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The AUD2IT-algorithm is a tool to structure the data, which is collected during an emergency treatment. The goal is on the one hand to structure the documentation of the data and on the other hand to give a standardised data structure for the report during handover of an emergency patient. AUD2IT-algorithm was developed to provide residents a documentation aid, which helps to structure the medical reports without getting lost in unimportant details or forgetting important information. The sequence of anamnesis, clinical examination, considering a differential diagnosis, technical diagnostics, interpretation and therapy is rather an academic classification than a description of the real workflow. In a real setting, most of these steps take place simultaneously. Therefore, the application of the AUD2IT-algorithm should also be carried out according to the real processes. A big advantage of the AUD2IT-algorithm is that it can be used as a structure for the entire treatment process and also is entirely usable as a handover protocol within this process to make sure, that the existing state of knowledge is ensured at each point of a team-timeout. PR-E-(AUD2IT)-algorithm makes it possible to document a treatment process that, in principle, does not have to be limited to the field of emergency medicine. Also, in the outpatient treatment the PR-E-(AUD2IT)-algorithm could be used and further developed. One example could be the preparation and allocation of needed resources at the general practitioner. The algorithm is a standardised tool that can be used by healthcare professionals of any level of training. It gives the user a sense of security in their daily work.

The paper argues that learning analytics as a research field can benefit from a theory-informed shared language to describe sensemaking of learning and teaching data. To make the case for such shared language, first, I critically review prominent sensemaking theories to then demonstrate how studies in learning analytics do not use coherent descriptions of sensemaking, eclectically combining the paradigms that have underlying differences. I then propose a conceptualization of sensemaking that overcomes the differences between these theories and explains how the concepts of "activity system," the "definition of the situation" and "affordances" can be used to capture individual differences in sensemaking. The paper concludes with a preliminary framework and examples demonstrating its utility in raising new theoretical questions, informing design principles and providing shared language for researchers in learning analytics.

An increasingly complex and demanding research landscape has seen university libraries rapidly evolve their services. While research data management, bibliometrics, and research impact services have predominantly featured in the literature to date, the full scope of support libraries are currently providing to their institutions is unknown. This paper aims to present an up-to-date view of the scope and extent of research support services by university libraries across Australia and Aotearoa New Zealand. A coding process analyzed content data from university library websites. Eleven research support areas were identified. Service delivery is split between synchronous and asynchronous modes. This paper describes a lived experience of an evidence-based library and information practice approach to improving research support services at two Australian university libraries, and while it highlights continued maturation of research support services, more research is needed to better understand influences on service development.

This paper proposes an online learning platform learner DM method based on the improved fuzzy C clustering (FCM) algorithm, constructs a learner feature database, and combines clustering analysis and SPSS statistical methods to statistically summarize the big data of law, thus improving the deficiencies of static and absolute classification of students in the student model. In the experiment paper, the improved algorithm is implemented and the experimental data is analyzed. The results show that the learner behavior feature extraction model in this paper has fewer errors and higher recall rate. Compared with the traditional CF algorithm, the error rate is reduced by 19.64% and the recall rate is increased by 22.85%. This study provides better targeted teaching programs and case resources for legal case teaching and promotes the innovation of legal case teaching mode.

Purpose: In today's digital world, data-driven digital artefacts pose challenges for education, as many students lack an understanding of data and feel powerless when interacting with them. This paper aims to address these challenges and introduces the data awareness framework. It focuses on understanding data-driven technologies and reflecting on the role of data in everyday life. The paper also presents an empirical study on young school students' data awareness. Design/methodology/approach: The study involves a teaching unit on data awareness framed by a pre- and post-test design using a questionnaire on students' awareness and understanding of and reflection on data practices of data-driven digital artefacts. Findings: The study's findings indicate that the data awareness framework supports students in understanding data practices of data-driven digital artefacts. The findings also suggest that the framework encourages students to reflect on these data practices and think about their daily behaviour. Originality/value: Students learn a model about interactions with data-driven digital artefacts and use it to analyse data-driven applications. This approach appears to enable students to understand these artefacts from everyday life and reflect on these interactions. The work contributes to research on data and artificial intelligence literacies and suggests a way to support students in developing self-determination and agency during interactions with data-driven digital artefacts.

