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Science funders, publishers, and data archives make decisions about how to responsibly allocate resources to maximize the reuse potential of research data. This paper introduces a dataset developed to measure the impact of archival and data curation decisions on data reuse. The dataset describes 10,605 social science research datasets, their curation histories, and reuse contexts in 94,755 publications that cover 59 years from 1963 to 2022. The dataset was constructed from study-level metadata, citing publications, and curation records available through the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan. The dataset includes information about study-level attributes (e.g., PIs, funders, subject terms); usage statistics (e.g., downloads, citations); archiving decisions (e.g., curation activities, data transformations); and bibliometric attributes (e.g., journals, authors) for citing publications. This dataset provides information on factors that contribute to long-term data reuse, which can inform the design of effective evidence-based recommendations to support high-impact research data curation decisions. [ABSTRACT FROM AUTHOR]

Ancient DNA is producing a rich record of past genetic diversity in humans and other species. However, unless the primary data is appropriately archived, its long-term value will not be fully realised. I surveyed publicly archived data from 42 recent ancient genomics studies. Half of the studies archived incomplete datasets, preventing accurate replication and representing a loss of data of potential future use. No studies met all criteria that could be considered best practice. Based on these results, I make six recommendations for data producers: (1) archive all sequencing reads, not just those that aligned to a reference genome, (2) archive read alignments too, but as secondary analysis files, (3) provide correct experiment metadata on samples, libraries and sequencing runs, (4) provide informative sample metadata, (5) archive data from low-coverage and negative experiments, and (6) document archiving choices in papers, and peer review these. Given the reliance on destructive sampling of finite material, ancient genomics studies have a particularly strong responsibility to ensure the longevity and reusability of generated data. [ABSTRACT FROM AUTHOR]

Scientific data are essential to advancing scientific knowledge and are increasingly valued as scholarly output. Understanding what drives dataset downloads is crucial for their effective dissemination and reuse. Our study, analysing 55,473 datasets from 69 data repositories, identifies key factors driving dataset downloads, focusing on interpretability, reliability, and accessibility. We find that while lengthy descriptive texts can deter users due to complexity and time requirements, readability boosts a dataset's appeal. Reliability, evidenced by factors like institutional reputation and citation counts of related papers, also significantly increases a dataset's attractiveness and usage. Additionally, our research shows that open access to datasets increases their downloads and amplifies the importance of interpretability and reliability. This indicates that easy access enhances the overall attractiveness and usage of datasets in the scholarly community. By emphasizing interpretability, reliability, and accessibility, this study offers a comprehensive framework for future research and guides data management practices toward ensuring clarity, credibility, and open access to maximize the impact of scientific datasets. [ABSTRACT FROM AUTHOR]

The demand for open data and open science is on the rise, fueled by expectations from the scientific community, calls to increase transparency and reproducibility in research findings, and developments such as the Final Data Management and Sharing Policy from the U.S. National Institutes of Health and a memorandum on increasing public access to federally funded research, issued by the U.S. Office of Science and Technology Policy. This paper explores the pivotal role of data repositories in biomedical research and open science, emphasizing their importance in managing, preserving, and sharing research data. Our objective is to familiarize readers with the functions of data repositories, set expectations for their services, and provide an overview of methods to evaluate their capabilities. The paper serves to introduce fundamental concepts and community-based guiding principles and aims to equip researchers, repository operators, funders, and policymakers with the knowledge to select appropriate repositories for their data management and sharing needs and foster a foundation for the open sharing and preservation of research data. [ABSTRACT FROM AUTHOR]

The ability to auto-generate databases of optical properties holds great potential for advancing optical research, especially with regards to the data-driven discovery of optical materials. An optical property database of refractive indices and dielectric constants is presented, which comprises a total of 49,076 refractive index and 60,804 dielectric constant data records on 11,054 unique chemicals. The database was auto-generated using the state-of-the-art natural language processing software, ChemDataExtractor, using a corpus of 388,461 scientific papers. The data repository offers a representative overview of the information on linear optical properties that resides in scientific papers from the past 30 years. Public availability of these data will enable a quick search for the optical property of certain materials. The large size of this repository will accelerate data-driven research on the design and prediction of optical materials and their properties. To the best of our knowledge, this is the first auto-generated database of optical properties from a large number of scientific papers. We provide a web interface to aid the use of our database. Measurement(s) refractive indices • dielectric constants Technology Type(s) natural language processing [ABSTRACT FROM AUTHOR]

A Clinical Data Repository (CDR) is a dynamic database capable of real-time updates with patients' data, organized to facilitate rapid and easy retrieval. CDRs offer numerous benefits, ranging from preserving patients' medical records for follow-up care and prescriptions to enabling the development of intelligent models that can predict, and potentially mitigate serious health conditions. While several research works have attempted to provide state-of-the-art reviews on CDR design and implementation, reviews from 2013 to 2023 cover CDR regulations, guidelines, standards, and challenges in CDR implementation without providing a holistic overview of CDRs. Additionally, these reviews need to adequately address critical aspects of CDR; development and utilization, CDR architecture and metadata, CDR management tools, CDR security, use cases, and artificial intelligence (AI) in CDR design and implementation. The collective knowledge gaps in these works underscore the imperative for a comprehensive overview of the diverse spectrum of CDR as presented in the current study. Existing reviews conducted over the past decade, from 2013 to 2023 have yet to comprehensively cover the critical aspects of CDR development, which are essential for uncovering trends and potential future research directions in Africa and beyond. These aspects include architecture and metadata, security and privacy concerns, tools employed, and more. To bridge this gap, in particular, this study conducts a comprehensive systematic review of CDR, considering critical facets such as architecture and metadata, security and privacy issues, regulations guiding development, practical use cases, tools employed, the role of AI and machine learning (ML) in CDR development, existing CDRs, and challenges faced during CDR development and deployment in Africa and beyond. Specifically, the study extracts valuable discussions and analyses of the different aspects of CDR. Key findings revealed that most architectural models for CDR are still in the theoretical phase, with low awareness and adoption of CDR in healthcare environments, susceptibility to several security threats, and the need to integrate federated learning in CDR systems. Overall, this paper would serve as a valuable reference for designing and implementing cutting-edge clinical data repositories in Africa and beyond. [ABSTRACT FROM AUTHOR]

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]

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]

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]

This short paper reports on the CNSM'2012 conference (International Conference on Network and Service Management). It presents an overview on the technical sessions, keynotes, tutorials, and panel, especially focusing on the original and dominating topics that raised the most comments and discussion. It then aims at indicating what the dominant research directions were in the network and service management community. [ABSTRACT FROM AUTHOR]

