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Neutronic parameter calculation of the reactor core is one of the bases for calculating the safety of a nuclear reactor operation. Neutronic calculations using reliable computer codes are needed to provide valid and accurate results. This paper aims to compare the neutronic calculation results of the two Monte Carlo computer codes, namely KENO-VI and MCNP6. The TRIGA 2000 research reactor becomes the object of study in this paper. The calculated neutronic parameters include excess reactivity, shutdown margin, and control rod worths. The calculation results show that the difference in reactivity values calculated by KENO-VI and MCNP6 increases when the number of control rods entering the core increases. The maximum reactivity difference was found to be 0.443 %dk/k for the shutdown margin parameter. The difference in cross-section library data is a factor causing the difference in the calculation results of the two computer codes. The calculation results also show that the KENO results tend to be lower than the MCNP results. Finally, it can be concluded that the differences between the KENO results and the MCNP results observed in this paper are aligned with the literature data. The difference between KENO-VI and MCNP6 calculation results become an important thing when the analyzed shutdown margin value is close to the commonly used shutdown margin limit value, which is −0.5 %dk/k. [ABSTRACT FROM AUTHOR]

The growth in data trafficking, data processing, e-learning, social networking, digitization of services and in general, the simple fact that almost everything is shifting digital, necessitates an ever-increasing demand in information handling and processing. The massive amounts of data are stored and processed in “servers,” “routers,” “Firewalls,” and storage devices (also known as IT load) installed within data centers (DCs). Data centers consume an important amount of electricity (about 2% per year), and it is forecasted that consumption will rise to more than 3.8% of global electricity consumption by 2030. Three key elements to be considered are the IT load rating, the layout-arrangement of the IT white space and cooling rating. These are related by the power density, which directly relates the data center size, the cooling capacity, and energy use. This paper covers the main technical infrastructure design for a case study of DC-500 kW in Hoa Lac-Vietnam, the design is following international guidelines and best practices. We present a better way for selecting DC’s power density and optimal layout-arrangement for IT racks. It is a conjunction of cooling load and airflow sizing, and computational fluid dynamics (CFD) simulations to analyze the heat distribution (cold air intake, hot air exhaust) in DC rack-rows. Airflow analysis is a key consideration during the initial phase of DC sizing since it encompasses all the design and configuration elements that go into restricting or preventing mixing between the cooling air provided to IT equipment and the hot air rejected from the IT room. [ABSTRACT FROM AUTHOR]

LHC@home was launched as a BOINC project in 2004 as an outreach project for CERN's 50 years anniversary. Initially focused on the accelerator physics simulation code SixTrack, the project was expanded in 2011 to run other physics simulation codes on Linux thanks to virtualisation. Later on the experiment and theory applications running on the LHC@home platform have evolved to use containers and take advantage of the CVMFS file system as well as content delivery networks. Furthermore, a substantial part of the contributed computing capacity nowadays is provided as opportunistic back-fill from data centers with spare capacity, in addition to enthusiastic volunteers. The paper will address the challenges with this distributed computing model, new applications to exploit GPUs and the future outlook for volunteer computing. [ABSTRACT FROM AUTHOR]

The Storage Group in the CERN IT Department operates several Ceph storage clusters with an overall capacity exceeding 100 PB. Ceph is a crucial component of the infrastructure delivering IT services to all the users of the Organization as it provides: i) Block storage for OpenStack, ii) CephFS, used as persistent storage by containers (OpenShift and Kubernetes) and as shared filesystems by HPC clusters and iii) S3 object storage for cloud-native applications, monitoring and software distribution across the WLCG. The Ceph infrastructure at CERN is being rationalized and restructured to allow for the implementation of a Business Continuity/Disaster Recovery plan. In this paper, we give an overview of how we transitioned from a single cluster providing block storage to multiple ones, enabling Storage Availability zones, and how block storage backups can be achieved. We also illustrate future plans for file systems backups through cback,a restic-based scalable orchestrator, and how S3 implements data immutability and provides a highly available, Multi-Data Centre object storage service. [ABSTRACT FROM AUTHOR]

Nuclear data uncertainties are provided as covariance matrices in standard nuclear data libraries and propagating them trough neutronics simulations helps quantify the associated uncertainties on the final result. However, processing these matrices often poses challenges. Currently, the IRSN nuclear data processing code GAIA processes cross sections via several modules like DOP (Reconstruction and Doppler), TOP (URR), and SAB (TSL), but lacks the capability to process covariances. This paper introduces a new module named COP (COvariance Processing). The COP module aims to process covariance matrices comprehensively, including cross section (File 33), angular distribution (File 34), and resonance parameters (File 32). The preliminary results obtained using the COP module of GAIA in comparison with the ERRORR module of NJOY and PUFF module of AMPX are presented. [ABSTRACT FROM AUTHOR]

Federated Learning enables different repositories of data to learn a shared model collaboratively and at the same time keep the privacy of each one becauseof the increasing awareness of large firms compromising on data security and user privacy. To accomplish federated learning, three learning ways were suggested; horizontal federated learning, vertical federated learning, and transfer federated learning. Vertical federated learning was adopted in the situation that data are spread among different parties. However, each one has different features from the others for identical objects. This paper is related to this type of federated learning. The learning task was clustering, using partitioning techniques. The proposed scenario adopted in this paper is that the clustering task was implemented with a k-means algorithm in each party independently from others. The final results from all parties are sent to the server to extract the combined results. At the server, k-modes were implemented on the last parties' results and produced one result. The results were evaluated based on the accuracy metric, and the comparison was applied with normal k-means algorithm results done with the unity data. The practical works were experimented with using different popular data sets. This scenario has some benefits, such as simplicity, and it reduces the traffic between servers and clients that hold each data party. The result Accuracy at the server by using K-modes for different Dataset as we noted in section 11, Breast cancer(92.79438), Musk(74.35587), Heart(79.12458), Ionosphere(71.79487), Sports Articles(79). [ABSTRACT FROM AUTHOR]

