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Industrial control terminals play an important role in industrial control scenarios. Due to the special nature of industrial control networks, industrial control terminal systems are vulnerable to malicious attacks, which can greatly threaten the stability and security of industrial production environments. Traditional security protection methods for industrial control terminals have coarse detection granularity, and are unable to effectively detect and prevent attacks, lacking real-time responsiveness to attack events. Therefore, this paper proposes a real-time dynamic credibility evaluation mechanism based on program behavior, which integrates the matching and symmetry ideas of credibility evaluation. By conducting a real-time dynamic credibility evaluation of function call sequences and system call sequences during program execution, the credibility of industrial control terminal application program behavior can be judged. To solve the problem that the system calls generated during program execution are unstable and difficult to measure, this paper proposes a partition-based dynamic credibility evaluation method, dividing program behavior during runtime into function call behavior and system call behavior within function intervals. For function call behavior, a sliding window-based function call sequence benchmark library construction method is proposed, which matches and evaluates real-time measurement results based on the benchmark library, thereby achieving symmetry between the benchmark library and the measured data. For system call behavior, a maximum entropy system call model is constructed, which is used to evaluate the credibility of system call sequences. Experiment results demonstrate that our method performs better in both detection success rate and detection speed compared to the existing methods. [ABSTRACT FROM AUTHOR]

This paper focuses attention on the problem of supporting multidimensional big data analytics tools over Big Web Knowledge Bases. These big data repositories are generated from web systems built on top of big datasets, as in several real-life examples such as web interfaces to social networks, e-government systems, smart city tools (e.g., for weather forecasting), and so forth. The problem of extracting actionable knowledge insights from big web knowledge bases is a relevant challenge now. In line with this emerging research field, the paper proposes models, issues and research trends in the context of multidimensional big data analytics over big web knowledge bases, along with a reference architecture that impersonates this paradigm. [ABSTRACT FROM AUTHOR]

The paper analyzes the issues and challenges of the current information and communication process of private sector healthcare institutions in Ethiopia. Both qualitative and quantitative research approaches were used to collect and analyze data using surveys, interviews, observation and related secondary source documents. The analysis indicated the research gaps in the current information and communication process models and the clear direction toward a solution. This study employed a mixed research design (both exploratory and applied research) and used Google Forms for a survey and interview-based fact-finding and analysis, Microsoft Visio for model designing, and Protopie for prototyping. As a final contribution, the study designed and validated a cloud-based green information and communication process model for the federated healthcare institutions in developing countries in general and the Southern Nations, Nationalities and Peoples Region (SNNPR) of Ethiopia in particular. This model can be used as an important instrument for inter or intra institutional collaboration and communications. Based on the user acceptance and demonstrated validation, the results indicate improvements in energy, efficiency, unified data repository, intra institutional collaboration, information sharing, trusted and ethical information and unified integration and share-ability. The test validated the model and its prototype as a new knowledge contribution to the domain. The research findings can be used as a basis for next-generation transformation of private sector healthcare institutions in developing countries such as Ethiopia. [ABSTRACT FROM AUTHOR]

Copyright of Arachnology Letters / Arachnologische Mitteilungen is the property of Arachnologische Gesellschaft e.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents selected aspects of a data platform to store agricultural data. It analyses the key user and system requirements for the data platform. The presented aspects were identified through a literature review, interviews and discussions with selected data experts and researchers and future users of the platform. The following issues of the data platform are discussed in the paper: architecture, data types, data source types, metadata, disciplinary interfaces, data sharing, data reusability, Open Science, FAIR data principles, and further data processing options. Part of the knowledge from this article was used in the design and implementation of the Institutional Data Repository called Data Management Platform (DaMP.) CZU (Czech University of Life Sciences Prague) (OPENAI, 2023). [ABSTRACT FROM AUTHOR]

This systematic review synthesised existing research papers that explore the available metadata standards to enable researchers to preserve, discover, and reuse research data in repositories. This review provides a broad overview of certain aspects that must be taken into consideration when creating and assessing metadata standards to enhance research data preservation discoverability and reusability strategies. Research papers on metadata standards, research data preservation, discovery and reuse, and repositories published between January 2003 and April 2023 were reviewed from a total of five databases. The review retrieved 1597 papers, and 13 papers were selected in this review. We revealed 13 research articles that explained the creation and application of metadata standards to enhance preservation, discovery, and reuse of research data in repositories. Among them, eight presented the three main types of metadata, descriptive, structural, and administrative, to enable the preservation of research data in data repositories. We noted limited evidence on how these metadata standards can be used to enhance the discovery and reuse of research data in repositories to enable the preservation, discovery, and reuse of research data in repositories. No reviews indicated specific higher education institutions employing metadata standards for the research data created by their researchers. Repository designs and a lack of expertise and technology know-how were among the challenges identified from the reviewed papers. The review has the potential to influence professional practice and decision-making by stakeholders, including researchers, students, librarians, information communication technologists, data managers, private and public organisations, intermediaries, research institutions, and non-profit organizations. [ABSTRACT FROM AUTHOR]

The paper addresses the challenge of transmitting a big number of files stored in a data center (DC), encrypting them by compilers, and sending them through a network at an acceptable time. Face to the big number of files, only one compiler may not be sufficient to encrypt data in an acceptable time. In this paper, we consider the problem of several compilers and the objective is to find an algorithm that can give an efficient schedule for the given files to be compiled by the compilers. The main objective of the work is to minimize the gap in the total size of assigned files between compilers. This minimization ensures the fair distribution of files to different compilers. This problem is considered to be a very hard problem. This paper presents two research axes. The first axis is related to architecture. We propose a novel pre-compiler architecture in this context. The second axis is algorithmic development. We develop six algorithms to solve the problem, in this context. These algorithms are based on the dispatching rules method, decomposition method, and an iterative approach. These algorithms give approximate solutions for the studied problem. An experimental result is implemented to show the performance of algorithms. Several indicators are used to measure the performance of the proposed algorithms. In addition, five classes are proposed to test the algorithms with a total of 2350 instances. A comparison between the proposed algorithms is presented in different tables discussed to show the performance of each algorithm. The result showed that the best algorithm is the Iterative-mixed Smallest-Longest-Heuristic (ISL) with a percentage equal to 97.7% and an average running time equal to 0.148 s. All other algorithms did not exceed 22% as a percentage. The best algorithm excluding ISL is Iterative-mixed Longest-Smallest Heuristic (ILS) with a percentage equal to 21,4% and an average running time equal to 0.150 s. [ABSTRACT FROM AUTHOR]

