bluuebunny/crossref_metadata_2025_split huggingface dataset:
From simulation to dissemination: automation of data and metadata management
Authors: Arif Mohammad Shafi , Schade Peter , Lehfeldt Rainer , Notay Vikram , Carstens Georg , Kösters Frank 
Published: January 2023

Abstract
Working with Computational Fluid Dynamics (CFD) can produce numerous datasets, which contain different physical parameters and study settings. Management and dissemination of such data benefits greatly from a comprehensive data description (metadata), ideally a specialized Metadata Information System (MIS), and adequate long-term storage (data warehouse). To avoid the manual creation of rival metadata, an automation method has been developed, which adds metadata automatically by the simulation and post-processing programs. The automation method described here is an example for data curation suitable for a professional work environment. This method begins with the collection and creation of metadata and ends with the dissemination and publication of the data. This procedure reduces the challenging amount of tedious and error-prone workload, avoids redundancy, enhances efficiency, and is thus a means of quality assurance. Basis for the automation process is an open-source metadata information system (MIS) which has been adapted to the techno-scientific demand of simulation metadata. It stores the metadata in an SQL database and provides Open Geospatial Consortium (OGC) compliant services and communication interface. A hierarchical metadata management concept was initiated for efficient management of numerous datasets. Subsequently, the metadata validation and dissemination are automated via a middleware, which compiles metadata in an XML file, imports the metadata into the MIS and transfers the data to a long-term repository via a Representational State Transfer (REST) interface. Finally, the data and metadata are interlinked and published.
Using artificial intelligence models to support water quality prediction in water distribution networks
Authors: Enriquez Laura , Saldarriaga Juan , Berardi Luigi , Laucelli Daniele , Giustolisi Orazio 
Published: January 2023

Abstract
Water chlorination is the most used disinfection method in water distribution networks (WDNs). Nonetheless, water quality parameters, including chlorine concentration, are not available at every point of the WDN, although such information is of direct relevance to drive the operation at water treatment plants to keep the correct chlorine residual through the system. This work proposes the use of data-driven models, i.e., Artificial Neural Networks and Evolutionary Polynomial Regression, to predict the water quality parameters in most areas of a WDN, using water quality data measures at few sampling points. The study is demonstrated on the case studies of the trunk network of Bogota’s water distribution system.
Water management issues in the context of music festivals
Authors: Giurgea Corina Maria , Safta Carmen-Anca , Lupu Ciprian , Ordean Mihaela , Opruţa Dan 
Published: January 2023

Abstract
In recent years, festivals, especially music festivals have become important vectors for promoting their host cities. Festivals are complex phenomena with high economic and socio-cultural impact. High CO2 emissions and huge resource consumption are commonly related to the festivals’ negative environmental impact. Ensuring the suitable amount of water for a large-scale event is essential for both the audience experience and the city residents, especially when their access to water is hindered by excessive water usage for the festival. Therefore, an accurate estimation of water consumption during festivals is a prerequisite for rigorous water planning. In the Covid-19 world, due to the worsening crisis of resources and to the increasing pressure exerted by audiences asking for more eco-conscious events, festival organizers are called to get together with other stakeholders and re-examine the water management issues. Water companies have already started to embrace digital solutions. Their further implementation will result in a large amount of environmental data that could contribute to a paradigm shift concerning festival sustainability. The present paper was conceived around this topic, with the goal of increasing the awareness of all the actors involved in festival operational processes. Outcomes of a desk research and a case study focused on the UNTOLD and Electric Castle music festivals are presented.
Analysis of planetary boundary layer feedbacks on the multiple equilibria of the continental water cycle
Authors: Dengri Abhinav , Yamada Tomohito 
Published: January 2023