Recent advances in digital technologies have lowered the costs and improved the quality of digital pathology Whole Slide Images (WSI), opening the door to apply Machine Learning (ML) techniques to assist in cancer diagnosis. ML, including Deep Learning (DL), has produced impressive results in diverse image classification tasks in pathology, such as predicting clinical outcomes in lung cancer and inferring regional gene expression signatures. Despite these promising results, the uptake of ML as a common diagnostic tool in pathology remains limited. A major obstacle is the insufficient labelled data for training neural networks and other classifiers, especially for new sites where models have not been established yet. Recently, image synthesis from small, labelled datasets using Generative Adversarial Networks (GAN) has been used successfully to create high-performing classification models. Considering the domain shift and complexity in annotating data, we investigated an approach based on GAN that minimized the differences in WSI between large public data archive sites and a much smaller data archives at the new sites. The proposed approach allows the tuning of a deep learning classification model for the class of interest to be improved using a small training set available at the new sites. This paper utilizes GAN with the one-class classification concept to model the class of interest data. This approach minimizes the need for large amounts of labelled data from the new site to train the network. The GAN generates synthesized one-class WSI images to jointly train the classifier with WSIs available from the new sites. We tested the proposed approach for follicular lymphoma data of a new site by utilizing the data archives from different sites. The synthetic images for the one-class data generated from the data obtained from different sites with minimum amount of data from the new site have resulted in a significant improvement of 15% for the Area Under the curve (AUC) for the new site that we want to establish a new follicular lymphoma classifier. The test results have shown that the classifier can perform well without the need to obtain more training data from the test site, by utilizing GAN to generate the synthetic data from all existing data in the archives from all the sites. [ABSTRACT FROM AUTHOR]

Developing and executing large-scale data analysis applications in parallel and distributed environments can be a complex and time-consuming task. Developers often find themselves diverted from their application logic to handle technical details about the underlying runtime and related issues. To simplify this process, ParSoDA, a Java library, has been proposed to facilitate the development of parallel data mining applications executed on HPC systems. It simplifies the process by providing built-in scalability mechanisms relying on the Hadoop and Spark frameworks. This paper presents ParSoDA-Py, the Python version of the ParSoDA library, which allows for further support of commonly used runtimes and libraries for big data analysis. After a complete library redesign, ParSoDA can be now easily integrated with other Python-based distributed runtimes for HPC systems, such as COMPSs and Apache Spark, and with the large ecosystem of Python-based data processing libraries. The paper discusses the adaptation process, which takes into consideration the new technical requirements, and evaluates both usability and scalability through some case study applications. [ABSTRACT FROM AUTHOR]

Software-defined networking will be a critical component of the networking domain as it transitions from a standard networking design to an automation network. To meet the needs of the current scenario, this architecture redesign becomes mandatory. Besides, machine learning (ML) and deep learning (DL) techniques provide a significant solution in network attack detection, traffic classification, etc. The DDoS attack is still wreaking havoc. Previous work for DDoS attack detection in SDN has not yielded significant results, so the author has used the most recent deep learning technique to detect the attacks. In this paper, we aim to classify the network traffic into normal and malicious classes based on features in the available dataset by using various deep learning techniques. TCP, UDP, and ICMP traffic are considered normal; however, malicious traffic includes TCP Syn Attack, UDP Flood, and ICMP Flood, all of which are DDoS attack traffic. The major contribution of this paper is the identification of novel features for DDoS attack detection. Novel features are logged into the CSV file to create the dataset, and machine learning algorithms are trained on the created SDN dataset. Various work which has already been done for DDoS attack detection either used a non-SDN dataset or the research data is not made public. A novel hybrid machine learning model is utilized to perform the classification. The dataset used by the ML/DL algorithms is a collection of public datasets on DDoS attacks as well as an experimental DDoS dataset generated by us and publicly available on the Mendeley Data repository. A Python application performs the classification of traffic into one of the classes. From the various classifiers used, the accuracy score of 99.75% is achieved with Stacked Auto-Encoder Multi-layer Perceptron (SAE-MLP). To measure the effectiveness of the SDN-DDoS dataset, the other publicly available datasets are also evaluated against the same deep learning algorithms, and traffic classification accuracy is found to be significantly higher with the SDN-DDoS dataset. The attack detection time of 216.39 s also serve as experimental evidence. [ABSTRACT FROM AUTHOR]

This review paper presents the practical development of imaging biomarkers in the scope of the PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project, as a noninvasive and reliable way to improve the diagnosis and prognosis in pediatric oncology. The PRIMAGE project is a European multi-center research initiative that focuses on developing medical imaging-derived artificial intelligence (AI) solutions designed to enhance overall management and decision-making for two types of pediatric cancer: neuroblastoma and diffuse intrinsic pontine glioma. To allow this, the PRIMAGE project has created an open-cloud platform that combines imaging, clinical, and molecular data together with AI models developed from this data, creating a comprehensive decision support environment for clinicians managing patients with these two cancers. In order to achieve this, a standardized data processing and analysis workflow was implemented to generate robust and reliable predictions for different clinical endpoints. Magnetic resonance (MR) image harmonization and registration was performed as part of the workflow. Subsequently, an automated tool for the detection and segmentation of tumors was trained and internally validated. The Dice similarity coefficient obtained for the independent validation dataset was 0.997, indicating compatibility with the manual segmentation variability. Following this, radiomics and deep features were extracted and correlated with clinical endpoints. Finally, reproducible and relevant imaging quantitative features were integrated with clinical and molecular data to enrich both the predictive models and a set of visual analytics tools, making the PRIMAGE platform a complete clinical decision aid system. In order to ensure the advancement of research in this field and to foster engagement with the wider research community, the PRIMAGE data repository and platform are currently being integrated into the European Federation for Cancer Images (EUCAIM), which is the largest European cancer imaging research infrastructure created to date. [ABSTRACT FROM AUTHOR]

