Database Model Research Articles

State of Health Estimation for Lithium-Ion Batteries Under Arbitrary Usage Using Data-Driven Multimodel Fusion

Accurately estimating the state of health (SoH) of batteries is indispensable for the safety, reliability, and optimal energy and power management of electric vehicles. However, from a data-driven perspective, complications, such as dynamic vehicle operating conditions, stochastic user behaviors, and cell-to-cell variations, make the estimation task challenging. This work develops a data-driven multi-model fusion method for SoH estimation under arbitrary usage profiles. All possible operating conditions are categorized into six scenarios. For each scenario, an appropriate feature set is extracted to indicate the SoH. Based on the obtained features, four machine learning algorithms are applied individually to train SoH estimation models using time-series data. In addition to the estimates at the current time step, a histogram data-based and online adaptive model is taken from previous work for predicting the next-step SoH. Then, a Kalman filter is applied to systematically fuse the results of all the estimation and prediction models. Experimental data collected from different types of batteries operated under diverse profiles verify the effectiveness and practicability of the developed method, as well as its superiority over individual models.

Investigation on Solid Particle Erosion Performance of Aluminum Alloy Materials for Leading-Edge Slat

This study aims to characterize solid particle erosion behaviors of three different aluminum alloys (AA2024-T351, AA6061-T651, and AA7075-T651) and reveal their erosion performances on leading-edge slat of airplane wings. Solid particle erosion tests were conducted using silicon carbide erodent particles under the conditions of six different impingement angles (20°-90°) and four different impact velocities (70-192 m/s). The erosion simulations of a leading-edge slat of the aforementioned aluminum alloys were numerically simulated at four different rotation angles (0°-15°) for three different impact velocities (130-250 m/s). A commercial ANSYS Fluent software using the Euler-Lagrange equation and an experimental data-based erosion model was used for the erosion simulations. The experimental results showed that the erosion rate increases with increasing impact velocity and the maximum erosion rate is obtained at the impingement angle of 30° which reflects the ductile manner. Of the three aluminum alloys, the AA6061-T651 exhibited the worst erosion behavior followed by the AA2024-T351 sample, whereas the AA7075-T651 had the best erosion resistance. The numerical results indicated that the erosion rate values of slat surfaces made up of three different aluminum alloys showed a slight increase after a slat rotation angle of 5°.

Design and optimization of a hybrid cooling configuration combining PCM and liquid cooling for Li-ion battery using data-based response surface approximation model

Effective thermal management is crucial for safe operation of electric vehicles. This study proposes a novel hybrid cooling system that using phase change material (PCM) and liquid cooling to address issues such as leakage, non-uniform temperatures under high discharge rates. A comprehensive 3D model was developed to analyze the influence of geometric parameters on the cooling system. Response surface approximation modeling was used to optimize the layout parameters of the PCM. The results show that the hybrid cooling outperforms other methods, maintaining the cell temperature with 35 ℃ and the temperature difference within 4 ℃. The thickness of the PCM is a significant factor, as it increases heat storage capacity and reduces temperature; however, exceeding a certain threshold leads to increased thermal resistance. A non-uniform PCM layout can reduce the temperature difference by 26 %. An optimal PCM thickness of 2.5 mm to 3 mm ensures that the maximum temperature remains below 32.5 ℃, with a temperature difference within 2 ℃. The lowest temperature difference is achieved with a vertical layout height of 70 mm and a horizontal layout thickness of 3 mm. These findings are valuable for enhancing the thermal management of lithium-ion battery.

An empirical experimental observations and MD simulation data-based model for the material properties of confined fluids in nano/Angstrom size tubes

