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The complexity of the data type and distribution leads to the increase in uncertainty in the relationship between samples, which brings challenges to effectively mining the potential cluster structure of data. Ensemble clustering aims to obtain a unified cluster division by fusing multiple different base clustering results. This paper proposes a three-way ensemble clustering algorithm based on sample's perturbation theory to solve the problem of inaccurate decision making caused by inaccurate information or insufficient data. The algorithm first combines the natural nearest neighbor algorithm to generate two sets of perturbed data sets, randomly extracts the feature subsets of the samples, and uses the traditional clustering algorithm to obtain different base clusters. The sample's stability is obtained by using the co-association matrix and determinacy function, and then the samples can be divided into a stable region and unstable region according to a threshold for the sample's stability. The stable region consists of high-stability samples and is divided into the core region of each cluster using the K-means algorithm. The unstable region consists of low-stability samples and is assigned to the fringe regions of each cluster. Therefore, a three-way clustering result is formed. The experimental results show that the proposed algorithm in this paper can obtain better clustering results compared with other clustering ensemble algorithms on the UCI Machine Learning Repository data set, and can effectively reveal the clustering structure. [ABSTRACT FROM AUTHOR]

Raman spectroscopy, widely used for material analysis, has formed an extensive spectral library. In practical applications, it is usually necessary to preprocess Raman spectroscopy of the target material and then identify the material through spectral-library comparisons. Baseline correction is an important step during pre-processing and it usually requires a special algorithm. However, it demands time and high-level professional skill, confining Raman spectroscopy to laboratories rather than large-scale applications. Therefore, to improve its efficiency and take advantage of the big data in the spectral library, this paper proposes a simple data-augmented deep learning method to achieve Raman spectroscopy recognition without baseline correction. In this method, a simple mathematical baseline (linear or sine function) is added to the spectrum in the database to complete data augmentation. Its training set is used to train the deep learning model. The trained deep-learning model can identify 20 minerals with 100% accuracy without additional baseline correction. Therefore, the method is effective for rapid and direct recognition of Raman spectra. [ABSTRACT FROM AUTHOR]

The transactional data typically collected/available on queueing systems are often subject to censoring as unsuccessful arrivals due to balking and/or unserved entities due to reneging are not recorded. In fact, in many situations, the true arrival, balking, and reneging events are unobservable, making it virtually impossible to collect data on these stochastic processes—information that is crucial for capacity planning and process improvement decisions. The objective of this paper is to estimate the true (latent) external arrival, balking, and reneging processes in queueing systems from such censored transactional data. The estimation problem is formulated as an optimization model and an iterative simulation-based inference approach is proposed to find appropriate input models for these stochastic processes. The proposed method is applicable in any complex queueing situation as long as it can be simulated. The problem is investigated under both known and unknown reneging distribution. Through extensive simulation experiments, general guidelines are provided for specifying the parameters of the proposed approach, namely, sample size and number of replications. The proposed approach is also validated through a real-world application in a call center, where it successfully estimates the underlying arrival, balking, and reneging distributions. Finally, to enable reproducibility and technology transfer, a working example, including all codes and sample data, are made available in an open online data repository associated with this paper. [ABSTRACT FROM AUTHOR]

With the development of society, the protection of historical buildings has been paid more and more attention. In recent years, China has increased the surveying and mapping of historical buildings buildings. This paper takes the historical building protection project in Zhejiang Province, China as an example. This paper introduces the method of combining UAV aerial photography, 3D laser scanning, close close-range photogrammetry and traditional surveying and mapping techniques. Mapping and information collection of historical buildings. And on this basis, the content and methods of quality inspection of historical architectural achievements are introduced. Guarantee the reliability of historical building archives and data quality quality. This will play a supporting role in the future repair and maintenance of historical buildings buildings. [ABSTRACT FROM AUTHOR]

In this paper, we propose a theoretical method to deduce the charge polarization (CP) and most probable charge for fission fragments for the selected range of mass numbers based on a quantum many-body framework, namely, a constrained Skyrme Hartree–Fock+BCS model. We investigate the CP on the low energy neutron-induced fission of 2 3 5 U as a representative reaction. We have found that the calculated CP has a noticeable dip that is clearly affected by the spherical shell structure around 1 3 2 Sn and deformed one around A = 144 and a peak for their counterparts, which are common features to those of widely-used Wahl's systematics, but otherwise, our CP values converge to zero. Especially, we could not see a deep valley and a big hump in the region of symmetric fission at A = 112 and 124 present in Wahl's systematics. The gradient of our CP near the symmetric fission is negative concerning the mass number of fission fragments, which is consistent with other previous theoretical studies; however, that is the opposite of the gradient in Wahl's systematics. This paper shows the difference between the charge distribution of fission fragments, which has been used practically so far, in comparison with the calculation by microscopic nuclear theory. [ABSTRACT FROM AUTHOR]

