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Value co‐creation as a research topic has been a critical proposition of interest to a wide range of disciplines. In recent years, the field of cultural heritage has also developed a range of information practices to promote different forms of value co‐creation. While the body of literature has begun to accumulate, we still lack a comprehensive understanding of conceptual foundations and how value co‐creation is implemented in cultural heritage information practices. This paper conducted a hermeneutic literature review to understand and conceptualize the core aspects of value co‐creation in various cultural heritage information works. In particular, this paper provides a research mapping of the genres of co‐creation in cultural heritage information practices, the actors, and the roles played by the stakeholders. Further, this review provides an overview of the dominant value co‐creation models in the cultural heritage field. It distills the drivers of value co‐creation and the core value consequences that arise from a process perspective. Finally, this paper puts forward a thematic, theoretical, and methodological agenda for future work based on these findings. The review contributes to the literature by proposing an integrated conceptual framework that summarizes the core elements of value co‐creation in cultural heritage information practices. The framework also serves as a call for action, providing a general understanding of the conceptual foundation for future research. [ABSTRACT FROM AUTHOR]

When writing a research paper, the author has to select information to include in the paper to support various arguments. The information has to be organized and synthesized into a coherent whole through relationships and information structures. There is hardly any research on the information structure of research papers, and how information structure supports rhetorical and argument structures. Thus, this study is focused on information organization in the Abstract and Introduction sections of sociology research papers, analyzing the information structure of research objective, question, hypothesis, and result statements. The study is limited to research papers reporting research that investigated cause–effect relations between two concepts. Two semantic frames were developed to specify the types of information associated with cause–effect and comparison relations, and used as coding schemes to annotate the text for different information types. Six link patterns between the two frames were identified—showing how comparisons are used to support the claim that the cause‐effect relation is valid. This study demonstrated how semantic frames can be incorporated in discourse analysis to identify deep structures underlying the argument structure. The results carry implications for the knowledge representation of academic research in knowledge graphs, for semantic relation extraction, and teaching of academic writing. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a modified consensus protocol algorithm is proposed for controlling a group of identical unmanned aerial vehicles (UAVs), which have been subjected to interfering signals during coordinate information exchange, using an unknown parameter in the consensus protocol. The maximum levels of interfering signals in the proposed protocol were adjusted by incorporating a hysteresis function with a dead zone consistent with the initial coordinates and the interfering signal levels. An adaptation algorithm is proposed to address a priori uncertainty regarding the consensus parameters, involving the correction of an unknown parameter by eliminating control signals exhibiting false sign changes. This correction relies on the coordinates in the phase plane, indicating that the delay in maneuver execution occurs at the beginning of the maneuver. Furthermore, by modeling synchronized motion, UAV group consensus is demonstrated for an ideal case devoid of a priori uncertainty regarding control protocol parameters, interfering signals, or consequences. The convergence of the adaptation algorithm was assessed by defining a vector function to track parameter changes during tuning. The monotonically decreasing nature of the resulting curve, along with the finite duration of the tuning process, provides confirmation of the convergence of the adaptation algorithm. [ABSTRACT FROM AUTHOR]

This paper explores the use of Fisher and Tronto's four phases of ethics of care (caring about, taking care of, caregiving, and care receiving) to three personal information management (PIM) frameworks, with a focus on PIM maintaining. The author argues that ethics of care can provide a theoretical foundation for PIM by using the four phases of caring to develop a perspective of PIM as a caring process using the categories of PIM as self‐care and PIM as caring for others. The paper begins by reviewing Fisher and Tronto's ethics of care, cites research in related fields that have applied ethics of care, and then describes how ethics of care could be applied to PIM research. To conclude, the author offers suggestions for how ethics of care can be applied to future PIM research in the following areas: better understand the motivations for PIM; the ways in which PIM can contribute to the social concepts of equality, justice, and trust and how social institutions can facilitate "good" PIM. [ABSTRACT FROM AUTHOR]

Non‐inferiority trials compare new experimental therapies to standard ones (active control). In these experiments, historical information on the control treatment is often available. This makes Bayesian methodology appealing since it allows a natural way to exploit information from past studies. In the present paper, we suggest the use of previous data for constructing the prior distribution of the control effect parameter. Specifically, we consider a dynamic power prior that possibly allows to discount the level of borrowing in the presence of heterogeneity between past and current control data. The discount parameter of the prior is based on the Hellinger distance between the posterior distributions of the control parameter based, respectively, on historical and current data. We develop the methodology for comparing normal means and we handle the unknown variance assumption using MCMC. We also provide a simulation study to analyze the proposed test in terms of frequentist size and power, as it is usually requested by regulatory agencies. Finally, we investigate comparisons with some existing methods and we illustrate an application to a real case study. [ABSTRACT FROM AUTHOR]