Abstract The transport of fluids in nanometer and Angstrom-sized pores has gotten much attention because of its potential uses in nanotechnology, energy storage, and healthcare sectors. Understanding the distinct material properties of fluids in such close confinement is critical for enhancing their performance in various applications. These properties dictate the fluid’s behavior and play a crucial role in determining flow dynamics, transport processes, and, ultimately, the performance of nanoscale devices. Remarkably, many researchers observed that the size of the geometry, such as the diameter of the confining nanotube, exerts a profound and intriguing influence on the material properties of nanoconfined fluids, including on the critical parameters such as density, viscosity, and slip length. Many researchers tried to model these material properties: viscosity η, density ρ, and slip λ using various models with many dependencies on the tube diameter. It is somewhat confusing and tough to decide which model is appropriate and needs to be incorporated in the numerical simulation. In this paper, we tried to propose a simple single equation for each nano confined material property such as for density ρ ( D ) / ρ o = a + b / ( D − c ) n , viscosity η ( D ) / η o = a + b / ( D − c ) n , and the slip length λ(D) = λ 1 D e −n D + λ o (where a, b, c, n, λ 1, λ o are the free fitting parameters). We model a wealth of previous experimental and MD simulation data from the literature using our proposed model for each material property of nanoconfined fluids at the nanometer and Angstrom scales. Our single proposed equation effectively captures and models all the data, even though many different models have been employed in the existing literature to describe the same material property. Our proposed model exhibits exceptional agreement with multiple independent datasets from the experimental observations and molecular dynamics simulations. Additionally, the model possesses the advantageous properties of continuity and a continuous derivative, so the proposed model is well-suited for integration into numerical simulations. Further, the proposed models also obey the far boundary conditions, i.e., when tube diameter D ⟹ ∞, the material properties tend to the bulk properties of the fluid. Due to the models’ simplicity, smooth, and generic nature, this heuristic model holds promise to apply in simulations to design and optimize nanoscale devices.

ЦИФРОВІ БАЗИ ДАНИХ ДЛЯ ОСВІТИ І ПРО ОСВІТУ: ДОСВІД ДАНІЇ

The article analyzes the content and specifics of Denmark's open educational electronic databases with the aim of adapting the existing Danish experience to the processes of digitization of the educational sphere in Ukraine. It was noted that these processes cover the sphere of the state policy of digitization of the educational realm (organizational, methodical, management, monitoring, accumulative mechanisms), education management (data storage to reflect the history of changes, monitoring of existing services and education quality control systems, accumulation of statistical data and their prognostic analysis), methodical support of the process of forming the digital competence of the educational process’ subjects (building information profiles, developing digital didactic subsystems, increasing the level of competitiveness of educational institutions as providers), the scientific sphere (digitalization of the open scientific environment, building a space of academic integrity, cooperation between scientists and stakeholders). The common characteristics of the National Electronic Research and Information System URIS and the Danish educational database MED were analyzed. A vision model of the MED database (subjects of educational activity and the content of their activity) was built. The structure of monitoring the quality of education in Denmark, based on open electronic databases, is presented; it includes evaluation of the professional activity of teachers, compliance of educational programs with the social order, effectiveness of state/regional types of certification of graduates, standardization and unification of the evaluation system, mandatory regional analysis of statistical data. It was noted that the identified problems and shortcomings of computer testing of school graduates in the context of assessing the quality of their activities became a significant motivating factor for the creation of the MED database. The conclusion was formulated that the open electronic database makes it possible to build statistically substantiated correlations between the existing social characteristics of teachers and students of all levels and types of schools. The main advantages of the created open MED database were noted: compliance with the global requirements for the assessment of the "added value of the teacher", high frequency of data collection, prognostic nature of conclusions regarding the formation of the state's educational policy.

Environmental causality calibration: Advancing WLAN RF fingerprinting for precise indoor localization.

In recent years, considerable and valuable research progress has been made in indoor positioning technologies based on WLAN Radio Frequency (RF) fingerprinting, identifying it as one of the most promising positioning technologies with substantial potential for wider adoption. However, indoor environmental factors significantly influence the propagation of wireless RF signals, resulting in a considerable decrease in positioning accuracy as the indoor environmental conditions vary. Thus, effectively mitigating the impact of indoor environmental factors on WLAN RF fingerprinting-based positioning systems has become a crucial research problem. Currently, there is a dearth of comprehensive research on the influence of indoor climatic factors, particularly the variations in relative humidity, on the propagation of WLAN RF signals within indoor spaces and its consequential impact on positioning accuracy. To address the aforementioned issues, this paper proposes an Adaptive expansion fingerprint database (AeFd) model based on a regression learning algorithm. The AeFd, through the design of a relationship model describing the interaction between fingerprint databases under varying relative humidity, allows the fingerprint database expanded by AeFd to dynamically adapt to the changes in indoor relative humidity. Our experiments show that using the AeFd model with the KNN algorithm, a 5% performance improvement was observed over 10 days and an 8% improvement over 10 months. According to experimental test results, the fingerprint database expansion model AeFd proposed in this paper can effectively expand the fingerprint database under different relative humidity levels, thereby significantly enhancing the positioning performance of the system and improving its stability.

Flu-CED: A comparative transcriptomics database of influenza virus-infected human and animal models.

