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In this article we study three types of uncitedness in Library and Information Science journals: uncitedness for articles, authors and topics. One important aspect in this study is giving accurate definitions of the indicators for measuring uncited papers, uncited authors and uncited topics. It is found that for the period 1991–2010 ratios of uncited papers fluctuate within the interval [0,0.1]. This ratio is relatively stable and not very high. Comparison of average number of pages, average number of references, average number of authors per paper and percentage of single-authored papers between cited and uncited papers shows that no matter the journal, the first three indicators’ values for uncited papers are lower, while the values of the fourth indicator are higher, than the corresponding values for cited papers. The fact that almost all uncited authors in a journal published only one paper in this journal illustrates that a journal's uncited authors are the least productive authors in this journal. Yet, productive and highly cited authors also publish uncited papers. As to why some topics fall into the group of uncited topics, the hypothesis is that the combination of unfamiliar keywords forms an unfamiliar topic, a topic authors have elected not to study further. Another assumption is that some uncited topics fall outside the field of Library and Information Science. Retrieval results in the Web of Science for a set of uncited keywords and keyword combinations support this assumption. [ABSTRACT FROM AUTHOR]

• This paper utilizes novel connected vehicle (CV) emulated data to predict real-time crash potential for arterials. • The CV emulated data are flexible to obtain. Different speed-related variables are estimated based on the CV emulated data to depict continuous traffic conditions • The results proved the feasibility of using CV emulated data for real-time crash potential prediction • The proposed methods can be applied to other types of vehicles Real-time crash potential prediction could provide valuable information for Active Traffic Management Systems. Fixed infrastructure-based vehicle detection devices were widely used in the previous studies to obtain different types of data for crash potential prediction. However, it was difficult to obtain data in large range through these devices due to the costs of installation and maintenance. This paper introduced a novel connected vehicle (CV) emulated data for real-time crash potential prediction. Different from the fixed devices' data, CV emulated data have high flexibility and can be obtained continuously with relatively low cost. Crash and CV emulated data were collected from two urban arterials in Orlando, USA. Crash data were archived by the Signal for Analytics system (S4A), while the CV emulated data were obtained through the data collection API with a high frequency. Different data cleaning and preparation techniques were implemented, while various speed-related variables were generated from the CV emulated data. A Long Short-term Memory (LSTM) neural network was trained to predict the crash potential in the next 5−10 min. The results from the model illustrated the feasibility of using a novel CV emulated data to predict real-time crash potential. The average and 50th percentile speed were the two most important variables for the crash potential prediction. In addition, the proposed LSTM outperformed Bayesian logistics regression and XGBoost in terms of sensitivity, Area under Curve (AUC), and false alarm rate. With the rapid development of the connected vehicle systems, the results from this paper can be extended to other types of vehicles and data, which can significantly enhance traffic safety. [ABSTRACT FROM AUTHOR]

This paper presents the design, implementation, and practical use of a specialized online data repository and an interactive visualization of a transportation dashboard. Specifically tailored to handle and interpret large-scale transportation data within the Qatari context, the combined platform serves as a comprehensive solution for managing extensive datasets, including GPS traces from taxis and e-scooters, which are examined as primary use-cases in this paper. The online data repository provides a centralized hub for efficient data storage and management of public transport including Mobility-as-a-Service (MaaS) data. Concurrently, the visualization dashboard fosters an intuitive, user-friendly interface for data exploration and analysis. Through real-world applications within Qatar's transportation ecosystem, we elucidate how these innovative developments can inform data-driven policy decisions in crucial areas such as infrastructure development, resource allocation, and safety measures. Ultimately, this study underscores the pivotal role of effective data management and advanced visualization in maximizing the potential of big data, providing valuable insights for urban mobility planning and enhancing the landscape of policy-making in Qatar and worldwide. [ABSTRACT FROM AUTHOR]

Distributed Scatterers Interferometry (DS-InSAR) has been widely applied to increase the number of measurement points (MP) in complex mountainous areas with dense vegetation and complicated topography. However, DS-InSAR method adopts batch processing mode. When new observation data acquired, the entire archived data is reprocessed, completely ignoring the existing results, and not suitable for high-performance processing of operational observation data. The current research focuses on the automation of SAR data acquisition and processing optimization, but the core time series analysis method remains unchanged. In this paper, based on the traditional Sequential Estimator proposed by Ansari in 2017, a Recursive Sequential Estimator with Flexible Batches (RSEFB) is improved to divide the large dataset flexibly without requirements on the number of images in each subset. This method updates and processes the newly acquired SAR data in near real-time, and obtains long-time sequence results without reprocessing the entire data archived, helpful to the early warning of landslide disaster in the future. 132 Sentinel-1 SAR images and 44 TerraSAR-X SAR images were utilized to inverse the line of sight (LOS) surface deformation of Xishancun landslide and Huangnibazi landslide in Li County, Sichuan Province, China. RSEFB method is applied to retrieve time-series displacements from Sentinel-1 and TerraSAR-X datasets, respectively. The comparison with the traditional Sequential Estimator and validation through Global Position System (GPS) monitoring data proved the effectiveness and reliability of the RSEFB method. The research shows that Xishancun landslide is in a state of slow and uneven deformation, and the non-sliding part of Huangnibazi landslide has obvious deformation signal, so continuous monitoring is needed to prevent and mitigate possible catastrophic slope failure events. [ABSTRACT FROM AUTHOR]

This paper estimates the effect of religious labor market concentration on clergy wages. Using data on the state-level Herfindahl-Hirschman Index (HHI) from the Association of Religion Data Archives (ARDA), and data on individual clergy wages from the Census/American Community Survey (ACS), I estimate the effect of monopsony in the market for religion on clergy wages for the years 1980 to 2020. Ordinary least squares (OLS) estimates suggest the elasticity of individual clergy wages to the state-level HHI ranges between -0.05 and -0.17. These results are robust to the inclusion of controls for individual clergy characteristics, state-level religious tradition adherent shares, state-level labor market concentration, state- and year-fixed effects, and religious group-specific time trends. Application of the Oster (2019) test for selection on unobservables suggests that OLS understates the negative effect of concentration on clergy wages. The OLS results are supported by an instrumental variables (IV) specification exploiting a Bartik-style instrument interacting historic concentration with national concentration growth. The IV estimates suggest elasticities between -0.11 and -0.19. [ABSTRACT FROM AUTHOR]

There are various paradigms of learning. One such paradigm is learning from knowledge assimilation. In machine learning, such a paradigm may be considered where concepts (classification models) generated from different algorithms applied to the same training data are combined to come up with a single integrated improved concept (classification model). This is different from combining outputs of various models as in Bagging or Boosting. Thus, learning from knowledge assimilation creates different alternative models that work on different parts of the search space. Such models can be separately optimized so that the best-performing model can be selected for the given problem. For optimization there are several algorithms and Chemical Reaction Optimization (CRO) is a chemistry-based nature-inspired optimization algorithm. In this algorithm, a set of optimization approaches are introduced corresponding to various types of chemical reactions. Classification models in our approach are optimized using a technique called Elitist CRO(ECRO). ECRO is an improvement over CRO, where the optimization approaches are modified making the convergence of the algorithm better than CRO. Although for classification CRO models based on neural networks exist, however, they suffer from interpretability. This paper proposes an application of a new Hybrid Elitist Chemical Reaction Optimization (HECRO) algorithm to generate interpretable classification rules. The proposed algorithm is an extension of CRO and ECRO, in the manner of region exploration of the search space, which is expanded by combining classification rules generated from various algorithms as the initial population following learning from knowledge assimilation. Experimental results with 20 datasets drawn from the UCI Machine Learning data repository show improvement over several performance measures in comparison to the base classifiers used for population generation in HECRO. [ABSTRACT FROM AUTHOR]

