During 2017, the authors conducted an evaluation of so-called “veteran trees” in Warsaw — selected specimens with outstanding historical, landscape and natural values, but not taken under legal protection, situated within public space (streets, squares, public facilities) — as part of a pilot program for the Green Board of Warsaw. The following were examined: the condition of trees, the surrounding ground’s condition, spatial conditions. The presented results include an example of two locations — prestigious streets, on which legible systems of street tree plantings from the beginning of the 20th century have been preserved (Piękna Street, J. Ch. Szucha Avenue). The protection of old trees — living witnesses of history and the maintenance of the original spatial form — classic avenue arrangement in both cases are essential for preservation of the historical pre-war scenery of this part of Warsaw.

Professor Krzysztof Spalik, Chairman of the PAS Committee for Environmental and Evolutionary Biology, tells us why the Białowieża Forest should be allowed to renew itself on its own.

Five models and methodology are discussed in this paper for constructing classifiers capable of recognizing in real time the type of fuel injected into a diesel engine cylinder to accuracy acceptable in practical technical applications. Experimental research was carried out on the dynamic engine test facility. The signal of in-cylinder and in-injection line pressure in an internal combustion engine powered by mineral fuel, biodiesel or blends of these two fuel types was evaluated using the vibro-acoustic method. Computational intelligence methods such as classification trees, particle swarm optimization and random forest were applied.

The article discusses the development of an approximation model of selected plastic and mechanical properties obtained from compression tests of model materials used in physical modeling. The use of physical modeling with the use of soft model materials such as a synthetic wax branch with various modifiers is a popular tool used as an alternative or verification of numerical modeling of bulk metal forming processes. In order to develop an algorithm to facilitate the choice of material model to simulate the behavior of real-metallic materials used in industrial production processes the induction of decision trees was used. First of all, the Statistica program was used for data mining, which made it possible to determine / find the relationship between the percentage of particular constituents of the model material (base material and modifiers) and yield strength, critical and maximum strain, and provide the opportunity to indicate the most important variables determining the shape of the stress – strain curve. Next, using the induction of decision trees, an approximation model was developed, which allowed to create an algorithm facilitating the selection of individual modifying components. The last stage of the research was verification of the correctness of the developed algorithm. The obtained research results indicate the possibility of using decision tree induction to approximate selected properties of modeling materials simulating the behavior of real materials, thus eliminating the need for costly and time-consuming experiments carried out on metallic material.

Fault Tree is one of the traditional and conventional approaches used in fault diagnosis. By

identifying combinations of faults in a logical framework it’s possible to define the structure

of the fault tree. The same go with Bayesian networks, but the difference of these probabilistic

tools is in their ability to reasoning under uncertainty. Some typical constraints to the

fault diagnosis have been eliminated by the conversion to a Bayesian network. This paper

shows that information processing has become simple and easy through the use of Bayesian

networks. The study presented showed that updating knowledge and exploiting new knowledge

does not complicate calculations. The contribution is the structural approach of faults

diagnosis of turbo compressor qualitatively and quantitatively, the most likely faults are

defined in descending order. The approach presented in this paper has been successfully

applied to turbo compressor, which represent vital equipment in petrochemical plant.

This paper describes the use of new methods of detecting faults in medium-voltage overhead lines built of covered conductors. The methods mainly address such faults as falling of a conductor, contacting a conductor with a tree branch, or falling a tree branch across three phases of a medium-voltage conductor. These faults cannot be detected by current digital relay protection systems. Therefore, a new system that can detect the above mentioned faults was developed. After having tested its operation, the system has already been implemented to protect mediumvoltage overhead lines built of covered conductors.

The application of churn prevention represents an important step for mobile communication

companies aiming at increasing customer loyalty. In a machine learning perspective,

Customer Value Management departments require automated methods and processes to

create marketing campaigns able to identify the most appropriate churn prevention approach.

