Data with censoring is common in many areas of science and the associated statistical models are generally estimated with the method of maximum likelihood combined with a model selection criterion such as Akaike’s information criterion. This manuscript demonstrates how the information theoretic minimum message length principle can be used to estimate statistical models in the presence of type I random and fixed censoring data. The exponential distribution with fixed and random censoring is used as an example to demonstrate the process where we observe that the minimum message length estimate of mean survival time has some advantages over the standard maximum likelihood estimate.

Routing in multidomain and multilayer networks is the subject of constant theoretical research, with special emphasis on routing optimization algorithms based on several criteria. Such research results in new proposals. The basic task of the algorithm is to perform the given task in a finite and reasonable period of time and with reasonable resource requirements. When new solutions are compared with previous solutions, it is necessary to consider as much information as possible about the characteristics and differences between these algorithms, which ultimately determines the degree of success of the algorithm. Routing algorithms depend on the goals to be achieved and most often solve a certain group of problems with certain simplifications of the overall problem and to the detriment of performance that are not crucial for a given routing optimization problem. Therefore, it is necessary to have acceptable methods for efficiency-complexity evaluation methods of routing algorithms with certain, universally applicable, metrics. Several theoretical approaches, including graph theory, optimization theory, complexity theory, allow approaches to compare the algorithms and the results achieved with the help of these algorithms.

This paper focuses on the problem of 5G network cell planning. In addition, it presents an example of a rough estimation of the required number of cells or base stations in a certain area for arbitrary number of users who are provided with a certain bandwidth per user within these cells. The cell number estimation is the initial step and the essence of planning and implementation of 5G network in an area. It is helpful for the operators to create and take into the account business plans in order to fully implement the network as a function of number of users which have to be served. Considering that, knowing the rough number of 5G base stations per user is very important for pre-sale activities and eventually necessity for widening of the initial investments. Therefore, the paper presents four scenarios that include different network parameters. Depending on the network parameters, the required number of base stations in a certain area changes. Given scenarios are examples of one 5G network in virtual area per bandwidth per user.

Cancer is the leading disease in the world by the increasing number of new patients and deaths every year. Hence, it is the most feared disease of our time. It is believed that lung cancer and breast cancer are most common types of cancer and they both are subtypes of the same group of cancer – carcinoma. With this type of cancer early detection is of great importance for patient survival. As it is the disease that has unfortunately been around for many years, today we have datasets with all necessary information for diagnosing and predicting cancer. Predicting cancer means deciding if the cancer is malignant or benign. The key to this answer lays in different values of parameters that have been stored when the disease was discovered. Machine learning plays the crucial role in predicting cancer, given the fact that algorithms such as Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and etc. are designed to find the pattern that occurs in large sets of data and based on that make a decision. In this paper, author's goal is to see how machine learning and its practical implementation on public datasets can help with early breast cancer diagnosis and hopefully help save more lives.

This paper presents a model push on which to determine the flow rates vehicles per share on the basis of circular intersections recorded entries and exits of vehicles in the aisles. In addition it is possible to analyze the influence of individual flows at intersections capacity, and determine the number of vehicles at the intersection of routes in the knowledge of the number of vehicles leaving the intersection at the next exit. Count the number of vehicles it is easier to manually and by using new technologies. Model (MIKR) results in a longer or shorter period of time which gives both static and dynamic characteristics.

The trend of continuous increase for mobility requirements results in a progressive increase in the use of vehicles of all modes of transport, which contributes to a significant increase in noise levels, especially in urban areas. The most significant noise in urban areas is traffic noise, where road traffic contributes the most. This paper reviews the treatment of road traffic noise in the European Union with a focus on Directive 2002/49 / EC. The paper presents the basics of the mechanisms of the institutional and management framework in the field of road traffic noise monitoring in EU countries. Also, the results of road traffic noise monitoring in EU countries, indicators of population exposure to road traffic noise, as well as indicators of the impact of road traffic noise on the health of the population, were presented.