The purpose of this paper is a celebration of Michael J. Roberts's illustrative genius. It provides details about the illustrations comprising the Michael J. Roberts archive at the NHM, explains how they can be accessed by scholars, discusses how they are housed and their conservation issues, and touches upon Roberts's illustrative techniques with examples from the archive. It also showcases several plates from each of his major publications to further exemplify Roberts's considerable talents. For the future, a potential collaborative project between the NHM and the British Arachnological Society is considered, to enable scholars to view more easily the illustrations online via a portal. This paper is of interest to anyone who is interested in spiders, who uses Roberts's illustrations for identification purposes and for scientific illustrators and artists. [ABSTRACT FROM AUTHOR]

Optimization of electrical discharge machining (EDM) processes is a critical issue due to complex material removal mechanism, presence of multiple input parameters and responses (outputs) and interactions among them and varying interest of different stakeholders with respect to relative importance assigned to the considered responses. Multi-criteria decision making (MCDM) techniques have become potent tools in solving parametric optimization problems of the EDM processes. In this paper, more than 130 research articles from SCOPUS database published during 2013-22 are reviewed extracting information with respect to experimental design plans employed, materials machined, dielectrics used, process parameters and responses considered and MCDM tools applied along with their integration with other mathematical techniques. A detailed analysis of those reviewed articles reveals that the past researchers have mostly preferred Taguchi's L9 orthogonal array as the experimental design plan; EDM oil as the dielectric fluid; medium and high carbon steels as the work materials; peak current and pulse-on time as the input parameters; material removal rate, tool wear rate and surface roughness as the responses; and grey relational analysis as the MCDM tool during conducting and optimizing EDM operations. This review paper would act as a data repository to the future researchers in understanding the stochastic behaviour of EDM processes and providing guidance in setting the tentative operating levels of varying input parameters along with achievable response values. The extracted dataset can be treated as an input to any of the machine learning algorithms for subsequent development of appropriate prediction models. This review also outlines potential future research avenues, emphasizing advancements in EDM technology and the integration of innovative multi-criteria decisionmaking tools. [ABSTRACT FROM AUTHOR]

Purpose: Prior research shows that religiosity affects the degree of managers' risk aversion. As a result, religious firms are less likely to invest in R&D activities. Moreover, US GAAP treats these investments as expenses. For this reason, religious firms have fewer expenses in their earnings and are less likely to be in financial distress. Design/methodology/approach: Data are collected from Worldscope and the Churches and Church Membership files of the American Religion Data Archive website from 1985 to 2018. With 18,199 observations in US context, the authors used the marginal effect to test the mediating effect of R&D accounting treatment. Findings: The authors find that the marginal effect of religiosity on financial distress with US GAAP is higher than the marginal effect of religiosity on financial distress with capitalization of R&D costs, which means that accounting treatment can explain the relation between religiosity and financial distress in the US context. Research limitations/implications: The authors used linear interpolation and linear extrapolation data to be able to conduct this research over a period of 1985–2018. For future researches, the authors propose to test other factors which can explain the relationship between religiosity and financial distress based on the ethics element. Practical implications: These results should be of interest to regulators because treating R&D activities as expenses can destroy the accounting performance of firms that prefer investing in risky projects. This favoritism prevents the comparison between two firms in the same industry with different risk-taking behaviors. This problem is more prevalent if the authors have two firms with different ratios of religiosity. This paper suffers from a major limitation related to data availability. Originality/value: This may be the first study that investigates why religious firms are less likely to be in financial distress. This paper notes that religious firms are less likely to be in financial distress because their conservative behavior towards R&D activities coincides with the conservative R&D accounting treatment. In fact, the mismatch between expenses and revenues from R&D activities can cause financial distress. [ABSTRACT FROM AUTHOR]

This paper gives an overview over all currently available data sets for the European Long-term Ecosystem Research (eLTER) monitoring site Gesäuse-Johnsbachtal. The site is part of the LTSER platform Eisenwurzen in the Alps of the province of Styria, Austria. It contains both protected (National Park Gesäuse) and non-protected areas (Johnsbachtal). Although the main research focus of the eLTER monitoring site Gesäuse-Johnsbachtal is on inland surface running waters, forests and other wooded land, the eLTER whole system (WAILS) approach was followed in regard to the data selection, systematically screening all available data in regard to its suitability as eLTER's Standard Observations (SOs). Thus, data from all system strata was included, incorporating Geosphere, Atmosphere, Hydrosphere, Biosphere and Sociosphere. In the WAILS approach these SOs are key data for a whole system approach towards long term ecosystem research. Altogether, 54 data sets have been collected for the eLTER monitoring site Gesäuse-Johnsbachtal and included in the Dynamical Ecological Information Management System – Site and Data Registry (DEIMS-SDR), which is the eLTER data platform. The presented work provides all these data sets through dedicated data repositories for FAIR use. This paper gives an overview on all compiled data sets and their main properties. Additionally, the available data are evaluated in a concluding gap analysis with regard to the needed observation data according to WAILS, followed by an outlook on how to fill these gaps. [ABSTRACT FROM AUTHOR]