Chinese students studying abroad have been increasing rapidly in the past decades and become a significant financial contribution to receiving countries. Accordingly, understanding their enrollment choice is essential to facilitate college marketing and admission strategies. Though the decision process is believed to be different from domestic students, empirical analysis of Chinese students' enrollment choices is still lacking. This paper fills the void by examining the influential factors of Chinese students' enrollment choice with novel student-level data. We find that in addition to factors domestic students typically consider, such as financial aid and academic quality, Chinese students particularly emphasize college ranking, reputation, and location in their decision process. Furthermore, unlike domestic students who usually prefer colleges with proximity to home, Chinese students' location preference is linked to job prosperity. We also find that the impact of the factors varies for students from different regions of China, which can be attributable to uneven economic development within the country.

Dyslexia is the most prevalent disorder in the world that causes difficulties with reading, writing, and spelling. Pupils with dyslexia show trouble with their cognitive skills. Various interventions were already introduced for their treatment but dyslexia is still a trending disorder. The available interventions utilized for these pupils' learning open the research area for the current state-of-art of learning interventions for pupils with dyslexia. The results of this Systematic Literature Review show the trending interventions, sensory approaches utilized, and difficulties for pupils with dyslexia learning. Papers published over a period of 5 years were reviewed and their data was collected using a rigid systematic process. Based on the gathered data, several analyses were conducted. The search shows that nowadays, technological-based interventions are trending specifically apps and games, in parallel haptics technology is in its very initial stage. The most predominant sensory approaches were visual and auditory, followed by kinesthetic and tactile, mainly intervening with non-technological and technological interventions. There are still many open issues and research opportunities in the field of learning interventions for pupils with dyslexia, as most researchers utilized the visual and auditory approaches for the feedback and guidance of these pupils, while they lack to utilize the kinesthetic and tactile.

The AUD2IT-algorithm is a tool to structure the data, which is collected during an emergency treatment. The goal is on the one hand to structure the documentation of the data and on the other hand to give a standardised data structure for the report during handover of an emergency patient. AUD2IT-algorithm was developed to provide residents a documentation aid, which helps to structure the medical reports without getting lost in unimportant details or forgetting important information. The sequence of anamnesis, clinical examination, considering a differential diagnosis, technical diagnostics, interpretation and therapy is rather an academic classification than a description of the real workflow. In a real setting, most of these steps take place simultaneously. Therefore, the application of the AUD2IT-algorithm should also be carried out according to the real processes. A big advantage of the AUD2IT-algorithm is that it can be used as a structure for the entire treatment process and also is entirely usable as a handover protocol within this process to make sure, that the existing state of knowledge is ensured at each point of a team-timeout. PR-E-(AUD2IT)-algorithm makes it possible to document a treatment process that, in principle, does not have to be limited to the field of emergency medicine. Also, in the outpatient treatment the PR-E-(AUD2IT)-algorithm could be used and further developed. One example could be the preparation and allocation of needed resources at the general practitioner. The algorithm is a standardised tool that can be used by healthcare professionals of any level of training. It gives the user a sense of security in their daily work. [ABSTRACT FROM AUTHOR]

School exclusion reduction in Scotland--and especially in the city of Glasgow--has received substantial media and policy attention in recent years. In London in particular, multiple governmental agencies have explicitly expressed a desire to replicate the exclusion reduction which recently occurred in Glasgow, often citing the connection between school exclusion and violence as a key motivating factor. In this paper, after presenting the statistical trends in school exclusions in Scotland, England, Glasgow and London, we mobilise original interview data to (1) explain how school exclusion reduction occurred so rapidly in Glasgow between 2007 and 2019, and (2) explore whether a similar reduction in exclusions could occur in contemporary London. We apply a theoretical framework to these issues which derives from Peters' work on policy coordination, allowing us to compare the conditions in Glasgow and London for well-coordinated pan-city exclusion reduction. Building on previous research which has contrasted national school exclusion policies in Scotland and England, we conclude that policy conditions surrounding school exclusion in the two cities differ substantially. There are substantial barriers to significant exclusion reduction in London, relating to both city- and national-level factors. There barriers include competition between different agencies working in relevant policy spaces; the fragmentation of the city's education system; the need for better incentivisation of inclusion by Ofsted and the Department for Education; and particular challenges to reframing the issue of school exclusion in London.