Thanks to the development of more powerful computers and efficient numerical techniques, numerical modelling has become a compulsory tool in solving various problems in the field of energetic materials. In cases where measuring techniques are still unable to measure a given parameter, numerical modelling may be the only option of obtaining a value. In addition, numerical modelling helps us to better understand some phenomena, particularly in understanding the influence of input parameters on output results, as well as saving time and money. The thermochemical equilibrium code EXPLO5 is such a tool which enables theoretical prediction of performance of high explosives, propellants and pyrotechnic compositions. The code is used by more than 80 research laboratories worldwide.

Tomato (Solanum lycopersicum L.), family Solanaceae, has become in the past fifty years one of the most important and extensively grown horticultural crops in the Mediterranean region and throughout the world. In 2019, more than 180 million tonnes of tomato have been produced worldwide, out of which around 42 million tonnes in Mediterranean countries. Due to its genetic properties, tomato is afflicted by numerous plant diseases induced by fungal, bacterial, phytoplasma, virus, and viroid pathogens. Not only is its genetic inheritance of great importance to the management of the numerous tomato pathogens, but equally as important are also the present climate changes, the recently revised phytopathological control measures, and the globalization of the seed industry. Thus, the recognition of symptoms and the knowledge of the distribution and spread of the disease and of the methods for early detection of the pathogens are the major prerequisites for a successful management of the disease. In this review, we will describe the main tomato pathogens in the Mediterranean area that impact mostly the tomato yield and provide the current and perspective measures necessary for their successful management.

Background Sustainable energy transition of a country is complex and long-term process, which requires decision-making in all stages and at all levels, including a large number of different factors, with different causality. The main objective of this paper is the development of a probabilistic model for decision-making in sustainable energy transition in developing countries of SE Europe. The model will be developed according to the specificities of the countries for which it is intended—SE Europe. These are countries where energy transition is slower and more difficult due to many factors: high degree of uncertainty, low transparency, corruption, investment problems, insufficiently reliable data, lower level of economic development, high level of corruption and untrained human resources. All these factors are making decision-making more challenging and demanding. Methods Research was done by using content analysis, artificial intelligence methods, software development method and testing. The model was developed by using MSBNx— Microsoft Research’s Bayesian Network Authoring and Evaluation Tool . Results Due to the large number of insufficiently clear, but interdependent factors, the model is developed on the principle of probabilistic (Bayesian) networks of factors of interest. The paper presents the first model for supporting decision-making in the field of energy sustainability for the region of Southeastern Europe, which is based on the application of Bayesian Networks. Conclusion Testing of the developed model showed certain characteristics, discussed in paper. The application of developed model will make it possible to predict the short-term and long-term consequences that may occur during energy transition by varying these factors. Recommendations are given for further development of the model, based on Bayesian networks.

Even though water-related forest ecosystem services are important for forestry and water management sectors, they have different definitions and are regulated differently in each sector, which makes them poorly recognized. How stakeholders from two main sectors (forestry and water management) perceive the importance of water-related forest ecosystem services, the trade-offs between ecosystem services and the effectiveness and implementation of payments schemes related to forest water ecosystem services were our areas of interest. We have conduct surveys with different groups of stakeholders from both sectors in four selected countries (the Federation of Bosnia and Herzegovina, Croatia, Slovenia and Serbia) with a lot of similarities and the potential to learn from each other. The results show that in spite of the spotted differences among analyzed countries, there is a high level of agreement among respondents on all investigated aspects. In addition, even though different payment schemes exist in three of four countries, stakeholders are rarely aware of their existence, or it is better to say that they do not recognize them as payment schemes for ecosystem services because of their names and definitions, which do not clearly define ecosystem services. Mostly, they use bundled services and non-voluntary payments and are designed and implemented by the states. Due to the strong role of states and the low transparency in the existing schemes, we looked at possible conditions reflected through stakeholders’ opinions for overcoming that obstacle for the development of new payment schemes. We found that there is a high level of acceptance of payments schemes as more effective than “command and control” schemes and of the involvement of other stakeholders in decision-making processes as those conditions that can positively influence development of new payment schemes in all four countries. These results give us hope that in spite of the strong role of the state in selected countries, the role of stakeholders will be more acknowledged and, by that, the future schemes will be more harmonized among the sectors and their goals and needs, contributing to its effectiveness as well.