In the 1950s, the Soviet Union expanded the range of its fisheries operations into international water traditionally fished by American vessels. In the USA, government officials and fisheries experts alike expressed concerns that the Soviets were overfishing Atlantic stocks, or even potentially engaging in off-shore espionage. Despite these fears, members of the US fisheries management community reached out and engaged with their Soviet counterparts directly. This initial communication led to a series of collaborations between US and Soviet scientific agencies aimed at developing a deeper understanding of marine ecosystems and improving the sustainability of international fisheries. The joint US–USSR research efforts beginning in the 1960s laid the foundation for continued cooperative studies through the 1980s and into the post-Soviet era. This paper uses historical records from the US National Archives and data products currently available on the web to examine the legacy of this Cold War cooperative research program. These materials demonstrate how American and Soviet scientists collaborated to generate and describe data on fisheries. It also considers how they negotiated recordkeeping and data management activities across radically different governmental structures, while also navigating the transition to digital recordkeeping and data exchange. This case offers perspective on the preservation of at-risk ecological records and the continued value of these data in our contemporary world. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-view representation kernel ensemble Support Vector Machine. Unlike the conventional multiple kernel learning techniques which utilizes a common similarity measure over the entire input space, with the aim of solely learning their models via linear combination of basis kernels in single Reproducing Kernel Hilbert Space (RKHS), our proposed model seeks to concurrently find multiple solutions in corresponding Reproducing Kernel Hilbert Spaces. To achieve this objective, we first derive our proposed model directly from the classical SVM model. Then, leveraging on the concept of multi- view data processing, we consider the original data as a multi - view data controlled by different sub models in our proposed model. The multi-view representations of the original data are subsequently transformed into ensemble kernel models where the linear classifiers are parameterized in multiple kernel spaces. This enables each model to co-optimize the learning of its optimal parameter via the minimization of a cumulative ensemble loss in multiple RKHSs. With this, there is an overall improvement in the accuracy of the classification task as well as the robustness of our proposed ensemble model. Since UCI machine learning data repository provides publicly available benchmark datasets, we evaluated our model by conducting experiments on several UCI classification and image datasets. The results of our proposed model were compared with other state-of-the-art MKL methods, such as SimpleMKL, EasyMKL, MRMKL, RMKL and PWMK. Among these MKL methods, our proposed method demonstrates better performances in the experiments conducted. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) in association with cloud technologies are the raising stars in information technology industry, which provides a lot of innovative gadgets to several industries to automate their needs as well as monitoring the events properly without any human interventions. The most common and emerging needs now-a-days is the development of new gadgets to support healthcare industry to rectify the medical flaws and save the human life. In one side the booming technologies such as Internet of Things and Cloud platforms are available and the other hand a drastic need to introduce an intelligent gadget for medical oriented needs to save one's life in critical situations. This paper aims to create a bridge between the two and introduce a new device to combine healthcare with recent technological developments. The proposed approach introduces a new algorithm called iCloud Assisted Intensive Deep Learning (iCAIDL), which provides support to healthcare medium as well as patients by means of applying the intelligent cloud system along with machine learning strategies and this proposed algorithm is derived from the base of deep learning norms. An iCloud Assisted Intensive Deep Learning algorithm initially begins with the flow of collecting the existing health records from the data repository and train the system with deep learning principles. Once the data training phase ends the proposed algorithm begins to get the live data from patient and this data is assumed as a testing data, which will be processed by using intensive deep learning principles and store the resulting summary into the Cloud repository by means of enabling Internet of Things feature in association with the proposed algorithm called iCAIDL. This is transparent in both the state of users like doctors as well as the patients to monitor the health records in intelligent manner. A Smart Medical Gadget is designed to collect the health record from patients and maintain it into the medical repository for testing phase, in which it collects the patient heart rate, pressure level, blood flow in intensive manner. The logic of IoT is connected with the machine learning process such as: the data accumulated from the smart Medical Gadget needs to be send to the Server end for processing, here the processing is mentioned as the machine learning based processing, in which the received data is considered to be the testing data and the results are emulated accordingly. Once the results are emulated that also will be coming for training part for the upcoming testing data. So, that the data coming from the Medical Gadget is considered to be the testing data and once the processing is done it will be considered to be the training data for further medical summaries. The performance evaluations of the proposed approach is estimated based on the following metrics such as data transfer ratio from Smart medical Gadget to the server end, storage accuracy and the communication efficiency. Empirical results are attained using simulation, in which it produces a drastical improvement of healthcare parameters by merge the proposed algorithm called iCloud Assisted Intensive Deep Learning. [ABSTRACT FROM AUTHOR]

Background: Social media platforms, especially Twitter, have turned out to be a major source of data repositories. They have become a platform that citizens can use to voice their concerns about issues that affect them. Most importantly, during the COVID-19 era, the platform was greatly used by governments and health organizations to sensitize people about the safety guidelines that they must adhere to so as to remain safe during the pandemic. As expected, people also used Twitter and other social media platforms to voice their opinions about how governments are handling the COVID-19 pandemic outbreak. Governments and organizations could, therefore, use these social media as a feedback mechanism that can help them know the view of the citizens about their policies. This could help them in making informed decisions about their policies. Aim: The aim of this paper is to explore the use of BiLSTM deep learning technique for sentiment analysis of COVID-19 tweets. Methodology: The study retrieved 197,327 tweets from the Nigeria Twitter domain using #COVID or #COVID-19 hashtags as keywords. The dataset was retrieved within the 1st month of COVID-19 vaccination in Nigeria, i.e., March 15–June 15, 2021. BiLSTM deep learning technique was trained using 789,306 sentiment annotated tweets obtained from Kaggle Sentiment140 tweet datasets. The preprocessed case study tweets were then used to evaluate the proposed model. Also, a precision of 78.26% and a recall value of 78.27% were also obtained. Results: With an accuracy of 78.29%, 98,545 (49.93%) positive sentiments and 98,782 negative sentiments (50.06%) were recorded. Also, a precision of 78.26% and a recall value of 78.27% were also obtained. However, the presence of outliers which are tweets not related to COVID but which used the hashtag was observed. Conclusion: This study has revealed the strength of BiLSTM deep learning technique for sentiment analysis. The results obtained revealed an almost balanced sentiments toward the pandemic with 49.93% positive disposition to the pandemic as compared to 50.06% negative disposition. This showed affirmed the impact of COVID vaccine in dousing citizen's tension when it was made available for public use. However, the presence of outliers in the classified tweets could be a pointer to the reason why aspect-based sentiment analysis could be preferred to sentence-based sentiment analysis. [ABSTRACT FROM AUTHOR]

The FAIR Principles are a set of good practices to improve the reproducibility and quality of data in an Open Science context. Different sets of indicators have been proposed to evaluate the FAIRness of digital objects, including datasets that are usually stored in repositories or data portals. However, indicators like those proposed by the Research Data Alliance are provided from a high-level perspective that can be interpreted and they are not always realistic to particular environments like multidisciplinary repositories. This paper describes FAIR EVA, a new tool developed within the European Open Science Cloud context that is oriented to particular data management systems like open repositories, which can be customized to a specific case in a scalable and automatic environment. It aims to be adaptive enough to work for different environments, repository software and disciplines, taking into account the flexibility of the FAIR Principles. As an example, we present DIGITAL.CSIC repository as the first target of the tool, gathering the particular needs of a multidisciplinary institution as well as its institutional repository. [ABSTRACT FROM AUTHOR]