The introduction of exotic plant species in the UAE poses substantial environmental challenges. Both poorly and well-adapted species can be detrimental to the local ecosystem by outcompeting native species and disrupting ecosystems. Native plants play a critical role in maintaining the ecological balance of any region. In the United Arab Emirates (UAE), native plants face significant threats due to habitat loss, climate change, and invasive species. Despite their ecological importance and potential benefits, a comprehensive and accessible database of native plant species for the UAE is currently lacking. Our review paper aims to address this gap by presenting an in-depth analysis of the native plants in Fujairah and the UAE. Our work covers almost all the native plants reported to date, which have been verified and added to our database. We provide valuable insights into the type, nature, physiology, habitat types, and other key aspects of these native plants. By presenting this information in a comprehensive and accessible manner, we hope to facilitate efforts toward biodiversity conservation and sustainable ecosystem management in the UAE. To address this issue, we have developed UAE Flora: The Native Plants Database of Emirates. This Web-based, virtual database contains over 760 plants identified from more than 400 published articles, online resources, and books from national and international authors. Each entry in UAE Flora includes over 50 data fields representing various ecological, biological, and pharmacological properties that have been manually extracted from the literature. This review paper will serve as a valuable resource for researchers, policymakers, and the public interested in promoting the conservation of native plants in the UAE. Flora will serve as a plant data repository that is the first of its kind in the United Arab Emirates [ABSTRACT FROM AUTHOR]

Health and demographic surveillance systems (HDSS) are community-based platforms that collect longitudinal data on core demographic and socioeconomic indices as well as key health indicators at regular intervals within defined geographical populations. These systems were previously paper-based. However, the advent of electronic data collection and processing with heterogeneous technologies has posed a problem for data validation and integration. This paper describes the development of ETL channels for automated import of HDSS data from ODK to OpenHDS. The setting was the Cross River HDSS, University of Calabar, South-south Nigeria with a surveillance population of 37,808. Data collection by Fieldworkers was done with Android smartphones running the ODK Collect, while the Supervisors used Android tablets devices to review data prior to submission to an intermediate cloud-based ODK Aggregate server. The validated data was serviced by the OpenHDS and resided on MySQL server maintained locally at the Data Centre. Channels were developed in Mirth Connect to read field data from ODK, validate against the OpenHDS repository and insert onto MySQL database, rejecting inconsistent data when found. Twenty-six channels were developed, tested and deployed for import of data onto the OpenHDS. With contextual modification, this tool can be extended to other health and demographic surveillance systems (HDSSs) under the INDEPTH Network. [ABSTRACT FROM AUTHOR]

Several sources of total cross sections for e+e− pair production by photon interaction with matter, used by major Monte Carlo codes for particle transport, are quantitatively evaluated with respect to experimental measurements collected from the literature. They include data libraries deriving from theoretical calculations and analytical formulae representing empirical fits to tabulations. Although most of these sources of cross sections are based on one theoretical reference, the statistical data analysis documented in this paper highlights differences in their compatibility with experiment. The cross sections collected in the 1997 version of the Evaluated Photon Data Library (EPDL) exhibit the lowest incompatibility with experiment; the paper details the validation results for all the examined cross section sources. The scarcity of experimental data does not allow the validation process to discern pair production in the field of the nucleus and in the field of atomic electrons; nevertheless, this paper documents relevant clues regarding the contribution of the latter to reproduce experimental measurements. [ABSTRACT FROM AUTHOR]

HAL is the national open repository for documents and data from French scientists. The paper analyses how grey literature is represented on HAL. It presents original empirical results from a follow-up study to former research, based on the scientometric assessment of deposits on the French national HAL repository by more than 1,200 research laboratories. These laboratories are affiliated to ten large French research universities and cover the whole range of scientific domains. We assessed the distribution of document types, the degree of openness, the use of open licenses and the attribution of a DOI. The results are discussed under three aspects: the development since 2019, reuse rights and identifiers. [ABSTRACT FROM AUTHOR]

The governance and development history of the Yellow River is an important part of Chinese history. Many historical documents and archives have been produced around the governance and development of the Yellow River as well as modern information resources such as databases and pictures, which together constitute a special collection of cultural heritage resources of the Yellow River Governance Landscape. In this paper, a literature search was conducted and unique resources collected for the construction of the Yellow River Governance Landscape, including the main literature, archives, pictures, databases, and other resources. Experts identified the concept and relevant resources of the Yellow River Governance Landscape, and librarians constructed the special collection. The future direction of digitization and database construction for the Yellow River Governance Landscape resources are also discussed. The special resources of the Yellow River Governance Landscape will help to increase the discipline collections and special collections of university libraries, provide resources for multidisciplinary teaching and research, and help to digitize and build databases of Yellow River Governance Landscape resources in the future as well as provide the content basis for open access and sharing of digital resources. [ABSTRACT FROM AUTHOR]

Social media, like Twitter, is a data repository, and people exchange views on global issues like the COVID-19 pandemic. Social media has been shown to influence the low acceptance of vaccines. This work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individual's sensitivities and feelings that lead to achievement. This work proposes a method to analyze the opinion of an individual's tweet about the COVID-19 vaccines. This paper introduces a sigmoidal particle swarm optimization (SPSO) algorithm. First, the performance of SPSO is measured on a set of 12 benchmark problems, and later it is deployed for selecting optimal text features and categorizing sentiment. The proposed method uses TextBlob and VADER for sentiment analysis, CountVectorizer, and term frequency-inverse document frequency (TF-IDF) vectorizer for feature extraction, followed by SPSO-based feature selection. The Covid-19 vaccination tweets dataset was created and used for training, validating, and testing. The proposed approach outperformed considered algorithms in terms of accuracy. Additionally, we augmented the newly created dataset to make it balanced to increase performance. A classical support vector machine (SVM) gives better accuracy for the augmented dataset without a feature selection algorithm. It shows that augmentation improves the overall accuracy of tweet analysis. After the augmentation performance of PSO and SPSO is improved by almost 7% and 5%, respectively, it is observed that simple SVMwith 10-fold cross-validation significantly improved compared to the primary dataset. [ABSTRACT FROM AUTHOR]