From July 2022 to December 2022, the World Data System (WDS) International Technology (ITO) and International Program (IPO) Offices conducted a review of strategic plans and technical roadmaps of all current WDS members and the set of Canadian repositories that participated in the Digital Research Alliance of Canada's CoreTrustSeal Certification Support and Funding Pilotv (Digital Research Alliance of Canada, 2022). In this paper, we describe how a new organizational assessment method was designed and utilized to identify the needs and challenges faced by the WDS and Canadian CTS Pilot members. Our method relied on reviewing public-facing documentation provided by the repositories, with a priority on strategic plans and technical road maps. In total, we reviewed 95 sources of information, including 33 strategic plans and 3 technical roadmaps describing a total of 95 out of the original 147 target organizations. In this paper, we also describe our assessment tool and the overarching challenges and goals we identified through the usage of this tool. Finally, we will describe the limitations of our methodology and provide recommendations from the World Data System on how best to assist the WDS members and the cohort of Canadian data repositories based on our findings. [ABSTRACT FROM AUTHOR]

To achieve a dynamic comprehensive risk assessment of road transport of hazardous chemicals, this paper first collected 213 road transport accidents involving hazardous chemicals that occurred between 2015 and 2020 to build a case library. Then, it established a risk assessment system (including 9 primary indicators and 28 secondary indicators) based on the frequencies of reasons for accident cases and explored the quantification methods for indicators based on their definitions. Subsequently, this paper calculated the weights of indicators based on the backpropagation neural network algorithm and the data in the case library and formulated a model for dynamic risk assessment of road transport of hazardous chemicals. The model takes into account the impact of comprehensive indicators such as drivers, vehicles, goods, roads, and the environment. The accuracy of the model calculation results is 93%. Eventually, the study verified the model using simulation data in real scenarios. The validation results show that the model could achieve a dynamic comprehensive assessment of risks in road transport of hazardous chemicals. [ABSTRACT FROM AUTHOR]

As cloud computing usage grows, cloud data centers play an increasingly important role. To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks and manage performance effectively. The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers. The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies, categories, and gaps. A literature review was conducted, which included the analysis of 463 task allocations and 480 performance management papers. The review revealed three task allocation research topics and seven performance management methods. Task allocation research areas are resource allocation, load-Balancing, and scheduling. Performance management includes monitoring and control, power and energy management, resource utilization optimization, quality of service management, fault management, virtual machine management, and network management. The study proposes new techniques to enhance cloud computing work allocation and performance management. Shortcomings in each approach can guide future research. The research's findings on cloud data center task allocation and performance management can assist academics, practitioners, and cloud service providers in optimizing their systems for dependability, cost-effectiveness, and scalability. Innovative methodologies can steer future research to fill gaps in the literature. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to explore what organizational adaptability means in the digitized context and to discuss how manufacturing companies achieve organizational adaptability during the digital transformation process. Design/methodology/approach: By conducting semi-structured interviews and acquiring archive data from a typical Chinese manufacturing company, this paper gathers extensive data. Based on this, a single-case study methodology is used to investigate organizational adaptability in digital transformation. Findings: This study identifies the process by which companies achieve organizational adaptability during digital transformation and deconstructs organizational adaptability into three dimensions: structural adaptability, operational adaptability and governance adaptability. This study also explores how organizational adaptability is affected by digital capabilities. Originality/value: This study proposes a process model to demonstrate how organizational adaptability may be attained during digital transformation and redefines organizational adaptability in the context of digitization. [ABSTRACT FROM AUTHOR]

Digital repositories play a crucial role in organizing and preserving vast collections of digital content. Efficiently ingesting large amounts of data into these repositories is a common challenge faced by institutions. This paper explores the use of bulk upload techniques in DSpace, an open-source digital repository software, to streamline the ingestion process and enhance repository management. We discuss the benefits of bulk upload in terms of time savings, metadata consistency, and scalability. Additionally, we delve into the technical aspects of implementing bulk upload in DSpace, covering the Simple Archive Format (SAF), metadata mapping, and handling of digital files. Furthermore, we highlight real-world examples and best practices for utilizing bulk upload in DSpace. By adopting this approach, institutions can significantly improve their efficiency in managing and preserving digital content, ensuring a seamless user experience, and facilitating knowledge dissemination. Here, an experimental method of research/case study technique is utilized to evaluate the efficiency and effectiveness of the design model for implementation of the bulk uploading of documents in Dspace at Bennett University is practiced. The feedback is gathered in order to identify the flaws and make the necessary improvements. Simple Archive Format (SAF) is a utility that converts Bitstream/Content files plus a metadata.csv file into a Simple Archive Format package, making bulk uploads to the DSpace repository simple. All question papers were digitized using a high-quality scanner, an Excel file with Dublin core information was created, and Excel was converted to CSV format in order to import all old question papers in bulk into the Bennett University Digital Repository Services. The study indicates that it is essential to pay close attention to the precise format of metadata leveraging the Dublin core and the file's location. It is an experiment conducted by the Bennett University Library and the research was confined to Bennett University digital repository. [ABSTRACT FROM AUTHOR]

The latest release of GRACE gravity field coefficients (Release-05, or RL05) are evaluated for ocean applications. Data have been processed using the current methodology for Release-04 (RL04) coefficients, and have been compared to output from two different ocean models. Results indicate that RL05 data from the three Science Data Centers - the Center for Space Research (CSR), GeoForschungsZentrum (GFZ), and Jet Propulsion Laboratory (JPL) - are more consistent than the previous RL04 data. Moreover, the variance of residuals with the output of an ocean model is 50-60% lower for RL05 data than for RL04 data. A more optimized destriping algorithm is also tested, which improves the results slightly. By comparing the GRACE maps with two different ocean models, we can better estimate the uncertainty in the RL05 maps. We find the standard error to be about 1 cm (equivalent water thickness) in the low- and mid-latitudes, and between 1.5 and 2 cm in the polar and sub-polar oceans, which is comparable to estimated uncertainty for the output from the ocean models. [ABSTRACT FROM AUTHOR]