Abstract
A simple soil-atmosphere interaction model is developed to study the bimodal distribution of soil moisture in regions where there is feedback between precipitation and soil moisture. Our study includes planetary boundary layer (PBL) feedback in the coupled analytical soil-atmosphere interaction model with a varying PBL height and entrainment from the free atmosphere. This study aims to reveal the dominant PBL feedbacks that impact the occurrence of soil moisture bimodality by comparing the evolution of prognostic variables for a detailed PBL scheme (with varying PBL height and entrainment of heat and moisture from the free atmosphere) with the simple PBL scheme (with fixed PBL height and no entrainment). This study has shown the existence of multiple equilibria in the continental water balance for both detailed PBL and simple PBL schemes when the model is integrated for different initial soil moisture up to equilibria. Although both with and without PBL feedback, the results are in qualitative agreement; the soil moisture initialization to realize multiple equilibria of a detailed PBL scheme is larger than the simple PBL scheme. Although the direct quantitative comparison with observation is clearly limited in the simple model, it also demonstrates how the non-linear dynamics of the coupled soil-atmosphere system may be expected to occur in observations.
Implementation of water related Sustainable Development Goals in Romania: overview of current and future challenges
Authors: Teau Camelia , Popescu Ioana , Florescu Constantin , Constantin Albert , Ciocan Cristian Moisescu , Vlaicu Valentin 
Published: January 2023

Abstract
The collection of information on the achievement of the water related Sustainable Development Goals (SDGs), as well as their analysis through research, is defined as being difficult especially in the absence of data and tools that can model these performances. An analysis of SDG indicators at the level of 2015, for 41 countries where the population has high and medium incomes shows that a special level of complexity in monitoring is given by the fact that each country measures certain indicators specifically, and these cannot be compared. It is recommended to formulate methods of measurement and monitoring that can be compared. All these consistent methods between countries must be subject to data protection rules, both in terms of access to them and in terms of their management. Present paper shows the main challenges in monitoring water related SDGs (mainly 6, 11 and 13) in Romania, what is the status so far and what are the challenges for the future.
Challenges & solutions for deterministic hydraulic modelling in Mediterranean coastal catchment. Application to the lower Paillons River, Nice, France
Authors: Game Paguédame , Wang Mingyang , Audra Philippe , Gourbesville Philippe 
Published: January 2023

Abstract
Different methods are used in hydrological and hydraulic modelling in a catchment. Deterministic modelling can be used to produce detailed information and optimize the organization of massive data collection. In the catchment of Paillons, there is a need to improve knowledge of hydrological processes through deterministic modelling. In fact, Nice Côte d’Azur metropolis faces numerous challenges in the Paillons catchment. The area provides water resources for large communities. However, it is exposed to flooding and droughts. Complex hydrological processes generate runoff over the 246 km2 watershed. Few existing monitoring stations provide runoff data. In addition, there are limitations in the understanding of surface hydraulics. Thus, this study uses DHI Mike21FM for surface hydraulics with 5 m grid size for riverbed and 2 m for tunnels. The study area is limited to the lower Paillons River. Observed and modelled water depths vary between 0.1 and 0.5 m for a maximum discharge of 38 m3/s in the tunnels. Flood maps created with a discharge of 1000 m3/s, show clearly high flood risk zones and flow directions. The selected CFL condition under 0.8 is respected. The tool is suitable for modeling flooding in areas of interest within the catchment of study. The results obtained are satisfactory and demonstrate that the constructed tool makes it possible to reproduce the overall behavior of surface hydraulics.
Changes in sediment erosion rate in the Azuma River basin after the Hokkaido eastern Iburi earthquake in 2018
Authors: Miyazaki Kotaro , Hoshino Tsuyoshi , Shimizu Keita , Shrivastava Sourabh , Yamada Tomohito J 
Published: January 2023

