Real-Time Management of Vessel Carbon Dioxide Emissions Based on Automatic Identification System Database Using Deep Learning

In this study, we propose an effective method using deep learning to strengthen real-time vessel carbon dioxide emission management. We propose a method to predict real-time carbon dioxide emissions of the vessel in three steps: (1) convert the trajectory data of the fixed time interval into a spatial–temporal sequence, (2) apply a long short-term memory (LSTM) model to predict the future trajectory and vessel status data of the vessel, and (3) predict the carbon dioxide emissions. Automatic identification system (AIS) database of a liquefied natural gas (LNG) vessel were selected as the sample and we reconstructed the trajectory data with a fixed time interval using cubic spline interpolation. Applying the interpolated AIS data, the carbon dioxide emissions of the vessel were calculated based on the International Towing Tank Conference (ITTC) recommended procedures. The experimental results are twofold. First, it reveals that vessel emissions are currently underestimated. This study clearly indicates that the actual carbon dioxide emissions are higher than those reported. The finding offers insight into how to accurately measure the emissions of vessels, and hence, better execute a greenhouse gases (GHGs) reduction strategy. Second, the LSTM model has a better trajectory prediction performance than the recurrent neural network (RNN) model. The errors of the trajectory endpoint and carbon dioxide emissions were small, which shows that the LSTM model is suitable for spatial–temporal data prediction with excellent performance. Therefore, this study offers insights to strengthen the real-time management and control of vessel greenhouse gas emissions and handle those in a more efficient way.

Recovery Efficiency of an Aquifer Storage and Recovery (ASR) Experiment from Saline Aquifer under Controlled Conditions

Present work was carried out in an experimental model developed at the institute, sand was used as prototype artificial aquifer and was saturated with highly saline water having Electrical Conductivity (EC) equal to 8500 µS/cm. Fresh water with average EC = 467.50 µS/cm and temperature = 25oC was injected in the known amount in the saline water and this water was extracted at a fixed time interval of 1, 1.5, 2, 2.5, 3, 3.5, 4, 4, 8 24, 48, 72, 96, 120, 144, and 168 hours in a cumulative time of 735.30 hours with average recovery efficiency of 63%. Recovered water has salinity equal to or less than 1000 µS/cm. Although, this experiment was carried out at a small scale but this can be tried at a bigger scale for skillfully managing the surface or reclaimed water in problematic areas where demand exceeds the supply.

On the numbers of infected and deceased in the second Corona wave

In Germany and other countries, a second wave of corona infections has been observed since July 2020, after the first wave has subsided. We have investigated both waves by a modified SIR-SI infection model, adapted to the data to the Robert-Koch-Institute (RKI) or the Johns- Hopkins-University (JHU). The first wave is characterized by the SIR model: in a perfect lockdown only a small part of the society is infected and the infections end after a certain time. The SI part considers the incompleteness of any lockdown: at the end of the first wave infections do not completely go down to zero, but continue to rise again, but only slowly due to mouth protection, hygiene and distance keeping. During this first wave the number of deceased people follows the number of infected persons with a fixed time interval and percentage: mostly symptomatic ill people have been tested. This applied to nearly all countries observed, with different intervals and percentages. In the present second wave, the number of daily infections has risen again significantly in some countries, and it may be questioned whether this is due to the increased number of tests. The answer may be given by looking at the daily number of deaths. In Germany, Austria, Italy, Great Britain and others this number has still remained at a constant level for six weeks. In these countries a second wave of died people has not yet arrived. The increased number of tests include obviously mostly asymptomatically infected persons, who do not fall ill or die from coronavirus. However, in some countries, like USA or Israel, the second wave did arrive. The numbers of infected and deceased people both have grown. A real second wave is a permanent threat to all countries.

Algorithm to Determine the Target State of a System and the Best Path to It

In the planning and management they usually decide how to move some object from the state in which it is in a fixed time interval (given, start, or initial state) to another state in a future time interval (desired, target, or planned state). The initial state of the object is known, definite unequivocally and exists. Future states can be many, and they exist only in the form of images, visions and ideas of the plan developers or persons who order the plan. It is assumed that the transition from the initial state to the desired one is possible. There are many possible ways of transition. The task is to choose the best, according to some criterion, a sequence of transition. The algorithm for determining the sequence of transfers of some object from a given state to the desired one I presented in this paper. The algorithm takes into account the presence of different possible transitions from one state to another one and shows a point-multiple mapping of the initial state of an object in the set of its desired states. The sequence of transfers, in which the total expected gain from changing the state of the object in a given period reaches its extreme — maximum or minimum, is found in the process of comparing different variants of transferring this object from one state to another. An example of finding the trajectory of transferring the object from a given state to one of its possible desired states, on which the maximum total expected result is achieved, I gave in this article.

Continuous limits of linear and nonlinear quantum walks

In this paper, we consider the continuous limit of a nonlinear quantum walk (NLQW) that incorporates a linear quantum walk as a special case. In particular, we rigorously prove that the walker (solution) of the NLQW on a lattice [Formula: see text] uniformly converges (in Sobolev space [Formula: see text]) to the solution to a nonlinear Dirac equation (NLD) on a fixed time interval as [Formula: see text]. Here, to compare the walker defined on [Formula: see text] and the solution to the NLD defined on [Formula: see text], we use Shannon interpolation.

Disaggregation of fixed time interval rainfall to continuous measured rainfall for the purpose of design rainfall estimation

Design rainfall estimates are primarily used in single-event deterministic design flood estimation methods where estimates of the peak discharge are based on the critical storm duration or time of concentration (TC) of a catchment. Therefore, daily design rainfall depths used in flood estimations must either be decreased or increased from durations less than or longer than 24 hours to the design rainfall depths for a rainfall duration of TC. This paper presents the comparison of two South African methods used to convert or scale 1-day fixed time interval observed rainfall (08:00 to 08:00) to continuous measures of n-hour rainfall for selected TC durations at a quaternary catchment level, in the C5 secondary drainage region in South Africa as pilot case study. In each quaternary catchment, the annual maximum series (AMS) of the 1-day fixed time interval point rainfall were extracted, infilled, converted and scaled to appropriate continuous measures of TC-hour point rainfall using conversion factors (Adamson, 1981) and scaling factors (Smithers and Schulze, 2003), respectively. Thereafter, all the TC-hour observed point rainfall values were averaged to observed catchment rainfall at a quaternary catchment level using the Thiessen polygon method. In using the two methods to estimate continuous short-duration n-hour (TC ≤ 24 hours) and long-duration n-hour (TC > 24 hours) catchment rainfall from 1-day fixed time interval point rainfall, an acceptable (0.71 < r2 ≤ 0.86) and high (r2 ≥ 0.93) degree of association were achieved, respectively, despite the different approaches used in each method. Overall, the results confirmed that fixed time interval rainfall should be scaled to continuous measures of rainfall using the Smithers-Schulze scale invariance approach for various TC durations in the case study area. In comparison to the Adamson conversion methodology, the Smithers-Schulze scaling methodology is also based on a more extensive and recent rainfall database as incorporated in software for design rainfall estimation in modern flood hydrology practice in South Africa.