Europe has set a target to become a decarbonised continent by 2050. To achieve this, intervention in buildings is crucial, as they serve as significant energy consumers and greenhouse gas emitters. This intervention encompasses two essential pathways: renovation and digitalisation. The combination of these two aspects gives rise to elements such as the Digital Building Logbook (DBL), a digital data repository expected to enhance the pace and quality of renovation efforts. This paper introduces, for the first time, a European DBL data model with a specific focus on building renovation purposes—the DBrL. It outlines its initial requirements, constituent entities, relationships, and attributes. While acknowledging the need to address issues related to data protection, integration with existing data sources, and connections with Building Information Modelling (BIM) and Geographic Information System (GIS) in subsequent design phases, the study's outcome represents a significant stride in defining this tool. [ABSTRACT FROM AUTHOR]

The majority of available climate data in global digital archives consist of data only from the 1940s or 1950s onwards, and many of these series have gaps and/or are available for only a subset of the variables which were actually observed. However, there exist billions of historical weather observations from the 1700s, 1800s, and early 1900s that are still in hard‐copy form and are at risk of being lost forever due to deterioration. An assessment of changes in climate extremes in several IPCC regions was not possible in IPCC AR6 WGI owing, in many cases, to the lack of available data. One such region is Africa, where the climate impact research and the ability to predict climate change impacts are hindered by the paucity of access to consistent good‐quality historical observational data. The aim of this innovative project was to use classroom‐based participatory learning to help transcribe some of the many meteorological observations from Africa that are thus far unavailable to researchers. This project transcribed quickly and effectively station series by enrolling the help of second‐year undergraduate students at Maynooth University in Ireland. The newly digitized African data will increase the temporal and spatial coverage of data in this important data‐sparse region. Students gained new skills while helping the global scientific community unearth new insight into past African climate. The project managed to transcribe 79 months of data at Andapa in Madagascar and 56 months of data for Macenta in Guinea. The digitized data will be openly and freely shared with the scientific and wider community via the Pangaea data repository, the C3S Climate Data Store, and the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Information (NCEI) data centre in the US. The project model has the potential for a broader roll‐out to other educational contexts and there is no shortage of data to be rescued. This paper provides details of the project, and all supporting information such as project guidelines and templates to enable other organizations to instigate similar programs. [ABSTRACT FROM AUTHOR]

Data centers (DCs) are highly energy-intensive facilities, where about 30%–50% of the power consumed is attributable to the cooling of information technology equipment. This makes liquid cooling, especially in two-phase mode, as an alternative to air cooling for the microprocessors in servers of interest. The need to meet the increased power density of server racks in high-performance DCs, along with the push towards lower global warming potential (GWP) refrigerants due to environmental concerns, has motivated research on the selection of two-phase heat transfer fluids for cooling servers while simultaneously recovering waste heat. With this regard, a heat pump-assisted absorption chiller (HPAAC) system for recovering waste heat in DCs with an on-chip two-phase cooling loop driven by the compressor is proposed in the present paper and the low GWP hydrofluoroolefin refrigerants, including R1224yd(Z), R1233zd(E), R1234yf, R1234ze(E), R1234ze(Z), R1243zf and R1336mzz(Z), are evaluated and compared against R245fa as server coolant. For the HPAAC system, beginning with the development of energy and economic models, the performance is analyzed through both a parametric study and optimization using the coefficient of performance (COP), energy saving ratio (ESR), payback period (PBP) and net present value (NPV) as thermo-economic indicators. Using a standard vapor compression cooling system as a benchmark, the results indicate that with the evaporation temperature between 50°C and 70°C and the subcooling degree ranging from 5°C to 15°C, R1233zd(E) with moderate compressor suction pressure and pressure ratio is the best refrigerant for the HPAAC system while R1234yf performs the worst. More importantly, R1233zd(E) is also superior to R245fa based on thermo-economic performance, especially under work conditions with relatively lower evaporation temperature as well as subcooling degree. Under the given working conditions, the overall COP, ESR, NPV, and PBP of R1233zd(E) HPAAC with optimum subcooling degree range from 4.99 to 11.27, 25.53 to 64.59, 1.13 to 4.10 × 107 CNY and 5.77 to 2.22 years, respectively. Besides, the thermo-economic performance of R1233zd(E) HPAAC under optimum working conditions in terms of subcooling degree varying with the evaporation temperature is also investigated. [ABSTRACT FROM AUTHOR]

Background: Lung cancer screening (LCS) using low-dose computed tomography (LDCT) is a strategy for early-stage diagnosis. The implementation of LDCT screening in countries with a high prevalence/incidence of tuberculosis (TB) is controversial. This systematic review and meta-analysis aim to identify whether LCS using LDCT increases early-stage diagnosis and decreases mortality, as well as the false-positive rate, in regions with a high prevalence of TB. Methods/Design: Studies were identified by searching BVS, PUBMED, EMBASE, and SCOPUS. RCT and cohort studies (CS) that show the effects of LDCT in LC screening on mortality and secondary outcomes were eligible. Two independent reviewers evaluated eligibility and a third judged disagreements. We used the Systematic Review Data Repository (SRDR+) to extract the metadata and record decisions. The analyses were stratified by study design and incidence of TB. We used the Cochrane "Risk of bias" assessment tool. Results: The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) were used. Thirty-seven papers were included, referring to 22 studies (10 RCTs and 12 cohorts). Few studies were from regions with a high incidence of TB (One RCT and four cohorts). Nonetheless, the evidence is compatible with European and USA studies. RCTs and CS also had consistent results. There is an increase in early-stage (I-II) diagnoses and reduced LC mortality in the LCDT arm compared to the control. Although false-positive rates varied, they stayed within the 20 to 30% range. Discussion: This is the first meta-analysis of LDCT for LCS focused on its benefits in regions with an increased incidence/prevalence of TB. Although the specificity of Lung-RADS was higher in participants without TB sequelae than in those with TB sequelae, our findings point out that the difference does not invalidate implementing LDCT LCS in these regions. Trial registration: Systematic review registration Systematic review registration PROSPERO CRD42022309581. [ABSTRACT FROM AUTHOR]

