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The interaction and exchange between local culture and Islamic culture dominate in influencing the social system. This research aims to explore the perspective of Hadith on the Gerudo and Topeng celebrations in Jambi. This research relies on an analytical descriptive method based on collecting library data from various paper and electronic sources and field data through observation and interviews. The researcher then used thematic methods in the takhrīj of hadith, involving the search for hadiths based on sentences related to the topic. In analyzing hadith texts, the researcher has employed a contextual method with comparison approach and has compared them with the opinions of 'ulamā' who are both supportive and critical of these approaches. The key finding of this research is that the taswīr prohibited by the Prophet Muhammad refers to taswīr that contains an 'illah or reason for its prohibition. This prohibition applies to taswīr used as a rival to Allah's creation, for worship, or due to elements in its form that contradict Islam. Therefore, Gerudo and Topeng are not considered prohibited taswīr because current practices no longer embody the reasons for their prohibition. [ABSTRACT FROM AUTHOR]

The number of studies investigating the effects of ocean acidification on marine organisms and communities increases every year. Results are not easily comparable since the carbonate chemistry and ancillary data are not always reported in similar units and scales and are not calculated using similar sets of constants. To facilitate data comparison, a data compilation hosted by the PANGAEA Data Publisher was initiated in 2008 and is updated on a regular basis (10.1594/PANGAEA.962556; Ocean Acidification International Coordination Centre (OA-ICC), 2023). By November 2023, a total of 1501 datasets (over 25 million data points) from 1554 papers had been archived. To easily filter and access relevant biological response data from this compilation, a user-friendly portal (https://oa-icc.ipsl.fr , last access: 9 November 2023) was launched in 2018. Here, we present an update of this data compilation since its second description by Yang et al. (2016) and provide an overview of the OA-ICC portal for ocean acidification biological response data, launched in 2018. Most of the study sites from which data have been archived are in the North Atlantic Ocean, North Pacific Ocean, South Pacific Ocean, and Mediterranean Sea, while polar oceans are still relatively poorly represented. Mollusca and Cnidaria are still the best-represented taxonomic groups. The biological processes most reported in the datasets are growth and morphology. Other variables that can potentially be affected by ocean acidification and are often reported include calcification/dissolution, primary production/photosynthesis, and biomass/abundance. The majority of the compiled datasets have considered ocean acidification as a single stressor, but their relative contribution has decreased from 68 % before 2015 to 57 % today, showing a clear tendency towards more data archived from multifactorial studies. [ABSTRACT FROM AUTHOR]

The article discusses the importance of reproducibility in academic research, particularly in the field of structural engineering. It acknowledges the challenges in achieving reproducibility, such as data storage and computing requirements, as well as proprietary issues. The authors argue for the development of a reproducibility policy in the Journal of Structural Engineering and the Journal of Bridge Engineering, taking into account the nature and culture of research in these subdisciplines. They propose a classification system that emphasizes the availability of data and metadata, and encourage authors to go beyond data availability by making models and other materials accessible. To incentivize research and data availability, the journals will provide a visible recognition badge for papers with open data/models/codes and may consider special recognition for these papers. The authors also outline the review process for data-verified papers, which involves three associate editors verifying the availability and representativeness of the data. [Extracted from the article]

This article discusses the importance of reproducibility in academic research, specifically in the field of bridge engineering. The authors argue that reproducibility is challenging due to factors such as paper documentation, technology constraints, and proprietary issues. They propose implementing a policy that encourages researchers to make their data and models available to others, although they acknowledge that full reproducibility may not always be practical. To incentivize this practice, the Journal of Bridge Engineering plans to introduce a visible recognition badge for papers that provide open data, models, and codes. The authors also outline the review process for papers seeking the "Data Verified Paper" designation. [Extracted from the article]

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]

The Semantic Web in general and the Linked Open Data Initiative, in particular, are a growing movement for organisations to make their existing data available in a machine-readable format. Thus, institutions are highly encouraged to publish, share and interlink their data publicly. The more data are opened on the Web (Open Data), the more integrated sets of data will be connected in the Semantic Web (Linked Open Data). Within this context, libraries can complement their data by linking it to other, external data sources. The purpose of this article is to identify papers that refer to linked data in libraries, emphasising the ways that linked data empower libraries to put their knowledge in the context of the open-world, thus enhancing semantic technology innovations. The study considered papers published between 2008 and 2019 in English and presents the collected literature by grouping it according to the topic each paper refers to. The results show that libraries are facing a period of continuing change which present several challenges and indicate that they are moving towards developing new practices, policies and services. [ABSTRACT FROM AUTHOR]

