Predictive maintenance (PdM) uses statistical and machine learning methods to detect and predict the onset of faults. PdM is often used in industrial IoT settings in the energy sector, where research works usually consider specific types of faults depending on the application. However, since PdM is mainly data-driven and needs to work in real time, the public availability of datasets is required in order to build efficient and effective models applicable across multiple domains. Unlike methods, the available datasets obtained from sensors in the energy sector have not been properly reviewed or categorized. In this work, we consider five subsectors of the energy sector: wind, solar, oil & gas, diesel & thermal, and electrical power grid. We provide a detailed description of the properties of the publicly available PdM datasets in these subsectors. The review of the available datasets is conducted on a number of scientific and commercial repositories: IEEE DataPort, UCI Machine Learning Repository, Kaggle, EDP, and Mendeley Data. The datasets are graded into three categories according to objective criteria. We also provide references to significant related research work that uses the considered datasets. The observed challenges in using the datasets in this field are thoroughly discussed. We find that there is a troublesome scarcity of publicly available datasets in the energy sector, more so of data coming from real, non-simulated sources. Three datasets, 3W (oil & gas), EDP-WT (wind), and OREC (wind) stand out as highly valuable for researchers in this field.

Major depressive disorder (MDD) is associated with substantial morbidity and mortality, yet its diagnosis and treatment rates remain low due to its diverse and often overlapping clinical manifestations. In this context, electroencephalography (EEG) has gained attention as a potential objective tool for diagnosing depression. This study aimed to evaluate the effectiveness of EEG in identifying MDD by analyzing 140 EEG recordings from patients diagnosed with depression and healthy volunteers. Using various machine learning (ML) classification models, we achieved up to 80% accuracy in distinguishing individuals with MDD from healthy controls. Despite its promise, this approach has limitations. The variability in the clinical and biological presentations of depression, as well as patient-specific confounding factors, must be carefully considered when integrating ML technologies into clinical practice. Nevertheless, our findings suggest that an EEG-based ML model holds potential as a diagnostic aid for MDD, paving the way for further refinement and clinical application.

The food processing industry is seeking new technologies to enhance product safety, extend shelf life, and optimise food quality in response to growing consumer demand for high-quality products. Since the discovery of plasma technology, its potential applications in food processing have been numerous. For most of these applications, plasma characterisation is key to successfully scaling up from laboratory to industrial settings. A highly valuable tool for plasma characterisation is optical emission spectroscopy (OES), which serves as a non-invasive diagnostic method to monitor reactive species, specifically excited atoms and molecules (reactive oxygen and nitrogen species—RONS) that are critical for food treatment. The main role of OES in food control is to enable species identification and real-time process monitoring, providing feedback on electron temperature and density to prevent thermal damage to sensitive food products. It also facilitates optimisation by adjusting voltage and gas flow rates to maximise the production of antimicrobial species. These results ensure that processes are reliable and repeatable, supporting the transition from laboratory-scale to industrial applications. The paper provides an overview of the use of optical emission spectroscopy in various applications of plasma technology in food processing, including the determination of the elemental composition of raw materials and final products, detection of contaminants, quality control, determination of characteristic plasma parameters, and ensuring compliance with food safety regulations.

Timely forecasting of respiratory disease activity can support more proactive planning of primary healthcare resources. The aim of this paper is to develop a machine learning framework for forecasting acute respiratory infection (ARI) and influenza-like illness (ILI) volumes using routinely collected primary care real-world data from Croatia. We used aggregated and pseudonymized EHRs from the Croatian Central Health Information System (CEZIH), covering an 11-year period (ISO 2015-W01 to 2025-W52), to construct weekly county-level ARI and ILI series based on ICD-10 codes. The predictor set included historical disease incidence, antimicrobial prescriptions, demographic and population data, localized meteorological and air-quality variables, seasonal features, and spatial information from neighbouring counties. Five machine learning regressors were used for one- to four-week-ahead forecasting, using the period 2024–2025 for testing. XGBoost consistently achieved the best performance for both ARI and ILI. Models predicting week-to-week changes outperformed those predicting absolute case counts in most scenarios. For ARI, the best XGBoost models achieved MAPE values of 11.19%, 13.98%, 15.53%, and 16.23% for one- to four-week horizons, respectively, compared with 14.96%, 19.06%, 22.74%, and 25.19% for ILI. Seasonal variables were the most influential predictors, and prediction intervals widened with increasing forecasting horizon. Our results demonstrate the potential of primary care EHR data and machine learning to support surveillance and proactive healthcare resource planning by public health authorities and medical professionals in Croatia.