Thermodynamic analysis of alternative marine fuels for marine gas turbine power plants

The marine shipping industry faces challenges to reduce engine exhaust emissions and greenhouse gases (GHGs) from ships, and in particular, carbon dioxide. International regulatory bodies such as the International Maritime Organization and National Environmental Agencies of many countries have issued rules and regulations to drastically reduce GHG and emissions emanating from marine sources. This study investigates the possibility of using natural gas and hydrogen as alternative fuels to diesel oil for marine gas turbines and uses a mathematical model to assess the effect of these alternative fuels on gas turbine thermodynamic performance. Results show that since natural gas is categorized as a hydrocarbon fuel, the thermodynamic performance of the gas turbine cycle using natural gas was close to that of the diesel case. However, the gas turbine thermal efficiency was found to be slightly lower for natural gas and hydrogen fuels compared to diesel fuel.     

Forecasting the Suez Canal traffic: a neural network analysis

Although the Suez Canal is the most important man-made waterway in the world, rivaled perhaps only by the Panama Canal, little research has been done into forecasting its traffic flows. This paper uses both univariate ARIMA (Autoregressive Integrated Moving Average) and Neural network models to forecast the maritime traffic flows in the Suez Canal which are expressed in tons. One of the important strengths of the ARIMA modelling approach is the ability to go beyond the basic univariate model by considering interventions, calendar variations, outliers, or other real aspects of typically observed time series. On the other hand, neural nets have received a great deal of attention over the past few years. They are being used in the areas of prediction and classification, areas where regression models and other related statistical techniques have traditionally been used. The models obtained in this paper provide useful insight into the behaviour of maritime traffic flows since the reopening of the Canal in 1975—following an 8-year closure during the Arab–Israeli wars (1967–1973)—till 1998. The paper also compares the performance of ARIMA models with that of neural networks on an example of a large monthly dataset.     

Trace element levels in some commercially valuable fish species from coastal waters of Mediterranean Sea, Egypt

Concentrations of 5 trace elements (Cd, Pb, Cu, Cr, Zn) were determined in muscle of fishes collected from two coastal areas of the Egyptian coast of the Mediterranean Sea west of Alexandria (El-Mex Bay and Eastern Harbour). Sardinella aurita, Alepes djedaba, Siganus luridus, Siganus rivulatus, Sphyraena chrysotoenia, and Scomberomorus commerson were collected from El-Mex Bay. While Boops boops, Lithognathus mormyrus, Sparisoma cretense, Serranus cabrilla and Synodus saurus were collected from the E.H. In all fishes zinc was highest (up to 57 mg/kg) followed by Cr, Cu, Pb and Cd. The concentrations of several metals in fishes were significantly different among the species in each area. For all trace element examined, the concentrations decreased significantly with body weight of some fishes. In contrast, a positive correlation with body weight was found especially for Zn and Cu concentrations in 5 fish species and for Pb in 4 fish species. The concentrations of Cd, Pb and Zn were higher in fishes from El-Mex Bay (3.76, 6.49 and 57.21 mg/kg) than those from Eastern Harbour, whereas the opposite trend was observed for Cu and Cr, revealing the direct sources of trace metal pollution present in El-Mex coastal area. Levels of Cr surpassed the Maximum Permissible Concentration in most fish tissues, followed by Pb and Cd in some species. Cu and Zn presented concentrations below the maximum permissible levels in fish tissues.     

Normal and risky driving patterns identification in clear and rainy weather on freeway segments using vehicle kinematics trajectories and time series cluster analysis

Enhancing traffic safety on freeways is the main goal for all transportation agencies. However, to achieve this goal, many analysis protocols of network screening models need to be improved through considering human factors while analyzing traffic data. This paper introduces one on the new analysis protocol of identifying and discriminating between normal and risky driving in clear and rainy weather. The introduced analysis protocol will consider the effect of human factors on updating the networking screening process of identifying hotspots of crash risk. This paper employs the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) data to investigate the behavior of normal and risky driving under both rainy and clear weather conditions. Near-crash events on freeways, which were used as Surrogate Measure of Safety (SMoS) for crash risk, were identified based on the changes in vehicle kinematics, including speed, longitudinal and lateral acceleration and deceleration rates, and yaw rates. Through a trajectory-level data analysis, there were significant differences in driving patterns between rainy and clear weather conditions; factors that affected crash risk mainly included driver reaction and response time, their evasive maneuvers such as changes in acceleration rates and yaw rates, and lane-changing maneuvers. A cluster analysis method was employed to classify driving patterns into two clusters: normal and risky driving condition patterns, respectively. Statistical results showed that risky driving patterns started on average one second earlier in rainy weather conditions than in clear weather conditions. Furthermore, risky driving patterns extended in average three seconds in rainy weather conditions, while it was two seconds in clear weather conditions. The identification of these patterns is considered as a primary step towards an automated development that would distinguish between different driving patterns in a Connected Vehicle CV environment using Basic Safety Messages (BSM) and to enhance the network screening analysis for increased crash risk hotspots.     

