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In marine vessel operations, fuel costs are major operating costs which affect the overall profitability of the maritime transport industry. The effective enhancement of using ship fuel will increase ship operation efficiency. Since ship fuel consumption depends on different factors, such as weather, cruising condition, cargo load, and engine condition, it is difficult to assess the fuel consumption pattern for various types of ships. Most traditional statistical methods do not consider these factors when predicting marine vessel fuel consumption. With technological development, different statistical models have been developed for estimating fuel consumption patterns based on ship data. Artificial Neural Networks (ANN) are some of the most effective artificial methods for modelling and validating marine vessel fuel consumption. The application of ANN in maritime transport improves the accuracy of the regression models developed for analysing interactive relationships between various factors. The present review sheds light on consolidating the works carried out in predicting ship fuel consumption using ANN, with an emphasis on topics such as ANN structure, application and prediction algorithms. Future research directions are also proposed and the present review can be a benchmark for mathematical modelling of ship fuel consumption using ANN.

Due to recent emission-associated regulations imposed on marine fuel, ship owners have been forced to seek alternate fuels, in order to meet the new limits. The aim of achieving low-carbon shipping by the year 2050, has meant that alternative marine fuels, as well as various technological and operational initiatives, need to be taken into account. This article evaluates and examines recent clean fuels and novel clean technologies for vessels. The alternative fuels are classified as low-carbon fuels, carbon-free fuels, and carbon neutral fuels, based on their properties. Fuel properties, the status of technological development, and existing challenges are also summarised in this paper. Furthermore, researchers have also investigated energy-saving devices and discovered that zero-carbon and virtually zero-carbon clean fuels, together with clean production, might play an important part in shipping, despite the commercial impracticability of existing costs and infrastructure. More interestingly, the transition to marine fuel is known to be a lengthy process; thus, early consensus-building, as well as action-adoption, in the maritime community is critical for meeting the expectations and aims of sustainable marine transportation.

Pressure on alternative fuels and strict environmental regulations are driving a strategic shift in the efficient use of renewable biofuels. One of the promising biofuel candidates recently interested by scholars is a biological or organic additive that is added into diesel or biodiesel fuel to improve engine performance and reduce pollutant emissions. With efforts to improve efficiency and combustion quality in cylinders, combustion characteristics, flame structure and emission formation mechanism in compression ignition (CI) engines using blended fuel with organic additives have been studied on the effect of additive properties on the combustion behaviour. In this review, the physicochemical properties of typical organic additives such as ethers compounds and their effects on engine performance and emission characteristics have been discussed and evaluated based on conclusions of recent relevant literature. The results of the analysis revealed the prospect of using ether additives to improve combustion in cylinders and reduce pollutant emissions from CI engines. Obviously, the presence of higher oxygen content, lower viscosity and density, and higher cetane number resulted in a positive change in the combustion dynamics as well as a chain of mechanisms for the formation of pollutant precursors in the cylinder. Therefore, ether additives have a significant contribution to the sustainable energy strategy of the transportation sector in the next period when internal combustion engines still dominate in the competition for energy system choices equipped on vehicles.

The thermal performance of a flat plate solar collector using MWCNT + Fe3O4/Water hybrid nanofluids was examined in this research. The flat plate solar collector was tested using different nanofluid concentrations and flow rates in an arid environment. A significant enhancement in coefficient of heat transfer (26.3%) with a marginal loss on pressure drop due to friction factor (18.9%). The data collected during experimental testing was utilized to develop novel prediction models for efficient heat transfer, Nusselt's number, friction factor, and thermal efficiency. The modern ensemble machine learning techniques Boosted Regression Tree (BRT) and Extreme Gradient Boosting (XGBoost) were used to develop prognostic models for each parameter. A battery of statistical methods and Taylor’s graphs were used to compare the performance of these two modern ML techniques. The value of R2 for the BRT-based prediction models were 0.9619 - 0.9994 and 0.9914 - 0.9997 for XGBoost-based models. The mean squared error was quite low for all the models (0.000081 - 9.11), while the mean absolute percentage error was negligible from 0.0025 to 0.3114. The comprehensive statistical analysis of the prognostic model was complemented with Taylor’s graphs to develop an improved comparison paradigm, to reveal the superiority of XGBoost over BRT.

This paper aims to develop a genetic algorithm to adjust an fuzzy controller for Vessels' Dynamic Positioning System (Vessels' DPs). It is well-known that nonlinearities affecting the control accuracy of the DPs are related to the arrangement of different types of thrusters in the vessels, such as azimuth thrusters (electric, L-drive, and Z-drive), bow thrusters, stern thrusters, water jets, and propulsion propellers with rudders. Compared with the traditional fuzzy control methods, the proposed Robust Adaptive Fuzzy Control using Genetic Algorithm (RAFC-GA) not only overcomes the influence of nonlinearities in the DPs, but also eliminates the impact of parameter uncertainties. Therefore, the tracking performance is excellent and robustness is maintained. The RAFC-GA control method is superior to the conventional fuzzy control methods in the two following aspects: 1) to find the optimal values for the fuzzy structure parameters to satisfy the robust condition under the effect of disturbances and nonlinearities in the DPs without weakening the output tracking performance and robustness, 2) to improve the quality of the system by optimizing values for the fuzzy structure using genetic algorithm which dynamically adjusted the coverage domain width and the overlap degree of membership functions. Simulation results of the RAFC-GA are evaluated in comparison with other methods. The RAFC-GA performs the desired transient response of DPs better than others in three case studies, which proves the effectiveness of the proposed solution.

