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Knowledge of the ice thickness distribution of glaciers and ice caps is an important prerequisite for many glaciological and hydrological investigations. A wealth of approaches has recently been presented for inferring ice thickness from characteristics of the surface. With the Ice Thickness Models Intercomparison eXperiment (ITMIX) we performed the first coordinated assessment quantifying individual model performance. A set of 17 different models showed that individual ice thickness estimates can differ considerably – locally by a spread comparable to the observed thickness. Averaging the results of multiple models, however, significantly improved the results: on average over the 21 considered test cases, comparison against direct ice thickness measurements revealed deviations on the order of 10 ± 24 % of the mean ice thickness (1σ estimate). Models relying on multiple data sets – such as surface ice velocity fields, surface mass balance, or rates of ice thickness change – showed high sensitivity to input data quality. Together with the requirement of being able to handle large regions in an automated fashion, the capacity of better accounting for uncertainties in the input data will be a key for an improved next generation of ice thickness estimation approaches.

Tick-borne encephalitis (TBE) is a widespread viral infection of the central nervous system with increasing incidence in Europe and northern Asia. Post-infectious sequelae are frequent, and patients with TBE commonly experience long-term fatigue and subjective sleep disturbances. Obstructive sleep apnea (OSA) may be a contributing factor, and objective sleep studies with polysomnography (PSG) are lacking. Forty-two adults, 22 TBE patients (cases), diagnosed in Region Västra Götaland, Sweden, between 2012 and 2015, and 20 controls without a known TBE history, underwent an overnight PSG, respectively. All participants responded to questionnaires. The cases and controls were similar regarding age, sex, obesity, concomitant diseases, smoking, and alcohol habits. Despite similar PSG characteristics such as total sleep time and OSA severity indices, the TBE cases reported statistically more sleep-related functional impairment on the Functional Outcome of Sleep Questionnaire (FOSQ) compared with the controls (median scores 18.1 vs . 19.9; p <0.05). In a multivariate analysis, TBE correlated significantly with the lower FOSQ scores (unstandardized β −1.80 [%95 confidence interval −3.02 - −0.58]; p = 0 . 005 ) independent of age, sex, total sleep time and apnea-hypopnea-index. TBE cases with OSA reported the lowest scores on the FOSQ compared with the other subgroups with TBE or OSA alone, and the ones with neither TBE nor OSA. TBE is associated with impaired functional outcomes, in which concomitant OSA may worsen the subjective symptoms. Further studies are warranted to determine the effect of treatment of concomitant OSA on functional outcomes with regard to optimal rehabilitation of TBE.

In this work, operational data collected from four Danish wastewater treatment plants (WWTP) are assessed for quality issues and analyzed to investigate the feasibility of data-driven modeling for control purposes. All plants have permanent N2O sensors installed in the biological reactors, and N2O data are collected on the same terms as other operational data. We present and deploy a six-dimensional data quality assessment to the operational data evaluating (1) relevance, (2) accuracy, (3) completeness, (4) consistency, (5) comparability, and (6) accessibility. To increase the accuracy and completeness of the stored data, it is suggested that future initiatives are taken toward the collection and storing of metadata in WWTPs. Furthermore, seasonal variations and time-varying relationships between N2O, nitrogenous variables, and oxygen are investigated and compared across various case plants and process designs. Results show that the quality of the operational data varies substantially between plants. The investigation of time-varying interrelation between N2O and nitrogenous variables showed no clear pattern within or across different case plants. Furthermore, it is recommended that future research should consider adapting models so that more influence is linked to reliable measurements, contrary to assuming that all variables are of equal quality.

A novel data-driven stochastic state space system for modeling and forecasting the sludge blanket height in secondary clarifiers is presented. The model is trained on sensor measurements of the sludge blanket height and uses as inputs (1) the clarifier sludge mass inflow rate, and (2) the clarifier recycle flow rate. The model's prediction accuracy is evaluated on data from two Danish wastewater treatment plants, for a summer and a winter month, by means of root-mean-square errors and compared with a persistence model. The model consistently outperforms the persistence model in the summer, but only one plant performs well in the winter month. The worst performing plant is challenging to model due to data quality issues and problematic (uneven and time-varying) flow distributions to the clarifiers. This led us to conclude that the best performance and stability is seen to require high data quality and well-controlled flow distribution. In summary the model achieves, in almost all cases, prediction error reductions in the order of 30–50% and 0.1–0.4 m in relative and absolute terms when compared with the predictions from a persistence model.