In this paper, we explore how techno-legal configurations shape the evolution of an information infrastructure (II) by focusing on data as its critical components. We define techno-legal configurations as assemblages, which are technologically determined by the functionalities for data storage, processing, sharing and usage, and legally determined by the basis for data processing, such as consent, data-processing agreements or laws. To study II’s evolution we conduct an 11-year study of a regional II in Norway as electronic patient record data and patient-generated healthcare data were shared within and across hospital organizations. We show how the considerations of data as internal and external to organizations are continuously renegotiated across techno-legal configurations, which we define as harmonized space and disparate space. We contribute to the II literature by raising the importance of the law in shaping the boundaries across which data can be produced, shared and used. [ABSTRACT FROM AUTHOR]

The recent growth of Cloud Computing technology has received a major attention on the research arena worldwide, mainly being focused on performance issues of greater and larger Data Centers. It is our interest to know about their cost regarding energy and power consumption. Are we getting to the point where it's too late to sustain a green environment, because of the high level of risk we are taking without analyzing? This study tends to give answers to these questions by presenting the current state of the art on green cloud computing. This paper provides an overview of researches and reports published in the latest three years, 2010-2013, in the fields of Cloud Computing Energy Efficiency improvements and awareness. We highlight the trend of researches on this area from the "Cloud Service Providers" and Environmentalists point of view. [ABSTRACT FROM AUTHOR]

The rate of death in heart failure patients keeps increasing, but the causes of death are always uncertain. Even though heart failure is a diverse syndrome classified by ejection fraction, the relationship among both ejection fraction and death rates is significant but elusive. According to health cardiology report, Patients with heart failure and reduced ejection fraction have greater risk of dying, compared with heart failure patients and preserved ejection fraction. However, the factors influencing the death rate of heart failure patients remains a challenging task for the doctors and researchers. With this motivation, this paper attempts to analyze the important features that influences the mortality rate of the patients. The Heart Failure dataset from UCI data warehouse repository is subjected for analyzing the heart failure patients. The dataset is preprocessed with data cleaning process and missing values. The preprocessed dataset is applied to all the classifiers and the performance of the mortality grading is analyzed before and after feature scaling. The raw dataset with entire 12 features are applied to Ada Boost, Random Forest, Extra Tree and Gradient Boost classifier to extract the top six important features to increase the accuracy performance. The feature importance dataset is then applied to all the classifier and the performance is analyzed in terms of accuracy, precision, recall, FScore and Run Time. The implementation results shows that random forest classifier is showing the accuracy of 89% after feature scaling and the random forest classifier with the feature importance of Gradient Boost classifier shows the accuracy of 97%. [ABSTRACT FROM AUTHOR]

Globally, the employability of students is a major concern of governments and higher education sectors. Number of educational institutions is using the methods to formulate the methods to maximize students' employability skills to fill the gap between students' skills and the needs of a competitive global market. Students, institutes, as well as society, can use it as an indicator of performance. By using data mining techniques, large repositories of data on student employability skills can be analyzed. As a component of intelligent computing, these techniques are capable of making predictions from the specific input patterns. This paper examines and forecasts the students' employability based unpublished papers between the years of 2010 and 2021 using data mining techniques to identify the employment gap. To deduce and analyze references for the same, we examined certain research and scientific databases such as Web of Science, IEEE, Scopus, Google Scholar, Science Direct, ACM, and Springer. Using meta-syntheses of references, only those papers will be selected to be studied which compare the accuracy of more than one data mining technique for predicting students' employability. The objective of this paper is to present the major work done in the field of evaluating employability of students using data mining techniques. [ABSTRACT FROM AUTHOR]

It is the aim of every educational system to develop wise heads in young people. As education is a direct way for a country to progress, all of our efforts should be directed toward ensuring and promoting education. The education system, from primary school to secondary all the way up to university, makes the mind nothing more than a repository of data and discourages creative thinking. Switching to online learning after having been in a classroom setting can be challenging for students. Due to sudden changes, they are unable to adapt to computer-based learning. When comparing online education and face-to-face, there are significant deficiencies in the online mode, such as lack of human connection, lack of opportunities for group learning, lack of supervision of teachers, and most importantly, lack of opportunities to learn complex subjects and engage in hands-on activities like Math and Science. How is this problem being addressed today? Primary education does not have many experiments to perform. Graduate students have various options including open-source, online platforms, virtual laboratories, and demonstrations. To execute an experiment at secondary school, the student must rely on a lab. Specifically, the paper aims to discuss issues with the existing solution, how the material can be developed to support practical online activities, as well as recommendations to make the project both subject matter and online educational methods aware. [ABSTRACT FROM AUTHOR]

Efficient digital archiving systems are indispensable for managing vast amounts of data, facilitating streamlined information retrieval, enabling remote data exchange, and ensuring robust data security. While existing techniques often introduce complexity and security concerns, necessitating larger storage spaces, this paper proposes a new and straightforward approach using mathematical coding to construct a secure archiving system. Our methodology prioritizes simplicity while maintaining robust security measures to archive higher education system information, particularly document images. The proposed system integrates three key domains: mathematical coding for security, image processing for high-quality image archiving, and archive system development. Specifically, information is encoded into a unique CODE using XOR coding for enhanced security and combined with student names to generate PDF file titles containing scanned documents. Additional security layers are implemented through password-protected PDF files. Benchmarking against other database types reveals that our approach yields a simple, secure system for HES records to be archived without requiring high storing abilities or security complexities. Our findings underscore the effectiveness of our methodology in achieving efficient digital archiving while maintaining data security. [ABSTRACT FROM AUTHOR]

