This study analyzes National Cyber Security Strategies (NCSSs) of G20 countries through a novel combination of qualitative and quantitative methodologies. It focuses on delineating the shared objectives, distinct priorities, latent themes, and key priorities within the NCSSs. Latent dirichlet allocation topic modeling technique was used to identify implicit themes in the NCSSs to augment the explicitly articulated strategies. By exploring the latest versions of NCSS documents, the research uncovers a detailed panorama of multinational cybersecurity dynamics, offering insights into the complexities of shared and unique national cybersecurity challenges. Although challenged by the translation of non-English documents and the intrinsic limitations of topic modeling, the study significantly contributes to the cybersecurity policy domain, suggesting directions for future research to broaden the analytical scope and incorporate more diverse national contexts. In essence, this research underscores the indispensability of a multifaceted, analytical approach in understanding and devising NCSSs, vital for navigating the complex, and ever-changing digital threat environment.

Recent developments in national health data platforms have the potential to significantly advance medical research, improve public health outcomes, and foster public trust in data governance. Across Europe, initiatives such as the NHS Research Secure Data Environment in England and the Data Room for Health-Related Research in Switzerland are underway, reflecting examples analogous to the European Health Data Space in two non-EU nations. Policy discussions in England and Switzerland emphasize building public trust to foster participation and ensure the success of these platforms. Central to building public trust is investing efforts into developing and implementing public involvement activities. In this commentary, we refer to three national research programs, namely the UK Biobank, Genomics England, and the Swiss Health Study, which implemented effective public involvement activities and achieved high participation rates. The public involvement activities used within these programs are presented following on established guiding principles for fostering public trust in health data research. Under this lens, we provide actionable policy recommendations to inform the development of trust-building public involvement activities for national health data platforms.

The REDATAM (retrieval of data for small areas by microcomputer) statistical package and format, developed by ECLAC, has been a critical tool for disseminating census data across Latin America since the 1990s. However, significant limitations persist, including its proprietary nature, lack of documentation, and restricted flexibility for advanced data analysis. These challenges hinder the transformation of raw census data into actionable information for policymakers, researchers, and advocacy groups. To address these issues, we developed Open REDATAM, an open-source and multiplatform tool that converts REDATAM data into widely supported CSV files and native R and Python data structures. By providing integration with R and Python, Open REDATAM empowers users to work with the tools they already know and perform data analyses without leaving their R or Python window. Our work emphasizes the need for a REDATAM official format specification to further enable informed policy debates that can improve policy processes’ implementation and feedback.

As data becomes a key component of urban governance, the night-time economy is still barely visible in datasets or in policies to improve urban life. In the last 20 years, over 50 cities worldwide appointed night mayors and governance mechanisms to tackle conflicts, foster innovation, and help the night-time economy sector grow. However, the intersection of data, digital rights, and 24-hour cities still needs more studies, examples, and policies. Here, the key argument is that the increasing importance of the urban night in academia and local governments claims for much-needed responsible data practices to support and protect nightlife ecosystems. By understanding these ecosystems and addressing data invisibilities, it is possible to develop a robust framework anchored in safeguarding human rights in the digital space and create comprehensive policies to help such ecosystems thrive. Night-time governance matters for the data policy community for three reasons. First, it brings together issues covered in different disciplines by various stakeholders. We need to build bridges between sectors to avoid siloed views of urban data governance. Second, thinking about data in cities also means considering the social, economic, and cultural impact of datafication and artificial intelligence on a 24-hour cycle. Creating a digital rights framework for the night means putting into practice principles of justice, ethics, and responsibility. Third, as Night Studies is an emerging field of research, policy and advocacy, there is an opportunity to help shape how, why, and when data about the night is collected and made available to society.

In the reliability analysis of multicomponent stress-strength models, it is typically assumed that strengths are either independent or dependent on a common stress factor. However, this assumption may not hold true in certain scenarios. Therefore, accurately estimating the reliability of the stress-strength model becomes a significant concern when strengths exhibit interdependence with both each other and the common stress factor. To address this issue, we propose an Archimedean copula (AC)-based hierarchical dependence approach to effectively model these interdependencies. We employ four distinct semi-parametric methods to comprehensively estimate the reliability of the multicomponent stress-strength model and determine associated dependence parameters. Furthermore, we derive asymptotic properties of our estimator and demonstrate its effectiveness through both Monte Carlo simulations and real-life datasets. The main original contribution of this study is the first attempt to evaluate the reliability problem under dependent strengths and stress using a hierarchical AC approach.

Blended language learning has recently experienced substantial growth, offering numerous potential benefits such as increased learning opportunities and personalization. However, digital inequalities persist, particularly affecting vulnerable groups like migrants with limited education. While the integration of technology in adult education may pose additional challenges for these groups, online learning paradoxically holds the promise of enhancing their basic skills. This study addresses this apparent contradiction, focusing on blended learning in Dutch second language (L2) education in Flanders (Belgium) for L2 learners with emerging literacy and limited formal education, representing the most vulnerable subgroup of L2 learners. This group is referred to as LESLLA learners (LESLLA is an acronym for Literacy Education and Second Language Learning for Adults). Through a combination of a systematic literature review and a needs analysis of stakeholders, including LESLLA learners themselves, the study explores the benefits and challenges of blended learning for LESLLA learners. The study reveals that while many affordances and limitations for adult L2 learners in general also apply to LESLLA learners, the significance varies based on their characteristics, curriculum goals, and context. In order to realize the affordances, while also tackling the challenges, effective blended education for low-literate L2 learners requires (1) a thoughtful design of the blend, in which instructional design principles are integrated with didactic principles for L2 teaching; (2) effective teacher conduct; and (3) powerful policy of adult education centers. This paper outlines the characteristics of each component, offering insights to strengthen blended L2 learning experiences for LESLLA learners.