Complications of detrital zircons databases provide a means for statistically analysing a variety of significant geological problems. In this work, we tried to collect a database about the South China Phanerozoic detrital zircon geochronology data. The data statistics of this paper rely on the OneSediment Working Group of The Deep‐time Digital Earth program (DDE). By November 2022, the database contains a total of 699 samples with 55,532 U–Pb ages and 3,770 effective Hf isotope data, from 130 papers. Abundant information including reference title, sample ID, locality, rock type, research institution, GPS coordinates, U–Pb ages, εHf(t) values, etc., have been involved in our database, and all data can be downloaded from DDE Data Publish & Repository website, https://repository.deep‐time.org/. Through the integrated data, we can improve the previous studies and avoid the waste of resources caused by a large number of repeated studies. [ABSTRACT FROM AUTHOR]

The document titled "INFORMATION FOR CONTRIBUTORS: EDITORIAL POLICIES AND STYLE GUIDELINES" is a resource for authors looking to submit their work to the Academy of Management Learning & Education (AMLE) journal. The journal focuses on management teaching and learning and has sections for research, reviews, critiques, and resources. The document provides guidelines for authors on the submission process, style and format, headings and sections, and citations. It also includes information on plagiarism detection and the review and publication process. [Extracted from the article]

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]

Franchising has played a pivotal role in driving China's economic growth in recent years. With the prominent problem of franchise asymmetry, the protection of franchisees has emerged as a significant concern. This paper conducts a comparative study of franchisee protection practices under franchising regulation in China and certain jurisdictions such as the United States, Australia, and Malaysia. This paper aims to analyze three aspects of franchisee protection, including information disclosure, registration, liability, and relief for violations. To this end, the researchers adopted library data collection methods. Franchisingrelated laws, regulations, rules, and cases are the primary data sources, while journals, books, industry reports, and network resources are the secondary data sources. The results of this study show that although China's franchisee protection is generally in line with international practice, there is still room for further improvement. The authors proposed recommendations such as strictly implementing registration requirements, introducing a disclosure exemption system, specifying the exact duration of the cooling-off period, and diversifying dispute resolution. The contribution of this paper is to learn from the advanced experience of foreign franchisee protection and improve the current franchisee protection system in China. [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]

Accounting and finance are intricately intertwined with the global quest for environmental sustainability by applying accounting and finance tools for carbon reduction initiatives. Clean energy financial investment is one of the many alternative tools through which accounting contributes to carbon reduction. Accordingly, this paper analysed the impact of separate and integrated clean energy investment alternatives on carbon reduction. Data on clean energy financial investment and carbon emission per capita were collected from the International Energy Agency (IEA) and Our World in Data archives, respectively. Data was analysed by using multiple pooled OLS to evaluate the impact of individual clean energy financial investments on carbon reduction and the impact of integrating the various clean energy financial investment alternatives on carbon reduction separately. Findings show that individual clean energy financial investments may not separately offer desired carbon reduction, hence, albeit some negative coefficients, individual clean investments showed no significant impact on carbon reduction. However, furthering the test by pooling all the clean energy financial investment alternatives shows a significant negative effect of clean energy financial investment on carbon reduction at a P-value of 0.05. This shows that an integration of different alternatives of clean energy financial investment may offer an enhanced reduction of carbon emission, which outweighs the effect of relying on a single clean energy investment alternative. The findings offer significant insight for policy makers' future strategies towards a combination of multiple clean energy financial investments. Furthermore, the findings from this paper are a further testament that accounting and finance are connected with the global quest for environmental sustainability through the application of accounting and financial investment tools in conducting clean energy financial investment. [ABSTRACT FROM AUTHOR]

Detrital zircon U–Pb chronology database of Precambrian deposits provides a context for the interpretation of the origin and evolution of ancient crust. Here, we tried to summarize the published literature containing detrital zircon U–Pb ages and Hf isotope data of Precambrian deposits in South China and then established a database, which contains details of information, such as reference, sample ID, locality, rock type, research institution, GPS coordinates, U–Pb ages and εHf(t) values. The data statistics of this paper rely on the OneSediment Working Group of The Deep‐time Digital Earth program (DDE). By November 2022, 610 samples with 38,278 U–Pb ages and 8,798 Lu‐Hf isotope data were collected from 136 papers, and these data can be downloaded from DDE Data Publish & Repository website, https://repository.deep‐time.org/. The purpose of the establishment of the dataset is to provide guidance and convenience for the research direction of future generations in South China and to improve the previous studies through the integrated data to avoid the waste of resources caused by a large number of repeated studies. [ABSTRACT FROM AUTHOR]

