Lighting is an important design criterion for ships, including the ro-ro ferry. The ship’s lighting system must meet certain standards. Therefore, the present study analysed and evaluated the artificial lighting of an Indonesian ro-ro ferry. The analysed ship in the present research was a 500 GT ro-ro ferry named KMP. Takabonerate that operated across the Selayar Islands of Indonesia. The artificial lighting systems in this ship’s three passenger spaces, which are reclining seat room, sofa seat room, and passenger open space, were evaluated and analysed. The main parameter to be investigated was the room illuminance produced by the ship’s lighting system. In order to achieve the study objective, the illuminance in those rooms was calculated using analytical formulas. The calculation results were validated using direct measurements and computer simulation. The direct measurements were conducted using a luxmeter, while computer simulations were performed using DIAlux Evo 10.0 software. With this software, the artificial lighting in the passenger spaces was also simulated and realistically visualized. The research results showed that the illuminance in the reclining seat room and the sofa seat room had met the minimum illuminance value set by the Indonesian National Standard (SNI), American Bureau of Shipping (ABS), and Indonesian Classification Bureau (BKI) standards. For the passenger open space, a re-arrangement of the lighting system was conducted to improve the light distribution so that all space illumination can meet the specified standards.

The optimum ship hull design solution has always been a concern, and in recent years, genetic algorithms to optimise the ship hull structure have been developed. The genetic algorithm’s fundaments generate alternative solutions and compare them with pre-defined constants and objectives. The development of design solutions evolves through competition and controlled variations. Minimising the ship hull structure weight is essential in reducing the ship’s capital (construction) expenditure and increasing the cargo capacity. The risk of the ship is associated with the loss of the ship, cargo, human life, environmental pollution, etc. It is a governing factor impacted by the chosen structural design solution and the measures taken to reduce the structural weight. A genetic algorithm will be employed to study the weight minimisation of a multi-purpose ship hull structure, controlling the associated risk by accounting for several structural design variables. The risk and best design solution are defined by the probability of compressive collapse of the stiffened plates, integral ship hull structure, and the associated cost due to failure. The Pareto frontier solutions, calculated by the non-dominated sorting genetic algorithm, NSGA-II, will be employed to determine feasible solutions for the design variables. The first-order reliability method will estimate the Beta reliability index based on the topology of the stiffened plates and ship hull structure as a part of the Pareto frontier solutions. The algorithm employed will not account for any manufacturing constraints and consequences due to the encountered optimal design solution.

Technical papers which attempt to analyse historic and ancient ship structures using modern techniques appear only spasmodically in today’s learned engineering journals. There could be many good reasons for this; the lack of commercial incentives, the paucity of accurate engineering data and perhaps a certain reluctance to cross into an area which is rightly the province of the nautical archaeologist. This, in the author’s view is a pity since simplified analytical techniques of the type routinely used in design offices can offer useful cost-effective insights into the physical behaviour of historic ships and craft, without endangering the physical reconstruction or its crew. In addition as the input data are often only loosely defined, less sophisticated methods are ideal for conducting “data-sensitivity” investigations. One such paper (Loscombe, 2022) described efforts to apply modern local and global structural design methods to Viking-age ships with a view to establishing plausible operational factors of safety for comparison with modern requirements. This follow-up contribution continues with the theme by focusing on the rudder. Rudder failure can endanger any ship, ancient, medieval or modern but the Viking ship rudder has a number of structural features not found on modern vessels which invite retrospective stress analyses. One component in particular, the lower bearing in modern terminology, appears to have a very short operational life, measured in days rather than years as is the expectation for today’s marine vehicles and hence the Viking ship rudder is a good candidate for such simplified numerical analyses.

In order to ensure the accuracy of propeller modelling and hydrodynamic performance prediction, first of all, compared with atlas modelling and OpenProp modelling, it is pointed out that the characteristics of the blade model established by PropCad are more complete. Secondly, through the analysis and discussion of the computational domain distribution, grid independence verification and turbulence model, the CFD calculation model is established systematically. Thirdly, the hydrodynamic analysis of AU5-80 propeller is carried out under different advance ratios, and the simulation results are in good agreement with the open water test data. The accuracy of CFD calculation model is proved. Then, the fluid-solid coupling analysis of the propeller is performed, and it is verified that the stress field of the propeller meets the material strength requirement. It provides support for the further optimization design of propeller structure. Finally, through the design and analysis of the PBCF propeller, some optimization parameters of the PBCF are obtained. Because the PBCF can diffuse and weaken the hub vortex, the propeller thrust, and efficiency are improved.

In the presented research work, LN2 supplied directly at rake face and in another localised machining condition, LN2 supplied at rake and flank face simultaneously. The DoE of performance of experiments was in accordance with Taguchi S/N ratio L18. It was found that flank wear length and crater wear width at LN2 supply at rake & flank face simultaneously declined by 23-38% and 20-30% respectively as compared to LN2 supply at rake face only. ANOVA gave the highest effect of contribution in the percentage to LN2 supply at rake & flank face simultaneously as 76.06% and 77.67%, next in decreasing order followed by speed, feed and depth of cut. SEM images depicted that flank wear length and crater wear width in both machining conditions. Tool wear was low during turning LN2 supply at rake and flank face. Optimized values of each response was confirmed by repeating the experiments.