Purpose: In light of a need for more critical education about datafication, this paper aims to develop a framework for critical datafication literacy that is grounded in theoretical and empirical research. The framework draws upon existing critical data literacies, an in-depth analysis of three well-established educational approaches - media literacy, the German "(politische) Bildung" and Freirean "critical pedagogy" - and empirical analyses of online educational resources about datafication. Design/methodology/approach: The study interconnects theoretical analyses with an empirical mixed methods investigation that includes expert interviews with creators of online educational resources about datafication and a qualitative survey with educators interested in teaching about data technologies. Findings: The research identified novel findings on the goals of resource creators and educators, such as a focus on empowering and emancipatory approaches, fostering systemic understanding of datafication and encouraging collective action. Such perspectives are rare in existing critical data literacy conceptualisations but show resemblance to traditional education scholarship. This highlights how much can be learnt from practitioners and from these more established educational approaches. Based on these findings, a framework for critical datafication literacy is suggested that aims for systemic understanding of datafication, encouraging critical thinking and enabling learners to make enlightened choices and take different forms of action. Originality/value: The study is unique in its interconnection of theoretical and empirical research, and it advances previous research by suggesting a grounded framework for critical datafication literacy.

Internet users are becoming ignorant with their data and the transparency of information due to the nature of high-speed internet today. Regrettably, internet users are deceived by engineering tactics performed by highly trained people, namely cybercriminals. Thus, in order to combat phishing attacks, internet users should be educated on security concerns, the influence of social engineering and anti-phishing knowledge. This paper presents a literature review of phishing, a type of cyber fraud, covering the types of phishing, the implications and governance. This study benefits the public to mitigate phishing attacks and increase phishing awareness.

This paper reports on the perceptions of Spanish academic libraries regarding Open Science (OS). OS is irrupting into academia and academic librarians need to support researchers. On the other side, researchers need to be ready to change their scientific behaviour in relation to publications and research data. We conducted a focus group with 8 academic librarians. We also sent a survey to (N = 67) academic libraries, obtaining a response rate of 71.6%. In the survey, we asked for drivers and constraints for OS services as well as for any training taking place. Our results show that facilitators are the system relationships (SD = 4.74) and internal promotion of systems relationships (SD = 4.54). In relation to the level of knowledge of OS, both researchers (SD = 3.27) and librarians have a high level in terms of the OA area (SD = 4.15) but little development of the rest of the components of OS. On the other hand, in relation to training librarians, results indicated that OA (SD = 4.79), Open Data (SD = 4.79) and new evaluation models (SD = 4.79) should be part of the training for researchers. The results of the focus group reinforce some of the indicators mentioned. We conclude that academic libraries may train researchers in OS through the acquisition of new skills and trainers-training and with the strategic support of the university. We argue that academic incentives and a change in research accreditation are also needed to shift researchers' perceptions in relation to OS.

The purpose of this paper is to explore 10-year trends in the use of Assistive Technology (AT). Using 2006 and 2015 Behavioral Risk Factor Surveillance System (BRFSS) data, univariate analysis were performed, and the rate of change was calculated. In both 2006 and 2015 data, a higher per cent of Blacks were using AT compared to Whites and Hispanics (p < 0.001); however, the rate of increase indicate that Blacks had the lowest per cent change (23.9%) compared to Whites (26.8%) and Hispanics (27.3%). Results indicated that AT use is statistically more prevalent in women, individuals who are not married (e.g. divorce, widowed, separated), the veteran population, lower educational attainment levels (e.g. some high school or less), lower income levels, the ageing population, unemployed (e.g. out of work, retired, unable to work) and individuals with limited functioning because of a disability. Similarly, individuals who have healthcare coverage, and individuals with concerns with medical costs used AT more. Even though most factors yielded higher utilisation rates when comparing 2006 to 2015 data, the rate of change had varying 'speeds' of improvement.