The continuing emergence of influenza virus has highlighted the value of public databases and related bioinformatic analysis tools in investigating transcriptomic change caused by different influenza virus infections in human and animal models. We collected a large amount of transcriptome research data related to influenza virus-infected human and animal models in public databases (GEO and ArrayExpress), and extracted and integrated array and metadata. The gene expression matrix was generated through strictly quality control, balance, standardization, batch correction, and gene annotation. We then analyzed gene expression in different species, virus, cells/tissues or after antibody/vaccine treatment and imported sample metadata and gene expression datasets into the database. Overall, maintaining careful processing and quality control, we collected 8064 samples from 103 independent datasets, and constructed a comparative transcriptomics database of influenza virus named the Flu-CED database (Influenza comparative expression database, https://flu.com-med.org.cn/). Using integrated and processed transcriptomic data, we established a user-friendly website for realizing the integration, online retrieval, visualization, and exploration of gene expression of influenza virus infection in different species and the biological functions involved in differential genes. Flu-CED can quickly query single and multi-gene expression profiles, combining different experimental conditions for comparative transcriptome analysis, identifying differentially expressed genes (DEGs) between comparison groups, and conveniently finding DEGs. Flu-CED provides data resources and tools for analyzing gene expression in human and animal models infected with influenza virus that can deepen our understanding of the mechanisms underlying disease occurrence and development, and enable prediction of key genes or therapeutic targets that can be used for medical research.

Business Intelligence applied to inventory management: case of a small retail company

Business Intelligence (BI) is a set of techniques that assist organizations in making data-driven decisions, enabling interactive access to information, often in real-time. In this way, its application provides precise business analyses, contributing to goal tracking, results projection, identification of opportunities, and anticipation of risks. Therefore, the objective of this work is to create a BI solution using data extracted from an Enterprise Resource Planning (ERP) to assist inventory management in a small-scale retail company specializing in party supplies. Factors such as shelf stockouts and inconsistencies between the system and the real environment have driven the development of this project. Facing ineffective inventory management and limited access to strategic information, a BI solution was developed in the company following steps such as problem analysis, data selection, extraction and processing, database modeling, BI implementation, and testing and evaluation. Thus, two focused dashboards were developed, one for the overall inventory report and another for monitoring safety stock. Through these dashboards, it is possible to assess inventory levels and product movement, devise strategies based on historical analyses, and identify the need to issue new purchase orders by product. The achieved results were positive, highlighting significant improvements in inventory management effectiveness and consolidating decision-making processes through quick and interactive access to strategic information.

Library management database design and application

Under the data-centric business environment, efficient database design is pivotal for efficient data management across various sectors. For libraries and bookstores, a well-tailored database is essential to categorize, index, and swiftly retrieve vast amounts of data, ensuring efficient operations. MySQL is an open-source database management system which facilitates efficient data manipulation. Based on MySQL, this essay delves into the realm of library database design and application, addressing the need for streamlined systems. This research addresses the need for adaptation in the evolving technology landscape and offers practical insights for librarians and researchers aiming to optimize information management in modern libraries. Employing the basic steps for database design, we explore one methodology for designing a library management database based on the design of database requirement, modeling, backend implementation, efficiency optimization and testing using MySQL. Through this approach, the study provides a potential solution that meets the basic requirements of libraries and enhances information management practices.

Cardiovascular Disease Preliminary Diagnosis Application Using SQL Queries: Filling Diagnostic Gaps in Resource-Constrained Environments

Cardiovascular diseases (CVDs) are chronic diseases associated with a high risk of mortality and morbidity. Early detection of CVD is crucial to initiating timely interventions, such as appropriate counseling and medication, which can effectively manage the condition and improve patient outcomes. This study introduces an innovative ontology-based model for the diagnosis of CVD, aimed at improving decision support systems in healthcare. We developed a database model inspired by ontology principles, tailored for the efficient processing and analysis of CVD-related data. Our model’s effectiveness is demonstrated through its integration into a web application, showcasing significant improvements in diagnostic accuracy and utility in resource-limited settings. Our findings indicate a promising direction for the application of artificial intelligence (AI) in early CVD detection and management, offering a scalable solution to healthcare challenges in diverse environments.