Research in radiation oncology often involves manual review and entry of large volumes of patient information into data repositories. This is labor intensive, costly, and may divert effort of learners (e.g. students and residents) away from more educational experiences. We hypothesize that we could use artificial intelligence (AI) to develop software to complete complex tasks related to data screening, cleaning, and entry into research databases. AI with ChatGPT4 was queried in an iterative process to build executable software using Python programming language and spreadsheet software macros to meet the data extract needs for two IRB-approved research projects. No patient data was shared with AI. In Project 1, paper questionnaires filled out by hand during clinic visits were obtained to collect patient and physician reported outcomes in the use of prostate radiotherapy. In Project 2, patient data from numerous global institutions were submitted to be included in a meta-analysis of individual patient data of hepatocellular carcinoma. Data were to be manually checked for completeness and inconsistencies, then comments were generated to be returned to the sender with requests to correct any remaining deficiencies. For both projects, we quantified the time taken to create the AI-derived software. For Project 1, where students have been employed over prior years and some have used time-tracking software to log their work, we quantified the time/cost that is predicted to be saved by avoiding manual data extraction. A radiation oncologist (TY), with experience using a programming environment (and no experience with Python or building spreadsheet software macros), used ChatGPT4 to build software to complete all required tasks for both Project 1 and 2 (Table 1). For Project 1, ≈40 hours of development produced software to digitize human markings on patient and physician reported outcomes into tabular form. In prior years, undergraduate and medical students have performed these tasks at a rate of ≈2-4 minutes/page with a departmental cost of ≈$0.7/page. Software generated by ChatGPT4 performs the same task at a rate of 1.6 seconds per page and can be run in perpetuity without cost. For Project 2, ≈35 hours of development were required to build a spreadsheet software macro to review and identify missing data, inconsistencies (e.g., incompatible dates), or unexpected variables (e.g., out-of-range laboratory results), as well as suggest comments to be returned to the sender. The macro was found to make no errors and is currently being used in lieu of hiring a research assistant. We were able to use AI to create software that can extract data accurately from paper forms and also review and clean large datasets with entry into registries to facilitate radiation oncology research. [ABSTRACT FROM AUTHOR]

In this paper we aim to analyse the adoption of Current Research Information Systems (CRIS) for Research Data Management (RDM). We show how CRISs hold a key role in facilitating the management and reporting of an institution's research activities and outputs – not only do they offer extensive functionality for researchers and research administrators to effectively manage all aspects of their research information, but are also integrating more and more with specialized RDM tools, Institutional Repositories (IR), and other external systems. This paper provides an overview of how CRISs have evolved and integrated to become a crucial part of the RDM chain, including the interoperability, registration, linking, and archiving of data. [ABSTRACT FROM AUTHOR]

Nuclear data evaluations available in existing nuclear data libraries are derived based on differential measurements that includes experimental effects such as target temperature, time-of-flight resolution, data normalization, self-shielding, multiple scattering, etc. Measurements are often made at temperatures corresponding to room temperature, 293.6 K, to avoid complexity in the experimental setup and costs of carrying out measurements for temperatures other than room temperature. This paper investigates the impact of experimental effects on the evaluation of a set of resonance parameters that fit the experimental differential data and its use in integral benchmark calculations. Given the importance of the temperature in integral benchmark results, the impact of the Doppler effect will be examined. Very seldom are experimental differential data available for temperatures below or above room temperature. Nuclear data measurements and evaluation needs are driven by reactor applications; consequently, the majority of data evaluations in nuclear data libraries are for temperatures above room temperature. Recently there has been a demand for nuclear data for low temperatures, below room temperatures, for criticality safety applications. Currently, calculations in response to low-temperature needs are based on extrapolating the existing data from the nuclear data libraries to temperatures below 293.6 K. For temperatures above 293.6 K, common practice is to process the data library to temperatures different from the temperature it was evaluated and use them in practical applications. Although this is an acceptable practice, care should be taken to understand whether the validity of the nuclear data can be extended to low and high temperatures. Issues in connection with temperature effects for low and high temperature nuclear data and their impact on practical applications are addressed in the paper. Given that experimental data for low- and high-temperatures are scarce, the results of the presented approach are based on data simulations. Simulated data for 235U in the resonance region, in particular the resolved resonance region, were used as part of the studies and demonstration. Furthermore, temperature effects were also investigated for thermal neutron scattering data, S (α , β) , for light water. However, the thermal scattering data are not based on simulation, but are the result of measurements carried out at the Spallation Neutron Source. A continuous-energy nuclear data library was used in Monte Carlo calculations to assess the impact in integral benchmark results. [ABSTRACT FROM AUTHOR]

• The methods to transform 3D gradation to 2D gradation are various, and there lacks a reliable method to verify different methods. • Semi whole scale cross section (SWSCS) method proposed in this paper shows that the phase separation method is the most reliable to transform 3D gradation to 2D gradation. • Geometry shape data library (GSDL) of aggregates and the assembling method developed in this paper is convenient to generate the designed two-dimensional virtual asphalt mixtures. • The FE simulation results are in accordance with literature data and the two-step Mori-Tanaka's analytical method in the study of dynamic modulus of the asphalt mixture with voids. This paper aims at finding a robust method to generate a two-dimensional virtual asphalt mixture and to verify the suitability of the generated virtual mixture. First, three methods namely stereological method, Walraven formula and phase separation method are adopted to convert the actual three-dimensional gradation curves into two-dimensional ones. Then, an approach called semi whole scale cross section (SWSCS) method is developed to verify the results obtained and to determine the most robust procedure to follow. Third, the numerical two-dimensional geometry shape data library (GSDL) of different aggregates and voids and a robust method to assemble representative volume elements (RVEs) are developed using Matlab. Finally, a gradation curve from the literature is considered to generate a virtual asphalt mixture specimen, which is then imported in the finite element (FE) software Abaqus to simulate the dynamic modulus test. The virtual test results are further verified using available literature and another homogenization numerical prediction method based on the Mori-Tanaka's approach. The results show that the phase separation method is effective to convert the three-dimensional gradation curve to a two-dimensional one. The simulation results of the virtual asphalt mixture developed in this study turn out to be in good agreement with both previously presented literature data and Mori-Tanaka's theory. [ABSTRACT FROM AUTHOR]

In the design of fusion reactors, extensive shielding calculations are necessary to verify the feasibility and safety of the design. The issue of deep penetration is a common and significant problem in fusion reactor shielding calculations. When the thickness of the shielding layer exceeds a certain value, the statistical error in Monte Carlo method calculations significantly increases. Therefore, it is imperative to adopt variance reduction techniques to enhance the computational accuracy for deep penetration problems. cosRMC is a reactor Monte Carlo code, and its accuracy in calculating neutron-deep-penetration shielding problems should be verified. For this purpose, three deep penetration benchmarks from Shielding Integral Benchmark Archive Database (SINBAD) were simulated by cosRMC and neutron cross section data library ENDF/B-VII.0. These benchmarks describe the penetration behaviors of neutrons in models with different geometric shapes and materials. This paper shows detailed cosRMC models based on benchmark experimental models, used to calculate neutron flux spectra at different penetration depths, which are recorded by detectors in the related benchmark experiments. In this way, a direct comparison between cosRMC results and experimental measurements is provided. In order to improve the accuracy of cosRMC in calculating deep penetration problems, the variance reduction technique of cell importance was used. The neutron flux spectrum calculated by cosRMC was compared with the neutron flux spectrum results calculated by MCNP5 and the experimental values of the benchmarks. The comparison results show that the calculations of cosRMC are in compliance with the MCNP5 reference values and experimental values, and the relative deviations between calculations of cosRMC and MCNP5 is within three time of standard errors. Therefore, this paper has verified that cosRMC can applies to neutron deep penetration problems of fusion reactors. [ABSTRACT FROM AUTHOR]