Documentation of CH is paramount to preserve its memory and allow its study, especially when CH is meant to be dismantled, as for the architectures of World's Fairs. Very few efforts are undertaken to achieve this goal and national and international institutions rarely adopt innovative digital technologies. Digital technologies and products like 3D digital models are successfully and largely applied in CH domains, while webGIS is less explored for CH. Nevertheless, 3D digital models are rarely adopted to accompany digital archives and Digital Humanities studies. Very few cases use 3D models for World's Fairs' study and documentation. WebGIS is not used for World's Fairs, and are scarcely adopted and not fully exploited by Digital Humanities and digital archives for sharing data and information about CH. Turin 1911 is the first digital project aimed to virtually document, recreate, and study an entire World's Fair. Combining digital technologies (geo-DB, 3D digital reconstruction, and 3D webGIS) with cataloging standards, Turin 1911 is a pioneering initiative that applies these innovations to Digital Humanities in order to share information online. In this paper, dedicated webGIS applications are developed for the Turin 1911 needs, reporting the designed procedure, challenges in the development phase, and potentialities for Digital Humanities research. Finally, BIM models are also integrated in webGIS apps, making visible no more visible architectures. The paper discusses how webGIS apps could become a way for sharing information and data, but also a working environment for Digital Humanities studies where the research takes place in 3D environments. [ABSTRACT FROM AUTHOR]

The main task of the National Research Program "Smart crop production", supported by the Ministry of Education and Science of Bulgaria and approved by the Council of Ministers, is the development of a regional data center to facilitate the work of farmers. The regional data center is part of the implementation of a smart crop production environment called ZEMEL which provides personal assistants supporting the work of farmers. The environment provides intelligent services for crop analysis and prevention and assists farmers in performing basic tasks related to crop production. The objective of the proposed article is to present the implementation of the architecture, infrastructure, and data architecture of a regional data center in the Plovdiv region. In order to clearly present the results of this work, which are the architectural and physical implementations of a regional data center and the storage of dynamic data and background knowledge, a methodology consisting of several steps is followed: the system infrastructure of the data center and the data architecture are discussed; one of the local pieces of infrastructure, implemented in the Institute of Plant Genetic Resources (IPGR) in the town of Sadovo in the Plovdiv region, is presented in detail, including the different types of sensors and their connection to the data center in wheat cultivation; the data repositories are discussed where dynamic data and background knowledge are stored. The paper pays special attention to background knowledge developed as ontologies for winter wheat cultivation. The results are summarized by drawing some conclusions and recommendations for the design of the local infrastructure of the center and the stored data to improve its performance. [ABSTRACT FROM AUTHOR]

In the modern era, digital data processing with a huge volume of data from the repository is challenging due to various data formats and the extraction techniques available. The accuracy levels and speed of the data processing on larger networks using modern tools have limitations for getting quick results. The major problem of data extraction on the repository is finding the data location and the dynamic changes in the existing data. Even though many researchers created different tools with algorithms for processing those data from the warehouse, it has not given accurate results and gives low latency. This output is due to a larger network of batch processing. The performance of the database scalability has to be tuned with the powerful distributed framework and programming languages for the latest real-time applications to process the huge datasets over the network. Data processing has been done in big data analytics using the modern tools HADOOP and SPARK effectively. Moreover, a recent programming language such as Python will provide solutions with the concepts of map reduction and erasure coding. But it has some challenges and limitations on a huge dataset at network clusters. This review paper deals with Hadoop and Spark features also their challenges and limitations over different criteria such as file size, file formats, and scheduling techniques. In this paper, a detailed survey of the challenges and limitations that occurred during the processing phase in big data analytics was discussed and provided solutions to that by selecting the languages and techniques using modern tools. This paper gives solutions to the research people who are working in big data analytics, for improving the speed of data processing with a proper algorithm over digital data in huge repositories. [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]