This paper presents the open-source Python software OSADCP developed for the processing of vessel-mounted Acoustic Doppler Current Profiler (VMADCP) data. At this stage, the toolbox is designed for processing VMADCP measurements from open-ocean applications of Teledyne RDI Ocean Surveyor ADCPs and the data acquisition software VMDAS. Based on the VMDAS ENX binary output format, the software contains implementations for cleaning and vector-averaging of single-ping velocity data, verification of the position data, and applying misalignment and amplitude corrections. The procedures of OSADCP are described in detail to encourage the scientific community to use it for their own purposes. The toolbox is an integral part of a workflow implemented on the German marine research vessels in the framework of the Underway Research Data project of the German Marine Research Alliance (DAM). It aims to ensure standardized data acquisition measures, reliable data transfer from the ADCP to shore both near-real-time and in delayed-mode, processing and quality control, and dissemination of the curated data product in the data repository PANGAEA. From PANGAEA, data sets are forwarded to the European marine data hubs Copernicus Marine Service and EMODnet. The workflow that forms the framework for OSADCP is described here as an example of scientific data management that follows the FAIR data guidelines. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

I'll leave it to others to talk about Dick's service to the Academy of Accounting Historians and his many career achievements. Unlike most US accounting academics, Dick's PhD was in history, and until then his research focused on British accounting practices. For Dick, the purpose of the "game" was to get the paper published, and Dick wanted to win that game more than anyone I've ever met. [Extracted from the article]

Modernizing existing buildings is vital to saving carbon emissions and counteracting global climate change. Many countries face the challenge of modernizing a considerable number of their buildings in the next two decades. Implementing related modernization measures requires a high number of craftworkers. However, current studies indicate that these craftworkers' current lack will increase. Examining the effects of craftworkers shortage on modernization strategies needs data concerning the realization time of modernization measures. We collected this data based on 90 expert interviews and provide it in this paper. The interview results comprise realization times for insulation measures of the envelope and changes in the heat supply system for typical buildings. This paper describes the data collection and presents the raw data available at a repository at Figshare. The data is internationally applicable in simulation and optimization approaches for building modernization. [ABSTRACT FROM AUTHOR]

How aggregators reconcile repositories' user-supplied subject keywords is a growing challenge in the metadata profession. While aggregators allow users to search across multiple databases to find information, the search capability is only as good as the supplied metadata. This paper is a case study of a project to reconcile harvested metadata keywords within a research data discovery service. The Federated Research Data Repository (FRDR) Discovery Service is a national, bilingual platform for discovering Canadian research data that harvests metadata from over 90 repositories. This paper outlines the work of a cross-Canada, volunteer group of experts who attempted to develop a semi-automated workflow to map the FRDR subject keywords to Faceted Application of Subject Terminology (FAST) to improve discoverability. The authors, who were members of the working group, discuss why the project failed, the problems encountered, and their thoughts on the future of automated metadata reconciliation. [ABSTRACT FROM AUTHOR]

Remaining useful life is one of the key indicators for mechanical equipment health and condition-based maintenance requirements. In fact, the field of prognostics and health management is heavily reliant on remaining useful life estimation. The availability of industrial big data has enabled promising research efforts in prognostics and health management. Deep learning techniques have been widely adopted, and proven to be successful in big data prognostics applications. However, deep learning approaches are considered black box approaches with interpretation difficulties and loss of information due to high-level feature extraction resulting from layer-by-layer processing. Enriching the deep learning input with temporal features can increase the performance of deep learning based approaches. This paper aims to improve the performance of deep learning techniques by incorporating dynamic mode decomposition into the deep learning schemes for the purposes of remaining useful life estimation. The developed method is capable of accurately predicting the remaining useful life in a data driven manner without prior knowledge of system equations. The input temporal information and health state are enriched by using dynamic mode decomposition which produces dynamic modes that approximate the infinite Koopman operator modes. The modes contain coherent time dynamics of the processed system which contribute to producing a health indicator that is representative of the system degradation. These time dependent dynamics are important characteristics of the system's health state. The degradation profile is incorporated into deep learning schemes that accurately predict the remaining useful life of the system. To validate the proposed model, two different experimental data repositories are used in this paper. The first one is a spiral bevel gear vibration dataset. The second one consists of turbofan engines vibration datasets. The validation results have shown improved remaining useful life estimation performance when dynamic mode decomposition technique is incorporated into the deep learning schemes presented in this paper. [ABSTRACT FROM AUTHOR]

OpenGrey, System for Information on Grey Literature in Europe, is your open access to 1 014 872 bibliographic references of grey literature produced in Europe and allows you to export records and locate the documents. Examples of grey (gray) literature include technical and research reports, doctoral dissertations, conference papers, official publications, and other types of grey literature. In 1980, this information resource first began as a bibliographic database under the name SIGLE. In 2006, the records migrated to OpenSIGLE - an open access database. In 2010 its name was changed to the OpenGrey Repository given that full-texts documents began to be ingested and preserved. And in 2020, Inist-CNRS (service provider) gave notice that OpenGrey will be discontinued. In order to guarantee open access to this resource, it is now archived as a database in DANS EASY - a data archive certified with the Nestor and CoreTrustSeal. [ABSTRACT FROM AUTHOR]

Call for Papers House Advertisement [ABSTRACT FROM AUTHOR]