Major evaluations have been updated in the latest release of the Evaluated Nuclear Data library (ENDF/B-VIII.0) and is the currently recommended pointwise library for criticality and depletion analysis in the open-source Monte Carlo code — OpenMC. However, the currently released OpenMC library does not yet include an accompanying windowed multipole library (WMP) that enables on-the-fly Doppler broadening. This paper revisits the windowed multipole representation and fixes a major drawback that has prevented its use for depletion analysis. The new WMP library presented in this paper explicitly treats key absorption reactions as separate channels such that proper reaction rates are provided during depletion analysis. Based on this improvement, the new WMP library is processed for ENDF/B-VIII.0 and then verified against the pointwise data through direct comparison, criticality benchmarks and fuel assembly depletion. The numerical results show good agreement and efficiency with the pointwise library. • The Windowed Multipole Library (WMP) for ENDF/B-VIII.0 has been released to support the OpenMC criticality and depletion analysis. • The current implementation of WMP library has been extended to include explicit absorption reaction cross sections in resolved resonance range. • The computational efficiency of WMP-based Doppler Broadening has been investigated against stochastic interpolation in criticality and depletion analysis. [ABSTRACT FROM AUTHOR]

Federated learning is a data decentralization privacy-preserving technique used to perform machine or deep learning in a secure way. In this paper we present theoretical aspects about federated learning, such as the presentation of an aggregation operator, different types of federated learning, and issues to be taken into account in relation to the distribution of data from the clients, together with the exhaustive analysis of a use case where the number of clients varies. Specifically, a use case of medical image analysis is proposed, using chest X-ray images obtained from an open data repository. In addition to the advantages related to privacy, improvements in predictions (in terms of accuracy, loss and area under the curve) and reduction of execution times will be studied with respect to the classical case (the centralized approach). Different clients will be simulated from the training data, selected in an unbalanced manner. The results of considering three or ten clients are exposed and compared between them and against the centralized case. Two different problems related to intermittent clients are discussed, together with two approaches to be followed for each of them. Specifically, this type of problems may occur because in a real scenario some clients may leave the training, and others enter it, and on the other hand because of client technical or connectivity problems. Finally, improvements and future work in the field are proposed. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of automating data-driven condition assessments for concrete bridges, hindered by limited data availability. The developed method consists of (1) data integration and standardization, and (2) condition assessment modules. The first module captures, structures, and integrates bridge data from diverse sources, including inspection reports, using web scraping and rule-based data extraction. It standardizes inspection data through text mining and natural language processing. The second module employs Bayesian belief networks to assess bridge deck conditions, leveraging standardized data. The method was validated on a set of bridges in Québec, Canada, resulting in a structured, integrated data repository with standardized data from 2255 inspection reports. This repository supports the development of advanced asset management tools for this class of bridges. The method achieved 94.6% accuracy, 95.0% precision, 94.7% recall, and 94.1% F1 score, demonstrating its potential to help transportation agencies improve bridge data and asset management efficiency. • Automated method for data-driven condition assessment of concrete bridges. • Automated system to capture and integrate bridge data scattered across diverse sources. • Bridge inspection data is standardized to support analytical use and improved condition assessments. • Bayesian belief networks to enable defect-based condition assessment of concrete bridge decks. [ABSTRACT FROM AUTHOR]

This study is focused on a comparison of experimental neutron flux data from the VR-1 reactor with results from Serpent calculation. Serpent data were calculated by the dynamic part of the code and the steady-state criticality calculation. The Serpent calculation was performed with the JEFF 3.3 and ENDF/B-VIII.0 nuclear data library. This paper expands previous work and focuses on the integral control rod worth. Comparison of the ENDF/B-VIII.0 and JEFF 3.3 nuclear data libraries with experimental data is especially valuable. The comparison shows better performance of JEFF 3.3 nuclear data instead of ENDF/B-VIII.0 for the transient calculation of the cases presented in this article. In addition, the comparison of static and dynamic reactivity provided an interesting difference, especially for small rod insertion. Moreover, the study confirms that the Serpent dynamic external source simulation mode provides an accurate description of reactor behavior, and that the shape of the integral control rod worth is close to one experimentally measured. • Control rod drop experiment was performer at VR-1 reactor. • Results from experiment were compared with Serpent dynamic calculation. • Diff. nuclear data libraries were used to study difference in delayed neutron data. • JEFF 3.3 nuclear data library provides better description compared to ENDF/B-VIII.0. [ABSTRACT FROM AUTHOR]

Automating ergonomic assessment improves both objectivity and cost-effectiveness. However, existing ergonomic scales remain susceptible to inaccuracy and insensitivity when assessing diversified construction activities. This susceptibility stems from the unreliable ranges, sharp boundaries, and oversimplified binary rules within conventional joint-level assessment rules, coupled with the restricted transformation rules to integer input/output. This paper presents a data-driven ergonomic assessment method grounded in statistical data from Construction Motion Data Library (CML) dataset, comprising: 1) Developing joint-level scoring models to primarily improve accuracy through leveraging Heuristics Gaussian Cloud Transformation (H-GCT), and 2) Designing fuzzy inference mechanism to enhance sensitivity through retaining risk information during transformation across levels. Subsequently, a three-step validation confirms accurate risk identification, sensitive risk feedback, and positive correlation with previous methods, contributing to occupational health and safety management. Future research could enhance the method by expanding the pose dataset, recruiting more participants, and refining the 3D pose estimation. • Conventional assessment rules within ergonomic scales impose restrictions to the accuracy and sensitivity. • Data-driven approach leverages H-GCT and statistical data from CML dataset. • Joint-level scoring models are developed to enhance unreliable ranges, sharp boundaries, and oversimplified binary rules. • Fuzzy inference mechanism is specially designed for retaining risk information during transformation across levels. • Validation confirms accurate risk identification, sensitive risk feedback, and positive correlation with previous methods. [ABSTRACT FROM AUTHOR]

Studies concerning solar eclipses have been rising significantly, yet, different circumstances during their occurrence provide uniqueness to every study. This paper studies the ionospheric and meteorological response to the total solar eclipses of August 21, 2017, March 20, 2015 and August 1, 2008. The ionospheric total electron content (TEC) was calculated from the signals beamed by the dual-frequency Global Positioning System (GPS) satellites and accessed from University NAVSTAR Consortium (UNAVCO) data archive. Similarly, the data of meteorological parameters were accessed from the historical climate archive of respective countries. The TEC drop of ∼2–7 TECU with a lag of ∼15–30 min is observed at varying latitudes which correspond with the findings of numerous past research. We analyzed the data of 15 stations under ∼100% obscuration to rule out the varying effects of different obscuration rates. Yet, given the turbulent nature of climate, we found varying changes at observed locations. A good relationship, however, was observed in 8 of the stations, where temperature drop ranged from 0.4 °C to 6.11 °C, and rise in relative humidity ranged from 0 to ∼77%. Wind speed has shown the most turbulent behavior. Their change was largely impacted by the eclipse on 5 of the stations, while the local factor was dominant on the others. In spite of this, the stations under observation showed distinct responses to the ionospheric change during the total solar eclipses, thus demonstrating the relation of meteorological parameters with eclipses. [ABSTRACT FROM AUTHOR]

