The coming into force of the regulations of the ISPS code was cause for significant hope. The new safety system based on the ISPS code created an international framework for cooperation aimed at detecting threats and undertaking appropriate preventative measures within the scope of protecting vessels, ports, and port facilities against potential terrorist attacks. Additionally, the ISPS code should have ensured the effective and rapid flow of all information regarding threats. During the implementation of the new safety system, many legal and practical problems arose which rendered the effectiveness of the system highly doubtful. The continuing inflation of regulations and the bureaucracy of the entire system will lead to the conclusion that the regulations of the ISPS code are de facto a system of 'paper safety' the implementation and maintenance of which are costly and incommensurable to the benefits of applying the new safety standards.
The purpose of the work was to predict the selected product parameters of the dry separation process using a pneumatic sorter. From the perspective of application of coal for energy purposes, determination of process parameters of the output as: ash content, moisture content, sulfur content, calorific value is essential. Prediction was carried out using chosen machine learning algorithms that proved to be effective in forecasting output of various technological processes in which the relationships between process parameters are non-linear. The source of data used in the work were experiments of dry separation of coal samples. Multiple linear regression was used as the baseline predictive technique. The results showed that in the case of predicting moisture and sulfur content this technique was sufficient. The more complex machine learning algorithms like support vector machine (SVM) and multilayer perceptron neural network (MPL) were used and analyzed in the case of ash content and calorific value. In addition, k-means clustering technique was applied. The role of cluster analysis was to obtain additional information about coal samples used as feed material. The combination of techniques such as multilayer perceptron neural network (MPL) or support vector machine (SVM) with k-means allowed for the development of a hybrid algorithm. This approach has significantly increased the effectiveness of the predictive models and proved to be a useful tool in the modeling of the coal enrichment process.
The article raised issues related to the design and execution of low-energy objects in Polish conditions. Based on the designed single-family house, adapted to the requirements of the National Fund for Environmental Protection and Water Management ("NF40" standard), the tools to assist investment decisions by investors were shown. An economic analysis and a multi-criteria analysis were performed using AHP method which had provided an answer to the question whether it is worthwhile to bear higher investment costs in order to adjust to the standards of energy-efficient buildings that fulfil a minimal energy consumption's requirements contained in Polish law. In addition, the variant of object that had optimal characteristics due to the different preferences of investors was indicated. This paper includes analysis and observations on the attempts to unify that part of the building sector, which so far is considered to be personalized, and objects in accordance with the corresponding idea are designed as "custom-made".