JENDL-5, the latest version of the Japanese evaluated nuclear data library, includes several sub-libraries to contribute to various applications. In this paper, we outline the evaluation and validation of the deuteron reaction sub-library developed mainly for the design of accelerator-based neutron sources and the alpha-particle reaction sub-library developed mainly for use in the back-end field. As for the deuteron sub-library, the data for 6,7Li, 9Be, and 12,13C from JENDL/DEU-2020 were partially modified and adopted. The data up to 200 MeV for 27Al, 63,65Cu, and 93Nb, which are important as accelerator structural materials, were newly evaluated based on the calculations with the DEURACS code. As for the alpha-particle sub-library, the data up to 15 MeV for 18 light nuclides from Li to Si isotopes were evaluated based on the calculations with the CCONE code, and then only the neutron production cross sections were replaced with the data of JENDL/AN-2005. Validation on neutron yield by Monte Carlo transport simulations was performed for both sub-libraries. As a result, it was confirmed that the simulations based on the sub-libraries showed good agreement with experimental data. [ABSTRACT FROM AUTHOR]

FUDGE (For Updating Data and Generating Evaluations) is an open-source code that supports reading, visualizing, checking, modifying, and processing nuclear reaction and decay data. For ease of use the front-end of FUDGE is written in Python while C and C++ routines are employed for computationally intensive calculations. FUDGE has been developed primarily at Lawrence Livermore National Laboratory (LLNL) with contributions from Brookhaven National Laboratory (BNL). It is used by the LLNL Nuclear Data and Theory (NDT) group to deliver high-quality nuclear data libraries to users for a variety of applications. FUDGE is also the world leader in converting data to the Generalized Nuclear Database Structure (GNDS) and working with GNDS data, including processing and visualizing. GNDS is a new extensible hierarchy that has been internationally adopted as the new standard for storing and using nuclear data libraries, replacing the previous standard ENDF-6. A new public release of FUDGE has recently been published on github. This paper gives an overview of nuclear data processing capabilities in FUDGE, as well as describing the latest release, new capabilities, future plans, and basic instructions for users interested in applying FUDGE to their nuclear data workflow. [ABSTRACT FROM AUTHOR]

In the last years, a new version of the Joint Evaluated Fission and Fusion File (JEFF) data library, namely JEFF-3.3, has been released with relevant updates in the neutron reaction, thermal neutron scattering and covariance sub-libraries. In the frame of the EU H2020 SANDA project, severale efforts have been made to enable the use of JEFF nuclear data libraries with the extensively tested and verified SCALE Code System. With this purpose, AMPX processing code has been applied to enable such application, allowing to provide insight into the interaction between the code and the new versions of JEFF data file. This paper provides an overview about the processing of JEFF-3.3 nuclear data library with AMPX for its application within the SCALE package. The AMPX-formatted cross-section library has been widely verified and tested using a comprehensive set of criticality benchmarks from ICSBEP, by comparing both with results provided by other processing and neutron transport codes and experimental. Processing of JEFF-3.3 covariances is also addressed along with their corresponding verification using covariances processed with NJOY. This work paves the way towards a successful future interaction between JEFF libraries and SCALE. [ABSTRACT FROM AUTHOR]

Internet of Things have attracted great attention due to their potential applications in many areas. In this paper, we propose an IoT based water level monitoring for flood early warning system. The system uses an ultrasonic sensor attached to a Raspberry Pi microcomputer to collect data and send it to the internet. This prototype was tested using two simulations. The first simulation is when the ultrasonic sensor reading value which has not exceeded the predetermined threshold, but the sensor is still reading and storing data on the website. The second simulation is when the sensor reading value has exceeded the predetermined threshold. The system then sends a signal in the form of a text via a telegram bot chat that tells the water level value as a warning sign so the administrator can post a live streaming video on Youtube social media. A webpage was also configured as data repository. The measured data and real-time video of the water level will be displayed on the webpage. [ABSTRACT FROM AUTHOR]

The data of the various organizations how placements are done and the factors affecting the placements for students are analyzed. This kind of data is very useful for college management to train the students. Thus, college succeeds in developing and improving its organization in a better way and also develops in all aspects of placements and improves the various standards of college in terms of admissions too. In this paper, we are going to collect the placement information of students and their status whether they are placed or not. It is also compared with other college placements and thus helping the Organization to improve its existing processes in order to give a healthy and good competition. This consists of various tasks like gender who got placed the most, in which companies did the students got placed (core or IT), programming languages required for placement, minimum CGPA required, No. of backlogs for students that affects their placements. Demographic wise placements, which board students have placed in campus and many more. Based on the analysis the organization can take necessary steps and help students get their desired company. Data Analytics are performed using Python programming language. Data manipulation, analysis, and visualization is done. Anaconda software is used as a coding platform which provides a Jupiter Notebook and also Spyder which supports many libraries for data analytics and preferred by most of the data analysts. [ABSTRACT FROM AUTHOR]