This paper focuses on data science in digital cultural heritage in Brazil, where there is a lack of systematized information and curated databases for the integrated organization of documentary knowledge. Thus, the aim was to systematically map the different forms of information organization and representation applied to objects from collections belonging to institutions affiliated with the federal government's Special Department of Culture. This diagnosis is then used to discuss the requirements of devising strategies that favor a better data science information infrastructure to reuse information on Brazil's cultural heritage. Content analysis was used to identify analytical categories and obtain a broader understanding of the documentary sources of these institutions in order to extract, analyze, and interpret the data involved. A total of 215 hyperlinks that can be considered cultural collections of the institutions studied were identified, representing 2,537,921 cultural heritage items. The results show that the online publication of Brazil's digital cultural heritage is limited in terms of technology, copyright licensing, and established information organization practices. This paper provides a conceptual and analytical view to discuss the requirements for formulating strategies aimed at building a data science information infrastructure of Brazilian digital cultural collections that serves as future projects. [ABSTRACT FROM AUTHOR]

Background: Deep learning methods, where models do not use explicit features and instead rely on implicit features estimated during model training, suffer from an explainability problem. In text classification, saliency maps that reflect the importance of words in prediction are one approach toward explainability. However, little is known about whether the salient words agree with those identified by humans as important. Objectives: The current study examines in‐line annotations from human annotators and saliency map annotations from a deep learning model (ELECTRA transformer) to understand how well both humans and machines provide evidence for their assigned label. Methods: Data were responses to test items across a mix of United States subjects, states, and grades. Humans were trained to annotate responses to justify a crisis alert label and two model interpretability methods (LIME, Integrated Gradients) were used to obtain engine annotations. Human inter‐annotator agreement and engine agreement with the human annotators were computed and compared. Results and Conclusions: Human annotators agreed with one another at similar rates to those observed in the literature on similar tasks. The annotations derived using the integrated gradients (IG) agreed with human annotators at higher rates than LIME on most metrics; however, both methods underperformed relative to the human annotators. Implications: Saliency map‐based engine annotations show promise as a form of explanation, but do not reach human annotation agreement levels. Future work should examine the appropriate unit for annotation (e.g., word, sentence), other gradient based methods, and approaches for mapping the continuous saliency values to Boolean annotations. Lay Description: What is known about subject matter: Explainability of deep learning models is complicated by their large size, design, and complexity.Explainability methods in deep learning exist but have not been rigorously tested.Saliency maps, as a form of explainability, can be used to generate engine annotations.Human annotations can serve as basis for comparison. What paper adds to knowledge: How well human annotations work in one domain (crisis paper identification).How well two engine annotation methods match human annotations. Implications of study findings: Human annotations have reasonable reliability.Engine annotations have lower reliability than humans.Model‐based engine annotations perform better than model‐agnostic engine annotations. [ABSTRACT FROM AUTHOR]

GLAM organizations have been digitizing their collections and making them available for the public for several decades. Recent methods for publishing digital collections such as "GLAM Labs" and "Collections as Data" provide guidelines for the application of computational methods to reuse the contents of cultural heritage institutions in innovative and creative ways. Jupyter Notebooks have become a powerful tool to foster use of these collections by digital humanities researchers. Based on previous approaches for quality assessment, which have been adapted for cultural heritage collections, this paper proposes a methodology for assessing the quality of projects based on Jupyter Notebooks published by relevant GLAM institutions. A list of projects based on Jupyter Notebooks using cultural heritage data has been evaluated. Common features and best practices have been identified. A detailed analysis, that can be useful for organizations interested in creating their own Jupyter Notebooks projects, has been provided. Open issues requiring further work and additional avenues for exploration are outlined. [ABSTRACT FROM AUTHOR]