Purpose: Upon graduating from university, many engineers will work in new product development and/or technology adoption for continuous improvement and production optimization. These jobs require employees to be cognizant of ethical practices and implications for design. However, little engineering coursework, outside the traditional ABET (Accreditation Board for Engineering and Technology) required Engineering Ethics course, accounts for the role of ethics within this process. Because of this, engineering students have few learning opportunities to practice and reflect on ethical decision-making. Design/methodology/approach: This paper highlights one approach to integrating ethics into an engineering course (outside of engineering ethics). Specifically, the study is implemented within a five-week module with a focus on big data ethics, as part of a Supply Chain Management Technology course (required for Industrial Engineering Technology majors), using metacognition as the core assessment. Findings: Four main themes were identified through the qualitative data analysis of the metacognitive reflections: (1) overreliance on content knowledge, (2) time management skills, (3) career connections and (4) knowledge extensions. Originality/value: Three notable points emerged which contribute to the literature. First, this study showcased one example of how an ethics module can be integrated into an engineering course (other than Engineering Ethics). Second, this study demonstrated how metacognitive reflections can be used to reinforce student self-awareness of the learning process and connections to big data ethics in the workplace. Finally, this study exhibited how metacognitive reflection assignments can be deployed as a teaching and learning assessment tool, providing an opportunity for the instructor to make immediate changes as needed.

This work reports on the implementation of a self-contained data-literacy exercise designed for use in undergraduate classes to help students practice data literacy skills such as interpreting and evaluating evidence and assessing arguments based on data. The exercises use already developed data-visualizations to test and develop students' ability to evaluate arguments based on data presented visually. Moreover, the exercises are designed to teach students positive lessons from evaluating flawed examples of data usage. We show that repeated use of these exercises has the potential to help students develop tools they can use across multiple contexts when evaluating data. Student feedback and panel survey data show that students like to learn using these tools and they report increased comfort levels in working with data. The paper argues that instructional tools like these data visualization exercises are a quick and effective tool for teaching data literacy skills and that they have significant utility in non-research-methods courses.

This paper outlines an initial analysis of 20 years of data held on an electronic bridge management database for approximately 3500 arch bridges across Northern Ireland (NI) by the Department for Infrastructure. Arch bridges represent the largest group of bridge types, making up nearly 56% of the total bridge stock in NI. This initial analysis aims to identify trends that might help inform maintenance decisions in the future. Consideration of the Bridge Condition Indicator (BCI) average value for the overall arch bridge stock indicates the potential for regional variations in the overall condition and the potential for human bias in inspections. The paper presents the most prevalent structural elements and associated defects recorded in the inspections of arch bridges. This indicated a link to scour and undermining for the worst-conditioned arch bridges. An Analysis of Variance (ANOVA) analysis identified function, number of spans, and deck width as significant factors during the various deterioration stages in a bridge's lifecycle. [ABSTRACT FROM AUTHOR]

Through an ethnographic study of Chinese IT professionals who integrate a form of data culture into the digital platforms they design, maintain, and operate daily within one of China's tech giants, this paper reveals numerous overlaps and interrelations between the data practices of Chinese IT professionals and the broader social implications that arise from them. The aim is to foster a more productive dialogue between the social studies of quantification and platform studies. This original research proposes the backstage as a potent methodology for inquiring into the role of Chinese IT professionals and domestic tech giants in advancing measuring systems and audit culture. This paper concludes by suggesting that such an approach can also be applied to wider studies of the paradox in quantification between its general claims and specific effects. [ABSTRACT FROM AUTHOR]

This paper focuses on the importance of equality, diversity, and inclusion (EDI) in the aquaculture industry, with a particular emphasis on the sector in Scotland. Aquaculture is a particularly important industry when it comes to EDI, given its potential to address Sustainable Development Goals on gender equality and diversity. The paper highlights the increasing attention being paid to EDI in many areas and the significant benefits to businesses that adopt EDI policies, including improved reputation, increased innovation, and greater profitability. This paper draws on a survey of EDI in Scottish aquaculture, a workshop and interviews with industry experts to suggest concrete actions that could improve EDI in the sector. A key priority is the collection and publication of workforce data in Scottish aquaculture as well as industry champions who raise awarenes and promote EDI, and supporting cross-sector organizations who provide EDI training. We conclude by calling for more research to support the development of EDI in Scottish aquaculture, which will contribute to future resilience and fairness as well as a dynamic, relevant, and accessible industry. [ABSTRACT FROM AUTHOR]