Recently, we noticed the emergence of several data management architectures to cope with the challenges imposed by big data. Among them, data lakehouses are receiving much interest from industrial and academic fields due to their ability to hold disparate multi-structured batch and streaming data sources in a single data repository. Thus, the heterogeneous and complex aspect of the data requires a dedicated process to improve their quality and retrieve value from them. Therefore, data curation encompasses several tasks that clean and enrich data to ensure it continues to fit the user requirements. Nevertheless, most existing data curation approaches need more dynamics, flexibility, and customization in constituting the data curation pipeline to align with end user requirements that may vary according to her/his decision context. Moreover, they are dedicated to curating only a single type of structure of batch data sources (e.g., semi-structured). Considering the changing requirements of the user and the need to build a customized data curation pipeline according to the users and the data source characteristics, we propose a service-based framework for adaptive data curation in data lakehouses that encompasses five modules: data collection, data quality evaluation, data characterization, curation service composition, and data curation. The proposed framework is built upon new data characterization and evaluation modular ontology and a curation service composition approach that we detail in the following paper. The experimental findings validate the contributions' performance in terms of effectiveness and execution time. [ABSTRACT FROM AUTHOR]

As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. The aim of this review is to describe archives that provide researchers with tools to store, share, and reanalyze both human and non-human neurophysiology data based on criteria that are of interest to the neuroscientific community. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. As the necessity for integrating large-scale analysis into data repository platforms continues to grow within the neuroscientific community, this article will highlight the various analytical and customizable tools developed within the chosen archives that may advance the field of neuroinformatics. [ABSTRACT FROM AUTHOR]

In order to improve the intelligence of the medical system, this paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. In order to improve the efficiency of traditional medical rehabilitation activities and enable patients to maximize their understanding of their treatment status, this paper designs a personalized health information system that allows patient users to understand their treatment and rehabilitation status anytime and anywhere, and all medical health data distributed in different independent medical institutions to ensure that these data are stored independently. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data are stored and analyzed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff. [ABSTRACT FROM AUTHOR]

Open Science practices bear great promise for making research in general more reproducible and transparent, and these goals are very important for preventive intervention research. From my perspective as a program co-developer, I note potential concerns and issues of how open science practices can be used in intervention research. Key issues considered are in the realms of pre-registration (making pre-registration a living document; providing rewards for hypothesis-generating research, in addition to hypothesis-testing research), data archiving (resources for data archiving of large datasets; ethical issues related to need for strong de-identification), and research materials (intervention manuals and materials, and characteristics, training and supervision of intervention staff). The paper focuses on easier-to-address and considerably harder-to-address issues and concerns in these three areas. [ABSTRACT FROM AUTHOR]

Advanced metering infrastructure (AMI) is one of the core aspects of the smart grid, and offers numerous possible benefits, such as load control and demand response. AMI enables two-way communication, but it is vulnerable to electricity theft. Due to the tempering of the smart meter, the abnormal pattern of fraud becomes difficult to detect, which introduces the increment of false data consumption. The majority of existing methods depend on factors such as predefined limits, extra information requirements, and the desire for labelled datasets. These factors are difficult to realize or have a poor degree of identification. This paper integrates two novel techniques to detect the False Data Injection attack. One is Principal Component Analysis, which is based on feature correlation, second is an unsupervised learning based technique Density-Based Spatial Clustering of Applications with Noise which helps to identify data patterns to detect outliers from a huge number of load profiles. The combination makes the proposed technique an appropriate tool for detecting an arbitrary pattern attack in high-dimensional data. Four different attack scenarios are analyzed on the Irish Science Data Archive smart meter data set. The efficacy of proposed theft detection method is evaluated by comparing the AUC, mAP, and time with those of other clustering approaches. The detection rate of the proposed scheme has been compared with the other approaches in the literature. The results demonstrate that the detection rate is substantially higher than that of other theft detection methods. Finally, the influence of varied abnormality ratios has been investigated. [ABSTRACT FROM AUTHOR]

At the former IMGC-CNR (now INRiM), the Italian national metrological Institute, a series of pure hydrocarbons were measured in past years to determine with high accuracy for the temperature of their triple point and of their solid-to-solid transition(s) when present. While results on methane, ethane, and propane were published; the 1982 measurements on n-butane were never so far. It seems useful to add these results now to the still scarce existing data for possible inclusion in reference data archives. The paper summarises the experimental setup and results, compared with previous determinations. For the triple point, a temperature Ttp,90 = 134.869(2) K was found showing a higher accuracy. For the s.s.t.-I, the temperature was T90,upper-end = 108.2(3) K, where the low accuracy is due to the peculiar s.s.t. properties. [ABSTRACT FROM AUTHOR]

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Addressing global scientific challenges requires the widespread sharing of consistent and trustworthy research data. Identifying the factors that influence widespread data sharing will help us understand the limitations and potential leverage points. We used two well-known theoretical frameworks, the Theory of Planned Behavior and the Technology Acceptance Model, to analyze three DataONE surveys published in 2011, 2015, and 2020. These surveys aimed to identify individual, social, and organizational influences on data-sharing behavior. In this paper, we report on the application of multiple factor analysis (MFA) on this combined, longitudinal, survey data to determine how these attitudes may have changed over time. The first two dimensions of the MFA were named willingness to share and satisfaction with resources based on the contributing questions and answers. Our results indicated that both dimensions are strongly influenced by individual factors such as perceived benefit, risk, and effort. Satisfaction with resources was significantly influenced by social and organizational factors such as the availability of training and data repositories. Researchers that improved in willingness to share are shown to be operating in domains with a high reliance on shared resources, are reliant on funding from national or federal sources, work in sectors where internal practices are mandated, and live in regions with highly effective communication networks. Significantly, satisfaction with resources was inversely correlated with willingness to share across all regions. We posit that this relationship results from researchers learning what resources they actually need only after engaging with the tools and procedures extensively. [ABSTRACT FROM AUTHOR]