Information and communication technology for development (ICT4D) is a field of research concerned with studying how information and communication technologies (ICTs) can be used to improve the socio‐economic situation of marginalized communities in developing countries. The authors identify the preservation of the information provided or accessed during ICT4D projects as a critical gap in ICT4D research. They argue that archival science, an information discipline concerned with the preservation of recorded information, provides theories and models that can help make ICT4D projects more sustainable. They discuss the creation of analog backups by participants in an ICT4D project in Bangladesh as an example of communities taking the initiative to remedy the limitations of an ICT4D project with simple pen and paper technology to preserve the information they wanted to keep. Conversely, they argue that insights on how marginalized communities interact with and preserve information gained through ICT4D projects can enrich archival science and foster the development of more inclusive theories and practices. Finally, they suggest areas for interdisciplinary research between ICT4D and archival science. [ABSTRACT FROM AUTHOR]

A second presidential social media transition in the United States occurred as Joe Biden took office on January 20, 2021. In the years since Barack Obama pioneered the use of platforms like Facebook and Twitter while President, Donald Trump shaped his Presidency around the use of Twitter, primarily through a personal account created before entering politics. In this paper, we examine Donald Trump's use of Twitter during his presidency as a lens through which to understand the ongoing archival preservation and data management challenges posed by social media platforms. The blurred lines between public and private records, deleting tweets, and the preservation issues that appeared after his suspension from Twitter and other platforms following the January 6, 2021 insurrection at the US Capitol all highlight an urgent, ongoing need by archivists, digital preservationists, and information scholars to consider how we might collect and manage social media records in an ever‐changing information landscape. This paper draws primarily on publicly available information from existing preservation initiatives to analyze the state of digital preservation for presidential records. Our findings highlight how both public and private entities manage and provide access to Donald Trump's tweets, pointing to broader implications for social media data preservation. [ABSTRACT FROM AUTHOR]

This paper proposes a theory of life transitions focused on information behavior. Through a process of meta‐ethnography, the paper transforms a series of influential theories and models into a theory of transitions for use in Information Science. This paper characterizes the psychological processes involved in transitions as consisting of three main stages, Understanding, Negotiating, and Resolving, each of which have qualitatively different information behaviors and which require different types of information support. The paper discusses the theoretical implications of this theory and proposes ways in which the theory can be used to provide practical support for those undergoing transitions. [ABSTRACT FROM AUTHOR]

Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas. [ABSTRACT FROM AUTHOR]

Information Technology (IT) investments in the public sector are large, and it is essential that they lead to benefits for the organisations themselves and wider society. While there is evidence suggesting a positive connection between the existence of benefits management practices and the realisation of benefits, less is known about how to implement such practices effectively. The aim of this paper is to provide insights into when benefits are most likely to be realised, and how benefits management practices and roles should be implemented, in order to have a positive effect on the success of projects in terms of realising benefits. We collected data relating to 10 public IT projects in Norway. For each project, information on benefits management was collected from project documents by interviewing the project owners and benefits owners and via follow‐up surveys. The benefits with the highest degree of realisation were those internal to the organisation, while those with the lowest degree were societal benefits. Projects assessed as having more specific, measurable, accountable, and realistically planned benefits were more successful in terms of realising benefits. Benefit owners were most effective when they were able to attract attention to the benefits to be realised, had a strong mandate, and had the domain expertise. [ABSTRACT FROM AUTHOR]

Zoos and aquaria, often regarded as preservation‐cum‐entertainment enterprises, are also actors in the effort to curb the biodiversity crisis: raising awareness, supporting conservation, and conducting research. We assessed trends in zoo and aquaria research topics and study organisms over time worldwide. For the zoos and aquaria registered in the Species360's Zoological Information Management System and the World Association of Zoos and Aquariums, we compiled metadata on their research published in the peer‐reviewed literature indexed in Scopus and carried out a keyword frequency analysis. The production of scientific papers by zoos increased at a much faster rate than the average accrual of scientific papers in the literature. Evolution of research themes ran parallel to that of biological sciences (e.g., development of molecular genetics or increased awareness about conservation). The focus of 48.5% of zoo‐led research was on vertebrates, of which mammal research was 33.7%. Whether zoos are effectively contributing to conservation may still be debatable, but our results highlight their institutional efforts to increase knowledge about the species in their care. Article impact statement: Research led by zoos has exploded over the last decade, particularly research on conservation and genetics. [ABSTRACT FROM AUTHOR]