Abstract
The Hokkaido Eastern Iburi earthquake in 2018 induced the largest landslide over the Azuma River basin in Hokkaido, the north island of Japan. This study estimated the sediment erosion rate over the Azuma River basin using the universal soil loss equation (USLE) for the pre- and post-earthquake conditions. The results showed that the potential sediment erosion rate from the entire basin increased by 8–29 times more than the pre-earthquake condition. The primary cause of the increase in sediment erosion rate was deforestation due to the landslide, which generated 13% of the bare land area in the entire basin. In addition, the influence of interannual variability of rainfall on sediment erosion rate was calculated from observed rainfall data. As a result, the minimum annual sediment erosion rate after the earthquake exceeded the maximum before the earthquake, indicating that the post-earthquake sediment erosion rate is substantially higher than the pre-earthquake rate. These findings suggest that in order to assess the sediment erosion risk over the Azuma River basin, it is necessary to predict and monitor the vegetation recovery, considering the interannual variability of rainfall.
Comparison of model-based techniques for pipe burst location in water distribution networks
Authors: Ferreira Bruno , Antunes André , Carriço Nelson , Covas Dídia 
Published: January 2023

Abstract
The current paper compares the performance of three model-based techniques for the automatic location of pipe burst events in water distribution networks. The first technique is based on nodal pressure sensitivities, the second solves an inverse analysis problem and the third technique uses hydraulic simulation to train a classifier. A real case study is used and a set of artificial measurements is generated for a number of pipe burst scenarios, with fixed burst location and variable pressure and flowrate noise levels and burst sizes. The performance of each technique is assessed based on the determined burst distance to the real burst location. Obtained results are discussed and the most relevant conclusions are drawn.
Predicting iron exceedance risk in drinking water distribution systems using machine learning
Authors: Kazemi Ehsan , Kyritsakas Grigorios , Husband Stewart , Flavell Katrina , Speight Vanessa , Boxall Joby 
Published: January 2023

Abstract
A Machine Learning approach has been developed to predict iron threshold exceedances in sub-regions of a drinking water distribution network from data collected the previous year. Models were trained using parameters informed by Self-Organising Map analysis based on ten years of water quality sampling data, pipe data and discolouration customer contacts from a UK network supplying over 2.3 million households. Twenty combinations of input parameters (network conditions) and three learning algorithms (Random Forests, Support Vector Machines and RUSBoost Trees) were tested. The best performing model was found to be Random Forests with input parameters of iron, turbidity, 3-day Heterotrophic Plate Counts, and high priority dead ends per District Metered Area. Different exceedance levels were tested and prediction accuracies of above 70% were achieved for UK regulatory concentration of 200 µg/L. Predicted probabilities per network sub-region were used to provide relative risk ranking to inform proactive management and investment decisions.
A data-driven model for the prediction of chlorine losses in water distribution trunk mains
Authors: Kyritsakas Grigorios , Speight Vanessa , Boxall Joby 
Published: January 2023

Abstract
A data-driven model that uses 4 different machine learning (ML) algorithms (Feed forward artificial neural networks (ANN), Nonlinear autoregressive exogeneous (NARX) ANN, support vector machine and Random Forest) was designed for the prediction of chlorine loss events in water distribution trunk mains. The model, firstly, identifies past chlorine loss events and their associate flow or temperature events. Then, the detected past flow events and their associate past chlorine loss events are used to train the ML algorithms. The model was tested in 3 trunk mains of the same drinking water distribution system with similar diameter but with different characteristics, using each time a different combination of parameters (flow (input) - past chlorine losses (output) or flow, temperature, and chlorine (input) - past chlorine losses (output)) and machine learning algorithms. Results indicate that the model could predict a future chlorine loss event with a period between 2 to 10 hours depending on the parameter and ML algorithm used and the trunk mains’ hydraulic characteristics.
Analysis of diffusion and dispersion processes in water distribution networks through the use of the Péclet number threshold
Authors: Piazza Stefania , Sambito Mariacrocetta , Freni Gabriele 
Published: January 2023