Flowing Bottomhole Pressure during Gas Lift in Unconventional Oil Wells

Summary We present artificial neural network (ANN) models for predicting the flowing bottomhole pressure (FBHP) of unconventional oil wells under gas lift operations. Well parameters, fluid properties, production/injection data, and bottomhole gauge pressures from 16 shale oil wells in Permian Basin, Texas, USA, are analyzed to determine key parameters affecting FBHP during the gas lift operation. For the reservoir fluid properties, several pressure-volume-temperature (PVT) models, such as Benedict-Webb-Rubin (BWR); Lee, Gonzalez, and Eakin; and Standing, among others, are examined against experimentally tuned fluid properties (i.e., viscosity, formation volume factor, and solution gas-oil ratio) to identify representative fluid (PVT) models for oil and gas properties. Pipe flow models (i.e., Hagedorn and Brown; Gray, Begs and Brill; and Petalas and Aziz) are also examined by comparing calculated FBHP against the bottomhole gauge pressures to identify a representative pipe flow model. Training and test data sets are then generated using the representative PVT and pipe flow models to develop a physics-based ANN model. The physics-based ANN model inputs are hydrocarbon fluid properties, liquid flow rate (qL), gas-liquid ratio (GLR), water-oil ratio (WOR), well true vertical depth (TVD), wellhead pressure (Pwh), wellhead temperature (Twh), and temperature gradient (dT/dh). A data-based ANN model is also developed based on only TVD, Pwh, qL, GLR, and WOR. Both physics- and data-based ANN models are trained through hyperparameter optimization using genetic algorithm and K-fold validation and then tested against the gauge FBHP. The results reveal that both models perform well with the FBHP prediction from field data with a normalized mean absolute error (NMAE) of around 10%. However, a comparison between results from the physics- and data-based ANN models shows that the accuracy of the physics-based model is higher at the later phase of the gas lift operation when the steady-state pipe flow is well established. On the contrary, the data-based model performs better for the early phase of gas lift operation when transient flow behavior is dominant. Developed ANN models and workflows can be applied to optimize gas lift operations under different fluid and well conditions.

Building ontology-based temporal databases for data reuse: An applied example on hospital organizational structures.

Keeping track of data semantics and data changes in the databases is essential to support retrospective studies and the reproducibility of longitudinal clinical analysis by preventing false conclusions from being drawn from outdated data. A knowledge model combined with a temporal model plays an essential role in organizing the data and improving query expressiveness across time and multiple institutions. This paper presents a modelling framework for temporal relational databases using an ontology to derive a shareable and interoperable data model. The framework is based on: OntoRela an ontology-driven database modelling approach and Unified Historicization Framework a temporal database modelling approach. The method was applied to hospital organizational structures to show the impact of tracking organizational changes on data quality assessment, healthcare activities and data access rights. The paper demonstrated the usefulness of an ontology to provide a formal, interoperable, and reusable definition of entities and their relationships, as well as the adequacy of the temporal database to store, trace, and query data over time.

Ultrasound breast images denoising using generative adversarial networks (GANs)

INTRODUCTION: Ultrasound in conjunction with mammography imaging, plays a vital role in the early detection and diagnosis of breast cancer. However, speckle noise affects medical ultrasound images and degrades visual radiological interpretation. Speckle carries information about the interactions of the ultrasound pulse with the tissue microstructure, which generally causes several difficulties in identifying malignant and benign regions. The application of deep learning in image denoising has gained more attention in recent years. OBJECTIVES: The main objective of this work is to reduce speckle noise while preserving features and details in breast ultrasound images using GAN models. METHODS: We proposed two GANs models (Conditional GAN and Wasserstein GAN) for speckle-denoising public breast ultrasound databases: BUSI, DATASET A, AND UDIAT (DATASET B). The Conditional GAN model was trained using the Unet architecture, and the WGAN model was trained using the Resnet architecture. The image quality results in both algorithms were measured by Peak Signal to Noise Ratio (PSNR, 35–40 dB) and Structural Similarity Index (SSIM, 0.90–0.95) standard values. RESULTS: The experimental analysis clearly shows that the Conditional GAN model achieves better breast ultrasound despeckling performance over the datasets in terms of PSNR = 38.18 dB and SSIM = 0.96 with respect to the WGAN model (PSNR = 33.0068 dB and SSIM = 0.91) on the small ultrasound training datasets. CONCLUSIONS: The observed performance differences between CGAN and WGAN will help to better implement new tasks in a computer-aided detection/diagnosis (CAD) system. In future work, these data can be used as CAD input training for image classification, reducing overfitting and improving the performance and accuracy of deep convolutional algorithms.

Explicit Data-Based Model for Predicting Oil-Based Mud Viscosity at Downhole Conditions.