Stable isotope data have made pivotal contributions to nearly every discipline of the physical and natural sciences. As the generation and application of stable isotope data continues to grow exponentially, so does the need for a unifying data repository to improve accessibility and promote collaborative engagement. This paper provides an overview of the design, development, and implementation of IsoBank (www.isobank.org), a community-driven initiative to create an open-access repository for stable isotope data implemented online in 2021. A central goal of IsoBank is to provide a web-accessible database supporting interdisciplinary stable isotope research and educational opportunities. To achieve this goal, we convened a multi-disciplinary group of over 40 analytical experts, stable isotope researchers, database managers, and web developers to collaboratively design the database. This paper outlines the main features of IsoBank and provides a focused description of the core metadata structure. We present plans for future database and tool development and engagement across the scientific community. These efforts will help facilitate interdisciplinary collaboration among the many users of stable isotopic data while also offering useful data resources and standardization of metadata reporting across eco-geoinformatics landscapes. [ABSTRACT FROM AUTHOR]

This paper describes a catalog of simultaneous observations of the Earth's magnetosheath by ESA's Cluster and NASA's MMS missions. The catalog is built from a visual inspection of summary plots provided by the two missions complemented by an analysis of high‐resolution magnetic field data. The catalog includes 117 events when Cluster 4 and MMS 4 crossed simultaneously the magnetosheath between January–April, 2017–2021. The dynamical and turbulent features of the magnetosheath are strongly influenced by θBn, the angle between the interplanetary magnetic field (IMF) and the shock normal direction. To facilitate such investigations, we also determine the bow shock geometry for each event based on two different approaches: (a) a minimum variance analysis of in‐situ magnetic field measurements, and (b) a geometrical approach which considers a bow shock model parameterized by OMNI data. A description of spacecraft trajectory during each event is also provided. Additional data describe the relative distances between Cluster 4 and MMS 4, a classification of each event as either quasi‐parallel or quasi‐perpendicular, and the distribution of events per magnetospheric flank. The time intervals for the Cluster ‐ MMS conjunctions are included in the catalog, as well as all associated figures and tables discussed in this paper are made available through an independent online data repository, and can be freely downloaded and used by any interested researcher. Key Points: We use in‐situ measurements from Cluster 4 and MMS 4 to build a catalog/database of simultaneous observations of the Earth's magnetosheathEmpirical models and minimum variance analysis of the magnetic field allow for an estimation of the bow‐shock orientation for each eventWe also provide details on the relative spacecraft position and an analysis of the bow‐shock geometry [ABSTRACT FROM AUTHOR]

The article "Editors' notes: Assessing needs and developing solutions" is from the IASSIST Quarterly journal. It discusses various topics related to assessing needs and developing solutions in the field of data management. The article highlights papers that introduce tools developed by different institutions to address operational challenges. These tools include a self-assessment checklist for data repositories, a method to identify needs and challenges faced by members of the World Data System and Canadian CoreTrustSeal Pilot, national surveys to gauge institutions' readiness for developing research data management strategies, and a controlled vocabulary for mapping social sciences survey variables. Additionally, the article describes a project that digitized a collection of old Yoruba newspapers in Nigeria to preserve their historical value. [Extracted from the article]

The article discusses the importance of integrity, transparency, and accessibility in publishing research in the field of developmental biology. It highlights the need for open exchange of data, materials, and methods, as well as maximum transparency in reporting research results. The journal, Development, upholds high standards for data and materials presentation and availability, and encourages authors to provide access to 'omic data and follow guidelines for reporting animal research, sex/gender equity, and stem cell research. The article also outlines specific requirements for data transparency and integrity, such as using appropriate graphs and providing raw images of western blots. The journal aims to promote best practices in data presentation and reporting to ensure the usefulness of published papers for readers. [Extracted from the article]