This paper reports on major outcomes of the ERA-PLANET (The European network for observing our changing planet) project, which was funded under Horizon 2020 ERA-net co-funding scheme. ERA-PLANET strengthened the European Research Area in the domain of Earth Observation (EO) in coherence with the European participation to Group on Earth Observation and the Copernicus European Union's Earth Observation programme. ERA-PLANET was implemented through four projects focused on smart cities and resilient societies (SMURBS), resource efficiency and environmental management (GEOEssential), global changes and environmental treaties (iGOSP) and polar areas and natural resources (iCUPE). These projects developed specific science-policy workflows and interfaces to address selected environmental policy issues and design cost-effective strategies aiming to achieve targeted objectives. Key Enabling Technologies were implemented to enhancing 'data to knowledge' transition for supporting environmental policy making. Data cube technologies, the Virtual Earth Laboratory, Earth Observation ontologies and Knowledge Platforms were developed and used for such applications. SMURBS brought a substantial contribution to resilient cities and human settlements topics that were adopted by GEO as its 4th engagement priority, bringing the urban resilience topic in the GEO agenda on par with climate change, sustainable development and disaster risk reduction linked to environmental policies. GEOEssential is contributing to the development of Essential Variables (EVs) concept, which is encouraging and should allow the EO community to complete the description of the Earth System with EVs in a close future. This will clearly improve our capacity to address intertwined environmental and development policies as a Nexus. iGOSP supports the implementation of the GEO Flagship on Mercury (GOS4M) and the GEO Initiative on POPs (GOS4POPs) by developing a new integrated approach for global real-time monitoring of environmental quality with respect to air, water and human matrices contamination by toxic substances, like mercury and persistent organic pollutants. iGOSP developed end-user-oriented Knowledge Hubs that provide data repository systems integrated with data management consoles and knowledge information systems. The main outcomes from iCUPE are the novel and comprehensive data sets and a modelling activity that contributed to delivering science-based insights for the Arctic region. Applications enable defining and monitoring of Arctic Essential Variables and sets up processes towards UN2030 SDGs that include health (SDG 3), clean water resources and sanitation (SDGs 6 and 14). • Earth Observation can support an efficient science-policy interaction by trusted information. • Policy targets are inherently multidisciplinary, posing challenges on integrating knowledge from different scientific domains. • Specific science-policy workflows and implemented applications were demonstrated through ERA-PLANET. • A comprehensive approach to generate and delivery knowledge from EO and Earth Sciences to policy-makers was developed. [ABSTRACT FROM AUTHOR]

Consolidation of the research information improves the quality of data integration, reducing duplicates between systems and enabling the required flexibility and scalability when processing various data sources. We assume that the combination of a data lake as a data repository and a data wrangling process should allow low-quality or "bad" data to be identified and eliminated, leaving only high-quality data, referred to as "research information" in the Research Information System (RIS) domain, allowing for the most accurate insights gained on their basis. This, however, would lead to increased value of both the data themselves and data-driven actions contributing to more accurate and aware decision-making. This cleansed research information is then entered into the appropriate target Current Research Information System (CRIS) so that it can be used for further data processing steps. In order to minimize the effort for the analysis, the proliferation and enrichment of large amounts of data and metadata, as well as to achieve far-reaching added value in information retrieval for CRIS employees, developers and end users, this paper outlines the concept of a curated data lake with the data wrangling process, showing how it can be used in CRIS to clean up data from heterogeneous data sources during their collection and integration. [ABSTRACT FROM AUTHOR]

Abstract In 2015, Radboud University (Nijmegen, the Netherlands) started a project to extend its CRIS (Metis) with functionalities that allow researchers to register (metadata) and archive (uploading files) their research data, while at the same time making the data available for reuse in a FAIR way (via national Dutch data archive DANS). The new functionality was integrated with already existing functions in the CRIS, thus offering a one-stop-shop interface to researchers in which registration and archiving of data is combined with registration of publications, the uploading of full text to the university's repository, the linking of datasets and publications and the creation of researcher's profile (CV) pages. Next to the functional extension of the CRIS, the project also included an organizational element: the establishment of support and management structures and workflows, including data curation processes, in order to assure the quality of the data registration process and to foster the FAIRness of the research data. In the period up to now, we continued to transform the university's CRIS, by bringing it in line with the research life cycle perspective and policy changes in Research Data Management (RDM), including a Data Management Plan (DMP) module and FAIR data. In this paper, it will be argued and demonstrated that both for researchers and research institutes, a CRIS oriented approach to RDM brings added value. We also point to future use cases that put a central role for CRIS's even earlier within the research life cycle, e.g. at pre-registration of research questions and informed consent/ethics approval procedures. We further envision our CRIS to play a linking pin function between storage and service locations of data during research and at publication. The paper will use Radboud University as a good practice of past, present and future use of CRIS's in the research life cycle that universities and research institutes as well as researchers and research support desks are currently dealing with in the FAIR data era. [ABSTRACT FROM AUTHOR]

Highlights • Net Present Value (NPV) investment analysis is simulated using significant factors. • District heating (DH) price is simulated using a dynamic pricing model. • Waste heat to DH from medium and large DCs provide positive NPV with high probability. • Small DCs are sensitive to input factors and may require alternative customers to DH. • The most influential factors are heat and electricity price and operative labor costs. Abstract The growth of computing and Internet use have attracted the attention of the general public concerning the carbon footprint of data centers (DCs). Previous research has focused on the implementations of energy efficiency activities. However, little research has been published on the economic evaluation of waste heat utilization in the DC industry. This paper aims to provide an economic investment assessment of DC waste heat utilization. The contribution of this paper is the assessment of three different sized cases with realistic input factors affecting the net present value (NPV) model. We contribute to the ongoing discussion on the energy efficiency of DCs and provide a transparent assessment model for DC and district heating operators. We identified the positive NPV cases with high probability. The medium case has an NPV of 1.04 M€ (with uncertainty, the results range from −0.332 M€ to 2.57 M€). The large case has an NPV of 16.3 M€ (with uncertainty, the results range from 4.1 M€ to 30.2 M€). Both of these are clear-cut waste heat utilization investment proposals. The small case NPV is −48.5 k€ (with uncertainty, the results range from −264 k€ to 143 k€). The small case is sensitive to input factor values. [ABSTRACT FROM AUTHOR]

Highlights • Realize IT load demand response controllability with data center resource scheduling. • Design server cluster workload scheduling method with time shift mechanism. • Design data center UPS energy storage dynamic response mechanism. • Holistic dynamic response method smoothens data center tie-line power fluctuation. Abstract With the rapid development of cloud computing, artificial intelligence technologies and big data applications, data centers have become widely deployed. High density IT equipment in data centers consumes a lot of electrical power, and makes data center a hungry monster of energy consumption. To solve this problem, renewable energy is increasingly integrated into data center power provisioning systems. Compared to the traditional power supply methods, renewable energy has its unique characteristics, such as intermittency and randomness. When renewable energy supplies power to the data center industrial park, this kind of power supply not only has negative effects on the normal operation of precision equipment, such as CPU/GPU chips and hard disk, in data center, but it would also impact the stability of the utility power grids operation. To solve this problem, this paper presents a novel tie-line power fluctuation smoothing algorithm with consideration of data center's holistic demand response. The contributions of this paper are: (1) overcoming the limitations of treating IT load as uncontrollable workload in the traditional demand response research, we design a data center resource scheduling model to realize IT load demand response controllability; (2) two novel mechanisms are proposed: (i) the server cluster workload scheduling method with time shift mechanism, and (ii) the data center UPS (Uninterruptible Power Supply) energy storage dynamic response mechanism. (3) Combining these two mechanisms as holistic demand response of data center, we present a tie-line power fluctuation smoothing algorithm to improve power supply reliability, which is beneficial to both the high density and precision IT equipment in the data center and the utility power grid. In the experiments, the results show that the new algorithm can effectively regulate the tie-line power fluctuations under different server cluster utilization ranges and scenarios of large-scale penetration of distributed renewable energy scenarios. The new algorithm is hence able to contribute beneficially to the reliability and stability of intelligent industrial park micro-grid and utility power grids. [ABSTRACT FROM AUTHOR]

• The compiled U2 segments database was divided into development and test datasets, and the prediction accuracy of the location-specific SPFs using the development dataset were evaluated using the test dataset independently, and compared with the HSM SPF calibrated using the same test dataset. • The assessment of the developed models was conducted using rigorous statistical tests, including likelihood ratio test, AIC and BIC statistics, Vuong test and rootograms. • This work presents the significant impact of crash location information on the value of the calibration factor. A significant range of fluctuation of the calibration factor, from 0.71 to 1.74 , sheds light on the fact that efforts made to manually extract the required roadway geometry and operational features data not included in available data repositories can easily be offset by the inaccurate or incomplete entries in crash databases, and the thresholds used to identify intersection related crashes. This paper presents the calibration and development process of the safety performance function for the undivided two-lane urban and suburban arterial segments in New Jersey. Data requirements, the availability of required data, and the data processing and extraction methods are presented, along with detailed results of the calibration and development process. Negative binomial, Poisson, zero-inflated Poisson and Hurdle models were generated using the development database. The best model fit was based on likelihood ratio test, AIC and BIC statistics, Vuong test and rootograms. The test database was used to calculate the calibration factor for U2 segments. The predictions of the location-specific count models were then evaluated and compared to those of calibrated Highway Safety Manual model, using the test dataset. The validation test results showed that the negative binomial and hurdle models exhibited better performance in terms of absolute residuals and absolute Pearson residual statistics. This paper also shows the impact of crash location information on analyses results, and underlines that efforts made to manually extract the missing required data can easily be offset by the inaccuracies in crash frequency databases, and the thresholds used to identify intersection related crashes. [ABSTRACT FROM AUTHOR]

