The rapid expansion of AI-based remote services has intensified debates about the long-term implications of growing structural concentration in infrastructure and expertise. As AI capabilities become increasingly intertwined with geopolitical interests, the availability and reliability of foundational AI services can no longer be taken for granted. This issue is particularly pressing for AI-enabled public services for citizens, as governments and public agencies are progressively adopting 24/7 AI-driven support systems typically operated through commercial offerings from a small oligopoly of global technology providers. This paper challenges the prevailing assumption that general-purpose architectures, offered by these providers, are the optimal choice for all application contexts. Through practical experimentation, we demonstrate that viable and cost-effective alternatives exist. Alternatives that align with principles of digital and cultural sovereignty. Our findings provide an empirical illustration that sovereign AI-based public services are both technically feasible and economically sustainable, capable of operating effectively on premises with modest computational and financial resources while maintaining cultural and digital autonomy. The technical insights and deployment lessons reported here are intended to inform the adoption of similar sovereign AI public services by national agencies and governments worldwide.
This study presents a comprehensive framework for evaluating the quality of public spaces across various urban typologies. Through a systematic review of 159 research studies, we identify universal quality factors that transcend spatial types as well as specialized factors unique to specific public environments. Our findings establish accessibility (73.6%), safety/security (58.4%), and comfort (52.8%) as foundational requirements across all public space types, while revealing distinct quality priorities for different typologies: open spaces emphasize comfort (70%), parks prioritize activities (60%), green spaces focus on aesthetics and natural elements (70% and 60%), and public facilities uniquely emphasize indoor environment quality (41.7%). The research reveals a hierarchical relationship between factors, where accessibility enables other qualities, safety serves as a prerequisite for utilization, and comfort determines engagement quality. We identify critical limitations in current assessment approaches, including artificial intelligence studies focused on easily quantifiable factors, domain-specific research confined within disciplinary boundaries, and overreliance on subjective perceptions without objective measures. This research provides a foundation for integrated approaches to public space assessment that acknowledge the complexity of public urban environments while addressing both universal human needs and context-specific requirements. The findings support urban planners, designers, and policymakers in developing balanced assessment methodologies that ensure both comparability across spaces and sensitivity to local conditions, ultimately contributing to the creation of high-quality public spaces that enhance urban life and community wellbeing.
We study reinforcement learning (RL) on volatility surfaces through the lens of Scientific AI. We ask whether axiomatic no-arbitrage laws, imposed as soft penalties on a learned world model, can reliably align high-capacity RL agents, or mainly create Goodhart-style incentives to exploit model errors. From classical static no-arbitrage conditions we build a finite-dimensional convex volatility law manifold of admissible total-variance surfaces, together with a metric law-penalty functional and a Graceful Failure Index (GFI) that normalizes law degradation under shocks. A synthetic generator produces law-consistent trajectories, while a recurrent neural world model trained without law regularization exhibits structured off-manifold errors. On this testbed we define a Goodhart decomposition \(r = r^{\mathcal{M}} + r^\perp\), where \(r^\perp\) is ghost arbitrage from off-manifold prediction error. We prove a ghost-arbitrage incentive theorem for PPO-type agents, a law-strength trade-off theorem showing that stronger penalties eventually worsen P\&L, and a no-free-lunch theorem: under a law-consistent world model and law-aligned strategy class, unconstrained law-seeking RL cannot Pareto-dominate structural baselines on P\&L, penalties, and GFI. In experiments on an SPX/VIX-like world model, simple structural strategies form the empirical law-strength frontier, while all law-seeking RL variants underperform and move into high-penalty, high-GFI regions. Volatility thus provides a concrete case where reward shaping with verifiable penalties is insufficient for robust law alignment.
Taija Kolehmainen, Reetta Ghezzi, Sami Hyrynsalmi
et al.