Responding to disasters triggered by natural hazards is a deeply political process, but it is usually presented by practitioners as an apolitical endeavour. This is striking when disasters occur in authoritarian and politically highly polarised conflict‐affected settings. Although the literature provides leads as to why and how humanitarians depoliticise aid, there has been little empirical research on the implications of depoliticisation, especially at the community level. Based on qualitative fieldwork that focused on the drought responses that overlapped with the 2016–19 sociopolitical crises in Zimbabwe, this paper details the practices, motivations, and implications of humanitarian depoliticisation. It differentiates between strategic, coerced, and routine managerial depoliticisation, and argues that, in an authoritarian conflict‐affected setting, depoliticisation strategically allows state and non‐state actors to defuse sensitive issues and actor relations and to remain safe. However, depoliticisation can also have implications for information management, monitoring, accountability, and protection, and thus ultimately for upholding humanitarian principles. [ABSTRACT FROM AUTHOR]

The digital management of engineering construction is one of the primary tasks of smart construction sites. In the current practice of construction site information management, there are a series of fraudulent behaviors such as engineering data being forged and modified and this seriously interferes with the safety supervision of engineering projects. In order to improve the security and traceability of multi‐type engineering data, this paper presents a framework of system for construction site information management using blockchain and smart contracts. In this method, multiple independent smart contracts are developed for different data types. It ensures that the system can dynamically deploy new smart contracts without interrupting service, facilitating the expansion of scenario services. More importantly, the data analysis function is extended to the terminal, which allows the integration of different data analysis algorithms such as deep learning into information management. The design breaks the restriction that only rule‐based data analysis can be performed. Meanwhile, the distributed federation architecture ensures that the data cannot be tampered with. Finally, the three most common types of data (namely text, time‐series, and image) were used to verify the security and stability of the management system. [ABSTRACT FROM AUTHOR]

Privacy‐enhancing technologies that incorporate a socially meaningful conception of privacy, one that meets people's expectations and is ethically defensible, need to factor in contextual privacy norms and information governance as part of their design. This involves understanding what information handling practices users deem acceptable, what factors influence users' perceptions and behaviors, and how informational norms evolve. In this paper, we present GKC‐CI, a unifying framework for examining contextual privacy norms and information governance in a given context to help structure research inquiries around these questions. [ABSTRACT FROM AUTHOR]

Background: Fungal skin infections are recognised as one of the most common health disorders globally, and dermatophyte infections of the skin, hair and nails are the most common fungal infections. Dermatophytes can be classified as anthropophilic, zoophilic or geophilic species based on their primary habitat association, and this classification makes epidemiological analysis useful for the prevention and control of these infections. The Irish contribution to the epidemiology of these infections has been scant, with just two papers (both reporting paediatric tinea capitis only) published in the last 20 years, and none in the last seven. Objectives: To perform a comprehensive retrospective epidemiological analysis of all dermatological mycology tests performed in University Hospital Limerick over a 20‐year period. Methods: All mycology laboratory test results were extracted from the Laboratory Information Management System (LIMS, iLab, DXC Technologies) from 2001 to 2020 inclusive for analysis. Specimen types were categorised according to the site of sampling. The data were analysed using Microsoft Excel. Results: About 12,951 specimens of skin, hair and nails were studied. Median patient age was 42 years (IQR 26–57) with a slight female preponderance (57.2%). Two thirds of samples (67%, n = 8633) were nail, 32% were skin scrapings (n = 4118) and 200 hair samples (1.5%) were received. Zoophilic dermatophytes were more commonly present in females (38% F, 23% M, proportion of dermatophytes) and in those under 10 years of age or from 45 to 70 years (36% and 34% zoophiles, respectively, proportion of dermatophytes), although anthropophiles predominated every age and gender category. Anthropophiles had their highest prevalence in the 10–20 years age category (80% anthropophiles, proportion of dermatophytes), and yeast infections were more prevalent in older patients (29% of >60 year olds vs. 17% of <60 year olds, proportion of all fungal positives). Trichophyton rubrum was the most prevalent pathogen detected, accounting for 53% of all dermatophytes detected, 61% of those detected from nail samples and 34% from skin and hair samples. Trichophyton tonsurans was the most prevalent dermatophyte in tinea capitis, accounting for 37% of dermatophytes detected. Both of these organisms are anthropophilic, and this group showed consistently increased prevalence in proportion to all fungal isolates. The proportion of this dermatophyte class (anthropophiles) increased among both nail samples and skin/hair samples during the study period, from 55% of samples in the first 5 years of the study (2001–2005) to 88% (proportion of dermatophytes) in the final 5 years. Conversely, yeast detection decreased. Conclusions: This study provides a detailed overview of the epidemiology of the fungal cultures of skin, nail and hair samples in the Mid‐West of Ireland over a 20‐year period. Monitoring this changing landscape is important in identifying likely sources of infections, to identifying potential outbreaks, and may help guide empiric treatment. To the best of our knowledge, this study provides the first detailed analysis from Ireland of fungal detections from skin, hair and nail samples, and is the first epidemiological fungal report of any kind in over 7 years. [ABSTRACT FROM AUTHOR]