Background: HIV diagnosis in South Africa is based on a point-of-care testing (PoCT) algorithm with paper-based record-keeping. Aggregated testing data are reported routinely. To facilitate improved HIV case-based surveillance, the Western Cape Province implemented a unique pilot intervention to digitise PoCT results, at an individual level, and generate an electronic register using the newly developed Provincial Health Data Centre (PHDC). We describe the intervention (phased) and present an evaluation of the operational feasibility of the intervention. We also offer implementation insights into establishing electronic capture of individual level testing data.Methods: Cross-sectional analyses were conducted on records of all patients attending a local Community Health Centre who had an HIV-PoCT during the study period. Data from the intervention were linked to the PHDC using a unique identifier and compared with aggregate data from the paper-based register. Correlation coefficients were calculated to quantify the correlation between the two monthly datasets. To support an understanding of the findings, the Department of Health project management team generated reflections on the implementation process, which were then grouped thematically into implementation lessons.Results: In total, 11,337 PoCT records were digitised (70% (7954) during Phase I; and 30% (3383) during Phase II). Linkage of forms to the PHDC was 96% in Phase I and 98% in Phase II. Comparison with aggregate data showed high correlation during Phase I, but notable divergence during Phase II. Divergence in Phase II was due to stringent data quality requirements and high clinical staff turnover. Factors supporting implementation success in Phase I included direct oversight of data capturing by a manager with clinical and operational insight. Implementation challenges included operational, health system, and high cost-related issues.Conclusions: We demonstrate that rapid digitisation of HIV PoCT data, without compromising currently collected aggregate data, is operationally feasible, and can contribute to person-level longitudinal HIV case-based surveillance. To take to scale, we will need to improve PoCT platforms and clerical and administrative systems. Although we highlight challenges, we demonstrate that electronic HIV testing registers can successfully replace manual registers and improve efforts to monitor and evaluate HIV testing strategies. [ABSTRACT FROM AUTHOR]

Marketing is always on a lookout to open new frontiers so that consumer decision making could be made easier and favourable. Neuromarketing as an emerging application field collaborates neurosciences with marketing & helps discern the complicated decision making process by scientifically studying the nervous system & cognitive mechanisms of humans. Either deliberate or intuitive, the decision making is neurotic in character. Neuromarketing acts as an instrument for enhancing advertising effectiveness and creating brand perceptions. This conceptual paper employs exploratory research design to unfold the realms of neuromarketing. The objective of the paper is to find how neuromarketing acts as an indispensable competitive weapon of marketing by enhancing advertising effectiveness & via creation of favourable brand perceptions. Systemic search data mining is used to derive relevant literature from various secondary data repositories. The qualitative research technique applied in the paper is conceptual framework analysis and the inferences so drawn are inductive in character. The findings of the paper signify the manner in which neuromarketing has emerged as a competitive tool that will resonate with consumer minds & bring them closer to positive purchase decision via brand imagery and brand preference. The paper is futuristic in nature and has implications for marketing strategists, consumers, image makers, policy formulators and advertising firms who can use neuromarketing as a vital tool for decoding consumers' complicated neuronal outcomes. [ABSTRACT FROM AUTHOR]

Traffic load monitoring based on deep learning and computer vision has garnered significant attention in bridge engineering worldwide. Unlike traditional traffic load monitoring systems, computer vision-based techniques can accurately extract the spatiotemporal load distribution across the entire bridge in an autonomous manner. However, many of the related studies in the literature used data sets that were collected from a few specific areas of different bridges, and there are very limited data sets that provide complete coverage of the entire bridge, making a detailed comparison of different computer vision methods difficult. This paper presents a benchmark data set that provides a series of annotations and field measurements required for traffic load detection, tracking, and continuous monitoring on the bridge. The data set was collected by five cameras and two weigh-in-motion systems installed on a cable-stayed bridge and is divided into three subsets. The first subset contains over 32,000 images and annotation files of 11 types of vehicle-related targets, which are necessary for the training of vehicle detection models. The second subset consists of photos of the calibration board and coordinates of reference points that are used for camera calibration. The last subset is designated for the field verification of various algorithms, providing synchronized vehicle weight data and monitoring videos covering the whole bridge. To the author's knowledge, this data set is the first open-source data set for vision-based traffic load monitoring in a bridge, which will have tremendous value in promoting research in the area of innovative bridge health monitoring technologies. Details of this data set will be available in the public domain through a Zenodo data repository. [ABSTRACT FROM AUTHOR]

This paper focused on the case of Semal Village, the region that will be demolished due to undergoing recent redevelopment in Paju. We experimented utilizing 3D spatial information to capture precise urban morphology of present status before redevelopment and to transform such data into multi-modal experience of the urban archive. We investigated feasible techniques of photogrammetry and hybrid 3D modelling that could render the situated reality of the village communicatively with various medium such as 3D-printed scale model, and augmented reality (AR) contents. Through drone photography, the entire Semal village was captured, and three-dimensional data were obtained using photogrammetry. Along with information from on-site surveys, the mesh model was segmented into buildings, terrain, and vegetation for focused work. Each models reconstructed and retextured using images from the on-site surveys. All data were compiled for full-colour 3D printing and assembly. The 3D-printed scale model replicating Semal is on display at the Paju Central Library. Additionally, an AR content was created using the 3D-printed scale model and ethnographic data, aiming to archive and share people's memories, thereby continuously building a sustainable archive of the village. [ABSTRACT FROM AUTHOR]