The most frequent cause of natural disasters in India is tropical cyclones. Early warning, real-time monitoring, impact and damage assessment, and relief operations all depend on remote sensing and GIS. The Bay of Bengal is frequently the source of cyclones of various intensities. In this paper, the actual goal is to predicate the tropical cyclone in the tropical region of the Bay of Bengal, for that the main satellite data set is collected from the NASA data repository. The overall work is being done by using the tool MATLAB, where all machine learning classifications are applied to the data set and trained. As a result, which classifier has the highest accuracy will be considered as the best result. The primary goal of this research is to forecast cyclones using day-by-day satellite data of coastal regions and a variety of weather factors. The objective of this work is threefold. First to design a model to reduce the dependency on a single classification technique, secondly to avoid the over-fitting of data, as well as to improve the accuracy of the prediction. Two number of steps in collective learning: Multiple machine learning models created using the same or different machine learning algorithm. [ABSTRACT FROM AUTHOR]

One major challenge in profile extrusion is the prediction of shrinking and warpage, leading to high amounts of off-specification goods, especially during start-up and product change. The determination of optimal process parameters requires either long trials or highly experienced line operators. There are numerous contributing factors to shrinking and warpage both in the planning and operating phase. Not only the die design, but also specific temperatures in the extruder, die and cooling setup during production contribute to shrinking and warpage. Cross-domain collaborations in the Cluster of Excellence "Internet of Production" enable agile research concerning the enhancement of Industry 4.0 applications. Real-time data analysis with model reduction and machine learning is used to build an application-specific Digital Shadow, which can be used for the development of an app-based decision support system for line operators, helping them to quickly determine the optimal process parameters during operation. In a first step, a measurement system was developed, enabling the retrofit of existing analogue extrusion lines. This is not only useful for general quality management purposes, but also a necessary step for creating an interface between the extrusion plant and the industry 4.0 network containing the machine learning backend. For the collection of training data for the machine learning backend of the decision support system, a modular profile extrusion die with exchangeable end plates suitable for three different profiles was rheologically and thermally designed. In the three different profiles, asymmetric cooling behaviour leads to different degrees of warpage. A common data base is developed, comprised of live data from the extrusion line, archive data from previous extrusion trials and archive data from cooling simulations. A fourth data set is created using model order reduction methods. Collectively, this common data base lays groundwork for the development of an invertible neural network, which creates a current Digital Shadow selecting the appropriate data from the four data sets. In this paper, the suitability of data sampling and preprocessing methods for model order reduction are examined and requirements for the archive data are determined. [ABSTRACT FROM AUTHOR]

The present research work explores historic data from NCAA men’s basketball datasets with the aim of providing decision-makers with relevant information and improving their judgment when hiring. However, such datasets may contain redundant, noisy, and irrelevant features that could potentially have a negative influence on decision-making processes. In particular, we have addressed a feature selection question to identify player attributes that contribute the most to being chosen by an NBA team for a professional contract. In this context, this paper proposes a data mining approach using a feature selection method to identify relevant characteristics and rank features to assist stakeholders in decision-making processes. Thus, we have used a data mining approach in terms of a feature selection method by testing some models and performing a combination of genetic algorithm with decision tree and SVM. Feature selection is an important step in the data mining process that allows for obtaining better results using a lower number of features. To this end, we performed an experimental study to evaluate the performance of the proposed method with datasets from the NCAA repository, mainly comparing it with conventional feature selection algorithms from the categories Wrapper, Filter, and Embedding, using SVM and Decision Tree as classifiers. The results show that the proposed feature selection technique outperforms the other used methods on this feature selection problem. The results also show that in this specific model, the dataset was reduced from 65 features to only 6, and metrics such as accuracy, recall, precision, and F1-score showed the best values for both the proposed feature selection technique. Furthermore, we found that dbpm, treb, and ogbpm are the three best predictors to determine the tendency of a player to reach the NBA. [ABSTRACT FROM AUTHOR]

Recommender systems are used with frequency so that content/items are offered in a personalized way for each user, and it is important that these algorithms can accurately recommend content/items to these users (good predictive performance), as well as have a satisfactory computational performance - so that it is not necessary to use too many computational resources. Thus, this paper aims to evaluate some recommendation algorithms that use the memory-based Collaborative Filtering (CF) technique and to evaluate the influence of similarity metrics on the performance of these algorithms. Both algorithms and metrics are available in the scikit-surprise library. Two public databases were used: MovieLens 100k and MovieLens 1M. After the experiments, it was observed that the choice of similarity metric might influence the predictive performance and the prediction and training time of the algorithms. The MSD metric was the one that stood out in influencing, in a positive way, these results. It was also noticed that the database could influence both the predictive performance of the algorithms, as well as the RAM consumption. [ABSTRACT FROM AUTHOR]

In modern data centres an effective and efficient monitoring system is a critical asset, yet a continuous concern for administrators. Since its birth, INFN Tier-1 data centre, hosted at CNAF, has used various monitoring tools all replaced, a few years ago, by a system common to all CNAF departments (based on Sensu, Influxdb, Grafana). Given the complexity of the inter-dependencies of the several services running at the data centre and the foreseen large increase of resources in the near future, a more powerful and versatile monitoring system is needed. This new monitoring system should be able to automatically correlate log files and metrics coming from heterogeneous sources and devices (including services, hardware and infrastructure) thus providing us with a suitable framework to implement a solution for the predictive analysis of the status of the whole environment. In particular, the possibility to correlate IT infrastructure monitoring information with the logs of running applications is of great relevance in order to be able to quickly find application failure root cause. At the same time, a modern, flexible and user-friendly analytics solution is needed in order to enable users, IT engineers and IT managers to extract valuable information from the different sources of collected data in a timely fashion. In this paper, a prototype of such a system, installed at the INFN Tier-1, is described with an assessment of the state and an evaluation of the resources needed for a fully production system. Technologies adopted, amount of foreseen data, target KPIs and production design are illustrated. [ABSTRACT FROM AUTHOR]