The information system PANGAEA provides targeted support for research data management as well as long-term data archiving and publication. PANGAEA is operated as an open access library for archiving, publishing, and distributing georeferenced data from earth and environmental sciences. It focuses on observational and experimental data. Citability, comprehensive metadata descriptions, interoperability of data and metadata, a high degree of structural and semantic harmonization of the data inventory as well as the commitment of the hosting institutions ensures the long-term usability of archived data. PANGAEA is a pioneer of FAIR and open data infrastructures to enable data intensive science and an integral component of national and international science and technology activities. This paper provides an overview of the recent organisational, structural, and technological advancements in developing and operating the information system. [ABSTRACT FROM AUTHOR]

The hosts of OH megamaser (OHM) are luminous infrared galaxies (LIRGs), in fact 1/3 of them are ultra-luminous infrared galaxies (ULIRGs), which imply that OHM phenomena should be related to the infrared radiation field. In this paper, we investigate the far infrared (FIR) properties of OHM host galaxies, through detailed infrared data covering broad bands. All known OHM sources and one control sample of (U)LIRGs without maser detections (non-OHM sources) are cross-identified with AKARI and Herschel photometric catalogs. Comparative analysis on the spectral energy distribution (SED) with broad coverage from J to 350 μ m (taken from 2MASS, WISE, Spitzer, and AKARI and Herschel archive data) shows that the OHM sources tend to have higher FIR luminosities than those of the non-OHM sources, which are more pronounced in the SED range covered by the AKARI. These are consistent with our statistical results of the FIR luminosities distribution of both the samples, which show that the OHM sources tend to have higher FIR luminosities, especially, at short FIR wavelength (i.e., the 65 and 90 μ m). However, the non-OHM sources tend to have much stronger emission than those of OHM sources at both the near infrared (NIR) and middle infrared (MIR) bands. The statistic analysis of the color–color properties at MIR and FIR bands shows that the OHM sources have much cooler MIR and warmer FIR colors than non-OHM sources. These clues could help us to choose OHM candidates for future OHM surveys with the Five-hundred-meter Aperture Spherical radio Telescope (FAST), where the OHM detection rate may exceed 40%. Further, one significant correlation of L OH ∝ L T FIR 1.18 ± 0.11 can be found between the maser luminosity and total FIR luminosity of OHM LIRGs. Combined with previous studies, we suggest that the OHM is dominantly pumped by the FIR, instead of NIR and MIR radiation fields. [ABSTRACT FROM AUTHOR]

Heart disease is a deadly disease in human life. The mortality rate from any disease is the highest in the world. Therefore, before reaching the final stage of this heart disease, all precautionary measures must be taken. For this reason without the help of any kind of traditional methods, if we can scientifically diagnose heart disease at an early stage through various decision support systems, then surely death rate of this disease will decrease in the whole world. Many researchers investigate the diagnosis of heart disease by creating various intelligent medical decision support systems. Artificial neural network concepts represent the highest predictive accuracy over medical data compared to other decision support systems. In this paper, we propose a better prediction method for the existence of heart disease through the scaled conjugate gradient backpropagation of artificial neural networks using K-fold cross-validation. For cardiac datasets, the University of California Irvine (UCI) Machine Learning Repository and IEEE data port have been used. For Cleveland processed heart dataset, the proposed system uses 13 input attributes and provides minimum 63.3803% and maximum 100% accurate results; similarly, for Cleveland Hungarian Statlog heart dataset, the proposed system uses 11 input attributes and provides minimum 88.4754% and maximum 100% accurate results by estimating the presence and absence of heart disease during testing. [ABSTRACT FROM AUTHOR]

The late-season Corn Stalk Nitrate Test (CSNT) is a well-known tool to help evaluate the after-the-fact performance of nitrogen management. The CSNT has the unique ability to distinguish between optimal and excessive corn nitrogen status, which makes it helpful for identifying the over-application of N so that farmers can adjust their future nitrogen decisions. This paper presents a multi-year and multi-location dataset of late-season corn stalk nitrate test measurements across the US Midwest from 2006 to 2018. The dataset consists of 32,025 corn stalk nitrate measurements from 10,675 corn fields. The nitrogen form, total N rate applied, US state, year of harvest, and climatic conditions are included for each corn field. When available, previous crop, manure source, tillage, and timing of N application are also informed. We provide a detailed description of the dataset to make it usable by the scientific community. Data are published through an R package and also available at the USDA National Agricultural Library Ag Data Commons repository and through an interactive website. [ABSTRACT FROM AUTHOR]

In this paper we describe an advanced database for the site characterization of seismic stations, named "CRISP—Caratterizzazione della RIsposta sismica dei Siti Permanenti della rete sismica" (http://crisp.ingv.it, quoted with https://doi.org/10.13127/crisp), designed for the Italian National Seismic Network (Rete Sismica Nazionale, RSN, operated by Istituto Nazionale di Geofisica e Vulcanologia). For each site, CRISP collects easily accessible station information, such as position, type(s) of instrumentation, instrument housing, thematic map(s) and descriptive attributes (e.g., geological characteristics, etc.), seismic analysis of recordings, and available geophysical investigations (shear-wave velocity [VS] profile, non-linear decay curve). The archive also provides key proxy indicators derived from the available data, such as the time-averaged shear-wave velocity of the upper 30 m from the surface (VS30) and site and topographic classes according to the different seismic codes. Standardized procedures have been applied as motivated by the need for a homogenous set of information for all the stations. According to European Plate Observing System infrastructural objectives for the standardization of seismological data, CRISP is integrated into pre-existing INGV instrument infrastructures, shares content with the Italian Accelerometric Archive, and complies map information about the stations, as well as local geology, through web services managed by Istituto Superiore per la Protezione e la Ricerca Ambientale. The design of the CRISP archive allows the database to be continually updated and expanded whenever new data are available from the scientific community, such as the ones related to new seismic stations, map information, geophysical surveys, and seismological analyses. [ABSTRACT FROM AUTHOR]

A research concept that was first presented on a poster at a Comprehensive Nuclear-Test-Ban Treaty (CTBT) conference in 2009 has resulted in a fully-fledged operational software product named NET-VISA (Network Processing Vertically Integrated Seismic Analysis), which performs the Network Processing step of the automatic processing at the International Data Centre (IDC). It has become one of the tools of the waveform analysts to review and improve the IDC standard automatic third Standard Events List (SEL3) bulletin and produce the Reviewed Events Bulletin (REB), the only global seismo-acoustic bulletin. The basic scientific concepts imbedded into NET-VISA are briefly summarized in this paper but the emphasis is on the process of adopting, developing, adapting, testing, and operationalizing the initial prototype. Extensive off-line testing has shown that one of the expected benefits of NET-VISA was the significant reduction in missed events rate compared to the current Network Processing software, Global Association (GA). NET-VISA also finds some events previously missed by manual review. Starting in July 2017, and as of January 2020, NET-VISA generates an automatic bulletin, called VSEL3, in parallel to SEL3, which is also used by analysts since January 2018. Tracing the origin of the REB events has confirmed the significant reduction in missed events when complementary VSEL3 events are reviewed in addition to the SEL3 events. If sufficient confidence is established, NET-VISA has the potential to replace GA in producing the standard automatic SEL3 bulletin. [ABSTRACT FROM AUTHOR]