Open access to research data is an important part of the concept of open science. The aim of this article was to identify Croatian authors/institutions that have recognized the importance of sharing research data, and that have given them access via one of the data repositories covered by Data Citation Index. A total of 265 documents were found in the database. Data papers by Croatian authors available on the Web of Science Core Collection platform were also analysed. Twenty-nine of them were found, mostly of multidisciplinary orientation. The authors of the largest number of data documents are affiliated to the University of Zagreb. Croatian researchers are archiving data in international repositories, because the infrastructure for their archiving at the national level has only recently been developed, and is just beginning to be used. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative solution to address the challenge of detecting latent malware in backup systems. The proposed detection system utilizes a multifaceted approach that combines similarity analysis with machine learning algorithms to improve malware detection. The results demonstrate the potential of advanced similarity search techniques, powered by the Faiss model, in strengthening malware discovery within system backups and network traffic. Implementing these techniques will lead to more resilient cybersecurity practices, protecting essential systems from hidden malware threats. This paper's findings underscore the potential of advanced similarity search techniques to enhance malware discovery in system backups and network traffic, and the implications of implementing these techniques include more resilient cybersecurity practices and protecting essential systems from malicious threats hidden within backup archives and network data. The integration of AI methods improves the system's efficiency and speed, making the proposed system more practical for real-world cybersecurity. This paper's contribution is a novel and comprehensive solution designed to detect latent malware in backups, preventing the backup of compromised systems. The system comprises multiple analytical components, including a system file change detector, an agent to monitor network traffic, and a firewall, all integrated into a central decision-making unit. The current progress of the research and future steps are discussed, highlighting the contributions of this project and potential enhancements to improve cybersecurity practices. [ABSTRACT FROM AUTHOR]

The recent and significant change in the architecture, engineering, and construction (AEC) industry is the increasing use of building information management (BIM) tools in design and how the generated digital data are consumed. Although designers have primarily published blueprints and model files, multiple parties are now involved in producing different aspects of the data and may be consuming data produced by others. This evolution has created new challenges in sharing and synchronizing information across organizational boundaries. Linked building data is a community effort identified as a potential means of overcoming the barriers to data interoperability. In this paper, we present how the industry can be strengthened using a peer-to-peer network based on the InterPlanetary File System (IPFS) framework to address the typical availability problems of web-based data. The paper particularly presents how Linked Data serialization of the Industry Foundation Classes (IFC) standard can be used in the context of IPFS and benchmarks the performance for the publication of models between IPFS versus HTTP protocol. We show that linked building data—in particular, IFC models converted into Resource Description Format (RDF) graphs according to the ifcOWL ontology—can be implemented using the framework, with initial indications of significant benefits of a peer-to-peer network in terms of performance, offline access, and immutable version history. Two use cases in distributed collaborative environments in the AEC/facility management (FM) sector using evolving multidomain models are used to evaluate the work. In a construction project, stakeholders must collaborate and share design data models for efficient and safe completion. Building information published using Linked Data techniques aims to facilitate data sharing between interested parties without a central intermediary. However, the challenges of Linked Data stem from using web protocols, resulting in limited availability, suboptimal performance, broken links, and lost history. This study explores using the InterPlanetary File System (IPFS), a communication protocol for computers to communicate with each other directly without needing an intermediary. It addresses issues related to data availability on the web and data-transfer performance. The study demonstrates the suitability of IPFS in addressing the challenges of sharing building data. The data are available even when the original publisher host is down. In addition, the content-based addressing provides a digital fingerprint that can ensure authenticity and uniqueness, eliminating the data's duplication. Therefore, it minimizes the storage space consumed by data backups and archives. However, because IPFS addresses and communication principles are incompatible with the Linked Data standard approach, we designed a way so that the construction data saved in IPFS can be accessed transparently as a part of the standard Linked Data network. [ABSTRACT FROM AUTHOR]

Personalized PageRank, as a basic algorithm in large graph analysis, has a wide range of applications in search engines, social recommendation, community detection, and other fields and it has been a hot problem of interest to researchers. The existing distributed personalized PageRank algorithms assume that all data are located in the same geographic location and the network environment is the same among the computing nodes where the data are located. However, in the real world, these data may be distributed in multiple data centers across continents, and these geo-distributed data centers are connected to each other through WANs, which are characterized by heterogeneous network bandwidth, huge hardware differences, and high communication costs. Moreover, the distributed personalized PageRank algorithm requires multiple iterations and random walk on the global graph. Therefore, the existing distributed personalized PageRank algorithms are not applicable to the geo-distributed environment. To address this problem, the GPPR (Geo-distributed Personalized PageRank) algorithm is proposed in this paper. The algorithm first preprocesses the big graph data in the geo-distributed environment and maps the graph data by using a heuristic algorithm to reduce the impact of network bandwidth heterogeneity on the iteration speed of the algorithm. Secondly, GPPR improves the random walk approach and proposes a probability-based push algorithm to further lower the number of iterations required by the algorithm by reducing the bandwidth load of data transmission between working nodes. We implement the GPPR algorithm based on the Spark framework and build a real geo-distributed environment in AliCloud to conduct experiments comparing the GPPR algorithm with several existing representative distributed personalized PageRank algorithms on eight open-source big graph datasets. The results show that the communication data volume of GPPRis reduced by 30% on average in the geo-distributed environment compared with that of other algorithms. In terms of algorithm running efficiency, GPPR improves by an average 2.5 factor compared with other algorithms. [ABSTRACT FROM AUTHOR]

In a continuously evolving environment, organizations, including public administrations, need to quickly adapt to change and make decisions in real-time. This requires having a real-time understanding of their context that can be achieved by adopting an event-native mindset in data management which focuses on the dynamics of change compared to the state-based traditional approaches. In this context, this paper proposes the adoption of an event-centric knowledge graph approach for the holistic data management of all data repositories in public administration. Towards this direction, the paper proposes an event-centric knowledge graph model for the domain of public administration that captures these dynamics considering events as first-class entities for knowledge representation. The development of the model is based on a state-of-the-art analysis of existing event-centric knowledge graph models that led to the identification of core concepts related to event representation, on a state-of-the-art analysis of existing public administration models that identified the core entities of the domain, and on a theoretical analysis of concepts related to events, public services, and effective public administration in order to outline the context and identify the domain-specific needs for event modeling. Further, the paper applies the model in the context of Greek public administration in order to validate it and showcase the possibilities that arise. The results show that the adoption of event-centric knowledge graph approaches for data management in public administration can facilitate data analytics, continuous integration, and the provision of a 360-degree-view of end-users. We anticipate that the proposed approach will also facilitate real-time decision-making, continuous intelligence, and ubiquitous AI. [ABSTRACT FROM AUTHOR]