Spatial climate data sets that extend back in time over many decades are an important resource for climate monitoring. The long‐term consistency of such data sets is, however, compromised by changes in the measurement systems over time. In this paper, we introduce a data set of monthly precipitation on a 5‐km grid over the European Alps that extends back to the late 19th century. In deriving the "long‐term Alpine precipitation reconstruction" (LAPrec), special care is taken of variations in the station network, in order for the data set to satisfy high standards in long‐term consistency. LAPrec builds on a reconstruction method that integrates the available information in two portions: The first is a set of high‐quality homogenized station series, taken from the HISTALP data archive, covering the entire period almost continuously. The second is a high‐resolution gridded precipitation analysis, taken from the "Alpine Precipitation Grid Data Set," constructed from thousands of rain‐gauges but covering a few decades only. We demonstrate how the reconstruction approach successfully introduces mesoscale structures that are not resolved by the available long‐term station series, more plausibly so than a predecessor data set using conventional interpolation. We also illustrate that LAPrec reveals long‐term precipitation trends that are spatially more consistent and more detailed than the trends in popular climate monitoring data sets. Over the period 1871–2017 a statistically significant increase is found in winter over the northern parts of the Alps (1%–2% per 10 years). LAPrec is available in two versions (back until 1871 and 1901 respectively) from the Copernicus climate data store. Plain Language Summary: Data sets extending back in time over many decades are an important resource for climate monitoring. In this paper, we introduce a data set on a regular grid of monthly precipitation over the European Alps that extends back to 1871. In deriving the "long‐term Alpine precipitation reconstruction" (LAPrec), special care is taken to keep the station network constant and make use of high‐quality homogenized time series, in order for the data set to be as consistent as possible over time. LAPrec builds on two sources of information: The first is a set of high‐quality station series from the HISTALP data archive, covering the entire period almost continuously. The second is a high‐resolution gridded precipitation analysis, the "Alpine Precipitation Grid Data Set," constructed from thousands of rain‐gauges but covering a few decades only. We demonstrate how this approach successfully introduces details that are not resolved by the available long‐term station series. We also illustrate that LAPrec reveals long‐term precipitation trends that are spatially more consistent and detailed than the trends in popular climate monitoring data sets. Over the period 1871–2017 a statistically significant increase in winter precipitation is found over the northern parts of the Alps. LAPrec is available from the Copernicus climate data store. Key Points: Long‐term spatial analyses of precipitation in the European Alps since 1871For applications such as climate monitoring and trend analysis, the data set meets high standards in long‐term consistency and homogeneityLong‐term Alpine precipitation reconstruction detects a significant trend in winter precipitation over the northern part of the Alps in the period starting from 1871 [ABSTRACT FROM AUTHOR]

LU and Cholesky matrix factorization algorithms are core subroutines used to solve systems of linear equations (SLEs) encountered when solving an optimization problem. Standard floating-point algorithms are highly efficient but remain susceptible to the accumulation of round-off errors, which can lead solvers to return feasibility and optimality claims that are actually invalid. This paper introduces a novel direct solution approach for solving sequences of closely related SLEs encountered in nonlinear programming efficiently and without round-off errors. Specifically, it introduces rank-one update algorithms for the round-off error–free factorization framework, a tool set built on integer-preserving arithmetic that has led to the development and implementation of extremely reliable subroutines for solving SLEs occurring in linear programming. The formal guarantees of the presented algorithms are established through the derivation of theoretical insights. Their advantages are supported with computational experiments, which demonstrate upward of 75× improvements over exact factorization runtimes on fully dense matrices with more than one million entries. A significant advantage of the featured integer-preserving framework is that the length of any matrix coefficient produced by its algorithms is bounded polynomially in the size of the inputs without having to resort to greatest common divisor operations, which are required by and thereby hinder an efficient implementation of exact rational arithmetic approaches. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms–Continuous. 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.2021.0331) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0331). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

The article provides an overview of the journal Molecular Ecology (MEC) and its achievements in the fields of ecology and evolution. It discusses the journal's high ranking and impact factor, as well as its policies on data and code archiving, indigenous perspectives, and the use of artificial intelligence. The article also mentions the journal's social media presence and blog. It concludes with information about prizes and notable papers published in the journal. The text discusses various research articles, opinion papers, and special issues covering a range of topics in molecular ecology. The journal aims to provide valuable insights and promote discussion in the field. The given text is an editorial written by several authors, but no information about its content is provided. [Extracted from the article]

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

In recent years, there has been a major expansion in digital storage capability for hosting raw diffraction datasets. Naturally, the question has now arisen as to the benefits and costs for the preservation of such raw, i.e., experimental diffraction datasets. We describe the consultations made of the global structural chemistry, i.e., chemical crystallography community from the points of view of the International Union of Crystallography (IUCr) Committee on Data, of which JRH was the Chair until very recently, and the IUCrData Raw Data Letters initiative, for which LKB is the Main Editor. The monitoring by the CCDC of CSD depositions which cite the digital object identifiers of raw diffraction datasets provides interesting statistics by probe (x-ray, neutron, or electron) and by home lab vs central facility. Clearly, a better understanding of the reproducibility of current analysis procedures is at hand. Policies for publication requiring raw data have been updated in IUCr Journals for macromolecular crystallography, namely, that raw data should be made available for a new crystal structure or a new method as well as the wwPDB deposition. For chemical crystallography, such a step requiring raw data archiving has not yet been recommended by the IUCr Commission on Structural Chemistry. [ABSTRACT FROM AUTHOR]

Copyright of All Azimuth: A Journal of Foreign Policy & Peace is the property of Ihsan Dogramaci Peace Foundation, Center for Foreign Policy & Peace Research 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.)