Online model predictive control (MPC) of water resource recovery facilities (WRRFs) requires simple and fast models to improve the operation of energy-demanding processes, such as aeration for nitrogen removal. Selected elements of the activated sludge model number 1 modelling framework for ammonium and nitrate removal were included in discretely observed stochastic differential equations in which online data are assimilated to update the model states. This allows us to produce model-based predictions including uncertainty in real time while it also reduces the number of parameters compared to many detailed models. It introduces only a small residual error when used to predict ammonium and nitrate concentrations in a small recirculating WRRF facility. The error when predicting 2 min ahead corresponds to the uncertainty from the sensors. When predicting 24 hours ahead the mean relative residual error increases to ∼10% and ∼20% for ammonium and nitrate concentrations respectively. Consequently this is considered a first step towards stochastic MPC of the aeration process. Ultimately this can reduce electricity demand and cost for water resource recovery, allowing the prioritization of aeration during periods of cheaper electricity.

Few patients scattered among centers complicate investigation of thrombotic thrombocytopenic purpura (TTP) and Evans syndrome (ES). Routinely collected Danish register data captures the total population and includes lifelong follow-up. We aimed to validate registered TTP and ES diagnoses and to explore clinical characteristics. We identified all patients in Denmark with diagnosis registrations indicative of TTP or ES in the Danish National Patient Registry 2000-2019, validated diagnoses through medical record review, and extracted and presented data on initial treatment and complications. Diagnoses for patients registered with TTP and ES were confirmed for 46% and 59%, respectively. Among validated TTP patients the most widespread complications at time of diagnosis were neurological symptoms or deficits, observed in 81% of cases. Other frequent types of complications in TTP patients were any organ failure (32%) and infection (25%). Initial management and complications did not change for patients diagnosed between 2000 and 2009 and 2010 and 2019, and survival remained constant (overall mortality 26%, median follow up of 8.4 years). Treatments and complications also remained unchanged for ES patients. Overall, diagnostic accuracy, complications and prognosis have remained relatively constant for patients over the study period. These now validated cohorts of Danish TTP and ES patients will be utilized in future studies to examine long-term health outcomes.

In this paper, we present two years of high-resolution nitrous oxide (\\(N_2O\\)) measurements for time series modeling and forecasting in wastewater treatment plants (WWTP). The dataset comprises frequent, real-time measurements from a full-scale WWTP, with a sample interval of 2 minutes, making it ideal for developing models for real-time operation and control. This comprehensive bio-chemical dataset includes detailed influent and effluent parameters, operational conditions, and environmental factors. Unlike existing datasets, it addresses the unique challenges of modeling \\(N_2O\\), a potent greenhouse gas, providing a valuable resource for researchers to enhance predictive accuracy and control strategies in wastewater treatment processes. Additionally, this dataset significantly contributes to the fields of machine learning and deep learning time series forecasting by serving as a benchmark that mirrors the complexities of real-world processes, thus facilitating advancements in these domains. We provide a detailed description of the dataset along with a statistical analysis to highlight its characteristics, such as nonstationarity, nonnormality, seasonality, heteroscedasticity, structural breaks, asymmetric distributions, and intermittency, which are common in many real-world time series datasets and pose challenges for forecasting models.

It is not clear whether high-quality coincidence gamma-PET (gPET) cameras can provide clinical data comparable with data obtained with dedicated PET (dPET) cameras in the primary diagnostic work-up of patients with suspected lung cancer. This study focuses on 2 main issues: direct comparison between foci resolved with the 2 different PET scanners and the diagnostic accuracy compared with final diagnosis determined by the combined information from all other investigations and clinical follow-up. Eighty-six patients were recruited to this study through a routine diagnostic program. They all had changes on their chest radiographs, suggesting malignant lung tumor. In addition to the standard diagnostic program, each patient had 2 PET scans that were performed on the same day. After administration of 419 MBq (range = 305-547 MBq) (18)F-FDG, patients were scanned in a dedicated PET scanner about 1 h after FDG administration and in a dual-head coincidence gamma-camera about 3 h after tracer injection. Images from the 2 scans were evaluated in a blinded set-up and compared with the final outcome. Malignant intrathoracic disease was found in 52 patients, and 47 patients had primary lung cancers. dPET detected all patients as having malignancies (sensitivity, 100%; specificity, 50%), whereas gPET missed one patient (sensitivity, 98%; specificity, 56%). For evaluating regional lymph node involvement, sensitivity and specificity rates were 78% and 84% for dPET and 61% and 90% for gPET, respectively. When comparing the 2 PET techniques with clinical tumor stage (TNM), full agreement was obtained in 64% of the patients (Cohen's kappa = 0.56). Comparing categorization of the patients into clinical relevant stages (no malignancy/malignancy suitable for treatment with curative intent/nontreatable malignancy), resulted in full agreement in 81% (Cohen's kappa = 0.71) of patients. Comparing results from a recent generation of gPET cameras obtained about 2 h later than those of dPET, there was a fairly good agreement with regard to detecting primary lung tumors but slightly reduced sensitivity in detecting smaller malignant lesions such as lymph nodes. Depending on the population to be investigated, and if dPET is not available, gPET might provide significant diagnostic information in patients in whom lung cancer is suspected.