Biodiesel, an environmentally-friendly bio-fuel, has been regarded as one of the most promising alternatives to fossil fuels whose use is rampant in the transportation sector. However, it is important that the corrosive effects of this fuel on engines are studied. This work reviews the corrosiveness that biodiesel exerts on various engine components, especially those made out of metals. First, an analysis of the corrosion mechanisms of metals exposed to biodiesel is provided. The conventional and advanced analysis methods will be applied to measure the level of corrosiveness in static immersion test, and to assess the formation of secondary products, if any, in biodiesel and any metal strips in contact with biodiesel-based fuel. The use of inhibitions to guard against corrosion will be mentioned. Lastly, several significant causes of metal corrosion, namely, the presence of dissolved oxygen and oxidation products, TAN change, a rise in dissolved water, the presence of metals, and the changes in biodiesel properties will also be presented. ©2019. CBIORE-IJRED. All rights reserved

Passenger satisfaction was considered as one of the most significant and primary factors in any service and industry field because of the direct relationship with passenger’s retention. According to recent study and estimates, the market share of urban public transport for buses was 41.3%, taxis were 30.6%, and contract car like Grab, Uber was 28.1%. This study work concentrates on the in-Ho Chi Minh city - people satisfaction with the public transportation system. A survey was set-up to explore the satisfaction for bus passengers. The results show that, most people living in Ho Chi Minh city satisfy with the Service and Price factor but not satisfy with the Contact factor. The paper results will help planners and decision makers to utilize this study findings aiming at not only focusing on the important attributes but also finding the disadvantages in urban transportation to improving the service quality. Besides, maintaining existing passengers and attracting new passengers, people satisfaction is the important rules to evaluate the success in public transport. Moreover, the study results will orientate to develop the city more sustainable through reduce in private cars.

Abstract High‐throughput sequencing has the potential to describe biological communities with high efficiency yet comprehensive assessment of diversity with species‐level resolution remains one of the most challenging aspects of metabarcoding studies. We investigated the utility of curated ribosomal and mitochondrial nematode reference sequence databases for determining phylum‐specific species‐level clustering thresholds. We compiled 438 ribosomal and 290 mitochondrial sequences which identified 99% and 94% as the species delineation clustering threshold, respectively. These thresholds were evaluated in HTS data from mock communities containing 39 nematode species as well as environmental samples from Vietnam. We compared the taxonomic description of the mocks generated by two read‐merging and two clustering algorithms and the cluster‐free Dada2 pipeline. Taxonomic assignment with the RDP classifier was assessed under different training sets. Our results showed that 36/39 mock nematode species were identified across the molecular markers (18S: 32, JB2: 19, JB3: 21) in UClust_ref OTUs at their respective clustering thresholds, outperforming UParse_denovo and the commonly used 97% similarity. Dada2 generated the most realistic number of ASVs (18S: 83, JB2: 75, JB3: 82), collectively identifying 30/39 mock species. The ribosomal marker outperformed the mitochondrial markers in terms of species and genus‐level detections for both OTUs and ASVs. The number of taxonomic assignments of OTUs/ASVs was highest when the smallest reference database containing only nematode sequences was used and when sequences were truncated to the respective amplicon length. Overall, OTUs generated more species‐level detections, which were, however, associated with higher error rates compared to ASVs. Genus‐level assignments using ASVs exhibited higher accuracy and lower error rates compared to species‐level assignments, suggesting that this is the most reliable pipeline for rapid assessment of alpha diversity from environmental samples.

Vietnamese Callisia fragrans (C. fragrans) has been considered as a valuable traditional plant with various medicinal properties. In this study, polyphenols were extracted from Vietnamese C. fragrans leaves by supercritical carbon dioxide (SC-CO2) extraction method using ethanol as a cosolvent. The investigation of four factors influencing the total polyphenol content (TPC) and antioxidant activity of the extracts obtained from each single-factor experiment was conducted including ethanol concentration, CO2 flow rate, extraction temperature, and pressure. Besides, the extraction efficiency of the SC-CO2 method under the best extraction conditions, namely ethanol concentration of 14%, CO2 flow rate of 20 g/min, extraction temperature of 45°C, and pressure of 200 bar was compared to that of the Soxhlet extraction (SE) method in terms of the TPC and antioxidant activity of the extracts. The results showed that using SC-CO2 method, the TPC and the half-maximal inhibitory concentration value obtained were of 87.42 ± 1.33 mg/g and 243.83 ± 5.30 μM TE/g, respectively, with much less time and solvent amount required while that obtained using SE method were of 85.34 ± 4.27 mg/g and 236.33 ± 7.66 μM TE/g, respectively. This indicated that SC-CO2 would be suitable for the industrial production of polyphenols with high antioxidant activity of the extracts obtained due to the restrictions of using the SE method and advantages of applying SC-CO2 method. Therefore, SC-CO2 method could be regarded as a potentially upcoming extraction technique which might be employed to replace the conventional SE method.