Copyright of Historia y Comunicación Social is the property of Universidad Complutense de Madrid 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 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]

Technological advancement has forced academic libraries to change their traditional services by adopting emerging technologies to respond to the changing information needs of their users, who are now more technologically inclined and prefer remote and timely access to scholarly information. Smart technologies are the recent trend in academic libraries. Smart technologies have the potential to enhance academic library services provision and also support the distance learning environment, especially now as higher education embraces online and distance learning. Therefore, it is essential to investigate the level of implementation of smart services at academic libraries and the challenges they face in developing and/or providing smart services. A questionnaire was developed to collect the required data from library staff (N = 340) at academic libraries in Jordan, where (246) responded and completed the questionnaire. The results indicate a moderate level of implementation of smart services at academic libraries in Jordan (M = 3.12). The result also shows that the challenges facing the libraries to implement and offer smart services was also moderate (M = 3.57). Resistance to change was the main challenge libraries face in their attempt to embrace smart technology to offer smart services. Also, privacy and confidentiality appeared to be one of the top rated challenges libraries have to deal with when planning for smart services deployment. Moreover, financial-related issues, such as poor infrastructure and staff training were among the main challenges librarians face to develop/offer smart services. Most importantly, challenges negatively affect the level of smart services at academic libraries in Jordan. This paper provides insights for academic librarians and decision-makers in planning for the provision of smart services at their libraries. It highlights the main challenges that might hinder the implementation and provision of smart services. [ABSTRACT FROM AUTHOR]

The followers of the Sufi path often associate their practices through the way of tariqa, particularly in their respect for the shaykh. However, there are those among them who take their reverence for the shaykh or guru to an excessive level. One form of homage is through kissing the feet. This action has become a debated issue on social media, leading to discussions and shifts within different factions. To what extent is the act of kissing the shaykh's feet rooted in and permitted by Islam? This paper aims to identify the relevant sources regarding the issue of kissing the shaykh's feet as a form of respect. The study takes a qualitative approach using library research and data analysis. The data is descriptively analysed thematically. The study's findings indicate that there is no valid evidence supporting the act of kissing the shaykh's feet as a basis for practicing proper etiquette towards the guru. Nevertheless, some scholars permit this act with the condition that it is not done in the manner of prostration and not frequently. However, the view that rejects it is stronger based on religious evidence and local customs. All parties should exercise caution and avoid excessive expressions of actions that could lead to forms of idolatry. Simultaneously, differing viewpoints should be handled harmoniously to reduce endless disputes and harm among various factions. [ABSTRACT FROM AUTHOR]

The article "Editors' notes: Developing systems to encourage FAIR and secure research data" is the first issue of Volume 48 of the IASSIST Quarterly journal. It features three papers that discuss different aspects of researchers' use of data archives. The first paper analyzes the search strategies of users of a social science data archive and suggests ways to improve their experience. The second paper describes the development of a web interface to make archival materials findable and accessible. The third paper discusses the development of training materials for researchers accessing restricted data through a virtual desktop environment. The article also mentions the ability for authors to link their ORCID iDs to their publications in the journal. [Extracted from the article]

A growing number of companies deploy their applications in green data centers (GDCs) and provide services to tasks of global users. Currently, a growing number of GDC providers aim to maximize their profit by deploying green energy facilities and decreasing brown energy consumption. However, the temporal variation in the revenue, price of grid, and green energy in tasks’ delay bounds makes it challenging for GDC providers to achieve profit maximization while strictly guaranteeing delay constraints of all admitted tasks. Unlike existing studies, a time-aware task scheduling (TATS) algorithm that investigates the temporal variation and schedules all admitted tasks to execute in GDC meeting their delay bounds is proposed. In addition, this paper provides the mathematical modeling of task refusal and service rates. In each iteration, TATS solves the formulated profit maximization problem by hybrid chaotic particle swarm optimization based on simulated annealing. Compared with several existing scheduling algorithms, TATS can increase profit and throughput without violating delay constraints of all admitted tasks. Note to Practitioners—This paper investigates the profit maximization problem for a green data center (GDC) while meeting delay constraints for all admitted tasks. Previous task scheduling algorithms do not jointly investigate temporal variation in revenue, green energy, and price of grid. Thus, they fail to meet the delay constraints of all admitted tasks. In this paper, a new approach that overcomes drawbacks of existing algorithms is proposed. It is obtained by using a hybrid metaheuristic algorithm that solves a constrained nonlinear optimization problem. Simulation results show that compared with several existing algorithms, it increases both throughput and profit. It can be readily incorporated into real-life industrial GDCs. The future work needs to investigate the repair/failure effect of GDCs on the proposed time-aware task scheduling. [ABSTRACT FROM AUTHOR]

An institution cannot exist and function properly without the presence of a library because the services provided by an academic library reflect the quality of the institution. This paper attempts to present a brief overview of the concept of library, research data, Research Data Management (RDM) and its constraints as well as the roles of academic libraries. These roles has experienced new paradigm shift, unprecedented reshaping and alterations as a result of emerging waves of information communication technology (ICT) and its accompanying sustainable and disruptive technologies. Scholars are now producing, storing and disseminating digital data in much larger volumes than the text. The continued existence and access of this data is an issue, since the data is not stored in libraries. It is therefore crucial to preserve data for future generations. This paper tries to delineate the emerging role of academic libraries in Research Data Management (RDM) in tertiary institutions. [ABSTRACT FROM AUTHOR]