The last decade has seen an explosion of machine learning applications in healthcare, with mixed and sometimes harmful results despite much promise and associated hype. A significant reason for the reversal in the reported benefit of these applications is the premature implementation of machine learning algorithms in clinical practice. This paper argues the critical need for 'data solidarity' for machine learning for embryo selection. A recent Lancet and Financial Times commission defined data solidarity as 'an approach to the collection, use, and sharing of health data and data for health that safeguards individual human rights while building a culture of data justice and equity, and ensuring that the value of data is harnessed for public good' (Kickbusch et al., 2021). [ABSTRACT FROM AUTHOR]

Highlights • The paper gives a comprehensive description on the virtualization and VM management in the cloud computing environments. • The paper tackles the problem of inefficient energy consumption in cloud data centers by proposing: (1) A distributed approach to energy efficient dynamic VM consolidation with novel ideas, (2) A novel VM placement strategy and formulate it mathematically. The strategy considers the types of jobs to be executed on the VMs before the process of VM placement. • The proposed work is evaluated using CloudSim simulator with real workload traces. The results of the performance evaluation demonstrate that the proposed approach is able to enhance the energy efficiency in the cloud data centers. Abstract To meet the growing demands on cloud services and applications, a sizeable number of large scale cloud data centers, hosting thousands of heterogeneous servers, is established by cloud service providers. The ever growth in establishing cloud data centers is accompanied by consuming enormous amounts of energy. Thus, proposing efficient management approaches to reduce the energy consumption in cloud data centers becomes a top priority for ensuring the scalability of the cloud computing architecture. In general, the main source of energy overconsumption in today's data centers is due to the inefficient use of the physical servers' resources, which results in poor server utilization patterns. So, the key aspect is to utilize the physical resources optimally while serving the cloud user demands. This paper investigates the design and implementation of virtual machine management strategies for energy efficient cloud data centers. Particularly, it considers the processes of virtual machine placement and virtual machine consolidation in enhancing the energy efficiency in cloud infrastructures. While addressing the virtual machine placement problem is important, virtual machine consolidation is even more important to enable continuous reorganization of the already-placed virtual machines on the least number of physical machines. This results in reducing the number of active physical machines by leveraging live virtual machine migration enabled by the virtualization concept. Moreover, since the virtual machine migration operations consume additional energy, the frequency of VM migrations needs to be limited and controlled as well. The paper presents a distributed approach to an energy-efficient dynamic virtual machine consolidation mechanism. This approach determines, based on novel algorithms, which virtual machines to migrate, and when. Then, the placement of the virtual machines selected for migration is achieved based on a generalization of the Knapsack Problem known as the Multiple Choice Knapsack Problem. The placement process suits both static and dynamic virtual machine placement. The results of the performance evaluation demonstrate that the proposed new algorithms are able to enhance the energy efficiency in cloud data centers. [ABSTRACT FROM AUTHOR]

Motivated by new technologies, such as Internet of Things, Big Data, Cloud Computing, data-driven based smart maritime related research is attracting more and more attention. With the establishment of China Automatic Identification System (AIS) Asia-Pacific Data Center and its network with IALA-Net, worldwide ship trajectory data is becoming increasingly available, thus providing plenty of materials for data-driven based smart maritime researches. This paper presents a novel approach to automatic maritime routing algorithm, given a set of ship trajectories, infer a routable road network by combining data-driven based algorithms, specially by focusing on: (i) After appropriate pre-processing, simplify AIS trajectory data using Douglas-Peucker algorithm, which can simplify AIS trajectory data by extracting characteristic points, in the meanwhile, compressing redundant information and improving subsequent processing efficiency. (ii) By setting similarity metric of characteristic points, cluster them using DBSCAN algorithm, consequently, deduce general behavior and spatial pattern of trajectories following the similar route. Also, the turning nodes of routes can be obtained in this step. Unlike past published works that focus on partitioned trajectory and require dense-sampling, trajectory point based method proposed in this paper has the merits of robustness to noise and disparity, low computational complexity and space complexity. (iii) Determine the connectivity of turning nodes according to ship trajectories. In this step, isolated turning nodes are linked together to form a coherent chain modeled as a directed graph. (iv) Given a starting location, infer the optimal route to the destination using Ant Colony Algorithm. By comparing the main route generated automatically and the macroscopic traffic flow situation, the result indicate that the method proposed in this paper is effective. [ABSTRACT FROM AUTHOR]

This paper presents the Korea Superconducting Tokamak Advanced Research (KSTAR) Real-Time Data Archiving System (RTDAS) for a real-time network. In most fusion experiments, a stable and low latency real-time network is essential for achieving real-time controllability. Inspecting and supervising the operation of a real-time network is essential for ensuring its stability. Archiving the network data makes it possible to analyze the performance and status of a network. In the KSTAR, RTDAS has been developed to monitor and archive real-time network data that is used to control the plasma and send diagnostic data to Plasma Control System (PCS) in real-time to ensure the stable operation of the real-time network. The KSTAR adopts reflective memory (RFM) as a real-time network and this paper presents the algorithm of the real-time data archiving system and the experimental results of this system. The real-time data archiving system operates in two phases. During the experiment, the archiving system stores data from these real-time networks in real-time. After the experiment is finished the system sends this archived data to the MDSplus data server; this system stores all data in accordance with the KSTAR timing system. Therefore, all data is synchronized with the KSTAR timing system. This system is configured with Experimental Physics and Industrial Control System (EPICS) and Asynchronous Driver Support (AsynDriver). This system operates in real-time, was implemented on Messaging Realtime Grid -Real time (MRG-R) kernel 3.10, and uses Multi-Core Utilities (MCoreUtil). The system was tested to validate the performance of this archiving system; the test results show this system’s real-time performance. [ABSTRACT FROM AUTHOR]

• The study compares the performance of 13 artificial intelligence-based fall detection algorithms when applied to accelerometer signals captured at the waist of the test subject with a wearable device. • Unlike most related studies, which just use a single data repository to test their proposals, the analysis employs up to 11 different public laboratory-based datasets, as well as two databases with signals measured during real falls, and four repositories with mobility samples gathered during long-term monitoring campaigns. • The study considers not only the typical intra-dataset evaluation procedure followed by the literature but also cross-dataset evaluation (i.e. using data from a different repository for testing than that considered for training). • The performance metrics show that the behavior of all algorithms significantly degrades under cross-dataset evaluation conditions, both when tested with samples from another dataset generated in the laboratory and with real fall or long-term monitoring samples. • The results show that the algorithms tend to overfit the patterns of emulated falls (obtained from laboratory based datasets) used for training. Thus they are not able to extrapolate their learning correctly to conventional movements or new fall patterns, which reflects in a high percentage of undetected falls and a high hourly rate of false alarms. • The obtained results clearly question the evaluation procedures of fall detectors used so far in the literature, highlighting the importance of testing this type of detectors in realistic scenarios with signals from real falls and actual daily life movements. The evaluation of fall detection systems based on wearables is controversial as most studies in the literature benchmark their proposals against falls that are simulated by experimental subjects under unrealistic laboratory conditions. In order to systematically investigate the suitability of this procedure, this paper evaluates a wide set of artificial intelligence algorithms used for fall detection, when trained with a large number of datasets containing acceleration samples captured during the emulation of falls and ordinary movements and then tested with the signals of both actual falls and long-term traces collected from the constant monitoring of users during their daily routines. The results, based on a large number of repositories, show a remarkable degradation in all performance metrics (sensitivity, specificity and false alarm hourly rate) with respect to the typical case in which the detectors are tested with the same types of laboratory movements for which they were trained. [ABSTRACT FROM AUTHOR]