The Xinjiang unearthed brocade as a significant cultural heritage records the historical and cultural information of diverse ethnic groups, and it also influences cultural product development. However, the existing knowledge concerning these brocades is disorganized, fragmented, and incomplete, hindering a comprehensive understanding of their intricate relationships. To address these issues, this study has developed an integrated ontological model for Xinjiang's unearthed brocades (XJUBontology). By applying Conceptual Reference Model of International Committee for Documentation (CIDOC CRM) ontology and the Remanufactured Process Planning (RPP) approach, this XJUBontology comprises knowledge about both the brocades and their cultural derivatives, and knowledge in these two domains are able to be organized and refined. The implementation of XJUBontology in Protégé and validation via SPARQL queries not only centralize and manage Xinjiang unearthed brocade-related knowledge but also establish vital connections among this fragmented information. The contribution is to help in associating, managing, and sharing knowledge in the domain of Xinjiang unearthed brocades, providing a framework for establishing related data repository, and helping researchers, apparel and accessories designers, museums, and other cultural and tourism institutions to access a more systematic information to assist their works. [ABSTRACT FROM AUTHOR]

Feature selection and classification are widely used in machine learning in the context of big data. In many data sets, both attributes and decision classes can be preference ordered. Therefore, to process the data and information based on preference-ordered attributes, dominance-based rough set approach (DRSA) has been proposed. DRSA considers dominance relation between objects and can process the information with preference-ordered attribute domains. The it should be noted that the majority of the algorithms based on DRSA use dependency as an underlying criterion measure for different tasks. However, calculating dependency using the conventional DRSA approach requires the calculation of lower and upper approximations which is a computationally expensive task. A new approach has been proposed in this paper which calculates the dominance-based rough set dependency measure without calculating the lower and upper approximations. The proposed methodology is called the "Incremental Dominance-based Dependency Calculation Method" (IDDC). To justify the proposed approach, both IDDC and conventional approaches are compared using various data sets from the UCI data set repository. Results have shown that the proposed approach outperforms the conventional approach by depicting on average 46 % and 98 % decrease in execution time and required runtime memory, respectively. [ABSTRACT FROM AUTHOR]

Context: Recent increases in ecological disturbances driven by climate change and our expanding human footprint make it challenging for natural resource managers to keep apprised of current conditions and adjust management plans accordingly. To effectively conserve species in highly dynamic landscapes requires more timely habitat monitoring and a more responsive adaptive management cycle. Objectives: We introduce a framework to automatically monitor and assess species habitats over a range of spatial and temporal scales. We then apply this framework by developing an automated habitat monitoring system for the Mexican spotted owl (MSO) in Arizona and New Mexico, USA, that will be linked to federal agency adaptive management plans. Methods: We automated the process of monitoring and assessing trends in MSO habitat on an annual schedule using the Google Earth Engine cloud-based spatial analysis platform and dynamic data repository. We ran this system retrospectively on historical data to monitor MSO habitat from 1986 to 2020. Results: The automated habitat monitoring system provided a 35-year MSO habitat time series with high accuracy. Widespread habitat gains and losses occurred every year, underscoring the need for continuous monitoring and the benefits of an automated workflow. Conclusions: Automated habitat monitoring linked to adaptive management holds great promise in helping managers track the impacts of recent disturbances and adjust plans to meet goals even in increasingly dynamic landscapes. In a companion paper, Jones et al. (2023) demonstrate the utility of this approach by analyzing our MSO habitat time series to assess trends, drivers of change, and management implications. [ABSTRACT FROM AUTHOR]

IoT edge computing is a new computing paradigm "in the IoT domain" for performing calculations and processing at the edge of the network, closer to the user and the source of the data. This paradigm is relatively recent, and, together with cloud and fog computing, there may be some confusion about its meaning and implications. This paper aims to help practitioners and researchers better understand what the industry thinks about what IoT edge computing is, and the expected benefits and challenges associated with this paradigm. We conducted a survey using a semi-structured in-depth questionnaire to collect qualitative data from relevant stakeholders from 29 multinational companies and qualitatively analyzed these data using the Constructivist Grounded Theory (Charmaz) method. Several researchers participated in the coding process (collaborative coding). To ensure consensus on the constructs that support the theory and thus improve the rigor of qualitative research, we conducted an intercoder agreement analysis. From the analysis, we have derived a substantive and analytic theory of what companies perceive about IoT edge computing, its benefits and challenges. The theory is substantive in that the scope of validity refers to the 29 surveys processed and analytic in that it analyzes "what is" rather than explaining causality or attempting predictive generalizations. A public repository with all the data related to the information capture process and the products resulting from the analysis of this information is publicly available. This study aims to strengthen the evidence and support practitioners in making better informed decisions about why companies are adopting edge computing and the current challenges they face. Additionally, the testing theory phase shows that the results are aligned with the ISO/IEC TR 30164 standard. [ABSTRACT FROM AUTHOR]

Natural gas has been increasingly used as a source of energy and presents itself as a strong trend for the future. In this context, regarding the high cost of installing pipelines, the design of gas networks requires highlight quality solutions, relating not only financial indicators but also reliability and security concerning demand. Thus, this paper proposes an approach for the design of natural gas networks under conditions of uncertainty of load evolution over a time horizon. A predefined network topology is assumed, where the pipe diameters define the design variables. We propose a Multiobjective Variable Neighborhood Search (MOVNS)-based algorithm, which is evaluated considering a set of test instances defined from the TSPLIB library data. The proposed methodology is also applied to a real case study being the results compared to those obtained by three engineers of a gas company with six years of experience on average. The solutions are investigated from a dominance analysis perspective, considering the criteria: installation cost, minimum gas pressure, feasibility rate, average cost of failure, and sensitivity. The results indicate solutions relatively different from those obtained by the engineers, presenting more robust and safe networks under conditions of uncertainties of load evolution. [ABSTRACT FROM AUTHOR]