Abstract
To model the water quality within the distribution networks, simplified models are used (such as EPANET), which use an advective-reactive approach and neglect relevant phenomena, such as diffusion and dispersion, for low values of the Reynolds number. Although valid in the presence of a purely turbulent flow regime, as they do not produce substantial modelling errors, these simplifications are not applicable equally to all distribution networks. Therefore, the present study defines a threshold of the Péclet number, which relates the effectiveness of mass transport by advection with the effectiveness of mass transport by dispersion or diffusion, in order to establish a priori the behaviour of the water distribution network and find the most suitable model to use. The numerical analysis was applied to a real case study (network of Oreto-Stazione) using EPANET advective model and EPANET-DDR (Dynamic-Dispersion-Reaction) model, developed by the authors.
Optimisation-based refinement of genesis indices for tropical cyclones
Authors: Ascenso Guido , Cavicchia Leone , Scoccimarro Enrico , Castelletti Andrea 
Published: January 2023

Abstract
Tropical cyclone genesis indices are valuable tools for studying the relationship between large-scale environmental fields and the genesis of tropical cyclones, supporting the identification of future trends of cyclone genesis. However, their formulation is generally derived from simple statistical models (e.g., multiple linear regression) and are not optimised globally. In this paper, we present a simple framework for optimising genesis indexes given a user-specified trade-off between two performance metrics, which measure how well an index captures the spatial and interannual variability of tropical cyclone genesis. We apply the proposed framework to the popular Emanuel and Nolan Genesis Potential Index, yielding new, optimised formulas that correspond to different trade-offs between spatial and interannual variability. Result show that our refined indexes can improve the performance of the Emanuel and Nolan index up to 8% for spatial variability and 16%–22% for interannual variability; this improvement was found to be statistically significant (p < 0.01). Lastly, by analysing the formulas found, we give some insights into the role of the different inputs of the index in maximising one metric or the other.
Strategies for fitting accurate machine-learned inter-atomic potentials for solid electrolytes
Authors: Wang Juefan , Panchal Abhishek A , Canepa Pieremanuele 
Published: January 2023

Abstract
Ion transport in materials is routinely probed through several experimental techniques, which introduce variability in reported ionic diffusivities and conductivities. The computational prediction of ionic diffusivities and conductivities helps in identifying good ionic conductors, and suitable solid electrolytes (SEs), thus establishing firm structure-property relationships. Machine-learned potentials are an attractive strategy to extend the capabilities of accurate ab initio molecular dynamics (AIMD) to longer simulations for larger systems, enabling the study of ion transport at lower temperatures. However, machine-learned potentials being in their infancy, critical assessments of their predicting capabilities are rare. Here, we identified the main factors controlling the quality of a machine-learning potential based on the moment tensor potential formulation, when applied to the properties of ion transport in ionic conductors, such as SEs. Our results underline the importance of high-quality and diverse training sets required to fit moment tensor potentials. We highlight the importance of considering intrinsic defects which may occur in SEs. We demonstrate the limitations posed by short-timescale and high-temperature AIMD simulations to predict the room-temperature properties of materials.
OmicsTIDE: interactive exploration of trends in multi-omics data
Authors: Harbig Theresa A , Fratte Julian , Krone Michael , Nieselt Kay 
Published: January 2023

Abstract

Motivation
The increasing amount of data produced by omics technologies has enabled researchers to study phenomena across multiple omics layers. Besides data-driven analysis strategies, interactive visualization tools have been developed for a more transparent analysis. However, most state-of-the-art tools do not reconstruct the impact of a single omics layer on the integration result.

