The gas-liquid interfacial area, which is determined by the gas hold-up and the Sauter mean bubble diameter, determines the production rate in many industrial processes. The effect of additives on this interfacial area is, especially in multiphase systems (gas-liquid-solid, gasliquid-liquid), often not understood. The addition of a third phase can cause the gas-liquid system to become completely opaque, which means that conventional techniques to study the interfacial area cannot be used. For this reason ultrasonic spectroscopy was used in this work to study the interfacial area and the bubble size distribution in these systems. The influence of different additives on the interfacial area was studied in a stirred vessel and in a bubble column under coalescing and noncoalescing conditions. It was found that the addition of toluene to a noncoalescing electrolyte system decreased the interfacial area to a large extent by turning it into a coalescing system, due to the interaction between gas bubbles and liquid organic droplets. Furthermore, around the toluene solubility concentration, both the gas hold-up (measured using an electric conductivity technique) and the interfacial area increased to values similar to those observed in noncoalescing systems. The cause of this remarkable phenomenon lies probably in the presence of a small toluene layer around the gas bubbles, which can be formed beyond the solubility point. This layer is absent at concentrations below the solubility limit and a large surface tension gradient exists between those two situations, which can be responsible for the sharp change in coalescence behavior.

Background Patient-reported outcome measurements (PROMs) are commonly used in clinical practice to support clinical decision making. However, few studies have investigated machine learning methods for predicting PROMs outcomes and thereby support clinical decision making. Objective This study investigates to what extent different machine learning methods, applied to two different PROMs datasets, can predict outcomes among patients with non-specific neck and/or low back pain. Methods Using two datasets consisting of PROMs from (1) care-seeking low back pain patients in primary care who participated in a randomized controlled trial, and (2) patients with neck and/or low back pain referred to multidisciplinary biopsychosocial rehabilitation, we present data science methods for data prepossessing and evaluate selected regression and classification methods for predicting patient outcomes. Results The results show that there is a potential for machine learning to predict and classify PROMs. The prediction models based on baseline measurements perform well, and the number of predictors can be reduced, which is an advantage for implementation in decision support scenarios. The classification task shows that the dataset does not contain all necessary predictors for the care type classification. Overall, the work presents generalizable machine learning pipelines that can be adapted to other PROMs datasets. Conclusion This study demonstrates the potential of PROMs in predicting short-term patient outcomes. Our results indicate that machine learning methods can be used to exploit the predictive value of PROMs and thereby support clinical decision making, given that the PROMs hold enough predictive power

Passenger comfort systems such as Heating, Ventilation, and Air-Conditioning units (HVACs) usually lack the data monitoring quality enjoyed by mission-critical systems in trains. But climate change, in addition to the high ventilation standards enforced by authorities due to the COVID pandemic, have increased the importance of HVACs worldwide. We propose a machine learning (ML) approach to the challenge of failure detection from incomplete data, consisting of two steps: 1. humanannotation bootstrapping, on a fraction of temperature data, to detect ongoing functional loss and build an artificial ground truth (AGT); 2. failure prediction from digital-data, using the AGT to train an ML model based on failure diagnose codes to foretell functional loss. We exercise our approach in trains of Dutch Railways, showing its implementation, ML-predictive capabilities (the ML model for the AGT can detect HVAC malfunctions online), limitations (we could not foretell failures from our digital data), and discussing its application to other assets.

Around 20% of the Dutch population is living with chronic musculoskeletal pain (CMP), which is a complex and multifactorial problem. This complexity makes it hard to define a classification system, which results in non-satisfactory referring from the general practitioner (GP). CMP is often explained using the biopsychosocial model in which biological, psychological and social factors cause and maintain the pain. The presented study investigated the factors related to the GPs’ referral for patients with CMP to further treatment.Using convenience sampling, semi-structured interviews and a focus group were conducted among 14 GPs. The interviews were iteratively analyzed using inductive conventional content analysis.Analysis of the interviews demonstrated that there were 28 referral factors that were mentioned by more than 50% of the interviewed GPs. The results showed that the GPs were mostly focussing on the physical (e.g. pain location) and psychological (e.g. acceptation of pain) factors, indicating that they lack focus on the social factors. Furthermore, unfamiliarity of GPs with treatment options was a noteworthy finding.The referral of patients with CMP by GPs is complex and based on multiple factors. To improve referral, it is recommended to include social factors in the decision-making process and to increase the familiarity of the GPs with available treatments.