One of the functions of drilling mud is to transport cuttings out of a drilled well. To fulfill this purpose, the mud must possess optimal viscous characteristics. The main goal of this study is to develop a model for estimating the plastic viscosity (PV) of oil-based muds (OBMs) at downhole conditions, especially when high-temperature, high-pressure wells are drilled. Existing methods used by previous researchers include the following: the multiplicative factor method, the relative dial readings approach, and the nonlinear regression technique. These methods do not result in flexible, generalizable, accurate, or robust models. Furthermore, existing literature mostly contains models that are only applicable under normal temperature and pressure conditions (surface conditions). To overcome these challenges, this study employed the artificial neural network (ANN) technique to predict the PV of OBM under downhole conditions. The data used to develop the model consists of 88 OBM laboratory PV measurements obtained from literature. The findings indicate that the performance of the developed model using the statistical metrics of means square error (MSE), root-mean-square error (RMSE), and correlation coefficient (R) was 0.0185, 0.136, and 0.967 respectively. To test for the generalizability of the developed model, a new data set consisting of 56 data points was used. In this regard, the model had an R value of 0.80, an MSE of 0.95, and an RMSE of 0.975. In order to ascertain the parametric importance of the inputs, a connection weight algorithm was utilized. In this regard, the downhole pressure had a higher influence on the PV (64.5%) than that of downhole temperature (35.5%). To make the models simple to incorporate in software applications, they were explicitly presented. In terms of memory requirements and processing speed for software applications, the models had a memory footprint of 48 bytes and required 12 floating-point operations to give an output. This model is valid for 20 °C ≤ temperature ≤315 °C and 0 psi ≤ pressure ≤40,000 psi. The characteristics of the models proposed in this work for which originality is claimed include the models' computational cost evaluation, their explicitness, and a suggestion for using them in the field. Upon utilizing the proposed models, time-consuming laboratory measurements of PV would no longer be necessary, and real-time results could be provided in the field.

Hierarchical higher-order dynamic mode decomposition for clustering and feature selection

This article introduces a novel, fully data-driven method for forming reduced order models (ROMs) in complex flow databases that consist of a large number of variables. The algorithm utilizes higher order dynamic mode decomposition (HODMD), a modal decomposition method, to identify the main frequencies and associated patterns that govern the flow dynamics. By incorporating various normalization techniques into an iterative process, clusters of variables with similar dynamics are identified, allowing the classification of different instabilities and patterns present in the flow. This method, known as hierarchical HODMD (h-HODMD), has been thoroughly tested in the development of ROMs using three different databases obtained from numerical simulations of a nonpremixed coflow methane flame. The effectiveness of h-HODMD has been demonstrated as it consistently outperforms HODMD in terms of modeling and reconstructing flow dynamics using a reduced number of modes. Additionally, the clusters of variables identified by h-HODMD reveal the algorithm's ability to group chemical species whose behavior is consistent from a kinetic perspective. h-HODMD allows for the construction of inexpensive reduced dynamical models that can predict flame liftoff, identify the occurrence of local extinction and blowout conditions, and facilitate control purposes.

Bioindication for Ecosystem Regeneration towards Natural conditions: the BERN data base and BERN model

The primary task of the BERN database is to document reference data on typical site parameters for the occurrence of plant communities in which their diagnostic species are in competitive equilibrium with each other and in homeostatic equilibrium with the site factors. Common approaches for the creation of a site-plant database such as ordination or bioindication based on individual species like PROPS or MultiMOVE model are of limited use because it is not possible to determine the potential occurrence of a plant species on the basis of site factors, since the competitive influences cannot be determined in advance according to current knowledge. Therefore, the BERN database takes into account the structure of plant communities with the abundance and dominance of species in the competitive equilibrium of plant communities as a reference for determining anthropogenically induced changes. Qualitative knowledge on the relationship between site types and vegetation communities is widely available, as can be seen from the extensive phytosociological publications. For this purpose, synoptic tables and their location descriptions of around 50,000 relevés were evaluated. The BERN database includes currently 887 central European plant communities and links to their diagnostically defining species composition. The database defines the niche of 2210 central European plant species for the soil properties pH, base saturation, carbon to nitrogen ratio, and wetness index and the climatic properties continentality, length of vegetation period, solar radiation and climatic water balance. The BERN model recombines the realised species niches that mainly form the competitively homeostatic structure of a plant community in order to determine the fundamental multifactorial niche of this community. The BERN database contains mainly historical recordings of more or less undisturbed sites. The BERN model (Bioindication for Ecosystem Regeneration towards Natural conditions) as an application module of the BERN database was developed to integrate ecological cause-effect relationships into studies on environmental status assessment and forecasting. The BERN database now has been published for the first time. The methodology of creating the BERN database and the BERN model are documented and applications are demonstrated with examples. The freely available database should invite you to supplement and modify it.