Any tool, apparatus, machine, appliance, implant, reagent for in vitro usage, software, material, or other like or related item that the producer intends to be used, either alone or in combination, for medical purposes is considered a medical device. A thorough review was conducted for the purpose of revealing the medical device data and medical device industry in the current paper. Some of the data incorporated in this paper has been taken from Government Websites, Press Releases, Media Reports, Deloitte Report, Union Budget 2022-23 and dully citation was mentioned. [ABSTRACT FROM AUTHOR]

Building on work which has shown the role of digital technologies in reframing environmental relations, this paper explores ethnographically how environmental data is reconfiguring the concept of place. The paper takes as its focus an action-research project within a UK based, citizen-oriented initiative called Newtown Energy Futures, in which we sought to enfold climate and energy data into a social-justice informed attempt at climate action. By exploring how the project used data as an invitation for citizens to engage with and participate in local infrastructural and environmental dynamics, the paper sheds light on how environmental data came to participate in the making of place and in doing so raised questions about how to rebuild the socio-material relations through which 'a sense of place' might be reproduced. As climate and energy data increasingly demand that places become enrolled into environmental projects our findings suggest that data enables place to emerge as a 'socio-technical potentiality' an observation that has implications both for both engagement with, and the study of data and place. In practical terms, we suggest that this refiguration of place has the effect of creating hopeful trajectories for change, whilst also posing difficult questions about the limits of participation in a data-infused form of place-based politics. [ABSTRACT FROM AUTHOR]

Relating electoral laws to electoral integrity has long been a focus of academic research. Using the strategic-relational approach, as outlined in this special issue, it is possible to better understand how electoral laws shape the voter experience and electoral outcomes. This paper contributes to this understanding by looking at Voter Identification (ID) laws. With no consolidated dataset of voter ID laws existing outside the U.S.A. it is difficult to answer research questions put forward in this special issue, especially the second. This paper begins to address this shortfall by presenting the Comparative Voter ID Law (CVIL) index. Which has collected data on 246 individual electoral jurisdictions. Data presented show how voter ID laws are distributed globally, regionally, by regime type and level of democracy. The second part of the analysis goes on to describe voter ID laws by whether a jurisdiction has compulsory ID laws, how many different types of ID are accepted and the minimum number of ID documents that must be shown. Thirdly, other variables within the dataset are described. The CVIL will provide opportunities to understand how Voter ID laws are part of institutional design, are used by actors, shape the voter experience and electoral out-comes. [ABSTRACT FROM AUTHOR]

This paper explores the mechanisms by which companies can gain access to data necessary for automated decision-making in scenarios without direct contractual agreements, focusing on market-driven approaches. It introduces the concept of the essential facilities doctrine under EU competition law and examines its applicability to sets of data, alongside an examination of current ex-ante regulatory instruments which grant data access rights, such as the Type Approval Regulation, the Open Data Directive, the Electricity Directive, the Digital Markets Act, and the Data Act. These legal instruments are analysed in terms of their ability to facilitate access to data necessary for the automation of decision-making processes. In addition, the study looks at the challenges and opportunities presented by these legal instruments, including the nuances of applying the essential facilities doctrine to data. The article concludes that the most efficient way for a company to gain access to sets of data required for automated decision-making (in the absence of a contractual agreement) is to base its data access claim on an act of ex-ante regulation. If, however, such legal basis does not exist, a company could still base its data access claim on the essential facilities doctrine. The practical applicability of the doctrine to sets of data, however, remains unclear. [ABSTRACT FROM AUTHOR]

An ensemble n-sub-epidemic modeling framework that integrates sub-epidemics to capture complex temporal dynamics has demonstrated powerful forecasting capability in previous works. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. In this tutorial paper, we introduce and illustrate SubEpiPredict, a user-friendly MATLAB toolbox for fitting and forecasting time series data using an ensemble n-sub-epidemic modeling framework. The toolbox can be used for model fitting, forecasting, and evaluation of model performance of the calibration and forecasting periods using metrics such as the weighted interval score (WIS). We also provide a detailed description of these methods including the concept of the n-sub-epidemic model, constructing ensemble forecasts from the top-ranking models, etc. For the illustration of the toolbox, we utilize publicly available daily COVID-19 death data at the national level for the United States. The MATLAB toolbox introduced in this paper can be very useful for a wider group of audiences, including policymakers, and can be easily utilized by those without extensive coding and modeling backgrounds. [ABSTRACT FROM AUTHOR]