The documentation of the tunneling process is a crucial task of every tunnel construction project. It provides evidence of the work performed and thus, serves as a basis for invoicing and for several further analyses. Therefore, continuous digitalisation of this documentation is essential. For this purpose, we provide a digital Tunneling Information Management System (TIMS), which is a prototypically implemented software tool for replacing the still common paper‐based documentation process of tunneling projects using the New Austrian Tunneling Method (NATM). The data model presented here defines the data structures managed by this tool. Based on this, the software architecture and the implementation of TIMS is shown. [ABSTRACT FROM AUTHOR]

Summary: With the development of cloud computing, the security of information has gradually become the focus of people. The patient information processed by the medical system has a very high sensitivity and privacy. If there is a leak, it will have a great impact on the patients. The purpose of this study is to explore the privacy information antistealing control method of medical system based on cloud computing. In this paper, through big data analysis, experimental research, variable comparison, and other methods, combined with modern security management means, we carry out the algorithm experiment and research on the privacy information antistealing control means. The result is that the running time of the three algorithms is 18, 16, and 10 s in turn when the algorithm runs 1000 times. It can be seen that the operation time of the enhanced encryption algorithm in this study is less than the initial algorithm by 8 s, the speed is increased by more than 40%. The conclusion is to improve the efficiency of data computing time and make the encryption process more efficient and accurate. The privacy data protection of medical system plays a good role. [ABSTRACT FROM AUTHOR]

In many orphan diseases and pediatric indications, the randomized controlled trials may be infeasible because of their size, duration, and cost. Leveraging information on the control through a prior can potentially reduce sample size. However, unless an objective prior is used to impose complete ignorance for the parameter being estimated, it results in biased estimates and inflated type‐I error. Hence, it is essential to assess both the confirmatory and supplementary knowledge available during the construction of the prior to avoid "cherry‐picking" advantageous information. For this purpose, propensity score methods are employed to minimize selection bias by weighting supplemental control subjects according to their similarity in terms of pretreatment characteristics to the subjects in the current trial. The latter can be operationalized through a proposed measure of overlap in propensity‐score distributions. In this paper, we consider single experimental arm in the current trial and the control arm is completely borrowed from the supplemental data. The simulation experiments show that the proposed method reduces prior and data conflict and improves the precision of the of the average treatment effect. [ABSTRACT FROM AUTHOR]

Background: Information specialists conducting searches for systematic reviews need to consider key questions around which and how many sources to search. This is particularly important for public health topics where evidence may be found in diverse sources. Objectives: The objective of this review is to give an overview of recent studies on information retrieval guidance and methods that could be applied to public health evidence and used to guide future searches. Methods: A literature search was performed in core databases and supplemented by browsing health information journals and citation searching. Results were sifted and reviewed. Results: Seventy‐two papers were found and grouped into themes covering sources and search techniques. Public health topics were poorly covered in this literature. Discussion: Many researchers follow the recommendations to search multiple databases. The review topic influences decisions about sources. Additional sources covering grey literature eliminate bias but are time‐consuming and difficult to search systematically. Public health searching is complex, often requiring searches in multidisciplinary sources and using additional methods. Conclusions: Search planning is advisable to enable decisions about which and how many sources to search. This could improve with more work on modelling search scenarios, particularly in public health topics, to examine where publications were found and guide future research. [ABSTRACT FROM AUTHOR]

This article considers the interdisciplinary opportunities and challenges of working with digital cultural heritage, such as digitized historical newspapers, and proposes an integrated digital hermeneutics workflow to combine purely disciplinary research approaches from computer science, humanities, and library work. Common interests and motivations of the above‐mentioned disciplines have resulted in interdisciplinary projects and collaborations such as the NewsEye project, which is working on novel solutions on how digital heritage data is (re)searched, accessed, used, and analyzed. We argue that collaborations of different disciplines can benefit from a good understanding of the workflows and traditions of each of the disciplines involved but must find integrated approaches to successfully exploit the full potential of digitized sources. The paper is furthermore providing an insight into digital tools, methods, and hermeneutics in action, showing that integrated interdisciplinary research needs to build something in between the disciplines while respecting and understanding each other's expertise and expectations. [ABSTRACT FROM AUTHOR]