Development of Data-driven Machine Learning Models and their Potential Role in Predicting Dengue outbreak.

Dengue fever is one of the most widespread vector-borne viral infections in the world, resulting in increased socio-economic burdens. The WHO has reported that 2.5 billion people are infected with dengue fever across the world, resulting in high mortalities in tropical and subtropical regions. The current article endeavors to present an overview of predicting dengue outbreaks through data-based machine-learning models. This artificial intelligence model uses real-world data such as dengue surveillance, climatic variables, and epidemiological data and combines big data with machine learning algorithms to forecast dengue. Monitoring and predicting dengue incidences have been significantly enhanced through innovative approaches. This involves gathering data on various climatic factors, including temperature, rainfall, relative humidity, and wind speed, along with monthly records of dengue cases. The study functions as an efficient warning system, enabling the anticipation of dengue outbreaks. This early warning system not only alerts communities but also aids relevant authorities in implementing crucial preventive measures.

Reaction Mechanisms and Plasma-Catalyst Interaction in Plasma-Assisted Oxidation of n-Butane: A Data-Driven Approach

Experimental investigations of n-butane oxidation under atmospheric-pressure plasma conditions and in He-dilution have provided detailed information on the power-dependence of the conversion of C4H10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {C}_{4}\ ext {H}_{10}$$\\end{document} into CO and CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {CO}_{2}$$\\end{document} at 450 K surface temperature. The rf-plasma discharge has been equipped with a MnO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {MnO}_{2}$$\\end{document}-catalyst, and a significant impact on the reaction chain due to the presence of the catalyst surface could be observed. We report on ongoing data-based model development. Recently, a reaction kinetic model has been published, which agrees well with the experimental data (Stewig et al. in Plasma Sources Sci Technol 32:105006, 2023). However, that model could not clearly identify the main mechanisms in the interaction of plasma and catalyst. We show that various models can be found that explain the data similarly well. Detailed sensitivity analysis shows that only a maximum of three parameters can be identified in all the models considered for the currently limited data. Despite this limitation, we intend to continue the data analysis using more general models and introduce possible surface effects. Such unified models simultaneously describe the experimental data from both measurements with and without catalyst using a single set of physical parameters. To evaluate the hypotheses, we present numerical results for certain ranges of experimental parameters, which, in a subsequent experimental verification, allows to exclude or confirm one or another model.

Safety Performance Assessment via Virtual Simulation of V2X Warning Triggers to Cyclists with Models Created from Real-World Testing

The overall crash statistics in the EU still show a very significant number of car–cyclist crashes. Within the Horizon 2020 project Safe-Up, countermeasures have been developed to reduce this number. One of these countermeasures involves a V2X-enhanced on-board unit for cycles, which can provide on-time warning triggers. The research assumption was based on studying the benefits of connectivity in enhancing cyclists’ safety. This study assessed the performance of this potential technology both qualitatively by analyzing volunteer feedback during physical testing and quantitatively by virtual simulations. The volunteers’ study showed positive findings on system’s safety relevance, user experience, and user acceptance. The method applied for the virtual simulation is a prospective safety performance assessment with reconstructed accident scenarios based on the GIDAS database and cyclist behavior models, obtained from physical testing. The results using a warning trigger 4 s prior to the collision showed a potential safety benefit of approximately 98%. It should be noted that this trigger time was found to be quite early in both physical testing and virtual simulation. Further research is required to evaluate the system’s performance in more complex urban scenarios, as well as to design the human–machine interaction strategies for optimal accident avoidance.

Data integration strategies for whole-cell modeling.

Data makes the world go round-and high quality data is a prerequisite for precise models, especially for whole-cell models (WCM). Data for WCM must be reusable, contain information about the exact experimental background, and should-in its entirety-cover all relevant processes in the cell. Here, we review basic requirements to data for WCM and strategies how to combine them. As a species-specific resource, we introduce the Yeast Cell Model Data Base (YCMDB) to illustrate requirements and solutions. We discuss recent standards for data as well as for computational models including the modeling process as data to be reported. We outline strategies for constructions of WCM despite their inherent complexity.