This paper will discuss Data, Information, Knowledge, and Wisdom, which is commonly referred to as DIKW. The DIKW Pyramid Model is a hierarchical model that is often referenced in both academic and practitioner circles. This model will be discussed and shown to be faulty on several levels, including a lack of definitional agreement. A new DIKW framework with systems orientation will be proposed that focuses on what the DIKW elements do in the way humans think, not what they are by definition. Information as a replacement for wasted physical resources in goal-oriented tasks will be a central organizing point. The paper will move the DIKW discussion to the computer-based concept of Digital Twins (DTs) and its augmentation of how we can use DIKW to be more effective and efficient. This will especially be the case as we move toward Intelligent Digital Twins (IDTs) with Artificial Intelligence (AI). [ABSTRACT FROM AUTHOR]

The purpose of this paper is to address the extraction of entities and relationships from unstructured Chinese text, with a particular emphasis on the challenges of Named Entity Recognition (NER) and Relation Extraction (RE). This will be achieved by integrating external lexical information and utilizing the abundant semantic information available in Chinese. We utilize a pipeline model that is applied separately to NER and RE by introducing an innovative NER model that integrates Chinese pinyin, characters, and words to enhance recognition capabilities. Simultaneously, we incorporate information such as entity distance, sentence length, and part-of-speech to improve the performance of relation extraction. We also delve into the interactions among data, models, and inference algorithms to improve learning efficiency in addressing this challenge. In comparison to existing methods, our model has achieved significant results. [ABSTRACT FROM AUTHOR]

Cybercrime is a growing concern in Bangladesh, and this paper will critically analyze the nature and extent of cybercrime in Bangladesh and its impact on individuals, businesses, and the economy as a whole. The research will begin by exploring the various types of cybercrime prevalent in Bangladesh and the methods cybercriminals use to carry out these crimes. It will then examine the reasons behind the increase in cybercrime in Bangladesh, including factors such as inadequate cyber-security measures, low awareness among the public, and the widespread use of digital technology. Moreover, the research question for this paper is: What are the main causes of the growing cybercrime problem in Bangladesh, and what can be done to mitigate its impact? Finally, this paper also discusses the challenges faced by law enforcement agencies in tackling cybercrime and the measures that can be taken to address this issue. [ABSTRACT FROM AUTHOR]

Over the last decade, data has exploded on cloud storage, and outsourcing data to cloud storage has become an appealing trend, which is not a fully reliable service. Data growth has made cloud storage management difficult. To provide more efficient and secure data storage on the cloud, cloud service providers employed a deduplication technique. Cloud data deduplication has developed as a popular research subject to boost the efficiency of cloud storage and minimize network communication traffic. It is not easy to provide security while increasing the huge volume of data on the cloud. Users are more concerned about the security of their data, as the data on the cloud is not secure and safe even after encryption. The various encryption and decryption available for providing the security to data. Conventional encryptions cannot be employed in data deduplication. To maintain the confidentiality and integrity of data, Convergent encryption and proof of ownership are applied. Various other approaches like proof of retrievability, Dekey, DupLESS, Identity-based encryption, Message-locked encryption, Attribute-based encryption, provable data possession, proof of storage with Deduplication, etc. are the security research topics these days. This paper presents a review of the literature on several proposed methodologies for safe deduplication techniques in cloud storage and current research trends. The primary contribution of the paper is to offer a complete understanding of the issues and solutions associated with safe data deduplication in cloud storage systems. The study dives into the various encryption solutions, security concerns, and potential challenges with data deduplication in the cloud. [ABSTRACT FROM AUTHOR]

