An electronic health record (EHR) is a vital high-dimensional part of medical concepts. Discovering implicit correlations in the information of this data set and the research and informative aspects can improve the treatment and management process. The challenge of concern is the data sources’ limitations in finding a stable model to relate medical concepts and use these existing connections. This paper presents Patient Forest, a novel end-to-end approach for learning patient representations from tree-structured data for readmission and mortality prediction tasks. By leveraging statistical features, the proposed model is able to provide an accurate and reliable classifier for predicting readmission and mortality. Experiments on MIMIC-III and eICU datasets demonstrate Patient Forest outperforms existing machine learning models, especially when the training data are limited. Additionally, a qualitative evaluation of Patient Forest is conducted by visualising the learnt representations in 2D space using the t-SNE, which further confirms the effectiveness of the proposed model in learning EHR representations.

Objectives: File fragment classification of audio file formats is a topic of interest in network forensics. There are a few publicly available datasets of files with audio formats. Therewith, there is no public dataset for file fragments of audio file formats. So, a big research challenge in file fragment classification of audio file formats is to compare the performance of the developed methods over the same datasets. Data description: In this study, we present a dataset that contains file fragments of 20 audio file formats: AMR, AMR-WB, AAC, AIFF, CVSD, FLAC, GSM-FR, iLBC, Microsoft ADPCM, MP3, PCM, WMA, A-Law, µ-Law, G.726, G.729, Microsoft GSM, OGG Vorbis, OPUS, and SPEEX. Corresponding to each format, the dataset contains the file fragments of audio files with different compression settings. For each pair of file format and compression setting, 210 file fragments are provided. Totally, the dataset contains 20,160 file fragments.

Process mining has proven effective in explaining the underlying processes of systems, thereby improving systems’ understanding, analysis, and operational efficiency. Process mining, however, often falls short in addressing multiple dimensions of systems’ behaviors, limiting its ability to provide comprehensive insights for systems’ performance and optimization opportunities. In this paper, we introduce an enhancement to conventional process mining that we term Multi-flow Process Mining (MFPM), which effectively extracts process flows across different system dimensions, such as time, energy, waste, and carbon footprint. MFPM enables a more comprehensive view of a system's dynamics, enabling holistic decision-making for enhanced system efficiency. We detail the framework of MFPM, outline corresponding data requirements, and introduce an expanded version of Petri nets—used here as a modeling formalism to describe and analyze multi-flow system processes. Through a detailed case study, we demonstrate the practical application of MFPM in capturing and analyzing multifaceted aspects of systems.

The rapid growth of the global economy and rising energy costs have underscored the need for efficient energy utilization across various sectors. To address this, we developed a novel data-informed digital shadow framework, enhancing energy management within a closed-loop control system, particularly in wastewater treatment facilities. Our approach centers on reducing biogas consumption in the fluidized bed reactor, a crucial component of the incineration process. The digital shadow simulates the entire incineration section by extracting model parameters for key components, including the furnace, dryer, mixer, and heat exchangers, from both physical models and operational data. System-level simulation was performed in AnyLogic software, integrating analytical calculations with data from positioning sensors. The model was informed by operational plant data and validated against historical system data. Evaluation of five operating scenarios demonstrated the capability of the digital shadow to capture system behavior and support operational decision-making. The evaluation of dynamic energy performance identified the most effective scenario, achieving a 40% reduction in biogas consumption while maintaining furnace operation within the temperature range specified by European standards. These findings demonstrate the effectiveness of the digital shadow approach and highlight its potential to deliver substantial annual cost savings in wastewater sludge incineration systems.