This paper examines the impact of religious participation on regional economic growth. Using data on GDP growth for United States counties from the Bureau of Economic Analysis (BEA) Regional Economic Accounts and data on county-level religious participation from the Association of Religion Data Archives (ARDA), this paper estimates the impact of religiosity on growth using two-way fixed-effects Barro regressions for the period 2000 to 2020. In our preferred specification, a ten percentage-point increase in the county religious adherent share reduces the 10-year compound annual growth rate of per-capita GDP by 0.14 percentage points (a 19% reduction relative to the sample mean). A battery of sensitivity checks suggests our results are unlikely to be driven by omitted variable bias: both the Oster (2019) adjustment for selection on unobservables and Kinky Least Squares (KLS) regression estimates indicate that OLS understates the negative impact of religion on regional economic growth. We argue that the negative impact of religion on regional economic growth is consistent with previous findings of increased business survival and increased small business activity in a framework where the social capital generated from religious participation results in inefficiently low regional dynamism. [ABSTRACT FROM AUTHOR]

When large scale of UAV photogrammetry and Lidar for mapping a Taiwanese aboriginal settlement with stone slab houses has been carried out, terrestrial 3D photogrammetry and scanning are applied to middle and small-scale recording individual buildings to get architectural form with decorations in detail. When constructing the basic data of cultural assets, especially in the mountain area, it is necessary to apply new technology to carry out efficient and accurate settlement mapping works. In this paper, taking an old Paiwan aboriginal settlement an example, a detailed examination of a stone slab house with three different 3D techniques is put into practice. The 3D data can be further used for digital archive, virtual presentation, and data query through network resources. The results of this study show that, in a large-scale of settlement surveys, aerial surveying technology is efficient for constructing the topography of a settlement and provides competent and spatial information of authentic landscape form, while terrestrial photogrammetry and scanning for middle and small scales record individual buildings to provide more details of architectural forms. They both together construct authentic landscape and architectural form of a settlement and furthermore are able to reveal the hidden settlements of archaeological sites under dense forests. The application of 3D technology, greatly increasing the efficiency and accuracy of mapping and recording, is very much helpful for visualization, virtual restoration, and conservation of indigenous settlements cultural heritage, especially in such high and steep forest. [ABSTRACT FROM AUTHOR]

OpenGrey, System for Information on Grey Literature in Europe, is your open access to 1 014 872 bibliographic references of grey literature produced in Europe and allows you to export records and locate the documents. Examples of grey (gray) literature include technical and research reports, doctoral dissertations, conference papers, official publications, and other types of grey literature. In 1980, this information resource first began as a bibliographic database under the name SIGLE. In 2006, the records migrated to OpenSIGLE - an open access database. In 2010 its name was changed to the OpenGrey Repository given that full-texts documents began to be ingested and preserved. And in 2020, Inist-CNRS (service provider) gave notice that OpenGrey will be discontinued. In order to guarantee open access to this resource, it is now archived as a database in DANS EASY -- a data archive certified with the Nestor and CoreTrustSeal. [ABSTRACT FROM AUTHOR]

The Public Retirement Research Lab (PRRL) was established to provide reliable data on public-sector defined contribution (DC) retirement plan participants. The PRRL Database is the repository for the data collected by the PRRL. To fully understand the impact of ongoing plan participation, it is crucial to analyze a consistent group of participants (a longitudinal sample) who have been part of the database for an extended period. This paper presents the first longitudinal study that analyzes public retirement plan participants who were present every year from 2019 through 2021. The study tracks balances, contributions, asset allocations, and loan activity of 1.1 million plan participants who held positive defined contribution account balances in the years 2019 through 2021 in the PRRL Database, covering a three-year period. [ABSTRACT FROM AUTHOR]

The ever-changing and temporary nature of Random Access Memory (RAM) presents distinct challenges and possibilities for digital forensic investigations. This research paper offers a comprehensive analysis of RAM data retrieval techniques within the realm of forensic studies. Starting with a comprehensive explanation of the importance of RAM in forensic investigations, this paper explores the different techniques used to extract volatile data from RAM. These methods involve conducting live system analysis, memory imaging, and memory dumping techniques, each with their own set of advantages and limitations. In addition, the paper delves into the complexities of data acquisition, preservation, and analysis, taking into account factors such as system state volatility, encryption, and anti-forensic measures. In addition, this paper explores the practicality and effectiveness of RAM data retrieval methods on various operating systems and hardware setups. It assesses the strengths and weaknesses of commonly used forensic tools and frameworks for RAM analysis, showcasing their performance in practical situations. In addition, the research explores the latest developments and trends in RAM forensics. This includes the use of machine learning algorithms to automate memory analysis and the creation of specialized forensic techniques for cloud environments. Finally, this paper brings together the findings to provide valuable insights into the best practices for RAM data retrieval in forensic investigations. It highlights the significance of meticulous documentation, maintaining a clear chain of custody, and adhering to legal standards. Through the synthesis of established knowledge and the exploration of emerging trends, this research makes a significant contribution to the field of RAM forensics. It offers valuable guidance for digital forensic practitioners, law enforcement agencies, and researchers, aiding in their understanding and advancement of the subject. [ABSTRACT FROM AUTHOR]

This paper presents a detailed study on the main parameters and performance evaluation of microjet engines, at take-off regime and at various engine working regimes, based on thermodynamic analysis of a particular engine data library, from different engine manufacturers such as JetCat and AMT Netherlands. The studied engines have the same spool design but different thrust classes ranging from 97 to 1569 N. The particular data library includes engine specifications from catalogs or data sheets as well as our own experimental data from the JetCat P80 microjet engine, obtained using the ET 796 Jet Turbine Module, a complete testing facility for gas turbine education purposes. Various ratios and differences between certain engine main parameters and performances are studied in order to calculate values through which the analyses can be performed. Even if the engines have different thrust classes, the study examines if there are close values of the ratios and differences of parameters, that can be defined as reference parameters through which the engine performance can be compared and evaluated. [ABSTRACT FROM AUTHOR]