In recent years, there has been a great increase in usage of cloud data centers which leads the energy consumption growth by about 10% a year continuously. Further, due to the increase in temperature of cloud data center, the hardware failure rate increases and maintenance cost is also increased. Hotspots and an irregular temperature distribution are the major research issues. Therefore, accurate and reliable thermal management of the data center is a challenging task. In order to select appropriate and efficient thermal management approach, this survey presents a state of art review on the development in this field, discusses the classification of thermal management approaches and thermal mapping models. This review also reveals the thermal management and heat management strategies at data center level to make the data centre more energy efficient. Further, it discusses various thermal aware task scheduling strategies. In thermal management, computing equipments are scheduled with the objective to minimize the hotspot and cooling cost. Finally, evaluation metrics for measuring thermal efficiency and open research issues in this field are provided. Both researchers and academicians find this review useful since it presents the significant research in the field of thermal management. [ABSTRACT FROM AUTHOR]

Optical character recognition (OCR) is an important research area in the field of pattern recognition. A lot of research has been done on OCR in the last 60 years. There is a large volume of paper-based data in various libraries and offices. Also, there is a wealth of knowledge in the form of ancient text documents. It is a challenge to maintain and search from this paper-based data. At many places, efforts are being done to digitize this data. Paper based documents are scanned to digitize data but scanned data is in pictorial form. It cannot be recognized by computers because computers can understand standard alphanumeric characters as ASCII or some other codes. Therefore, alphanumeric information must be retrieved from scanned images. Optical character recognition system allows us to convert a document into electronic text, which can be used for edit, search, etc. operations. OCR system is the machine replication of human reading and has been the subject of intensive research for more than six decades. This paper presents a comprehensive survey of the work done in the various phases of an OCR with special focus on the OCR for ancient text documents. This paper will help the novice researchers by providing a comprehensive study of the various phases, namely, segmentation, feature extraction and classification techniques required for an OCR system especially for ancient documents. It has been observed that there is a limited work is done for the recognition of ancient documents especially for Devanagari script. This article also presents future directions for the upcoming researchers in the field of ancient text recognition. [ABSTRACT FROM AUTHOR]

The first-order intuitionistic logic is a formal theory from the family of constructive logics. In intuitionistic logic, it is possible to extract a particular example x = a and a proof of a formula P(a) from a proof of a formula ∃xP(x). Owing to this feature, intuitionistic logic has many applications in mathematics and computer science. Many modern proof assistants include automated tactics for the first-order intuitionistic logic, which simplify the task of solving challenging problems, such as formal verification of software, hardware, and protocols. In this paper, a new theorem prover (called WhaleProver) for full first-order intuitionistic logic is presented. Testing on the ILTP benchmarking library has shown that WhaleProver performance is comparable with the state-of-the-art intuitionistic provers. Our prover has solved more than 800 problems from the ILTP version 1.1.2. Some of them are intractable for other provers. WhaleProver is based on the inverse method proposed by S.Yu. Maslov. We introduce an intuitionistic inverse method calculus which is, in turn, a special kind of sequent calculus. It is also described how to adopt for this calculus several existing proof search strategies proposed for different logical calculi by S.Yu. Maslov, V.P. Orevkov, A.A. Voronkov, and others. In addition, a new proof search strategy is proposed that allows one to avoid redundant inferences. The paper includes results of experiments with WhaleProver on the ILTP library. We believe that Whale- Prover can be used as a test bench for different inference procedures and strategies, as well as for educational purposes. [ABSTRACT FROM AUTHOR]

Due to the lack of appropriate tailoring of the inter-connection weights, the segmentation performance of the recently suggested Quantum-inspired Self-supervised Neural Network models suffers from the slow convergence problem. As a result, using quantum-inspired meta-heuristics in Quantum-Inspired Self-supervised Neural Network models improves their hyper-parameters and inter-connection weights. The goal of this paper is to propose an improved version of a Quantum-Inspired Self-supervised Neural Network (QIS-Net) model for brain lesion segmentation. The proposed Optimized Quantum-Inspired Self-supervised Neural Network (Opti-QISNet) model is based on the QIS-Net architecture, and its operations are used to get the best segmentation results. A Quantum-Inspired Optimized Multi-Level Sigmoidal (Opti-QSig) activation is the optimized activation function used in the described model. Three quantum-inspired meta-heuristics improve the Opti-QSig activation function, with fitness evaluated using Otsu's multi-level thresholding. Experiments were carried out using brain MR images from the Cancer Imaging Archive (TCIA) in the Nature data repository. The results show that the proposed self-supervised Opti-QISNet model outperforms our recently established QIBDS Net and QIS-Net models in brain lesion segmentation, and it is a potential candidate to extensively supervised neural network based architectures (UNet and FCNNs). [ABSTRACT FROM AUTHOR]

With the growing volume of data produced these days, we see more and more users using different types of systems, such as professional and scientific data storage systems. Given the large amount of stored data, the difficulty of finding candidates with appropriate profiles for a particular activity is remarkable. In this context, to try to solve this problem the retrieval or search of experts arises, a branch of information retrieval, which consists in, given a query, documents are retrieved and are related as indirect units of information of the specialties of the candidates, with this, some technique is used to aggregate these documents generating a score. Having a smaller number of related searches, the search for experts in the scientific area with neural models is an even greater challenge due to the complexity of these models and the need for large volumes of data with relevance judgments or labels for their training. Given this, this paper proposes a technique for expansion and generation of weakly supervised data where relevance judgments are created with heuristic techniques, making it possible to use models that require large volumes of data. Furthermore, a technique using deep autoencoder is proposed to select negative documents or irrelevance judgments and finally a ranking model based on recurrent networks called Dual Embedding LSTM that was able to outperform all the compared baselines. [ABSTRACT FROM AUTHOR]

Access to data is seen as a key priority today. Yet, the vast majority of digital cultural data preserved in archives is inaccessible due to privacy, copyright or technical issues. Emails and other born-digital collections are often uncatalogued, unfindable and unusable. In the case of documents that originated in paper format before being digitised, copyright can be a major obstacle to access. To solve the problem of access to digital archives, cross-disciplinary collaborations are absolutely essential. The big challenges of our time—from global warming to social inequalities—cannot be solved within a single discipline. The same applies to the challenge of "dark" archives closed to users. We cannot expect archivists or digital humanists to find a magical solution that will instantly make digital records more accessible. Instead, we need to set up collaborations across disciplines that seldom talk to each other. Based on 21 interviews with 26 archivists, librarians and other professionals in cultural institutions, we identify key obstacles to making digitised and born-digital collections more accessible to users. We outline current levels of access to a wide range of collections in various cultural organisations, including no access at all and limited access (for example, when users are required to travel on-site to consult documents). We suggest possible solutions to the problems of access—including the ethical use of Artificial Intelligence to unlock "dark" archives inaccessible to users. Finally, we propose the creation of a global user community who would participate in decisions on access to digital collections. [ABSTRACT FROM AUTHOR]

