Ongoing debates among historians of early modern philosophy are concerned with how to best understand the context of historical works and authors. Current methods usually rely on qualitative assessments made by the historians themselves and do not define constraints that can be used to profile a given context in more quantitative terms. In this paper, we present a computational method that can be used to parse a large corpus of works based on their linguistic features, alongside some preliminary information that can be retrieved from the associated metadata. The goal of the method is to use the available information about the corpus to create broad groups that can work as sub-contexts for better understanding different sorts of works and authors. In turn, this makes it possible to better profile each group and identify its most distinguishing linguistic features. Once these features are clarified, it will eventually become possible to also identify what the most representative works and authors in each group are and which of them may be worth exploring in greater detail. This classification method thus allows historians to integrate their qualitative assessments with quantitative studies in order to better define the relevant context for any given work.

Disasters, both natural and human-made, pose significant challenges to public health systems worldwide. This Research Letter examines the latest strategies and interventions in disaster preparedness and response. Our study highlights key practices that enhance the readiness and resilience of healthcare professionals and communities against disasters. The strategies reviewed include comprehensive emergency planning, simulation exercises, continuous education, interagency coordination, community engagement, and technological advancements. Our findings underscore the importance of multifaceted approaches that significantly improve disaster preparedness and response outcomes. This research provides valuable insights into effective disaster management practices and establishes an important foundation for future studies.

This paper draws attention to the untapped potential of international law (IL) in understanding how security communities develop. It focuses, among others, on ‘transnational legal processes’ – a key overlooked variable – by highlighting what international relations (IR) theory can learn from IL. In so doing, the paper contributes to the literature in three ways. First, it proposes a definition and conceptualisation of regional norms in the study of security communities. Second, by pointing out legal and judicial factors that facilitate or hinder the legal internalisation of regional norms, and consequently affect the development of a security community, it suggests new important research questions that can help broaden the ontology of security communities and bring theoretical heft to the fundamental concept of peaceful change. Third, the paper discusses how and under what conditions regional norms contribute to maintaining reasonable expectations of peaceful change not only at the systemic or state elite level, but equally at the domestic societal level.

Indoor positioning systems (IPS) are essential for mobile robot navigation in environments where global positioning systems (GPS) are unavailable, such as hospitals, warehouses, and intelligent infrastructure. While current surveys may limit themselves to specific technologies or fail to provide practical application-specific details, this review summarizes IPS developments directed specifically towards mobile robotics. It examines and compares a breadth of approaches that vary across non-radio frequency, radio frequency, and hybrid sensor fusion systems, through the lens of performance metrics that include accuracy, delay, scalability, and cost. Distinctively, this work explores emerging innovations, including synthetic aperture radar (SAR), federated learning, and privacy-aware AI, which are reshaping the IPS landscape. The motivation stems from the’ increasing complexity and dynamic nature of indoor environments, where high-precision, real-time localization is essential for safety and efficiency. This literature review provides a new conceptual, cross-border pathway for research and implementation of IPS in mobile robotics, addressing both technical and application-related challenges in sectors related to healthcare, industry, and smart cities. The findings from the literature review allow early career researchers, industry knowledge workers, and stakeholders to provide secure societal, human, and economic integration of IPS with AI and IoT in safe expansions and scale-ups.

The COVID-19 pandemic caused widespread disruption to early childhood education and care services worldwide, affecting children’s well-being and placing unprecedented caregiving burdens on families. This paper compares the childcare-related social policy responses in three countries representing distinct welfare regimes: South Korea (Productivist/East Asian), France (Conservative-Corporatist), and the UK (Liberal). Focusing on four key domains – ECEC services, family leave, work environment, and financial support – it examines how each country addressed childcare challenges during the pandemic. The findings show that, while some similarities emerged in responding to shared challenges, the policy responses diverged considerably. These differences were shaped not only by pandemic-specific health strategies but also by pre-existing welfare structures and childcare systems. France utilised its strong public infrastructure and introduced special childcare leave; Korea expanded temporary family leave and financial aid while relying heavily on informal care; and the UK prioritised employment protection with limited direct caregiving support. The study underscores the importance of institutional flexibility and multi-layered care systems in building crisis-resilient childcare policies.