Intelligent learning represents a dynamic approach to education that provides innovative technologies and personalized methodologies to enhance learning outcomes. Intelligent teaching adapts instruction to the individual needs, preferences, and progress of each student. This approach enables educators to tailor curriculum delivery, identify areas for improvement, and provide timely feedback, fostering a more engaging and effective learning environment. Moreover, intelligent teaching promotes collaborative learning experiences and encourages critical thinking skills, preparing students for success in an increasingly digital and interconnected world. This paper proposed a framework of Generative Platform-Oriented Intelligent Deep Neural Network (GPoIDNN) for Korean language teaching in Universities. The proposed GPoIDNN network comprises a social media platform for the promotion of Korean language teaching among students. With the GPoIDNN platform, a Generative network is implemented for the analysis of the factors involved in Language teaching in universities. The platform considered for the proposed model is Weibo for acquiring in-depth information about the language learning process. Upon the estimated features GPoIDNN uses the Generative Deep Neural Network platform for the classification and examination of the student performance. With the Weibo platform in social media, the Generative network constructs the intelligent teaching system for the Korean language teaching process in University students. The examination of student performance demonstrated that the proposed GPoIDNN model improves the student learning of Korean language with improved by 73% through the intelligent model. Further, the keywords and opinions classified with the GPoIDNN model exhibits a higher classification rate of 0.98 based on the opinion of the students in the universities.

This paper highlights the use of aluminum oxide nanoparticles as an additive in diesel-butanol blends to show its effect on fuel consumption, emissions and performance. In the present experiment, different concentrations of aluminium oxide nanoadditives (30, 50, and 70 ppm) are used in alcohol-based biodiesel. Butanol has been used in concentrations of 5 and 10 % in diesel and therefore all blends are termed as B5 and B10 in addition to nanoparticle concentration to avoid complexity. These different blends (B5+30, B5+50, B5+70, B10+30, B10+50, B10+70) are tested for various engine loads at a constant speed of 1500 rpm. The experiment was performed on a Variable compression ratio (VCR) engine at varying compression ratios of 16, 17, and 18. Engine characteristics at different compositions of the blend at different compression ratios were provided by the interfaced computer through the software. The enhanced performance effects can be easily seen from the outcomes in the increment in brake thermal efficiency of the blends as compared to neat diesel. Considerable decrement can be observed in carbon monoxide (CO) and unburnt hydrocarbon (HC) values with an increase in compression ratio. Moderate reduction can be observed in NOx at higher loads in contrast to neat diesel.

Pitch synthesis with violin sound involves the generation of musical pitches using technology to mimic the distinctive tonal characteristics of a violin. This process typically employs digital signal processing techniques to recreate the timbre, articulation, and nuances of a real violin. Advanced algorithms analyze and model the acoustic properties of a violin sound, allowing for the synthesis of realistic pitch variations and expressive qualities. Whether utilized in electronic music production, virtual instruments, or sound design, pitch synthesis with violin sound aims to emulate the rich and complex sonic palette of the violin, offering musicians and composers versatile tools for creative expression and sonic exploration. In this paper proposed Fuzzy Pitch Clustering Machine Learning (FPC-ML) for the violin Music Pitch Synthesis using Machine Learning. The proposed FPC-ML model uses the Fuzzy Clustering model for the estimation of pitches in the violin music signal. Based on the Fuzzy clustering model membership degree is computed for the proposed FPC-ML for the estimation of the pitch in the violin music. With the estimation of linguistic variables, clustering is performed in the Music signal for the computation of pitches. With the estimated pitches in the violin music, the features are trained in the machine learning model for the classification and estimation of features in the Violin Music. Simulation analysis demonstrated that the proposed FPC-ML model computes the features of the Violin Music Pitch values based on the estimated clustering values synthesis performed for the classification of the Violin Music signal. The proposed FPC-ML technique achieves an accuracy value of 0.98 for the violin signal with an iteration of 20. With the increase in several iterations and epoch, the accuracy of the FPC-ML model is further increased for the synthesis of the Violin Music.

In present study, the ideal engine functioning condition for pollutants as well as functionality was determined using RSM. The L16 Orthogonal Array experiment table was designed using Minitab 16 software with Taguchi's design of experiments methodology with Three variables—fuel type, engine speed, and engine load, each of which was varied across four distinct levels. After a comprehensive model examination, the R2 and modified R2 values are close, indicating a low risk of including unimportant components. The model found that an engine load of 6.85 kgf, an engine speed of 2000 rpm, and the 20% OPB blended fuel (OPB20) would optimise BTE, EE, BSFC, and NO, HC, and CO emissions. The model's maximum desirability was 86.79%, indicating that the predicted optimum answers and experimental responses were similar. The utilisation of RSM optimisation in conjunction with OPB fuel has the potential to enhance engine performance and mitigate emissions.