Electricity supplier selection by a household in the Czech Republic in 2017 and 2018 – Monte Carlo simulation approach

The situation on the electricity retail market in the Czech Republic is not clear because of the number of suppliers and its products. Although the information about the prices for the electricity consumption for households is available on the web and each household can change the supplier nearly with no extra effort and cost, households are still often not familiar with the individual price items of the products. In this article the analysis of the Czech electricity market from the distribution rate D25d point of view is made for the years 2017-2018 when the household annual consumption is simulated via Monte Carlo simulation model. The aim of this paper is to select such a supplier and product that minimizes the total costs of the electricity for a household for the selected distribution rate and compare it with the results from the previous years.

Pollution routing problem with time window and split delivery

In most classic vehicle routing problems, the main goal is to minimise the total travel time or distance while, the green vehicle routing problem, in addition to the stated objectives, also focuses on minimising fuel costs and greenhouse gas emissions, including carbon dioxide emissions. In this research, a new approach in Pollution Routing Problem (PRP) is proposed to minimise the CO2 emission by investigating vehicle weight fill level in length of each route. The PRP with a homogeneous fleet of vehicles, time windows, considering the possibility of split delivery and constraint of minimum shipment weight that must be on the vehicle in each route is investigated simultaneously. The mathematical model is developed and implemented using a simulated annealing algorithm which is programmed in MATLAB software. The generated results from all experiments demonstrated that the application of the proposed mathematical model led to the reduction in CO2 emission.

Optimizing energy efficiency in distributed mHealth networks

Energy efficiency in mobile health applications is a relevant problem for long-term monitoring and user acceptance. Various parameters influence the runtime of the system to some degree. One of the parameters is the sampling rate of the individual distributed sensors. Increasing the sampling rate can lead to an increase in energy consumption within the system. By contrast, a reduction can lead to a loss of the data quality, which reduces the informative value of the results of algorithms that use this data. Using optimization methods from reinforcement learning and deep learning to adaptive adjust the sampling rates during runtime, energy efficiency could be improved in only 40 training runs without losing data quality during sampling.

Study about the rigor of tightness tests and the existance of an empirical constant to validate theoretical simulations to approximate the real model

"Due to the great risk of contamination by leaking in underground fuel storage tanks (UST) of gas stations all over the world, the establishment of effective monitoring methods in this environment is extremely necessary. Among UST monitoring methods the tightness test is one of the most effective ones in identifying leaks, it can be done in two different ways, either wet part test or dry part test. But while both of the tests are permitted, they show a great difference in rigorousness, when it comes to approving or not a tank. This study envisions to deeply explore the causes of the difference of rigorousness between both tests, and discover ways in which simulations can approach the real situation. The research allowed us to identify not only the cause of such difference in rigor, but also to establish a constant that approximates the theory to the real situation."

Modeling and analysis of the impact of texturing angles on doping profiles in ion implanted N-type solar cells

"The aim of this paper is to develop an accurate model to study the impact of texturing angles on doping profiles in ion implanted solar cells. This study will help designers and manufacturers choose an optimal angle in texturing the surfaces of innovative solar cells. Using an optimal texturing angle will improve the performance of solar cells. Randomly chosen texturing angles may decrease the absorption of the sun light or introduce excessive defects as clustering or channeling. These defects will represent recombination centers for active electrons and holes. This will contribute seriously to the loss of the active carriers and then to the loss of solar cell efficiency. This loss is known as a recombination loss. This loss alone may reduce the efficiency of a solar cell by 20%. Numerical results showing the effects of texturing angles on doping profiles will be presented, analyzed, and validated."