In the ongoing academic discussion regarding what happens to our data after we die, how our data are utilised for commercial profit-making purposes, and what kinds of death-related practices our posthumous data figure in, the notion of digital afterlife is attracting increasing attention. While the concept of digital afterlife has been approached in different ways, the main focus remains on the level of individual loss. The emphasis tends to be on the role of posthumous digital artefacts in grief practices and death-related rituals or on data management issues relating to death. Building on a socio-technical view of digital afterlife, this paper offers, as a novel contribution, an understanding of digital afterlife as a techno-affective assemblage. It argues for the necessity of examining technological and social factors as mutually shaping and brings into the discussion of digital afterlife the notions of relationality, materiality, and the affective potential of data. The paper ends with ruminations about digital afterlife as a posthumanist project. [ABSTRACT FROM AUTHOR]

This paper discusses the role of technology under the framework of Critical Technical Practice specifically in the form of constructing artefacts and deconstructing tools in order to produce what Philip Agre would describe as 'reflexive work of critique' (Agre, 1997:155). By presenting the activities and methods used in the teaching and shaping of undergraduate courses, this paper aims to show how technical objects, such as data, datasets, application programming interfaces and machine learning models, can be considered as discursive subjects, demonstrating pedagogical understanding across fields. The courses operate in the humanities tradition and take critical technical practice as a didactic approach, insofar as software and data are understood and manipulated on an instrumental level, while encouraging critical engagement and embodied reflection that bridge the technical and social/cultural domains. Within this pedagogical approach, critical is not only understood as a paradigm of rationality or quantitative, data-driven argumentation, but as adopting a critical position – that is, to research and reflect on the social structures and cultural phenomena entangled with digital objects, bodies, tools, methods and software production. By embracing work-in-progress and reflexive exploration, we aim to extend the notion of critical technical practice by unfolding how (de)constructing machines can be achieved beyond thinking of technology as neutral instrumentalisation. The challenge is how to find a balance, not only as researchers but as educators, unfolding aspects of both formality and functionality as well as questioning and understanding technology at a discursive and critical level. We argue that learning technical practice in an educational setting is not an end, but rather a means to question existing technological structures and create further changes in socio-technical systems. [ABSTRACT FROM AUTHOR]

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Originality/value: 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. [ABSTRACT FROM AUTHOR]

Military operations necessitate swift and informed choices under duress. This paper explores the intersection of military and engineering sciences, proposing a novel framework that leverages artificial intelligence (AI) and advanced engineering simulations to bolster military decision-making. GenAi, using a cutting-edge large language model, is employed to analyze vast datasets of historical military campaigns, engineering feats, and terrain data. By integrating GenAi's analytical prowess with physics-based engineering simulations, the framework enables the creation of high-fidelity virtual battlefields. These simulations can incorporate real-world factors like weather patterns, troop movements, and equipment capabilities. Military commanders can utilize these simulations to experiment with various strategies and assess potential outcomes before committing troops. The framework facilitates the exploration of complex scenarios, including combined arms maneuvers, logistical support chains, and the efficacy of novel weaponry. Through iterative simulations, commanders can refine their plans, identify potential weaknesses, and optimize resource allocation. This paper highlights the advantages of this AI-powered approach, including enhanced situational awareness, improved risk assessment, and the ability to train for unforeseen circumstances. It also acknowledges the challenges associated with data security, model bias, and the ethical considerations of employing AI in warfare. Finally, the paper proposes future research directions to refine the framework and ensure its responsible implementation within the military domain. [ABSTRACT FROM AUTHOR]

Digital and data-driven technologies are having substantial impacts on global production, with growing analysis within established frameworks such as Global Value Chains (GVC) and Global Production Networks (GPN). Given the claims, however, that digitalization is leading to transformations in the patterns of production and labor, further theoretical work is needed to consider how these frameworks fit with evolving dynamics. Beginning with critiques that mainstream GVC/GPN have poorly theorized the concept of value, the paper argues that a re-centering of value is crucial for improved understanding of digitalization. To do this, broader debates in the literature on the digital economy—on rent and surplus value—are reviewed. These debates provide an expanded perspective of value including a broader understanding of forms of techno-economic rent and the growing debates on heterogeneous forms of labor, shaping production. A stronger orientation towards value within mainstream GVC/GPN studies can absorb some of these ideas, but considering the evolving forms, conventional notions of governance and upgrading may be less viable. [ABSTRACT FROM AUTHOR]

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