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In describing these projects, the authors hope to encourage academic librarians and archivists to participate in, and even facilitate, similar work at their own institutions. Although both of these projects began in the library and included readings and discussions related to library and archival practices, the most generative conversations rapidly shifted from “how should the library handle these materials?” to “what might this institution do to reckon with its history?” When traditional library practices were de-centered and community perspectives were sought on the college archive, the authors were able to have more inclusive, authentic conversations about the college's history and future.

This case study explores two projects undertaken at a liberal arts college: a working group and a credit-bearing course intended to reckon with racist, xenophobic or otherwise harmful materials in the college archive. Both projects were informed by the authors' engagement with Tema Okun's White Supremacy Culture and guided by inclusive pedagogies and practices that participants had explored in workshops and within the context of the college's Engaged Pluralism Initiative.

The working group and the course underscore the centrality of relationships, trust-building and time to the work of addressing difficult histories. The “campus-wide conversations” the authors had hoped to have about the college archive evolved into smaller spaces developed with intention and care. The diverse perspectives of working group members and students in the course demonstrate the value of bringing together viewpoints from outside the library and beyond institutional or disciplinary silos, to consider far-reaching systemic issues.

Many US colleges and universities have begun, or will begin, to investigate the myriad ways in which racism, racial exclusion, or racial violence have marked their institutions and how these troubled legacies persist in the present day. This case study proposes possible approaches that academic libraries and librarians may take to contribute to this essential work.

These two projects propose that work that typically happens solely within libraries and archives (cataloging and description of potentially harmful materials) or within institutional or disciplinary silos (reckoning with legacies of racism and bias) can be discussed, debated, and shared among the campus community. All of the participants in the working group and the course, regardless of their title, role, or academic credentials, bring necessary expertise and experience to these projects. Inclusive practices, when paired with grassroots energy, suggest ways in which a college archive can be used as a site of evidence, reflection, interrogation, and repair.

This chapter analyzes the link between the digital divide, infrastructure regulation, and disaster planning and relief through a case study of the flood in San Jose, California triggered by the Anderson dam’s overtopping in February 2017 and an examination of communication failures during the 2018 wildfire in Paradise, California. This chapter theorizes that regulatory decisions construct social and disaster vulnerability. Rooted in the Whole Community approach to disaster planning and relief espoused by the United Nations and the Federal Emergency Management Agency, this chapter calls for leadership to end the digital divide. It highlights the imperative of understanding community information needs and argues for linking strategies to close the digital divide with infrastructure and emergency planning. As the Internet’s integration into society increases, the digital divide diminishes access to societal resources including disaster aid, and exacerbates wildfire, flood, pandemic, and other risks. To mitigate climate change, climate-induced disaster, protect access to social services and the economy, and safeguard democracy, it argues for digital inclusion strategies as a centerpiece of community-centered infrastructure regulation and disaster relief.

The purpose of this paper is to make visible the field's propensity to center whiteness even in engaging inclusive practices in information literacy classrooms. This paper offers abolitionist pedagogy as a means to understand and address these concerns.

This paper uses interdisciplinary research methods in the fields of education, library science, feminist studies, Black studies and abolition studies to examine and provide an analysis of current information literacy practices by using abolitionist pedagogy to articulate how it is possible to expand information literacy instruction practices.

Current information literacy practices and methods that seek to create inclusive learning environments for racialized and minoritized learners rely on a set of institutionalized practices such as critical information literacy and culturally sustaining pedagogies. An examination of these practices through an abolitionist pedagogical lens reveals how the field has engaged in reductive and uncritical engagement with these methods despite employing them to create inclusive spaces. Using abolitionist pedagogy as a lens, this critical essay examines the field's foundations in whiteness and illustrates pathways for transformative educational justice.

There has been much work on inclusive teaching practices that discusses challenging information literacy structures' reliance on dominant culture.? To date, there has been little to no scholarship on how information literacy practices could engage in abolitionist pedagogical praxis.

The purpose of this article is to investigate digital public spaces and audiences and to explore the relationship of digital public spaces to both ideas of nationhood and physical public institutions.

The article investigates tensions arising from the conjuncture of public spaces and digital culture through the lens of the Digital Public Library of America (DPLA). This research uses qualitative content analysis of a range of data sources including semi-structured interviews, primary texts and secondary texts.