The JUNO (Jiangmen Underground Neutrino Observatory) Monte Carlo production tasks are composed of complicated workflow and dataflow linked by data. The paper will present the design of the JUNO production system based on the DIRAC transformation framework to meet the requirements of the JUNO Monte Carlo production activities among JUNO data centres according to the JUNO computing model. The approach allows JUNO data-driven workflow and dataflow to be chained automatically with availability of data and also provides a convenient interface for production groups to create and monitor production tasks. The functions and performance tests for evaluating the prototype system would be also presented in the paper. [ABSTRACT FROM AUTHOR]

The Cloud computing concepts provide everything as a service to global users without any limitation on regions and Technology. The individual user requirement, medium or large scale industry needs will be fulfill virtual infrastructure bycloud service provider with less cost. The existing most of data center has major issue on energy management, pollution and carbon emission. The power conception would be monitor in all the levels in data center helps to implementing optimizationtechnique will be to reduce energy usage. The server consolidation, schedule, optimization, automation technique will reduce power and carbon emission. The effective hardware and software usage and selection of right power source also reducepollution. This paper has been used virtualization technology for the green cloud data center to provide resources such asapplication, desktop VDI, hosted servers and server virtualization. Those resources have been hosted on hypervisors. It was monitoring and based on resource usage category, load, priority, threshold and implemented possible optimization, to reduceenergy. The experimental setup based on monitoring data and implemented referred optimization techniques helps to achievereduce energy and carbon emission. [ABSTRACT FROM AUTHOR]

One of the objectives of reactor dosimetry is determination of activity of irradiated dosimeters, which are placed on reactor pressure vessel surface, and calculation of neutron flux in their position. The uncertainty of calculation depends mainly on the choice of nuclear data library, especially cross section used for neutron transport and cross section used as the response function for neutron activation. Nowadays, number of libraries already exists and can be still used in some applications. In addition, new nuclear data library was recently released. In this paper, we have investigated the impact of the cross section libraries on activity of niobium, one of the popular materials used as neutron fluence monitor. For this purpose, a MCNP6 model of VVER-1000 was made and we have compared the results between 14 commonly used cross section libraries. A possibility of using IRDFF library in activation calculations was also considered. The results show good agreement between the new libraries, with the exception of the most recent ENDF/B-VIII.0, which should be further validated. [ABSTRACT FROM AUTHOR]

Database Management Systems (DBMS) are software systems that implement different data structuring methods in practice. Formally, a Data Model defines data structuring facilities and operations that can be applied to such structured data. Potentially, databases can operate with well-structured data or semi-structured data. In the case of well-structured data, database structure can be separated from actual information content. Such a generic database structure is defined as a so-called database schema. Thus, the database can be seen as just instances of data types predefined in the database schema. A typical sample of well-structured data is the relational data model. Semi-structured data are kept as software repositories that contain both data structures and actual content. A typical sample of such semi-structured data repository is a collection of WWW documents along with navigable links. This paper proposes a method of structuring data repositories suitable for the development of semi-structured databases. The method is based on the concept of so-called structured collection (S-Collections). An S-Collection is an object that encapsulates other data objects and relationships between them. Database may be seen as a set of S-Collections. S-Collections may contain other S-Collections even recursively. Developing a database can be seen as creating individual data objects, combining the objects into S-Collections and inclusion of S-Collections into other S-Collections. Editing operations are always applied to a particular data object (S-collection), what presents a mechanism for supporting the logical integrity of complex semi-structured databases. [ABSTRACT FROM AUTHOR]

The challenge for any nuclear data evaluation project is to periodically release a revised, fully consistent and complete library, with all needed data and covariances, and ensure that it is robust and reliable for a variety of applications. Within an evaluation effort, benchmarking activities play an important role in validating proposed libraries. The Joint Evaluated Fission and Fusion (JEFF) Project aims to provide such a nuclear data library, and thus, requires a coherent and efficient benchmarking process. The aim of this paper is to present the activities carried out by the new JEFF Benchmarking and Validation Working Group, and to describe the role of the NEA Data Bank in this context. The paper will also review the status of preliminary benchmarking for the next JEFF-3.3 candidate cross-section files. [ABSTRACT FROM AUTHOR]

The neutron reference field produced by a heavy-water moderated 252Cf source is used at PTB for calibrating neutron-measuring devices. Knowledge of the precise neutron spectrum is very important for the investigation of operational radiation protection quantities. Recently, new fission spectrum data of 252Cf has been proposed based on the latest nuclear data library version. At PTB, earlier calculations for the D2O moderated 252Cf neutron source were carried out more than 20 years ago, thus updated and more detailed calculations are required. In this paper, a detailed simulation model of the PTB moderated 252Cf source assembly has been prepared and investigated using new spectral data and two different Monte Carlo transport codes MCNP6.1 and PHITS3.22, with ENDF/BVIII.0, ENDF/B-VII.1, ENDF/B-V, and ENDL85 evaluated nuclear data libraries. The results show that the evaluated nuclear data libraries influence the calculated operational quantities by (3-5) %. The dosimetric quantities calculated with the PHITS code and the ENDF/B-VII.1 data library agree well with the MCNP6 results. [ABSTRACT FROM AUTHOR]