Public actors are often seen as slow, especially in renewing information systems, due to complex tendering and competition regulations, which delay decisions. This challenge is even greater in multi-company ecosystems. However, when faced with a common threat, the ecosystem needs to unite to face the challenge. This study explores how the Omaolo ecosystem in Finland evolved from traditional public-private cooperation to an alliance model during the COVID-19 pandemic from 2020 to 2022. It highlights how the crisis accelerated changes in operations and collaboration between public and private participants, identifying key shifts, benefits, and challenges. Key findings include the removal of traditional barriers and the creation of an alliance approach that sped up the development of Omaolo's symptom assessment tool. This improved collaboration, service scalability, and responsiveness to healthcare needs despite the initial regulatory and stakeholder alignment challenges. The study concludes that crises can drive agile responses in public ecosystems. The new collaboration model helped Omaolo to adapt quickly to changing service demands, managing healthcare patient loads more effectively. These findings highlight the value of flexible, collaborative strategies for responding to emergencies in public software ecosystems.
Naga VS Raviteja Chappa, Charlotte McCormick, Susana Rodriguez Gongora
et al.
The Public Health Advocacy Dataset (PHAD) is a comprehensive collection of 5,730 videos related to tobacco products sourced from social media platforms like TikTok and YouTube. This dataset encompasses 4.3 million frames and includes detailed metadata such as user engagement metrics, video descriptions, and search keywords. This is the first dataset with these features providing a valuable resource for analyzing tobacco-related content and its impact. Our research employs a two-stage classification approach, incorporating a Vision-Language (VL) Encoder, demonstrating superior performance in accurately categorizing various types of tobacco products and usage scenarios. The analysis reveals significant user engagement trends, particularly with vaping and e-cigarette content, highlighting areas for targeted public health interventions. The PHAD addresses the need for multi-modal data in public health research, offering insights that can inform regulatory policies and public health strategies. This dataset is a crucial step towards understanding and mitigating the impact of tobacco usage, ensuring that public health efforts are more inclusive and effective.
This article explores the current state and future prospects of developing a green economy in Afghanistan, focusing on renewable energy and fossil resources. It also examines regional cooperation and Afghanistan’s politico-economic relations with its neighbors, especially Uzbekistan.
Afghanistan has a significant potential for a green economy due to its reserves of lithium and rare earth metals, essential for modern green technologies. The country is rich in renewable energy resources, which could address environmental challenges, reduce fossil fuel dependence, and create new economic opportunities. This study looks into renewable energy infrastructure, sustainable agriculture, and related challenges and opportunities.
The paper starts by providing a literature review which analyzes the data on Afghanistan’s geology, economy, and environmental issues. It conducts stakeholder analysis by collecting data on perceptions and expectations from local communities, environmental organizations, and industry experts. The analysis is conducted through reviewing the current mining sector policies and comparing them with successful international models to propose policy reforms.
Key areas for development include expanding renewable energy infrastructure, such as solar and wind power projects, and promoting sustainable agriculture practices. International organizations and donors are supporting these initiatives.
In conclusion, Afghanistan’s transition to a green economy is viable and beneficial, requiring sustained efforts from the government, international partners, and the private sector. Strategic investments and cooperation can unlock the full potential of Afghanistan’s green economy, contributing to sustainable development and environmental protection.
Scientific cooperation on an international level has been well studied in the literature. However, much less is known about this cooperation on the intercontinental level. In this paper, we address this issue by creating a collection of approximately 13.8 million publications around the papers by one of the highly cited author working in complex networks and their applications. The obtained rank-frequency distribution of the probability of sequences describing continents and number of countries -- with which authors of papers are affiliated -- follows the power law with an exponent $-1.9108(15)$. Such a dependence is known in the literature as Zipf's law and it has been originally observed in linguistics, later it turned out that it is very commonly observed in various fields. The number of distinct ``continent (number of countries)'' sequences in a function of the number of analyzed papers grows according to power law with exponent $0.527(14)$, i.e. it follows Heap's law.
Aniruddha Rajendra Rao, Chandrasekar Venkatraman, Robert Ellis
et al.
Public utilities are faced with situations where high winds can bring trees and debris into contact with energized power lines and other equipments, which could ignite wildfires. As a result, they need to turn off power during severe weather to help prevent wildfires. This is called Public Safety Power Shutoff (PSPS). We present a method for load reduction using a multi-step genetic algorithm for Public Safety Power Shutoff events. The proposed method optimizes load shedding using partial load shedding based on load importance (critical loads like hospitals, fire stations, etc). The multi-step genetic algorithm optimizes load shedding while minimizing the impact on important loads and preserving grid stability. The effectiveness of the method is demonstrated through network examples. The results show that the proposed method achieves minimal load shedding while maintaining the critical loads at acceptable levels. This approach will help utilities to effectively manage PSPS events and reduce the risk of wildfires caused by the power lines.