Results
We developed a data classification scheme focusing on different aspects of multi-omics datasets for a systemic understanding. Based on this classification, we developed the Omics Trend-comparing Interactive Data Explorer (OmicsTIDE), an interactive visualization tool for the comparison of gene-based quantitative omics data. The tool consists of a computational part that clusters omics datasets to determine trends and an interactive visualization. The trends are visualized as profile plots and are connected by a Sankey diagram that allows for an interactive pairwise trend comparison to discover concordant and discordant trends. Moreover, large-scale omics datasets are broken down into small subsets that can be analyzed functionally using Gene Ontology enrichment within few analysis steps. We demonstrate the interactive analysis using OmicsTIDE with two case studies focusing on different experimental designs.

Availability and implementation
OmicsTIDE is a web tool available via http://omicstide-tuevis.cs.uni-tuebingen.de/.

Supplementary information
Supplementary data are available at Bioinformatics Advances online.
Cerebrospinal fluid cytokines after autologous haematopoietic stem cell transplantation and intrathecal rituximab treatment for multiple sclerosis
Authors: Burman Joachim , Zjukovskaja Christina , Svenningsson Anders , Freyhult Eva , Wiberg Anna , Kultima Kim 
Published: January 2023

Abstract
Multiple sclerosis has been established as an inflammatory disease of the central nervous system. Many aspects of the pathophysiology are still unknown and it is presently unclear how different treatments affect the immunopathology of multiple sclerosis. In this study, we explored cytokines discriminating between individuals with multiple sclerosis and healthy controls and then how these cytokines were affected by treatment intervention with autologous haematopoietic stem cell transplantation or intrathecal rituximab. CSF from individuals with multiple sclerosis and healthy controls were analysed with a proximity extension assay to simultaneously determine the level of 92 cytokines and other inflammation-related proteins. In total, CSF from 158 multiple sclerosis patients and 53 healthy controls were analysed. Sixty-four patients with relapsing-remitting multiple sclerosis and 27 with progressive multiple sclerosis took part in a cross-sectional study and underwent lumbar puncture on a single occasion. Forty-five patients with relapsing-remitting multiple sclerosis were treated with autologous haematopoietic stem cell transplantation and underwent lumbar puncture at baseline and then at follow-up visits made at 1-, 2- and 5 years. Twenty-two patients with progressive multiple sclerosis were treated with intrathecal rituximab and followed with lumbar punctures at baseline and then at follow-up visits made at 3-, 6- and 12 months. Of the 92 studied cytokines, 16 were found to be altered in multiple sclerosis and 11 were decreased after treatment with autologous haematopoietic stem cell transplantation. None of the studied cytokines was affected by treatment with intrathecal rituximab for progressive multiple sclerosis. Some proteins were highly associated with each other. Therefore, a cluster analysis was made and then the highest-ranked protein from the four highest-ranked clusters was used for the subsequent analyses. CCL3, IL-12B, CXCL10 and IL-8 discriminated between multiple sclerosis patients and controls, but only IL-12B differed between patients with relapsing-remitting and progressive multiple sclerosis. The CSF concentrations of CCL3, IL-12B and CXCL10 were decreased after autologous haematopoietic stem cell transplantation, whereas IL-8 appeared to be unaffected by this intervention. High concentrations of IL-8 were associated with worse outcome in both treatment groups. Overall, the results suggest a profound effect of autologous haematopoietic stem cell transplantation on the inflammatory milieu of the CSF in multiple sclerosis.
Linear focal elastosis
Authors: Palaniappan Vijayasankar , Selvaarasan Jayapratha , Murthy Aravind Baskar , Karthikeyan Kaliaperumal 
Published: January 2023

Abstract
Linear focal elastosis (LFE) is an uncommon, benign, acquired elastotic condition with uncertain pathogenesis. It is characterized clinically by asymptomatic, multiple, yellowish, elevated, irregularly indurated, striae-like lines or bands distributed horizontally across the lower and middle part of the posterior trunk. The histopathological hallmark of LFE is a focal increase of elastic fibres in the dermis. The differential diagnosis is varied, and striae distensae is the closest mimic of LFE. Response of LFE to treatment is often poor. The focus of this article is to provide insights into this condition for dermatologists.