Introduction: Electromagnetic imaging is an emerging technology which promises to provide a mobile, and rapid neuroimaging modality for pre-hospital and bedside evaluation of stroke patients based on the dielectric properties of the tissue. It is now possible due to technological advancements in materials, antennae design and manufacture, rapid portable computing power and network analyses and development of processing algorithms for image reconstruction. The purpose of this report is to introduce images from a novel, portable electromagnetic scanner being trialed for bedside and mobile imaging of ischaemic and haemorrhagic stroke. Methods: A prospective convenience study enrolled patients (January 2020 to August 2020) with known stroke to have brain electromagnetic imaging, in addition to usual imaging and medical care. The images are obtained by processing signals from encircling transceiver antennae which emit and detect low energy signals in the microwave frequency spectrum between 0.5 and 2.0 GHz. The purpose of the study was to refine the imaging algorithms. Results: Examples are presented of haemorrhagic and ischaemic stroke and comparison is made with CT, perfusion and MRI T2 FAIR sequence images. Conclusion: Due to speed of imaging, size and mobility of the device and negligible environmental risks, development of electromagnetic scanning scanner provides a promising additional modality for mobile and bedside neuroimaging.

Null Hypothesis Significance Testing is a statistical procedure widely used in cognitive development research. There is widespread concern that the results of this statistical procedure are misinterpreted and lead to unsubstantiated claims about studies’ outcomes. Two particularly pertinent issues for research on cognitive development are: i) treating a non-significant result as evidence of no difference or no effect, and ii) treating a non-significant result in one group/condition and a significant result in another as evidence of a difference between groups/conditions. The current study focuses on quantifying the extent to which these two issues can be observed in the published literature on cognitive development. To this end, we will systematically search for empirical studies investigating cognitive development in 0-to-16-year-old children that have been published at two time points, namely in 1999 and 2019. For each of the two issues, we will extract information from 300 published articles, 150 per publication year.

Background Increasing urbanization as well as global warming requires an investigation of the influence of different construction methods and ground surfaces on the urban heat island effect (UHI effect). The extent of the influence of the urban structure, the building materials used and their surfaces on the UHI effect can be significantly reduced already in the planning phase using a designated OpenFOAM-based solver “uhiSolver”. Results In the first part of this research work, it is shown that inner building details and components can be neglected while still obtaining sufficiently accurate results. For this purpose, the building model was divided into two layers: a surface layer without mass, where the interaction with radiation takes place, and a component layer, which contains all relevant components and cavities of the building represented with mass-averaged material properties. It has become apparent that the three parameters—albedo, heat capacity and thermal resistance—which have a decisive influence on the interaction, have different effects on the component temperatures and the surface temperatures. In the second part of this research work, dynamic 3D computational fluid dynamics (CFD) simulations are performed with uhiSolver for a residential block in Vienna. Comparing the simulation results with measurement data collected on site, it is shown that the simplified assumption of homogeneous material data for building bodies provides very good results for the validation case investigated. However, the influence of the greening measures in the courtyard of the residential block on the air temperature is found to be negligible. Furthermore, it was observed that due to locally higher radiation density, lower air velocities and higher air humidity, the apparent temperature in the courtyard is sometimes perceived to be higher than in the adjacent streets, despite the lower air temperature. Conclusions Simplifying the modeling process of the uhiSolver software by reducing the model complexity helps to reduce manual work for setting up appropriate boundary conditions of buildings. Compared to market competitors, good results are obtained for the validation case Kandlgasse presented in this research work, despite the simplifications proposed. Thus, uhiSolver can be used as a robust analytical tool for urban planning.