Public private partnerships (PPPs) are increasingly common in health research, with large European investment over the last 20 years and renewed focus in the wake of the global health crisis COVID-19. PPPs have been used for health research that seeks to collect, analyse and share personal data from research participants, often on the basis of informed or broad consent. PPPs are underpinned by contracts, both to govern the use of data and samples necessary for health research, and to govern the agreement between the public and private contracting parties of a project. This raises the question of how far contracts adequately protect public interests, for example in privacy and data protection when patient data are exposed to a broader range of potential uses from the private sector. A core principle of contract law is that you cannot contract for unlawful activity. As such, contracts could be void if their design or performance entails a breach of statute or common law, for example data protection and privacy laws or the common law duty of confidentiality. This paper analyses the implications of this general principle of illegality for contracts underpinning PPPs in health research, particularly to understand the extent to which it could operate to protect the public interest as conceived by privacy and data protection law. The paper will show how this heavily policy-driven doctrine has scope to ensure that contracts and contract terms that are contrary to public policy are void or unenforceable which, in the context of PPPs using personal information for health innovation and research, is a welcome, though limited, accountability mechanism in private law that could operate to serve the public interest.
Law, Law in general. Comparative and uniform law. Jurisprudence
Emily Escamilla, Martin Klein, Talya Cooper
et al.
The definition of scholarly content has expanded to include the data and source code that contribute to a publication. While major archiving efforts to preserve conventional scholarly content, typically in PDFs (e.g., LOCKSS, CLOCKSS, Portico), are underway, no analogous effort has yet emerged to preserve the data and code referenced in those PDFs, particularly the scholarly code hosted online on Git Hosting Platforms (GHPs). Similarly, the Software Heritage Foundation is working to archive public source code, but there is value in archiving the issue threads, pull requests, and wikis that provide important context to the code while maintaining their original URLs. In current implementations, source code and its ephemera are not preserved, which presents a problem for scholarly projects where reproducibility matters. To understand and quantify the scope of this issue, we analyzed the use of GHP URIs in the arXiv and PMC corpora from January 2007 to December 2021. In total, there were 253,590 URIs to GitHub, SourceForge, Bitbucket, and GitLab repositories across the 2.66 million publications in the corpora. We found that GitHub, GitLab, SourceForge, and Bitbucket were collectively linked to 160 times in 2007 and 76,746 times in 2021. In 2021, one out of five publications in the arXiv corpus included a URI to GitHub. The complexity of GHPs like GitHub is not amenable to conventional Web archiving techniques. Therefore, the growing use of GHPs in scholarly publications points to an urgent and growing need for dedicated efforts to archive their holdings in order to preserve research code and its scholarly ephemera.
We consider the price-optimal earliest arrival problem in public transit (POEAP) in which we aim to calculate the Pareto-set of journeys with respect to ticket price and arrival time in a public transportation network. Public transit fare structures are often a combination of various fare strategies such as, e.g., distance-based fares, zone-based fares or flat fares. The rules that determine the actual ticket price are often very complex. Accordingly, fare structures are notoriously difficult to model, as it is in general not sufficient to simply assign costs to arcs in a routing graph. Research into POEAP is scarce and usually either relies on heuristics or only considers restrictive fare models that are too limited to cover the full scope of most real-world applications. We therefore introduce conditional fare networks (CFNs), the first framework for representing a large number of real-world fare structures. We show that by relaxing label domination criteria, CFNs can be used as a building block in label-setting multi-objective shortest path algorithms. By the nature of their extensive modeling capabilities, optimizing over CFNs is NP-hard. However, we demonstrate that adapting the multi-criteria RAPTOR (MCRAP) algorithm for CFNs yields an algorithm capable of solving POEAP to optimality in less than 400 ms on average on a real-world data set. By restricting the size of the Pareto-set, running times are further reduced to below 10 ms.
