical experience. The global opinion of the model obtained a score of 4.40. In addition, all surgeons answered that this rabbit model showed the same complexity as newborns and young children in the repair of this type of defect. The inclusion of new models through rabbits in pediatric surgery programs will allow the development of advanced skills of pediatric residents and surgeons.