The construction of the public library space as a digital entity does not attract anticipated audiences. Additionally, the national framing of the DPLA is not compatible with how audiences engage with digital public spaces.

Drawing on original, qualitative data, this article engages with the prevalent but undertheorized concept of digital public spaces. The article addresses unreflexive uses of the digital public and the assumptions connected to the imagined audiences for platforms like the DPLA.

The accurate modeling of magnetic hysteresis in electrical steels is important in several electrical and electronic applications. Numerical models have long been known that can correctly reproduce some typical behaviours of these magnetic materials. Among these, the model proposed by Jiles and Atherton must certainly be mentioned. This model is intuitive and fairly easy to implement and identify with relatively few experimental data. Also, for this reason, it has been extensively studied in different formulations. The developments and numerical tests made on this hysteresis model have indicated that it is able to accurately reproduce symmetrical cycles, especially the major loop, but often it fails to reproduce non-symmetrical cycles. This paper aims to show the positive aspects and highlight the defects of the different formulations in predicting the minor loops of electrical steels excited by non-sinusoidal currents.

The different formulations are applied to different electrical steels, and the data coming from the simulations are compared with those measured experimentally. The direct and inverse Jiles–Atherton models, including the introduction of the dissipative factor approach, are presented, and their limitations are proposed and validated using the measurements of three non-grain-oriented materials. Only the measured major loop is used to identify the parameters of the Jiles–Atherton model. Furthermore, the direct and inverse Jiles–Atherton models were used to simulate the minor loops as well as the hysteresis cycles with direct component (DC) bias excitation. Finally, the simulation results are discussed and compared to measurements for each study case.

The paper indicates that both the direct and the inverse Jiles–Atherton model formulations provide a good agreement with the experimental data for the major loop representation; nevertheless, both models can not accurately predict the minor loops even when the modification approaches proposed in the literature were implemented.

The Jiles–Atherton model and its modifications are widely discussed in the literature; however, some limitations of the model and its modification in the case of the distorted current waveform are not completely highlighted. Furthermore, this paper contains an original discussion on the accuracy of the prediction of minor loops from distorted current waveforms, including DC bias.

Session-based recommendation aims to predict the user's next preference based on the user's recent activities. Although most existing studies consider the global characteristics of items, they only learn the global characteristics of items based on a single connection relationship, which cannot fully capture the complex transformation relationship between items. We believe that multiple relationships between items in learning sessions can improve the performance of session recommendation tasks and the scalability of recommendation models. At the same time, high-quality global features of the item help to explore the potential common preferences of users.

This work proposes a session-based recommendation method with a multi-relation global context–enhanced network to capture this global transition relationship. Specifically, we construct a multi-relation global item graph based on a group of sessions, use a graded attention mechanism to learn different types of connection relations independently and obtain the global feature of the item according to the multi-relation weight.

We did related experiments on three benchmark datasets. The experimental results show that our proposed model is superior to the existing state-of-the-art methods, which verifies the effectiveness of our model.

First, we construct a multi-relation global item graph to learn the complex transition relations of the global context of the item and effectively mine the potential association of items between different sessions. Second, our model effectively improves the scalability of the model by obtaining high-quality item global features and enables some previously unconsidered items to make it onto the candidate list.

In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.

We propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.

The literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.

Based on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.

This study aims to explore whether globalization and technology are harmful to health using a global panel data set of 52 countries over the period 1990–2019.

The study focused on four continents: Africa, the Americas, Asia/Oceania and Europe. The authors used four advanced econometric methodologies, which include the standard panel fixed effect (FE), Arellano–Bover/Blundell–Bond dynamic panel, Hausman–Taylor specification and two-stage least squares (FE-2SLS)/Lewbel-2SLS approaches.

The empirical evidence highlights the significance of globalization and technology in promoting global health. The findings suggest that globalization has various impacts on global health indicators and that technology is useful in tracking, monitoring and promoting global health. In addition, the empirical evidence indicates that a truly health-centred process of globalization and technological innovation can only be realized by ensuring that the interests of countries and vulnerable populations to health risks are adequately considered in international decision-making regarding global economic integration.

The authors suggest that achieving the aspiration of global health will entail the use of globalization and information technology to extend human activities and provide equal access to global health.