This paper aims to study neutronic modeling using decommissioned fuel pins of the URANIE subcritical assembly at the Cadarache center of CEA for the future Tunisian Subcritical Assembly. Indeed, the CNSTN intends to exploit the fuel to implementing its subcritical assembly. Therefore, a comprehensive computational study of the URANIE fuel was carried out to analyze the neutronic parameters for future facilities including criticality safety, the neutron flux, and neutron spectrum, for future facilities. Indeed, MCNP Monte Carlo code was used to develop a three-dimensional sketch for URANIE core. Two versions were used to estimate an effective multiplication factor (k eff): MCNP5 and MCNP6. The k eff was varied from 0.85842 to 0.86463 with a maximal difference of 623 pcm and depended on ENDF/B data library releases. The nominal value of k eff is acquired at the optimum lattice pitch of 4.6 cm, 30 cm in reflector thickness, and 185 cm in moderator level. Moreover, the reactivity of the facility is always negative. In addition, the energy spectrum was depicted at the three planes using 283 energy groups. Furthermore, the radial neutron flux was evaluated at eight positions. The axial neutron flux was estimated at various spatial planes of the assembly core. Inside the reactor core, the findings revealed indicate that the fast flux is higher and can be reach 7 × 10−4 n/cm2.Q and decreased steadily moving away from the center to the reflector part about 8 × 10−4 n/cm2.Q. • 3-D Monte Carlo simulating of URANIE is caried out using MCNP code. • Criticality analysis of URANIE is performed using KCODE method. • Neutron flux spectrum and thermal neutron distributions are calculated. • Results obtaining from MCNP5 and MCNP6 with ENDF releases are compared and discussed. [ABSTRACT FROM AUTHOR]

Multi-source heterogeneous, multi-modal, and multi-type open scientific data (e.g., thematic sharing sites, metadata, journal articles, etc.) on earth surface systems (EES) provide important data sources for knowledge mining, discovery, and accurate recommendations, and also pose increasing challenges, resulting in the need to develop appropriate tools to address these challenges and support decision-making. This paper constructs an interoperable software system to store, visualize, and publish open science data of ESS. Utilizing an open scientific data catalogue repository encompassing EES information as foundational input and employing an integrated modeling methodology, this system endeavors to synthesize heterogeneous surface data of diverse linguistic, sourced, and typological origins. The objective is to facilitate multidimensional data retrieval and precise data auto-recommendation, thereby fostering the dissemination of scientific data and facilitating value-added services within EES domain. The tool may be used by stakeholders including researchers, data analysts, policymakers and national authorities to support decision-making on questions ranging from locating the location of open data related to the topic, to discovering high-quality data, selecting the data with the better overall evaluation. Along with a description of the system/platform design process, its structure, and the constituent models, key results are presented relating to the user interface, and several application examples. Software systems can help modelers to use the best features of a single software tool to answer open scientific data-related questions that seek to discovery, use, comparison or synthesis within or across topics of ESS. • Developed a novel tool for constructing a scientific data association network. • Presented an interoperable software system. • Proposed a 'time-space-topic' model for building associative networks. [ABSTRACT FROM AUTHOR]

The exponential growth in data generated by satellites, radars, sensors, and analysis and reanalysis from model outputs for the hydrological domain requires efficient real-time data management and distribution mechanisms. This paper introduces HydroRTC, a web-based data transfer and communication library designed to accelerate large-scale data sharing and analysis. Leveraging next-generation web technologies like WebSockets, WebRTC and Node.js, the library enables seamless peer-to-peer sharing, smart data transmission, and large dataset streaming. Three primary scenarios are presented as use cases, demonstrating the potential of HydroRTC as server-to-peer with intelligent data scheduling and large data streaming, peer-to-peer data sharing, and peer-to-server for data exchange. HydroRTC offers a promising solution for collaborative infrastructures in the hydrological and environmental domain, allowing real-time and high-throughput data sharing and transfer for enhancing research efficiency and collaboration capabilities. • HydroRTC accelerates large-scale data sharing with next-gen web technologies. • Three primary scenarios: server-to-peer, peer-to-peer, peer-to-server data exchange. • Promising solution for collaborative infrastructures in hydrological and environmental domains. • Exponential growth in data necessitates efficient real-time management mechanisms. • Leverages WebSockets, WebRTC, Node.js for seamless peer-to-peer sharing. [ABSTRACT FROM AUTHOR]

Autonomous driving is assisted by different sensors, each providing information about certain parameters. What we are looking for is an integrated perspective of all these parameters to drive us into better decisions. To achieve this goal, a system that can handle these Big Data issues regarding volume, velocity and variety is needed. This paper aims to design and develop a real-time Big Data Warehouse repository, integrating the data generated by the multiple sensors developed in the context of IVS (In-Vehicle Sensing) systems; the data to be stored in this repository should be merged, which will imply its processing, consolidation and preparation for the analytical mechanisms that will be required. This multisensory fusion is important because it allows the integration of different perspectives in terms of sensor data, since they complement each other. Therefore, it can enrich the entire analysis process at the decision-making level, for instance, understanding what is going on inside the cockpit. [ABSTRACT FROM AUTHOR]

The cantilever bending test is one of the simplest and most widely used methods to estimate the bending stiffness of textile materials. A nonlinear moment–curvature relationship can be computed from a single image of a cantilevered textile specimen. However, the calculation of curvature involves second-order differentiation of noisy data, which leads to noise amplification. Traditionally, this is handled by subjectively choosing one of many functions to fit the data or by manual tuning of fitting parameters. The user choices ultimately lead to uncertainties in the data fit. This paper presents a novel automatic data processing method for the cantilever test using smoothing splines with automatic parameter selection through cross-validation. The method is verified on a simulated deflection curve with known bending stiffness and then used to characterise real textile specimens. Finally, the method is validated by simulating the deflection curve using the computed stiffness. This method makes it possible, for the first time, to accurately predict the textile curvature even in the presence of severe noise, without needing user inputs prone to human error. The code used for this paper is freely available with sample data on the repository at https://doi.org/10.5281/zenodo.7376939. [ABSTRACT FROM AUTHOR]

The paper presents a study, conceived within the GEYSER FP7 co-funded European research project, on the ancillary services that Green Data Centres (GDCs) can provide, focusing, in particular, on spinning reserve. More in detail, the main contribution of the paper is to show how GDCs can be involved in the secondary frequency regulation process of a smart distribution grid, for contrasting the disturbing effects of unexpected variation of the energy production. With this aim, a real data centre, owned by Engineering S.p.A., is simulated as connected to the Medium Voltage IEEE 14-bus test grid where some RES-based generators are supposed to exist. Simulations are carried out in order to evaluate the impact of power management actions to be executed by the data centre during under-frequency transients, due to an event of renewable energy facilities power production decrease. During this event, the effects of the spinning reserve provided by the data centre are deeply analysed. [ABSTRACT FROM AUTHOR]