In the last three years the research program was conducted in the Czech Republic, which was aimed on the in-situ penetration tests results interpretation. Geotechnical engineers and statisticians from academic environment and engineering geologists from private companies dealing with geological survey were incorporated into the research team. The main objective of the research was to improve methodology of interpretation the dynamic penetration test (DPT) and cone penetration test (CPT) records. This was achieved through the specification of more accurate correlations between the values of parameters measured during a penetration test and geomechanical parameters of penetrated soils. The on-line database enabling direct statistical processing of big amount of archive data (more than 700 probes) was build first. Based on this database the new correlations were established. Correlations were next incorporated into the newly developed application for direct evaluation of measured data. New correlations are presented, and the process of their development is described in paper. [ABSTRACT FROM AUTHOR]

In this study we investigate the characteristics of malicious account behaviors on Twitter based on the analysis of the published data archive. We investigate emergent behavior of malicious accounts that Twitter tagged as connected to state-backed information operations, identified as malicious and removed from the Twitter network. We focus on the analysis of four types of malicious accounts' features: (1) Account reputation, (2) Account tweeting frequency, (3) Age of account and (4) Account activity score. With the use of descriptive statistics and unsupervised learning, we attempt to extend past research that defined behavioral patterns of malicious actors on Twitter. Our research contributes to the understanding of behavior of malicious actors and enriches current research in that area. In this paper we analyze the dataset published by Twitter in January 2019, which contains details on suspended malicious accounts' activities initiated in Bangladesh. [ABSTRACT FROM AUTHOR]

This paper covers the activities of creating a database for the study of modern crustal movements in the region of Sofia and Southwestern Bulgaria, including data from GNSS measurements of the geodynamic network and the results of their processing and analysis, data from permanent stations and archive data from Synthetic-aperture radar, processing and obtaining interferometric images. The local archive of GNSS measurement includes: manual sketches of the location of the points; log sheets of the measurement with information for the height of the antenna, start and end time of measurement and operator name; raw GNSS measurements and data in RINEX format. Data from permanent stations are extracted from external repositories such as IGS or SOPAC are also included in the archive. The local archive of interferometric images contains scenes from the Sentinel-1 satellite downloaded from the repository of the European Space Agency's Science Center. Data for a digital terrain model are extracted from external sources, such as SRTM or ASTER repositories are also included. In order of the envisaged integration with GNSS data, it should be borne in mind that both types of data must be in the same reference system. Measurement campaigns in the geodetic network are carried out in a certain period of time and these periods are used as reference, which allows comparisons to the information extracted from interferometric images. The available experience shows that there is a good correlation between the two types of data. The processing and analysis of the obtained results is another important part of the local archive. The database thus created contains important information and allows for a more accurate analysis of ongoing geodynamic processes in the study area and obtaining reliable information for better regional planning from local authorities. [ABSTRACT FROM AUTHOR]

The opportunity of using Cloud resources on a pay-as-you-go basis and the availability of powerful data centers and high bandwidth connections are speeding up the success and popularity of Cloud systems, which is making on-demand computing a common practice for enterprises and scientific communities. The reasons for this success include natural business distribution, the need for high availability and disaster tolerance, the sheer size of their computational infrastructure, and/or the desire to provide uniform access times to the infrastructure from widely distributed client sites. Nevertheless, the expansion of large data centers is resulting in a huge rise of electrical power consumed by hardware facilities and cooling systems. The geographical distribution of data centers is becoming an opportunity: the variability of electricity prices, environmental conditions and client requests, both from site to site and with time, makes it possible to intelligently and dynamically (re)distribute the computational workload and achieve as diverse business goals as: the reduction of costs, energy consumption and carbon emissions, the satisfaction of performance constraints, the adherence to Service Level Agreement established with users, etc. This paper proposes an approach that helps to achieve the business goals established by the data center administrators. The workload distribution is driven by a fitness function, evaluated for each data center, which weighs some key parameters related to business objectives, among which, the price of electricity, the carbon emission rate, the balance of load among the data centers etc. For example, the energy costs can be reduced by using a "follow the moon" approach, e.g. by migrating the workload to data centers where the price of electricity is lower at that time. Our approach uses data about historical usage of the data centers and data about environmental conditions to predict, with the help of regressive models, the values of the parameters of the fitness function, and then to appropriately tune the weights assigned to the parameters in accordance to the business goals. Preliminary experimental results, presented in this paper, show encouraging benefits. [ABSTRACT FROM AUTHOR]

Libraries are starting to use Linked Open Data (LOD) to provide their data (li-brary data) for reuse and to enrich them. However, most initiatives are only available for textual resources, whereas non-textual resources stay aside. Firstly, this paper discusses the potential of library data to be published as LOD. Secondly, it focuses on the library data related to the management of audiovisual scientific materials in the TIB|AV-Portal. The use LOD Standards to support multilingual functionalities and data reuse is outlined. Future developments lead to the building of semantic applications based on LOD Struc-tures. [ABSTRACT FROM AUTHOR]

In this paper, we analyse how three social science data archives (SSDA) have adapted to provide value to different stakeholders over time. Drawing on historical administrative documents and interviews with current and former staff at three long standing SSDA, we examine how these archives have provided value, to whom, and how they have situated themselves vis‐à‐vis alternative archives over 20‐40 year time spans. Although data archives have been in operation for decades, how they have sustained themselves over time by continuing to provide value to stakeholders in changing conditions is less well understood. Studies of value have tended to focus on a snapshot in time rather than providing a view that emphasizes change over time. We conclude with a comparative analysis of changes in value across archives and suggestions for future work [ABSTRACT FROM AUTHOR]