Digitalisation, accelerated by the pandemic, has brought the opportunity for companies to expand their businesses beyond their geographic location and has considerably affected networks around the world. Cloud services have a better acceptance nowadays, and it is foreseen that this industry will grow exponentially in the following years. With more distributed networks that need to support customers in different locations, the model of one-single server in big financial centres has become outdated and companies tend to look for alternatives that will meet their needs, and this seems to be the case with Fortaleza, in Brazil. With several submarine cables connections available, the city has stood out as a possible hub to different regions, and this is what this paper explores. Making use of real traffic data through looking glasses, we established a latency classification that ranges from exceptionally low to high and analysed 800 latencies from Roubaix, Fortaleza and Sao Paulo to Miami, Mexico City, Frankfurt, Paris, Milan, Prague, Sao Paulo, Santiago, Buenos Aires and Luanda. We found that non-developed countries have a big dependence on the United States to route Internet traffic. Despite this, Fortaleza proves to be an alternative for serving different regions with relatively low latencies. [ABSTRACT FROM AUTHOR]

The intention of this paper is to analyse religiosity as a factor that potentially affects tax compliance. Studies in the 1990s have shown that the puzzle of tax compliance is 'why so many individuals pay their taxes' and not 'why people evade taxes.' It has been noted that compliance cannot be entirely explained by the level of enforcement. Countries set the levels of audit and penalty so low that most individuals would evade taxes if they were rational, because it is unlikely that cheaters will be caught and penalized. Nevertheless, a high degree of compliance is observed. Therefore, studies that analyse a variety of factors other than detection and punishment are need. Religiosity can play an important role in determining one's tax compliance decision. We use religious adherence data from the American Religious Data Archive and measure tax compliance as a discrepancy in reported income between Internal Revenue Service and Census data to analyse independent effects of church adherence rates on tax compliance in the United States at the county level. To our knowledge, this paper is the first study that analyzes the effect of religiosity on actual tax compliance behaviour. [ABSTRACT FROM AUTHOR]

Subsidence-related vertical ground deformation due to natural and anthropogenic factors may lead to considerable damages to structures and infrastructures, and may increase the occurrence probability of consequential events, such as floods, especially on sea and lake shores. Como city, placed in the north of Italy, adjacent to Como Lake, is subjected to significant subsidence phenomenon, which has been monitored by geodetic levelling networks. In this work the historical geodetic levelling measurements and satellite-based Synthetic Aperture Radar (SAR) data archive are integrated to assess the accuracy of Atmospheric Delay and Deformation Rate estimations obtained through Persistent Scatter Interferometry (PSI) techniques. Tree levelling measurement datasets acquired in 1990, 1997 and 2004 are used in order to obtain the precise deformation rate at the benchmarks for two periods of 1990–1997 and 1997–2004. For multi-temporal InSAR analysis, 106 SAR images (1992–2004) and 41 SAR images (1992–2004) in Ascending Track orbit from ERS-1/2 missions are used in this study. The assessment is performed through a statistical comparison between two sets of vertical land deformation rates obtained by integrated methods. The results of the validation represented a good consistency between deformation rates derived by both techniques. Also, this study has revealed the potential of SAR images acquired in gyro-less mode by ERS-2 mission (2001–2004) in terms of the estimation of ground deformation. [ABSTRACT FROM AUTHOR]

This paper highlights the gap in the use of genomic data of Africans for global research efforts for disease cures. Genomic data represents an important tool used in disease research for understanding how diseases affect several populations and how these differences can be harnessed for the development of effective cures especially vaccines that have an impact at the genetic level e.g., RNA vaccines. This paper then provides a review of global genomic data status where three continents are reported to be the major contributor of genomic data to repositories used for disease research and the development of vaccines and medicines around the world. We reviewed the most recently published information about genetic data inclusiveness of populations, explaining how genomic data of Africans is lacking in global research efforts that cater towards the eradication of pandemics via the development of vaccines and other cures. We also discuss the implication of this non-inclusiveness for global disease burdens and indicate where changes need to be made in the last part of the paper. Lastly, the entire centers on some general policy recommendations to fully include African genomic data in such global genetic repositories. These recommendations can be implemented in African countries to improve genetic data collection, storage, and usage policies. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to report a study of how research literature addresses researchers' attitudes toward data repository use. In particular, the authors are interested in how the term data sharing is defined, how data repository use is reported and whether there is need for greater clarity and specificity of terminology. Design/methodology/approach: To study how the literature addresses researcher data repository use, relevant studies were identified by searching Library Information Science and Technology Abstracts, Library and Information Science Source, Thomas Reuters' Web of Science Core Collection and Scopus. A total of 62 studies were identified for inclusion in this meta-evaluation. Findings: The study shows a need for greater clarity and consistency in the use of the term data sharing in future studies to better understand the phenomenon and allow for cross-study comparisons. Furthermore, most studies did not address data repository use specifically. In most analyzed studies, it was not possible to segregate results relating to sharing via public data repositories from other types of sharing. When sharing in public repositories was mentioned, the prevalence of repository use varied significantly. Originality/value: Researchers' data sharing is of great interest to library and information science research and practice to inform academic libraries that are implementing data services to support these researchers. This study explores how the literature approaches this issue, especially the use of data repositories, the use of which is strongly encouraged. This paper identifies the potential for additional study focused on this area. [ABSTRACT FROM AUTHOR]