Optimal research data management and archiving is a key condition for progress in modern science and of vital importance from both the point of view of research as such as well as research policy and management. More specifically, it is a conditio sine qua non for the realization of Open Science and at the same time it is indispensable for the monitoring and assessment of the quality and integrity of research. Various aspects play a role here: optimal infrastructures and tools for the actual handling of data during the research lifecycle, appropriate metadata to describe the datasets, and – last but not least – an adequate organizational framework to curate and archive the datasets professionally and provide optimal support and services to the researchers. The paper presents the Research Data Services (RDS) project of Radboud University (the Netherlands) in cooperation with one of the Dutch national research data archives: DANS (Data Archiving and Networked Services). In this project, a model is worked out for the archiving of research datasets via the CRIS (Current Research Information System) of the university, including both the registration of the metadata as well as the actual upload of the data files to the DANS archive. It is argued that an optimal solution is not only a technical matter, but also requires the definition and organization of appropriate support, management structures and workflows, involving both local and national partners. In this respect, attention is paid to the explanation of the frontoffice-backoffice model(FoBo) that is being defined and implemented as part of the project and which forms the organizational backbone of the solution worked out. The paper starts by arguing that aCRIS-oriented approach in research data archiving holds substantial added value, and it ends with an overview of lessons learned and a peek into the future of the RDS-project. [ABSTRACT FROM AUTHOR]

In the current information age, usually the data resources are much higher than the value of the computer equipment itself. In recent years, with the rapid development of network technology, the formation of a high concentration of data stored in the network environment, the simplified data management, improve operational efficiency, reduce the cost of investment at the same time, the data security is experiencing more severe test. As a kind of data disaster recovery data security technology, through a complete copy of the data in real time to the disaster recovery center, in order to keep a copy of the basic means of providing a last line of defense for the security of the data. Since the disaster recovery started, people have made a lot of research, but there are still some problems first, as the diversification of the storage subsystem structure of infrastructure, so to build a disaster recovery system need to do a lot of personalized treatment followed by a single, storage technology has been unable to meet the needs of various industries on the disaster intensity of third and if the security of the backup data cannot be guaranteed, for sensitive confidential data, even worse than no backup. To solve the above problems, this paper related to the key technology of disaster recovery data backup based on virtual storage research, the main work is as follows. Based on the characteristics of storage technology in disaster tolerant system in the analysis, choose to use the virtual storage technology support as the basis of disaster recovery system to realize data backup, and through in-depth analysis of the virtual storage technology and its application status in the disaster recovery system, pointed out that the current application of virtual storage technology in disaster tolerant system in the challenge and the starting point of this article. Analysis of the structure of the internal computer system with virtual memory and the disaster recovery system based on the needs of the design of a virtual network storage model and disaster tolerant architecture based on this model. The advantages and disadvantages of three virtual memory mapping mechanism at present, designed for disaster recovery system virtual memory mapping mechanism and cache mechanism. On the basis of this mapping mechanism is established on data disaster tolerance strategy can be realized, and the upper user language abstraction, form a complete set of disaster recovery mechanism. Through a detailed analysis of the virtual disk transparent encryption mechanism, proposed the use of the mechanism to realize the transparent protection of backup data, combined with the characteristics of backup data, and optimized the virtual disk transparent encryption mechanism, focus on strengthening the management of keys, the encryption mechanism is analyzed on how the role of disaster recovery system. Quasi double live disaster recovery storage based cloud computing data center technology problems of the equipment malfunction need to manually switch the business system, business recovery time. In order to solve this problem, this paper uses Net App 3250 double live disaster recovery storage technology Metro Cluster to build a double live a real sense of cloud computing data center. When the double live data center renyiyitai main memory based equipment malfunction, another main memory automatically and quickly take over the business, to achieve zero down live migration, to ensure the continued operation of the business system, provide a solid guarantee for the information construction unit. [ABSTRACT FROM AUTHOR]

Data warehousing is a traditional domain of relational databases, and there are two main reasons for that: (1) data warehouses mostly are used in enterprises with large-scale data sets created in different legacy systems with relational data storages, (2) though rapidly developing non-relational databases are still rather unusual in data processing tasks. This paper discusses the possibilities to create data warehouse solutions by using NoSQL database management systems. The main challenge is to find a good balance between characteristics of classical data warehouses using relational data base management systems and opportunities offered by NoSQL database management systems. The paper describes processes of creation and production of data warehouse using a NoSQL data mart and outlines requirements for technology necessary for such processes. The research is based on practical experience when implementing NoSQL data marts with MongoDB and Clusterpoint DB. [ABSTRACT FROM AUTHOR]

We present the results concerning the development in Geant4 of a new data driven library, called here the ANSTO HF library. This X-ray fluorescence library is based on an approach of particular interest for PIXE simulation applications; however, it can be used in any Geant4 applications where X-ray fluorescence needs to be described. The X-ray fluorescence transition probabilities were calculated within the Hartree-Fock (HF) approach, which is recognised to better reproduce PIXE experimental values compared with the Hartree-Slater approach, adopted in the current default Geant4 EADL data library. These HF X-ray fluorescence transition probabilities were integrated into a new Geant4 library and will be released within Geant4 in the near future. In this paper, we compare the fluorescence X-ray spectra generated by the ANSTO HF library and by the currently available library (EADL-1991 [1]) within Geant4, for targets irradiated with protons and α particles with energies up to 10 MeV, a range of interest for PIXE applications. The comparisons were performed for a large set of sample materials spanning a broad range of target atomic numbers. These two approaches were compared to existing experimental measurements performed at the ANSTO heavy ion microprobe beamline using 2 MeV and 3 MeV proton and 10 MeV He2+ ion beams. This work represents a useful upgrade to the Geant4 atomic de-excitation package. [ABSTRACT FROM AUTHOR]

Lunar Laser Ranging (LLR) measures the distance between observatories on Earth and retro -reflectors on Moon since 1970. In this paper, we study the effect of non-tidal station loading (NTSL) in the analysis of LLR data. We add the non-tidal loading effect provided by three data centres: the German Research Centre for Geosciences (GFZ), the International Mass Loading Service (IMLS) and EOST loading service of University of Strasbourg in France, as observation level corrections of the LLR observatories in our analysis. This effect causes deformations of the Earth surface up to the centimetre level. Its addition in the Institute of Geodesy (IfE) LLR model, it leads to a change in the uncertainties (3- σ values) of the station coordinates resulting in a 0.60% improvement, an improvement in the post-fit LLR residuals of up to 9%, and a decrease in the power of the annual signal in the LLR post-fit residuals of up to 57%. [ABSTRACT FROM AUTHOR]

• Comprehensive investigation of metadata complexities in online portals and data repositories. • Automating layout preparation to streamline metadata files for accurate element detection. • Improving metadata structural quality through the use of syntactic preparators. • Elevating the contextual quality of metadata by applying semantic preparators. • Conducting performance evaluations on the proposed methodologies to gain valuable insights. With the exponential growth of data production, the generation of metadata has become an integral part of the process. Metadata plays a crucial role in facilitating enhanced data analytics, data integration, and resource management by offering valuable insights. However, inconsistencies arise due to deviations from standards in metadata recording, including missing attribute information, publishing URLs, and provenance. Furthermore, the recorded metadata may exhibit inconsistencies, such as varied value formats, special characters, and inaccurately entered values. Addressing these inconsistencies through metadata preparation can greatly enhance the user experience during data management tasks. This paper introduces MDPrep, a system that explores the usability and applicability of data preparation techniques in improving metadata quality. Our approach involves three steps: (1) detecting and identifying problematic metadata elements and structural issues, (2) employing a keyword-based approach to enhance metadata elements and a syntax-based approach to rectify structural metadata issues, and (3) comparing the outcomes to ensure improved readability and reusability of prepared metadata files. [ABSTRACT FROM AUTHOR]