CPU-FPGA heterogeneous architectures feature flexible acceleration of many workloads to advance computational capabilities and energy efficiency in today's datacenters. This advantage, however, is often overshadowed by the poor programmability of FPGAs. Although recent advances in high-level synthesis (HLS) significantly improve the FPGA programmability, it still leaves programmers facing the challenge of identifying the optimal design configuration in a tremendous design space. In this paper we propose the composable, parallel and pipeline (CPP) microarchitecture as an accelerator design template to substantially reduce the design space. Also, by introducing the CPP analytical model to capture the performance-resource trade-offs, we achieve efficient, analytical-based design space exploration. Furthermore, we develop the AutoAccel framework to automate the entire accelerator generation process. Our experiments show that the AutoAccel-generated accelerators outperform their corresponding software implementations by an average of 72x for a broad class of computation kernels. [ABSTRACT FROM AUTHOR]

Since 2005 Data Archiving and Networked Services (DANS) promotes sustained access to digital research data. DANS offers several services to support this, including the online archiving system EASY, the Dutch Dataverse Network and the portal NARCIS. In this paper these services will be presented, including the differences we encounter between the disciplines using these services. Within the disciplines served by DANS - mostly belonging to the Social Sciences and Humanities - differences can be discerned regarding the 'openness' of the data. As case study the archeology datasets in EASY will be discussed. Many of them contain reports of archeological excavations done in the Netherlands. Should we consider these as research data or grey literature? And, should we open up the access to these datasets? And if not, why not? These, and other questions, will be addressed, providing a view on how DANS currently deals with open data, grey literature and disciplinary differences. [ABSTRACT FROM AUTHOR]

The design, implementation, and maintenance of data center networks must meet numerous dependability requirements to guarantee the quality of service at a high level of reliability. This paper investigates dependability metrics and performs sensitivity analysis for data center networks with different redundancy levels. Our approach is based on hierarchical modeling in which we use reliability block diagrams. We applied the parametric sensitivity analysis technique in the proposed experiments to assess how sensitive the availability is concerning the model components' failure and repair times. [ABSTRACT FROM AUTHOR]

RANKERN 16 is the latest version of the point-kernel gamma radiation transport Monte Carlo code from AMEC Foster Wheeler’s ANSWERS Software Service. RANKERN is well established in the UK shielding community for radiation shielding and dosimetry assessments. Many important developments have been made available to users in this latest release of RANKERN. The existing general 3D geometry capability has been extended to include import of CAD files in the IGES format providing efficient full CAD modelling capability without geometric approximation. Import of tetrahedral mesh and polygon surface formats has also been provided. An efficient voxel geometry type has been added suitable for representing CT data. There have been numerous input syntax enhancements and an extended actinide gamma source library. This paper describes some of the new features and compares the performance of the new geometry capabilities. [ABSTRACT FROM AUTHOR]

Two different use cases for monitoring are analysed in this paper: the CERN video conference system – a complex ecosystem, which is being used by most HEP institutes, together with Swiss Universities through SWITCH; and the CERN Audio-Visual and Conferencing (AVC) environment – a vast Internet of Things (IoT), which includes a great variety of devices accessible via IP. Despite the differences between both use cases, a common set of techniques underpinned by IT services is discussed in order to tackle each situation. [ABSTRACT FROM AUTHOR]

This paper introduces a generic and scalable anomaly detection framework. Anomaly detection can improve operation and maintenance efficiency and assure experiments can be carried out effectively. The framework facilitates common tasks such as data sample building, retagging and visualization, deviation measurement and performance measurement for machine learning-based anomaly detection methods. The samples we used are sourced from Ganglia monitoring data. There are several anomaly detection methods to handle spatial and temporal anomalies within the framework. Finally, we show the rudimental application of the framework on Lustre distributed file systems in daily operation and maintenance. [ABSTRACT FROM AUTHOR]

Since 2013 CERN's local data centre combined with a colocation infrastructure at the Wigner data centre in Budapest [1] have been hosting the compute and storage capacity for Worldwide LHC Computing Grid (WLCG) [2] Tier-0. In this paper we will describe how we try to optimize and improve the operation of our local data centre to meet the anticipated increment of the physics compute and storage requirements for Run 3, taking into account two important changes on the way: the end of the co-location contract with Wigner in 2019 and the loan of 2 out of 6 prefabricated compute modules being commissioned by the LHCb experiment [3] for their online computing farm. [ABSTRACT FROM AUTHOR]

High Energy Physics (HEP) experiments rely on the networks as one of the critical parts of their infrastructure both within the participating laboratories and sites as well as globally to interconnect the sites, data centres and experiments instrumentation. Network virtualisation and programmable networks are two key enablers that facilitate agile, fast and more economical network infrastructures as well as service development, deployment and provisioning. Adoption of these technologies by HEP sites and experiments will allow them to design more scalable and robust networks while decreasing the overall cost and improving the effectiveness of the resource utilization. The primary challenge we currently face is ensuring that WLCG and its constituent collaborations will have the networking capabilities required to most effectively exploit LHC data for the lifetime of the LHC. In this paper we provide a high level summary of the HEPiX NFV Working Group report that explored some of the novel network capabilities that could potentially be deployment in time for HL-LHC. [ABSTRACT FROM AUTHOR]

VyPR (http://pyvypr.github.io/home/) is a framework being developed with the aim of automating as much as possible the performance analysis of Python programs. To achieve this, it uses an analysis-by-specification approach; developers specify the performance requirements of their programs (without any modifications of the source code) and such requirements are checked at runtime. VyPR then provides tools which allow developers to perform detailed analyses of the performance of their code. Such analyses can include determining the common paths taken to reach badly performing parts of code, deciding whether a single path through code led to variations in time taken by future observations, and more. This paper describes the developments that have taken place in the past year on VyPR's analysis tools to yield a Python shell-based analysis library, and a web-based application. It concludes by demonstrating the use of the analysis tools on the CMS Experiment's Conditions Upload service. [ABSTRACT FROM AUTHOR]