The MET Norway Ice Service (NIS) celebrated its fiftieth year as a formal operational sea ice information provider in 2020. Prior to the 1970's, support to navigation had started off with ad-hoc observations from coastal stations on Svalbard in the 1930's, before developing as a research programme in the 1960's. Activity in the region has steadily increased, and now the NIS also supports a large number of research, tourist, and resource exploration vessels, in addition to the ice chart archive being a resource for climate change research. The Ice Service has always been at the forefront in the use of satellite Earth Observation technologies, beginning with the routine use of optical thermal infrared imagery from NASA TIROS and becoming a large user of Canadian RADARSAT-2 Synthetic Aperture Radar (SAR), and then European Copernicus Sentinel-1, in the 2000's and 2010's. Initially ice charts were a weekly compilation of ice information using cloud-free satellite coverage, aerial reconnaissance, and in situ observations, drawn on paper at the offices of the Norwegian Meteorological Institute (MET Norway) in Oslo. From 1997 production moved to the Tromsø office using computer-based Geographical Information System (GIS) software and the NIS developed the ice charting system Bifrost. This allowed the frequency of production to be increased to every weekday, with a greater focus on detailed sea ice concentrations along the ice edge and coastal zones in Eastern Greenland and in the Svalbard fjords. From 2010, the NIS has also provided a weekly austral summer ice chart for the Weddell Sea and Antarctic Peninsula. To further develop its capabilities, NIS engages in a number of national and international research projects and led the EU Horizon 2020 project, Key Environmental monitoring for Polar Latitudes and European Readiness (KEPLER). This paper summarises the overall mandate and history of the NIS, and its current activities including the current state of routine production of operational ice charts at the NIS for maritime safety in both the Arctic and Antarctic, and future development plans. [ABSTRACT FROM AUTHOR]

In identifying the status of cloud computing in a state of sustainability in terms of contribution and impact of cloud computing, the authors have gathered articles and journals and applied different methods to achieve the purpose of the study. The author hopes to identify the dominant challenges or issues that data centers have faced today in achieving cloud sustainability, specifically in the context of promoting greener practices within companies. Furthermore, the study will try to evaluate how cloud computing technologies help businesses in economics, environment, and social systems, also focusing on their environmental impact reduction strategies. Lastly, assessing the Gartner Hype Cycle for 2023, particularly to Cloud Sustainability, exploring when cloud sustainability is expected to reach its plateau of productivity, which is projected to occur within 2 to 5 years from the current timeframe. The study used a qualitative analysis framework that focuses on collecting data through experiences, opinions, and ideas related to the topic. Further, the author used thematic analysis to deeply understands the collected data systematically. Lastly, the author used mind mapping to provide a visual presentation and easily categorized the collected data. The paper discussed the contribution and advantages of cloud computing in economics, environment, and social systems. Furthermore, it also identified challenges and issues of data centers that offer cloud services. Thus, Data Centers are now starting to address these issues and this paper discusses all possible solutions that data centers are now starting to implement. Moreover, to support this argument this paper cited top 5 data center companies promoting sustainability. The authors conclude cloud computing contributes to all businesses enhancing their efficiency and competitiveness in the market. Further, cloud computing helps other technologies to enhance their capacity. However, since there is no 100% perfect technology there are implication in cloud computing. Thus, businesses and data centers provide solutions and keep on improving and capacitate these solutions to achieve sustainability. [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]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The classic pump scheduling or optimal water flow (OWF) problem for water distribution networks (WDNs) minimizes the cost of power consumption for a given WDN over a fixed time horizon. In its exact form, the OWF is a computationally challenging mixed-integer nonlinear program (MINLP). It is complicated by nonlinear equality constraints that model network physics, discrete variables that model operational controls, and intertemporal constraints that model changes to storage devices. To address the computational challenges of the OWF, this paper develops tight polyhedral relaxations of the original MINLP, derives novel valid inequalities (or cuts) using duality theory, and implements novel optimization-based bound tightening and cut generation procedures. The efficacy of each new method is rigorously evaluated by measuring empirical improvements in OWF primal and dual bounds over 45 literature instances. The evaluation suggests that our relaxation improvements, model strengthening techniques, and a thoughtfully selected polyhedral relaxation partitioning scheme can substantially improve OWF primal and dual bounds, especially when compared with similar relaxation-based techniques that do not leverage these new methods. History: Accepted by David Alderson, Area Editor for Network Optimization: Algorithms & Applications. Funding: This work was supported by the U.S. Department of Energy (DOE) Advanced Grid Modeling project, Coordinated Planning and Operation of Water and Power Infrastructures for Increased Resilience and Reliability. Incorporation of the PolyhedralRelaxations Julia package was supported by Los Alamos National Laboratory's Directed Research and Development program under the project Fast, Linear Programming-Based Algorithms with Solution Quality Guarantees for Nonlinear Optimal Control Problems [Grant 20220006ER]. All work at Los Alamos National Laboratory was conducted under the auspices of the National Nuclear Security Administration of the U.S. DOE, Contract No. 89233218CNA000001. This work was also authored in part by the National Renewable Energy Laboratory, operated by the Alliance for Sustainable Energy, LLC, for the U.S. DOE, Contract No. DE-AC36-08GO28308. 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.2022.0233) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0233). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]