To enhance the production of short-lived isotopes, higher beam powers are sought, which require targets able to accommodate them. One such target prototype is a liquid metal target LIEBE, developed at CERN. In this paper, a simulation of the proton beam interaction with the LIEBE target is presented. Simulations were performed by a series of proton transport calculations using the MCNP Monte Carlo code. The latest LIEBE target MCNP input was created in high-fidelity geometry, and the FENDL-3.1 cross-section data library was used. Flux and dose rate maps in the LIEBE target obtained from the simulations are presented in the paper. The maximum obtained dose around the target is roughly 361 Sv/h for gamma rays and 214 Sv/h for neutrons. The 70 MeV–100 µA proton beam penetrates roughly 7 mm deep into the liquid eutectic lead–bismuth. Based on this, further required changes to the LIEBE target can be evaluated. [ABSTRACT FROM AUTHOR]

This paper describes the new International Comprehensive Ocean–Atmosphere Data Set (ICOADS) near-real-time (NRT) release (R3.0.2), with greatly enhanced completeness over the previous version (R3.0.1). R3.0.1 had been operationally produced monthly from January 2015 onward, with input data from the World Meteorological Organization (WMO) Global Telecommunication Systems (GTS) transmissions in the Traditional Alphanumeric Codes (TAC) format. Since the release of R3.0.1, however, many observing platforms have changed, or are in the process of transitioning, to the Binary Universal Form for Representation of Meteorological Data (BUFR) format. R3.0.2 combines input data from both BUFR and TAC formats. In this paper, we describe input data sources; the BUFR decoding process for observations from drifting buoys, moored buoys, and ships; and the data quality control of the TAC and BUFR data streams. We also describe how the TAC and BUFR streams were merged to upgrade R3.0.1 into R3.0.2 with duplicates removed. Finally, we compare the number of reports and spatial coverage of essential climate variables (ECVs) between R3.0.1 and R3.0.2. ICOADS NRT R3.0.2 shows both quantitative and qualitative gains from the inclusion of BUFR reports. The number of observations in R3.0.2 increased by nearly 1 million reports per month, and the coverage of buoy and ship sea surface temperatures (SSTs) on monthly 2° × 2° grids increased by 20%. The number of reported ECVs also increased in R3.0.2. For example, observations of SST and sea level pressure (SLP) increased by around 30% and 20%, respectively, as compared to R3.0.1, and salinity is a new addition to the ICOADS NRT product in R3.0.2. Significance Statement: The International Comprehensive Ocean–Atmosphere Data Set (ICOADS) is the largest collection of surface marine observations spanning from 1662 to the present. A new version, ICOADS near-real-time 3.0.2, includes data transmitted in the Binary Universal Form for Representation of Meteorological Data (BUFR) format, in combination with Traditional Alphanumeric Codes (TAC) data. Many of the organizations that report observations in near–real time have moved to BUFR, so this update brings ICOADS into alignment with collections and archives of these international data distributions. By including the BUFR reports, the number of observations in the upgraded version of ICOADS increased by nearly one million reports per month and spatial coverage of buoy and ship SSTs increased by 20% over the previous version. [ABSTRACT FROM AUTHOR]

While a great deal of recent effort has focused on addressing a perceived reproducibility crisis within brain structural magnetic resonance imaging (MRI) and functional MRI research communities, this article argues that brain positron emission tomography (PET) research stands on even more fragile ground, lagging behind efforts to address MRI reproducibility. We begin by examining the current landscape of factors that contribute to reproducible neuroimaging data analysis, including scientific standards, analytic plan pre-registration, data and code sharing, containerized workflows, and standardized processing pipelines. We then focus on disparities in the current status of these factors between brain MRI and brain PET. To demonstrate the positive impact that further developing such reproducibility factors would have on brain PET research, we present a case study that illustrates the many challenges faced by one laboratory that attempted to reproduce a community-standard brain PET processing pipeline. We identified key areas in which the brain PET community could enhance reproducibility, including stricter reporting policies among PET dedicated journals, data repositories, containerized analysis tools, and standardized processing pipelines. Other solutions such as mandatory pre-registration, data sharing, code availability as a condition of grant funding, and online forums and standardized reporting templates, are also discussed. Bolstering these reproducibility factors within the brain PET research community has the potential to unlock the full potential of brain PET research, propelling it toward a higher-impact future. [ABSTRACT FROM AUTHOR]

Chert is a sedimentary rock abundant and conspicuous throughout the geologic record. It serves as an essential geological archive and is vital for palaeogeographic reconstruction. The Chert Database Working Group is part of the OneSediment Working Groups of the Deep‐time Digital Earth (DDE) Big Science Program. To facilitate the sharing of chert information and promote chert research, we have compiled a summary of literature containing chert information worldwide and established the Global Chert Database (GCDB). The main body of the current database consists of seven data tables, each providing details on references, lithology, depositional age, geographic location, depositional environment and geochemical information for each sample. As of December 2023, the GCDB contains 8417 sample data from 617 pieces of literature, which can be downloaded from the 'DDE Data Publish & Repository'. [ABSTRACT FROM AUTHOR]

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