What drives changes in the thematic focus of state-linked manipulated media? We study this question in relation to a long-running Iranian state-linked manipulated media campaign that was uncovered by Twitter in 2021. Using a variety of machine learning methods, we uncover and analyze how this manipulation campaign’s topical themes changed in relation to rising Covid-19 cases in Iran. By using the topics of the tweets in a novel way, we find that increases in domestic Covid-19 cases engendered a shift in Iran’s manipulated media focus away from Covid-19 themes and toward international finance- and investment-focused themes. These findings underscore (i) the potential for state-linked manipulated media campaigns to be used for diversionary purposes and (ii) the promise of machine learning methods for detecting such behaviors.

The principal function of an open recirculating system (ORS) is to remove heat from power plant equipment. In particular, the presence of scale on the internal surfaces of ORS heat exchange equipment can reduce heat transfer efficiency, which leads to increased energy consumption and operating costs. The purpose of this article is to investigate the process of calcium carbonate (CaCO3) precipitation formation in terms of the components of the carbonate system and parameters affecting the shift of carbonate equilibrium in an ORS. An appraisal model was used to represent the processes occurring during the operation of an ORS. In this study, it is demonstrated that water heating in ORS condensers leads to the excretion of carbon dioxide (CO2) from the water, while cooling in the cooling towers results in CO2 uptake by the water. These processes significantly influence the state of carbonate equilibrium within the ORS. The study used the results of chemical control of the make-up and cooling water at the ORS Rivne Nuclear Power Plant (RNPP) for 2022. Furthermore, the dependencies of changes in the components of the carbonate system on the pH levels of the make-up (pH 7.51–9.52) and cooling (pH 8.21–9.53) water were revealed, and changes in the cycles of concentration (CоC), total hardness (TH), total dissolved solids (TSD), and total alkalinity (TA) were estimated. Taking into account the obtained correlation dependencies, in general, it was found that the lower the CoC levels, the lower the TA reduction value, and it is possible to increase or decrease the cooling water pH levels, which is determined by the initial state of carbonate equilibrium of make-up water. These findings enable the prediction and control of CaCO3 scale formation through continuous monitoring of water chemistry, making the process more efficient, reliable, and sustainable. The results emphasize the importance of data-driven modeling for optimizing water treatment and reducing operational costs in power plants by reducing CaCO3 scale formation.

The operational reliability of large mechanical equipment is typically influenced by the functional effectiveness of key components. Consequently, prompt repair before their failure is necessary to ensure the dependability of mechanical equipment. The prognostic and health management (PHM) technology could track the system’s health state and timely detect faults. Therefore, the remaining useful life (RUL) prediction as one of the key components of PHM is rather important. Accurate RUL prediction results could be the data support for condition-based equipment maintenance plans. Also, it could increase the dependability and safety of mechanical equipment while reducing the loss of human and financial resources and meet the requirements of sustainable manufacturing in the Industry 4.0 era. However, with the widespread use of deep learning in the field of intelligent manufacturing, there is a lack of review on RUL prediction based on deep learning. In this paper, different deep learning-based RUL prediction methods for mechanical components are summarized and classified, along with their pros and cons. Then, the case study on the C-MAPSS dataset is mainly conducted and different methods are compared. And finally, the difficulties and future directions of the RUL prediction in practical scenarios are discussed.

The complex socioeconomic landscape of conflict zones demands innovative approaches to assess and predict vulnerabilities for crafting and implementing effective policies by the United Nations (UN) institutions. This article presents a groundbreaking Augmented Intelligence-driven Prediction Model developed to forecast multidimensional vulnerability levels (MVLs) across Afghanistan. Leveraging a symbiotic fusion of human expertise and machine capabilities (e.g., artificial intelligence), the model demonstrates a predictive accuracy ranging between 70% and 80%. This research not only contributes to enhancing the UN Early Warning (EW) Mechanisms but also underscores the potential of augmented intelligence in addressing intricate challenges in conflict-ridden regions. This article outlines the use of augmented intelligence methodology applied to a use case to predict MVLs in Afghanistan. It discusses the key findings of the pilot project, and further proposes a holistic platform to enhance policy decisions through augmented intelligence, including an EW mechanism to significantly improve EW processes, thereby supporting decision-makers in formulating effective policies and fostering sustainable development within the UN.

Answer set programming (ASP), a well-known declarative logic programming paradigm, has recently found practical application in Process Mining. In particular, ASP has been used to model tasks involving declarative specifications of business processes. In this area, Declare stands out as the most widely adopted declarative process modeling language, offering a means to model processes through sets of constraints valid traces must satisfy, that can be expressed in linear temporal logic over finite traces (LTL$_{\text {f}}$). Existing ASP-based solutions encode Declare constraints by modeling the corresponding LTL$_{\text {f}}$ formula or its equivalent automaton which can be obtained using established techniques. In this paper, we introduce a novel encoding for Declare constraints that directly models their semantics as ASP rules, eliminating the need for intermediate representations. We assess the effectiveness of this novel approach on two Process Mining tasks by comparing it with alternative ASP encodings and a Python library for Declare.