As data centers proliferate, their energy intensity deserves close attention. Always-on operations and growing usage for cloud and other backend processes make servers the fundamental driver of data center energy use. Yet servers' power draw under real-world conditions is poorly understood. This paper explores characteristics of volume servers that affect energy use, quantifying differences in power draw between higher-performing Standard Performance Evaluation Corporation (SPEC) and ENERGY STAR servers and that of a typical server. First, we establish general characteristics of the US installed base, before reporting hardware configurations from a major online retail website. We then compare idle power across three datasets (one unique to this paper) and explain their differences via the hardware characteristics to which power draw is most sensitive. We find idle server power demand to be significantly higher than benchmarks from ENERGY STAR and the industry-released SPEC database, and SPEC server configurations—and likely their power scaling—to be atypical of volume servers. Next, we examine power draw trends among high-performing servers across their load range to consider whether these trends are representative of volume servers, before inputting average idle power load values into a recent national server energy use model. Lastly, results from two surveys of IT professionals illustrate the incidence of more efficient equipment and operational practices in server rooms/closets. Future work should include server power field measurements in data centers of different sizes, accounting for variations in configurations and setting changes post-purchase, as well as investigating the linkage between time and server energy efficiency. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the design and application of a robust learning environment using Chinese as a foreign language in the context of web-based teaching. The study employs a carefully planned environment with cutting-edge educational technology and pedagogical techniques for comprehensive language and cultural learning. It creates a browser/server (B/S) framework for teaching Chinese as a foreign language, including system structure, logical design, database integration, and operations, respectively. Specific features like the five-tier logical framework spanning representation and business logic levels are elaborated. The research also presents a resource model that optimizes user preferences and knowledge links using an adaptive multi-objective particle swarm optimization technique, resulting in an efficient and effective learning system. Finally, this study proposes a system for Teaching and Learning Chinese as a foreign language (TCFL) that uses a client-side browser and a server-side browser. Client-side browser provides access to educational materials. Interaction and navigation by the user lead to content presentation and rendering, which displays text, videos, and interactive features. Data management and retrieval manage user requests and business logic on the server side, drawing from a data repository. The scalability and performance of the proposed TCFL system assure smooth operation, while centralized control and governance manage courses and material. The experimental assessment demonstrates the remarkable performance of the suggested TCFL system across critical criteria. The suggested system outperforms other techniques by achieving outstanding results such as increased learning efficiency of 82.4%, increased user engagement and interaction rates of 87.3%, and tailored adaptability with a customization index of 0.865 and adaptive learning rate of 89.5%. The system's outstanding technical integration, excellent learning outcome enhancement of 82.4%, and practical pedagogical techniques highlight its complete and revolutionary design. This work marks a crucial turning point in the history of learning environments for foreigners. It covers critical language acquisition, user engagement, personalization, and adaptation by pioneering a holistic and technology-infused TCFL system. These accomplishments can change foreign language learning and provide the groundwork for future research and innovation in building global optimum and immersive learning environments for learners. [ABSTRACT FROM AUTHOR]

The paper presents a methodology for the digitization and processing of our own spectral data archive and the results of comparing the obtained data with those of modern observations. An Epson Perfection V850 Pro scanner with optional SilverFast8 software was used to scan photographic films. More than 2,000 archive spectra of Seyfert galaxies obtained in 1970–1990 with the AZT-8 telescope have been scanned to date (resolution 2400 dpi). The work describes the reduction of distortion for the scanned spectra using the program code, created in Python. Our code has been registered on the web service "GitHub" and a link to the code is given in the work. The results of digitization and subsequent spectra processing are presented in the example of the Seyfert galaxy Mrk 3. For the absolute calibration of the early spectra (Jan. 25, 1976) the radiation fluxes in the emission lines of [SII] were used. The lines were measured on the modern spectrogram obtained in 2023 on telescope AZT-8 (Mar. 14, 2023) [ABSTRACT FROM AUTHOR]

The plethora of available and stored temporal data necessitated the development of effective algorithms for information retrieval. The previous research on temporal information retrieval predominantly focused on the correctness of the retrieval results and supported wider types of temporal operators for retrieval. Many of these algorithmic approaches are based on high-level data structures and libraries supported by high-level programming languages, thus limiting the running time performance of these approaches. In this paper, we develop querying and information retrieval for temporal queries based on Allen's interval algebra that provides a calculus for temporal reasoning by defining thirteen basic relations between two intervals. To increase the retrieval performance, we propose using bitmaps and bitwise operations to identify all of Allen's thirteen relations between any two events across the entirety of the data where events are represented as bitmaps. The indexes in the bitmap represent various time instances in the data, and the values 1 and 0 correspond to the presence and absence of an event. Using bitwise operators such as AND, OR, and bit-shifts, in our compressed representation of the events, we establish expressions for each of Allen's relations. Our experiments show that, for two events with roughly 5 × 10 6 intervals in each, the bitwise operation-based methods are almost 42 times faster than conventional interval-based linear lookups and almost 21 times faster than conventional pattern-finding parallel techniques inherently available. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is used for the performance evaluation of a set of decision making units (DMUs). Such performance scores are necessary for taking managerial decisions like allocation of resources, improvement plans for the poor performers, and maintaining high efficiency of the leaders. In classical DEA, it is assumed that the DMUs are operating in a similar environment. But in practice, this assumption is normally broken as DMUs operate in a varied environment due to several uncontrollable factors like socio-economic differences, competitiveness in the region and location. In order to address this issue, categorical DEA was proposed for the construction of peer groups by creating crisp categories based on the level of competitiveness. However, such categorizations suffer from indeterminate factors, for example, human judgment and biases, linguistic ambiguity and vagueness. In this paper, we propose a more realistic DEA approach which is capable of handling categories defined in natural languages or with vague boundaries and generates efficiency as triangular fuzzy number. The analysis indicates that if a higher degree of fuzziness is allowed while defining the boundaries of the reference set, it results in (1) a compromise with the accuracy, signified by the spread of the fuzzy efficiency, (2) degradation of the quality, signified by the centre of the fuzzy efficiency, of the decision. Finally, the applicability of this approach has been demonstrated using public library data for different regions in Tokyo city. The sensitivity of the optimal decisions to the changes in fuzzy parameters has also been investigated. [ABSTRACT FROM AUTHOR]