Monitoring the health of lithium batteries is a crucial undertaking in ensuring the safe and dependable functioning of electric vehicles. Data‐driven methods have been proved to be an effective method for identifying the complex degradation process of batteries. To augment the precision of predicting the remaining useful life (RUL), this paper introduces a pioneering architecture for a denoising autoencoder (DAE). This architecture integrates a stacked convolutional neural network with subsequent layers of bidirectional gated recurrent units within an encoder–decoder framework. The utilization of the DAE network is employed as a means to effectively capture and represent the intricate and nonlinear knowledge associated with degradation data acquired from measured sources. Simultaneously, the reconstruction loss is incorporated into the total loss to improve the accuracy and generalization of the prediction model. The efficacy of the proposed approach is substantiated through the utilization of data sets sourced from the NASA Ames Prognostics Data Repository. The comparative findings suggest that the proposed approach demonstrates an exceptional ability to achieve precise and robust estimation in predicting the RUL, surpassing other advanced methodologies. [ABSTRACT FROM AUTHOR]

To explore complex biological questions, it is often necessary to access various data types from public data repositories. As the volume and complexity of biological sequence data grow, public repositories face significant challenges in ensuring that the data is easily discoverable and usable by the biological research community. To address these challenges, the National Center for Biotechnology Information (NCBI) has created NCBI Datasets. This resource provides straightforward, comprehensive, and scalable access to biological sequences, annotations, and metadata for a wide range of taxa. Following the FAIR (Findable, Accessible, Interoperable, and Reusable) data management principles, NCBI Datasets offers user-friendly web interfaces, command-line tools, and documented APIs, empowering researchers to access NCBI data seamlessly. The data is delivered as packages of sequences and metadata, thus facilitating improved data retrieval, sharing, and usability in research. Moreover, this data delivery method fosters effective data attribution and promotes its further reuse. This paper outlines the current scope of data accessible through NCBI Datasets and explains various options for exploring and downloading the data. [ABSTRACT FROM AUTHOR]

A recent study on the classical capacitated vehicle routing problem (CVRP) introduced an adaptive version of the widely used iterated local search paradigm, hybridized with a path-relinking (PR) strategy. The solution method, called adaptive iterated local search (AILS)-PR, outperformed existing meta-heuristics for the CVRP on benchmark instances. However, tests on large-scale instances suggest that PR is too slow, making AILS-PR less advantageous in this case. To overcome this challenge, this paper presents an AILS combined with mechanisms to handle large CVRP instances, called AILS-II. The computational cost of this implementation is reduced, whereas the algorithm also searches the solution space more efficiently. AILS-II is very competitive on smaller instances, outperforming the other methods from the literature with respect to the average gap to the best-known solutions. Moreover, AILS-II consistently outperforms the state of the art on larger instances with up to 30,000 vertices. History: Accepted by Ted Ralphs, Area Editor for Software Tools. This paper has been accepted for the INFORMS Journal on Computing Special Issue on Software Tools for Vehicle Routing. Funding: This work was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo [Grants 2013/07375-0, 2019/22067-6, and 2022/05803-3] and the Conselho Nacional de Desenvolvimento Científico e Tecnológico [Grants 309385/2021-0 and 403735/2021-1]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0106) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0106). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

The optimization community has made significant progress in solving vehicle routing problems (VRPs) to optimality using sophisticated branch-cut-and-price (BCP) algorithms. VRPSolver is a BCP algorithm with excellent performance in many VRP variants. However, its complex underlying mathematical model makes it hardly accessible to routing practitioners. To address this, VRPSolverEasy provides a Python interface to VRPSolver that does not require any knowledge of mixed integer programming modeling. Instead, routing problems are defined in terms of familiar elements, such as depots, customers, links, and vehicle types. VRPSolverEasy can handle several popular VRP variants and arbitrary combinations of them. History: Accepted by Ted Ralphs, Area Editor for Software Tools. This paper has been accepted for the INFORMS Journal on Computing Special Issue on Software Tools for Vehicle Routing. Funding: This work was supported by Faperj [Grant E-26/202.887/2017] and Conselho Nacional de Desenvolvimento Científico e Tecnológico [Grant 305684/2022-1]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0103) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0103). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

We introduce PyVRP, a Python package that implements hybrid genetic search in a state-of-the-art vehicle routing problem (VRP) solver. The package is designed for the VRP with time windows (VRPTW) but can be easily extended to support other VRP variants. PyVRP combines the flexibility of Python with the performance of C++ by implementing (only) performance-critical parts of the algorithm in C++ while being fully customizable at the Python level. PyVRP is a polished implementation of the algorithm that ranked first in the 2021 DIMACS VRPTW challenge and, after improvements, ranked first on the static variant of the EURO meets NeurIPS 2022 vehicle routing competition. The code follows good software engineering practices and is well documented and unit tested. PyVRP is freely available under the liberal MIT license. Through numerical experiments, we show that PyVRP achieves state-of-the-art results on the VRPTW and capacitated VRP. We hope that PyVRP enables researchers and practitioners to easily and quickly build on a state-of-the-art VRP solver. History: Accepted by Ted Ralphs, Area Editor for Software Tools. This paper has been accepted for the INFORMS Journal on Computing Special Issue on Software Tools for Vehicle Routing. Funding: Funding was provided by TKI Dinalog, Topsector Logistics, and the Dutch Ministry of Economic Affairs and Climate Policy. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0055) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0055). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. There is a video associated with this paper. Click here to view the Video Overview. To save the file, right click and choose "Save Link As" from the menu. [ABSTRACT FROM AUTHOR]