Medicine is practiced in environments of volatility, uncertainty, complexity, ambiguity, and disruption (VUCAD). Medical educators are tasked with providing an education that prepares graduates to successfully practice medicine in those types of environments. While medical education is continually evolving to incorporate new content and educational approaches, there are two mega‐evolutions that have permeated medical education: increased curricular structure and content integration. Structure and integration are assumed to be positive changes; however, they may alter the development of higher cognitive skills such as information management, planning, breadth of approach, strategy, and initiative. As new graduates will practice in VUCAD environments, it seems prudent to consider the effects of medical education's mega‐evolutions on the development of higher cognitive skills critical to those types of environments. This paper explores the potential consequences of the increased curricular structure and content integration in medical education for learners who will practice in VUCAD environments and proposes a research agenda to better understand the relationship between curricular structure and integration and higher cognitive skill development. [ABSTRACT FROM AUTHOR]

The architectures of many neural networks rely heavily on the underlying grid associated with the variables, for instance, the lattice of pixels in an image. For general biomedical data without a grid structure, the multi‐layer perceptron (MLP) and deep belief network (DBN) are often used. However, in these networks, variables are treated homogeneously in the sense of network structure; and it is difficult to assess their individual importance. In this paper, we propose a novel neural network called Variable‐block tree Net (VtNet) whose architecture is determined by an underlying tree with each node corresponding to a subset of variables. The tree is learned from the data to best capture the causal relationships among the variables. VtNet contains a long short‐term memory (LSTM)‐like cell for every tree node. The input and forget gates of each cell control the information flow through the node, and they are used to define a significance score for the variables. To validate the defined significance score, VtNet is trained using smaller trees with variables of low scores removed. Hypothesis tests are conducted to show that variables of higher scores influence classification more strongly. Comparison is made with the variable importance score defined in Random Forest from the aspect of variable selection. Our experiments demonstrate that VtNet is highly competitive in classification accuracy and can often improve accuracy by removing variables with low significance scores. [ABSTRACT FROM AUTHOR]

The information professions need a paradigmatic shift to address the epistemicide happening within our field and the ways we have systematically undermined knowledge systems falling outside of Western traditions. Epistemicide is the killing, silencing, annihilation, or devaluing of a knowledge system. We argue epistemicide happens when epistemic injustices are persistent and systematic and collectively work as a structured and systemic oppression of particular ways of knowing. We present epistemicide as a conceptual approach for understanding and analyzing ways knowledge systems are silenced or devalued within Information Science. We extend Fricker's framework by: (a) identifying new types of epistemic injustices, and (b) by adding to Fricker's concepts of Primary and Secondary Harm and introducing the concept of a Third Harm happening at an intergenerational level. Addressing epistemicide is critical for information professionals because we task ourselves with handling knowledge from every field. Acknowledgement of and taking steps to interrupt epistemic injustices and these specific harms are supportive of the social justice movements already happening. This paper serves as an interruption of epistemic injustice by presenting actions toward justice in the form of operationalized interventions of epistemicide. [ABSTRACT FROM AUTHOR]

The study aims to construct a client/server model of a three‐tier architecture based on the convolutional neural network (CNN), which is responsible for information management in admission office, including eight aspects of the registration management, admission management, prepayment management, inpatient charge management, settlement management, report statistics management, information query management, and help management. The test results of the system show that the management system constructed can run 48 h trouble‐free for registration services, no‐fee discharge services, inpatient charge services, and settlement services, and the average response time of the system is less than 10 s. The management system constructed for the admission office realizes the medical informatization, and heightens the work efficiency of relevant workers. [ABSTRACT FROM AUTHOR]

The International Conference on Geographical Information Systems Theory, Applications and Management aims at creating a meeting point of researchers and practitioners that addresses new challenges in geospatial data sensing, observation, representation, processing, visualisation, sharing and managing, in all aspects concerning both information and communications technologies (ICTs) as well as management information systems and knowledge-based systems. Conference areas: Data Acquisition and Processing Remote Sensing Modelling, Representation and Visualisation Knowledge Extraction and Management Domain Applications Although the conference is back to the normal mode (i.e., in-person), speakers are allowed to present remotely if unable to travel to the venue (hybrid support). The conference welcomes original papers of either practical or theoretical nature, presenting research or applications, of specialised or interdisciplinary nature, addressing any aspect of geographic information systems and technologies. [Extracted from the article]