Moving towards a big data-driven environment, a deeper understanding of data

provided by churn processes and client operations is needed. In this context, a procedure

aiming at reducing the number of churners by planning a customized marketing campaign

is deployed through a data-driven approach. Decision Tree methodology is applied to drow

up a list of clients with churn propensity: in this way, customer analysis is detailed, as well

as the development of a marketing campaign, integrating the individual churn model with

viral churn perspective. The first step of the proposed procedure requires the evaluation of

churn probability for each customer, based on the influence of his social links. Then, the

customer profiling is performed considering (a) individual variables, (b) variables describing

customer-company interactions, (c) external variables. The main contribution of this work

is the development of a versatile procedure for viral churn prevention, applying Decision

Tree techniques in the telecommunication sector, and integrating a direct campaign from

the Customer Value Management marketing department to each customer with significant

churn risk. A case study of a mobile communication company is also presented to explain

the proposed procedure, as well as to analyze its real performance and results.

Treating a sick forest: evidence-based medicine?

Construction planning always requires labour productivity estimation. Often, in the case of monolithic construction works, the available catalogues of productivity rates do not provide a reliable assessment. The paper deals with the problem of labour estimation for reinforcement works. An appropriate model of labour prediction problem is being introduced. It includes, between others, staff experience and reinforcement buildability. In the paper it is proposed, that labour requirements can be estimated with aggregated classifiers. The work is a continuation of earlier studies, in which the possibility of using classifier ensembles to predict productivity in monolithic works was investigated.

Work safety control and analysis of accidents during the construction performance are some of the most important issues of the construction management. The paper focuses on the post-accident absence as an element of the occupational safety management. The occurrence of the post-accident absence of workers can be then treated as an indicator of building performance safety. The ability to estimate its length can also facilitate works planning and scheduling in case of the accident. The paper attempts to answer the question whether it is possible and how to use decision trees and their ensembles to predict the severity of the post-accident absence and which classification algorithm is the most promising to solve the prediction problem. The paper clarifies the model of the prediction problem, introduces 5 different decision tress and different aggregation algorithms in order to build the model. Thanks to the use of aggregation methods it is possible to build classifiers that predict precisely and do not require any initial data treatment, which simplifies the prediction process significantly. To identify the most promising classifier or classifier ensemble the prediction accuracy measures of selected classification algorithms were analyzed. The data to build the model was gathered on national (Polish) construction sites and was taken from literature. Models obtained within simulations can be used to build advisory or safety management systems allowing to detect threats while construction works are being planned or carried out.

Fruit tree orchards were present in some public parks from the very beginning of their existence in the 19th century. Apart from the utilitarian role, in the 20th and 21st centuries, they also gained different ones: ornamental — on account of high aesthetic qualities of fruit trees in the flowering and fruit-bearing seasons, environmental and ecological — related to supporting biodiversity, cultural — in the context of memory of old forms of using rural and allotment gardens, social — as a space for leisure, and even therapeutic — as an element of hortitherapy. The growing popularity of orchards indicates a change in the trends in contemporary public parks development.

Despite the considerable progress that has recently been made in medicine, the treatment of viral infections is still a problem remaining to be solved. This especially concerns infections caused by newly emerging patogenes such as: human immunodeficiency virus, hepatitis C virus or SARS-coronavirus. There are several lines of evidence that the unusual genetic polymorphism of these viruses is responsible for the observed therapeutic difficulties. In order to determine whether some parameters describing a very complex and variable viral population can be used as prognostic factors during antiviral treatment computational methods were applied. To this end, the structure of the viral population and virus evolution in the organisms of two patients suffering from chronic hepatitis C were analyzed. Here we demonstrated that phylogenetic trees and Hamming distances best reflect the differences between virus populations present in the organisms of patients who responded positively and negatively to the applied therapy. Interestingly, the obtained results suggest that based on the elaborated method of virus population analysis one can predict the final outcome of the treatment even before it has started.