• Analysis of ASTRA benchmark through consistent code-to-code comparisons was performed. • Double heterogeneity models used in Serpent and MCU-HTR were compared. • Impact of different graphite thermal scattering libraries on k eff was examined. • Regular and random representation of the pebble-bed were compared. Key problems in the simulation of pebble-bed high-temperature gas-cooled reactors include approximations of TRISO fuel particles and pebble-bed models, choice of nuclear data libraries, and uncertainty in graphite impurity content. This study is focused on the investigation of these problems for the ASTRA critical facility simulation with MCU-HTR and Serpent Monte Carlo codes. This paper presents a comparative analysis of the ASTRA benchmark (IEU-COMP-THERM-008) through consistent code-to-code comparisons. Some studies were performed on the simplified models of the ASTRA facility. A special care was taken to develop similar models of the ASTRA facility for the calculations using MCU-HTR and Serpent. A good agreement in the multiplication factor (less than 160 pcm) between the simulations of ASTRA critical configurations using MCU-HTR and Serpent with ENDF/B-VII.0 cross-section libraries was obtained. In addition, the reactivity impact of different graphite thermal scattering libraries included in ENDF/B-VIII.0 release on the calculated results for the ASTRA benchmark was examined. Graphite evaluations assuming 0, 10, and 30 % porosity were considered in Serpent simulations with ENDF/B-VIII.0 cross-section libraries. The spread of the calculated k eff for the measured critical configurations having different core height was found to be approximately 200 pcm for most selected models. Consequently, model's ability to correctly capture the procedure of a reactor loading during startup was proven. However, a significant overestimation of the k eff (1000–1500 pcm) was observed when we used the equivalent boron content (EBC) in the reflectors graphite adjusted to reproduce the measured graphite absorption cross-section, the EBC in matrix graphite of 1 ppm wt., ENDFB/VIII.0 cross-sections, and 30 % or 10 % porous graphite thermal scattering libraries in the Serpent simulations. [ABSTRACT FROM AUTHOR]

Fluvial sediments preserve evidence of past hydroclimatic regimes and are important but potentially under-utilised indicators of palaeoenvironmental change in drylands. This paper presents a new chronological database of 603 ages from late Quaternary fluvial sediments at 70 sites in southern Africa's western dryland regions. This synthesis assesses the spatial and temporal trends in both the palaeoenvironmental record and the supporting data that underpins these interpretations. We find that topographic and climatic characteristics of river catchments influence the nature of fluvial archive formation and preservation. Channel confinement, legacies of research, and sampling strategies in specific river basins are found to control the sedimentary context and length of documented record available for palaeoenvironmental reconstruction. The age of dated deposits and associated magnitude of chronological uncertainties influence the temporal range and resolution of climate and fluvial variability that can be discerned. Where calendar year uncertainties are small enough, predominantly dating to the last millennium, individual flood events and multi-year episodes within flash flood deposits can be linked to synoptic climate patterns, yet when uncertainties are larger, interpretations are restricted to understanding multidecadal to centennial-scale phases of flow regimes. This paper collates a scattered literature to detail the current state of southwestern Africa's fluvial record and highlights opportunities for future research. We consider controls on fluvial archive interpretation that are pertinent to wider dryland contexts and argue that fluvial records provide useful information for inclusion in regional palaeoenvironmental syntheses. Following a filtering process, quality-assessed chronologies are compiled to establish the combined fluvial history of 22 rivers to consider the response of fluvial systems to hydroclimatic drivers. At the Last Glacial Maximum (LGM; 24–18 ka), records detail a regionally consistent signal of intermittent to perennial flow under relatively humid conditions in what are currently ephemeral systems. The mid Holocene (MH; 8-4 ka) is also characterised by the occurrence of sustained river flow and higher discharge events than present, but with greater spatial variability. Sedimentary archives display evidence of both intermittent flow and extreme flood events within ephemeral regimes, and two rivers, the Hoanib and Molopo, are characterised by reduced fluvial activity. Within the last millennium, flash flood regimes within an arid to hyper-arid climate were established with coherence in the timing of flood frequency variations recorded between catchments. • A new chronological database synthesises >600 ages from southwestern Africa's fluvial sediments. • Fluvial chronologies span the last 132 ka, with most studied sediments dated within the last 50 ka. • The combined record reveals enhanced hydrological activity at the LGM and mid-Holocene, and a flash flood dominated regime from 1 ka. • Analysis of topographic and climate setting establishes controls on archive formation and preservation. • Results highlight the utility of quality-assessed fluvial records as sources of palaeoenvironmental information. [ABSTRACT FROM AUTHOR]

This scientific review paper aims at challenging a common point of view on metadata as a necessary evil and something mandatory to the data creating and dataset publishing process. Metadata are instead presented as a crucial element to ensure the findability of data services and repositories. This paper describes a way through four levels of metadata management and publication, from default unstructured data, through schema-based metadata with literal values and/or URIs, towards linked open (meta)data providing explicit linkage between reliable data resources. Such research was conducted within the European Union's project PoliVisu. Special attention is given to the following: (1) guidance on publication aimed at the broad audience of search engine users and (2) the publication of geo (meta)data not only via standard technologies, such as the OGC Catalogue Service for Web and open data portals, but also through leading search engines (that are Schema.org-based). • Metadata developed in line with open linked data concept are scarce in geosciences. • The presented paradigm revokes an artificial border between data and metadata. • Open linked (meta)data are advertised attractively in search engines. • Multiple-times more users can be attracted, both, within as well beyond geosciences. • Incremental implementations approach is a novel one towards open linked (meta)data. [ABSTRACT FROM AUTHOR]

This paper introduces the EGI Open Data Platform and the EGI DataHub, outlines their functionality and explains how this meets the requirements of EGI end users. The paper also explains how these new services can support the European Open Science Cloud and will fit into the future European Strategy Report on Research Infrastructures (ESFRI). [ABSTRACT FROM AUTHOR]

Inter-cloud Challenges, Expectations and Issues Cluster objective is to enable collaboration among European Research projects addressing topics of multi-cloud and inter-cloud. Today these projects analyze the question from diverse perspectives and focusing on specific parts of the problem. This position paper provides the work done in collaboration by all these projects to define research areas and challenges for 2020. It identifies a Cluster's vision of Inter-Cloud topics development by 2020, as well as, research areas in order to realize the provided vision. An extended version of this work is available on the Inter-Cloud Cluster position paper 1,2 . [ABSTRACT FROM AUTHOR]

The design of a complex power supply infrastructure and the satisfaction of the concentrated high energy demand is a constant challenge for data centre owners. Furthermore, economics of the operation and ideas of sustainability are getting more and more attention lately as well. Green data centres are designed to minimize the effect on the natural environment. One of the primary goals of such units is to decrease the energy needs both for the computing infrastructure and the supporting systems (e.g. thermal management), which can be achieved either by increasing the efficiency of processes or by integrating renewable energy sources. Present paper introduces results of an ongoing research and development cooperation between the Centre for Energy Research of the Hungarian Academy of Sciences and Persecutor Ltd., supported by the Hungarian Government’s “PIAC_13” programme. The project aims to develop a new, highly energy efficient data centre infrastructure, which is able to supply IT needs at a high-level, while minimizing power losses. Present paper focuses on decreasing power losses in the distribution and power conversion infrastructure. A power loss model, capable of supporting the optimal selection of different subsystems of the technological chain, is introduced and two case studies are shown to demonstrate the effect of different loss reduction options. [ABSTRACT FROM AUTHOR]

This paper is the second paper on the study regarding the feasibility of low-enriched uranium (LEU) fuel in nuclear thermal rocket (NTR) applications. While the first paper dealt primarily with the issue of achieving and demonstrating criticality of the LEU-loaded NTR, this companion paper addresses other important reactor parameters which need to be verified in order to demonstrate the practicability of the LEU-NTR. Following the conclusions of the first paper, this second paper seeks to characterize various neutronic and performance parameters in order to definitely demonstrate the technical feasibility of the LEU-NTR. The parameters characterized and presented in this study are the reactor burn-up behavior including effects due to the build-up of Xe-135, the temperature coefficient of reactivity, the reactivity capability of the control drum system, and a rocket performance comparison with previous NTR systems. Impacts of the transient Xe reactivity change during operation have been characterized though various reactor depletion analyses using the MCNP5.1 and MCNPX codes in combination with the ENDF/B-VII.0 nuclear data library. The necessary level of excess reactivity of the LEU-loaded reactor has been quantified and the reactivity control drum, system has been assessed in term of the reactor shutdown capability. In this work, the reference core design presented in the first paper has been modified in order to improve the operational performance of the LEU-NTR to operate with a 900 s specific impulse and a 110 kN thrust. [ABSTRACT FROM AUTHOR]