一种基于蚁群算法的聚类组合方法

蚂蚁等群居类昆虫被看作能解决复杂问题的分布式系统,研究者从它们的协作性能以及自组织、信息素通信、任务划分等机理中获得灵感,已在组合优化、通信网络、机器人等许多应用领域找到解决问题的新方法。聚类作为一种无监督的学习,能根据数据间的相似程度自动地进行分类。基于蚁群算法的聚类方法已经在当前数据挖掘研究中得到应用。本文提出的基于蚁群算法的聚类组合新方法,模仿多蚁群的协作性能,将运动速度类型各异的多个蚁群,独立而并行地进行聚类分析,然后组合其聚类结果为超图,再用蚁群算法对超图进行2次划分。实验结果表明,该方法能自动决定聚类的数目,聚类组合方法能明显改善聚类质量。     

Forecasting the Suez Canal traffic: a neural network analysis

Although the Suez Canal is the most important man-made waterway in the world, rivaled perhaps only by the Panama Canal, little research has been done into forecasting its traffic flows. This paper uses both univariate ARIMA (Autoregressive Integrated Moving Average) and Neural network models to forecast the maritime traffic flows in the Suez Canal which are expressed in tons. One of the important strengths of the ARIMA modelling approach is the ability to go beyond the basic univariate model by considering interventions, calendar variations, outliers, or other real aspects of typically observed time series. On the other hand, neural nets have received a great deal of attention over the past few years. They are being used in the areas of prediction and classification, areas where regression models and other related statistical techniques have traditionally been used. The models obtained in this paper provide useful insight into the behaviour of maritime traffic flows since the reopening of the Canal in 1975—following an 8-year closure during the Arab-Israeli wars (1967-1973)—till 1998. The paper also compares the performance of ARIMA models with that of neural networks on an example of a large monthly dataset.     

Wave Energy Potential Analysis in the Casablanca-Mohammedia Coastal Area (Morocco)

In the last two decades, the exploitation of marine renewable energies (70％of the globe is made up of oceans), especially wave energy, has attracted great inter...     

Modeling drivers' diversion from normal routes under ATIS using generalized estimating equations and binomial probit link function

Advanced Traveler Information Systems (ATIS) provide travelers with real time traffic information to optimize their travel choices. The objective of this paper is to model drivers' diversion from their normal routes in the provision of ATIS. Five different scenarios of traffic information are used. Generalized Estimating Equations (GEE) framework with repeated observations and binomial probit link function is introduced and implemented. GEE with four different correlation structures including the independent case are developed and compared with each other and with regular Maximum Likelihood Estimation (MLE). A travel simulator was used. Sixty-five subjects have traveled 10 simulated trial days each on a 40-link realistic network with real historical congestion levels. The results showed that providing traffic information increases the probability of drivers' diversion from their normal routes. Adding advice to the pre-trip and/or en-route information encourages drivers to divert. Providing en-route in addition to the pre-trip information with or without advice increases the diversion probability. High travel time on the normal route and less travel time on the diverted route increase the probability of diversion. High-educated drivers are less likely to divert. Expressway users are more likely to divert from their normal routes under ATIS. Drivers' familiarity with the device that provides the information and high number of traffic signals on the normal route increase the diversion probability.     

Modeling Travel Time Under ATIS Using Mixed Linear Models

The objective of this paper is to model travel time when drivers are equipped with pre-trip and/or en-route real-time traffic information/advice. A travel simulator with a realistic network and real historical congestion levels was used as a data collection tool. The network included 40 links and 25 nodes. This paper presents models of the origin-to-destination travel time and en-route short-term route (link) travel time under five different types and levels of advanced traveler information systems (ATIS). Mixed linear models with the repeated observation's technique were used in both models. Different covariance structures (including the independent case) were developed and compared. The effect of correlation was found significant in both models. The trip travel time analysis showed that as the level of information increases (adding en-route to the pre-trip and advice to the advice-free information), the average travel time decreases. The model estimates show that providing pre-trip and en-route traffic information with advice could result in significant savings in the overall travel time. The en-route short-term (link) travel time analysis showed that the en-route short-term (link) information has a good chance of being used and followed. The short-term qualitative information is more likely to be used than quantitative information. Learning and being familiar with the system that provides the information decreases en-route short-term delay.