In recent years, there have been many studies on quantum computing and the construction of quantum computers which are capable of breaking conventional number theory-based public key cryptosystems. Therefore, in the not-too-distant future, we need the public key cryptosystems that withstand against the attacks executed by quantum computers, so-called post-quantum cryptosystems. A public key cryptosystem based on polar codes (PKC-PC) has recently been introduced whose security depends on the difficulty of solving the general decoding problem of polar code. In this paper, we first implement the encryption, key generation and decryption algorithms of PKC-PC on Raspberry Pi3. Then, to evaluate its performance, we have measured several related parameters such as execution time, energy consumption, memory consumption and CPU utilization. All these metrics are investigated for encryption/decryption algorithms of PKC-PC with various parameters of polar codes. In the next step, the investigated parameters are compared to the implemented McEliece public key cryptosystem. Analyses of such results show that the execution time of encryption/decryption as well as the energy and memory consumption of PKC-PC is shorter than the McEliece cryptosystem.
In the judgment in question, the Court of Justice of the European Union (CJEU) for the first time ever carried out such broad interpretation of Article 9 (2) (2) (e) of Directive 2011/95/EU in the context of non-formalized refusal to perform military service by a young Syrian who escaped from his country of origin. The paper analyses the impact of the CJEU judgment on the functioning of the guarantee of the right to conscientious objection to military service within the EU asylum law. It also asks two key questions. First, in the light of the analysed judgment, should any potential Syrian conscript who in reality does not support the government (non-opportunist) and who evades military service be granted protection? Secondly, do all Syrian conscripts who join the army make themselves subject in the future to automatic exclusion from protection?
Law, Political institutions and public administration (General)
Lasse S. Liebst, Peter Ejbye-Ernst, Marijn de Bruin
et al.
Abstract Face masks have been widely employed as a personal protective measure during the COVID-19 pandemic. However, concerns remain that masks create a false sense of security that reduces adherence to other public health measures, including social distancing. This paper tested whether mask-wearing was negatively associated with social distancing compliance. In two studies, we combined video-observational records of public mask-wearing in two Dutch cities with a natural-experimental approach to evaluate the effect of an area-based mask mandate. We found no observational evidence of an association between mask-wearing and social distancing but found a positive link between crowding and social distancing violations. Our natural-experimental analysis showed that an area-based mask mandate did not significantly affect social distancing or crowding levels. Our results alleviate the concern that mask use reduces social distancing compliance or increases crowding levels. On the other hand, crowding reduction may be a viable strategy to mitigate social distancing violations.
INTRODUCTION: Employment examinations and periodic examinations have an important place in the screenings of health workers within the context of Occupational Health and Safety measures, which started to be implemented in accordance with the Occupational Health and Safety Law No. 6331 in the public hospitals that are in the high risk workplace class. In this context, it is important to evaluate the results of Hepatitis B, Hepatitis C, HIV (Human immunodeficiency virus) transmitted via parenteral route and Hepatitis A due to the increased seronegativity in the health screenings of apprentices and trainees. It is aimed to take necessary precautions by performing serological screenings at the time of the employment examinations of apprentices, and trainees, and vaccinate them before they start working. METHODS: In this study, results of screening tests performed for viral hepatitis and HIV in 226 apprentices and trainees before starting their training were evaluated retrospectively. Seronegative Hepatitis B and Hepatitis A cases were included in the vaccination program.
RESULTS: As a result of the screening tests, no HBs Ag, HCV and HIV positivity was encountered. From one student HIV confirmation test was requested and followed up. Anti HBs- and Anti HAV IgG-positivities were found to be 85.14%, and 10.17% of the cases, respectively. DISCUSSION AND CONCLUSION: Viral hepatitis and HIV screening and vaccination in the employment examinations of apprentices and trainees before starting to work in hospitals that were very dangerous workplaces is important in terms of preventing the consequences of work accidents and occupational diseases that may develop within the scope of the Occupational Health and Safety Law No. 6331. It should be ensured that all health screening procedures are performed by an occupational physician, vaccination procedures are applied and protective measures are taken.