Large scientific data centers have recently begun providing a number of different types of data storage in order to satisfy the various needs of their users. Users with interactive accounts, for example, might want a POSIX interface for easy access to the data from their interactive machines. Grid computing sites, on the other hand, likely need to provide an X509-based storage protocol, like SRM and GridFTP, since the data management system is built upon them. Meanwhile, an experiment producing large amounts of data typically demands a service that provides archival storage for the safe keeping of their unique data. To access these various types of data, users must use specific sets of commands tailored to their respective storage, making access to their data complex and difficult. BNLBox is an attempt to provide a unified and easy to use storage service for all BNL users, to store their important documents, code and data. It is a cloud storage system with an intuitive web interface for novice users. It provides an automated synchronization feature that enables users to upload data to their cloud storage without manual intervention, freeing them to focus on analysis rather than data management software. It provides a POSIX interface for local interactive users, which simplifies data access from batch jobs as well. At the same time, it also provides users with a straightforward mechanism for archiving large data sets for later processing. The storage space can be used for both code and data within the compute job environment. This paper will describe various aspects of the BNLBox storage service. [ABSTRACT FROM AUTHOR]

The Information Service (IS) is an integral part of the Trigger and Data Acquisition (TDAQ) system of the ATLAS experiment at the Large Hadron Collider (LHC) at CERN. The IS allows online publication of operational monitoring data, and it is used by all sub-systems and sub-detectors of the experiment to constantly monitor their hardware and software components including more than 25000 applications running on more than 3000 computers. The Persistent Back-End for the ATLAS Information System (PBEAST) service stores all raw operational monitoring data for the lifetime of the experiment and provides programming and graphical interfaces to access them including Grafana dashboards and notebooks based on the CERN SWAN platform. During the ATLAS data taking sessions (for the full LHC Run 2 period) PBEAST acquired data at an average information update rate of 200 kHz and stored 20 TB of highly compacted and compressed data per year. This paper reports how over six years PBEAST became an essential piece of the experiment operations including details of the challenging requirements, the failures and successes of the various attempted implementations, the new types of monitoring data and the results of the time-series database technology evaluations for the improvements towards LHC Run 3. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic emerged as word's major issue. COVID-19 is the greatest test the world has faced since the end of World War II. It comes at the point that none of the countries on earth are ready to face it nevertheless status of the country neither developed nor poor. COVID-19 is a global health crisis killing people and spreading human suffering across region. This outbreak gave significant impact to all sort of sectors including security. There are winners and losers since the 'pandora-virus-box' has opened, the world's focus has changed to securing health and food industry rather than capitalism economic base. The Covid-19 pandemic has the potential to devastate fragile and conflict affected area such as Palestine, Syria, Yemen, Somalia, Nigeria, Myanmar, Iraq, Afghanistan and many more. These states facing lots of socio-economy problem and have faltering health systems especially to the refugees. Separatists and freedom fighters halt their movement as they also having very tough-time right now. This paper is qualitative in nature, emphasized the best practice used in minimising security threat by COVID-19 impact, using secondary data from library research. The outcome, understanding the impact and threat of COVID-19 to the world's security was intended to help governments and policy makers to revisit their whole of security related policy and prevent their country from catasthropic made by COVID-19 or any pandemic threat in future. [ABSTRACT FROM AUTHOR]

The rapid development of Open Government Data (OGD) and the increasing attention on data use/reuse have stimulated many studies on data‐related issues. However, the findability of OGD is still one of the major challenges. Aiming to ameliorate the situation that "data is hard to find", this paper examines OGD users' needs and accessing behaviors when interacting with local OGD portals. Transaction log analysis and web content mining were used in order to obtain insights from large groups of OGD users in an unobtrusive manner. Through analyzing transaction log data from three local OGD portals, including Open Data Philly (opendataphilly.org), Western Pennsylvania Regional Data Center (wprdc.org) and Analyze Boston (data.boston.gov), our study shows that users relied on different channels to enter local OGD portals, and such channels have different impacts on user success in finding the sought‐after data. We also find that OGD users prefer browsing over searching when inside the portals, the utilization of different browsing entries, and users' data needs. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper reports on how LIS authors depict the concept of sustainability of digital archives, repositories and libraries in English language texts from 2000- 2015 indexed in three major LIS databases. Our results show that sustainability is not as popular a topic as one might expect. Results show that most authors discuss sustainability at a superficial level rather than in-depth. Sustainability is a multi-faceted concept, and we explore the prevalence of nine codes, representing different facets of sustainability, in the texts. We found most authors discussed sustainability in terms of technology, management, relationships or revenue. Fewer described assessment, disaster planning or policy facets. We also describe the range and variation in subthemes we encountered within each code. We conclude with suggestions for advancing conversation about organizational sustainability in the LIS literature. [ABSTRACT FROM AUTHOR]

The fraction of general internet traffic carried over IPv6 continues to grow rapidly. The transition of WLCG central and storage services to dual-stack IPv4/IPv6 is progressing well, thus enabling the use of IPv6-only CPU resources as agreed by the WLCG Management Board and presented by us at CHEP2016. By April 2018, all WLCG Tier-1 data centres should have provided access to their services over IPv6. The LHC experiments have requested all WLCG Tier-2 centres to provide dual-stack access to their storage by the end of LHC Run 2. This paper reviews the status of IPv6 deployment in WLCG. [ABSTRACT FROM AUTHOR]