The aim of the paper is to propose a new approach to forecast the energy consumption for the next day using the unique data obtained from a digital twin model of a building. In the research, we tested which of the chosen forecasting methods and which set of input data gave the best results. We tested naive methods, linear regression, LSTM and the Prophet method. We found that the Prophet model using information about the total energy consumption and real data about the energy consumption of the top 10 energy-consuming devices gave the best forecast of energy consumption for the following day. In this paper, we also presented a methodology of using decision trees and a unique set of conditional attributes to understand the errors made by the forecast model. This methodology was also proposed to reduce the number of monitored devices. The research that is described in this article was carried out in the context of a project that deals with the development of a digital twin model of a building.

Patient multi-omics datasets are often characterized by a high dimensionality; however, usually only a small fraction of the features is informative, that is change in their value is directly related to the disease outcome or patient survival. In medical sciences, in addition to a robust feature selection procedure, the ability to discover human-readable patterns in the analyzed data is also desirable. To address this need, we created MAINE—Multi-omics Analysis and Exploration. The unique functionality of MAINE is the ability to discover multidimensional dependencies between the selected multi-omics features and event outcome prediction as well as patient survival probability. Learned patterns are visualized in the form of interpretable decision/survival trees and rules. MAINE is freely available at maine.ibemag.pl as an online web application. Supplementary data are available at Bioinformatics online.

New diseases constantly endanger the lives of populations, and, nowadays, they can spread easily and constitute a global threat. The COVID-19 pandemic has shown that the fight against a new disease may be difficult, especially at the initial stage of the epidemic, when medical knowledge is not complete and the symptoms are ambiguous. The use of machine learning tools can help to filter out those sick patients who do not need to be tested for spreading the pathogen, especially in the event of an overwhelming increase in disease transmission. This work presents a screening support system that can precisely identify patients who do not carry the disease. The decision of the system is made on the basis of patient survey data that are easy to collect. A case study on a data set of symptomatic COVID-19 patients shows that the system can be effective in the initial phase of the epidemic. The case study presents an analysis of two classifiers that were tuned to achieve an assumed acceptable threshold of negative predictive values during classification. Additionally, an explanation of the obtained classification models is presented. The explanation enables the users to understand the basis of the decision made by the model. The obtained classification models provide the basis for the DECODE service, which could serve as support in screening patients with COVID-19 disease at the initial stage of the pandemic. Moreover, the data set constituting the basis for the analyses performed is made available to the research community. This data set, consisting of more than 3000 examples, is based on questionnaires collected at a hospital in Poland.

Currently, efficiency in the supply domain and the ability to make quick and accurate decisions and to assess risk properly play a crucial role. The role of a decision support system (DSS) is to support the decision-making process in the enterprise, and for this, it is yet not enough to have up-to-date data; reliable predictions are necessary. Each application area has its own specificity, and so far, no dedicated DSS for liquefied petroleum gas (LPG) supply has been presented. This study presents a decision support system dedicated to support the LPG supply process from the perspective of gas demand analysis. This perspective includes a short- and medium-term gas demand prediction, as well as the definition and monitoring of key performance indicators. The analysis performed within the system is based exclusively on the collected sensory data; no data from any external enterprise resource planning (ERP) systems are used. Examples of forecasts and KPIs presented in the study show what kind of analysis can be implemented in the proposed system and prove its usefulness. This study, showing the overall workflow and the results for the use cases, which outperform the typical trivial approaches, could be a valuable direction for future works in the field of LPG and other fuel supply.

Efforts of the scientific community led to the development of multiple screening approaches for COVID-19 that rely on machine learning methods. However, there is a lack of works showing how to tune the classification models used for such a task and what the tuning effect is in terms of various classification quality measures. Understanding the impact of classifier tuning on the results obtained will allow the users to apply the provided tools consciously. Therefore, using a given screening test they will be able to choose the threshold value characterising the classifier that gives, for example, an acceptable balance between sensitivity and specificity. The presented work introduces the optimisation approach and the resulting classifiers obtained for various quality threshold assumptions. As a result of the research, an online service was created that makes the obtained models available and enables the verification of various solutions for different threshold values on new data.