The production and circulation of common wares during the late antique period in North Africa has been largely overlooked by past scholarship, despite their potential to shed light on late antique production, workshop organisation and regional ceramic economies. This paper provides the first detailed study of a distinctive type of late antique, wheelmade common ware, the so-called African ‘painted ware’ (APW). It first presents a critical overview of the distribution of painted wares and their typology, decoration and chronology based on existing publications. It then develops a typology of vessel shapes, but also decoration patterns based on a large, well-preserved assemblage of painted ceramics recently excavated by the DAI, INP and UCL at the archaeological sites of Bulla Regia and Chimtou in the Medjerda valley, Tunisia. To understand the composition, technology and provenance of the wares, petrographic and chemical analysis was conducted on 57 painted sherds from the two sites. The results suggest the existence of a production centre in the Medjerda Valley, with potters using local calcareous clay tempered with sand, while the decoration was obtained using iron-based pigments. Comparison with published painted wares at other sites contributes to an initial insight into regional distribution patterns of the painted ware.

The objectives of the study were to determine somatic cell count (SCC) and evaluate the presence of pathogens (IMI – intramammary infection) in late lactation (LL), followed by the start (colostrum, CL) and approximate peak (established lactation, EL) of the next lactation, as well as to assess the possible transmission of IMI from lactation to lactation. The study was performed on a dairy farm in northern Slovakia. A total of 489 half udder milk samples (242, 80 and 167 in LL, CL and EL, respectively) were collected. Pathogens were identified using MALDI-TOF MS and PathoProof (the latter only in LL). SCC was determined only in LL and EL. Samples were divided according to SCC in four groups from lowest (SCC1 < 500 × 103 cells mL−1) to highest (SCC4 ≥ 2000 × 103 cells mL−1). SCC was higher in LL than in EL. The prevalence of pathogens identified using MALDI-TOF MS was 16.5, 38.8 and 12.6% in LL, CL and EL, respectively. Non-aureus staphylococci and mammaliicocci (NASM) were the most common isolated pathogens in goat milk and colostrum. Staphylococcus (S.) caprae and S. epidermidis species tended to cause persistent IMI in the next lactation. The identification of pathogens using PathoProof was higher than with MALDI-TOF MS. Of all the pathogens (n = 262) identified using PathoProof, the most common were Staphylococcus spp. (86.7%) of which 65.8% exhibited the β-lactamase gene. Additionally, Escherichia coli (4.2%), S. aureus (2.7%), Enterococcus spp. (2.3%), Streptococcus uberis (1.9%), Mycoplasma spp., Protetheca spp. (0.8% each), Arconabacterium pyogenes/Peptoniphilus indolicus and yeast (0.4% each) were also detected using PathoProof. Better identification of pathogen presence in samples with high SCC could contribute to the discussion about SCC as an indicator of subclinical mastitis in goats.

Water hyacinth is an invasive aquatic plant that has been associated with major negative economic and ecological impacts in water systems worldwide, including Rwanda, since its establishment in the country in the 1960s. While biological control is considered the most sustainable management method, the success of biocontrol agents depends on various abiotic factors, with temperature being critical. This study assessed the suitability of potential water hyacinth biocontrol agents such as: Neochetina weevils, Megamelus scutellaris Berg (Hemiptera: Delphacidae), and Cornops aquaticum Bruner (Orthoptera: Acrididae) for regions with a temperate climate by testing their thermal boundaries. Using thermal physiology limits and CLIMEX modelling, we found that Neochetina eichhorniae Warner and N. bruchi Hustache (Coleoptera: Curculionidae) had lower thermal minimums (CTmin) of 2.4°C and 2.6°C, respectively, compared to Megamelus scutellaris (4.7°C) and Cornops aquaticum (6.2°C). CLIMEX modelling predicted the suitability of Neochetina weevils and C. aquaticum across Rwanda, while M. scutellaris appeared unsuitable for the colder northern regions of the country but appropriate for the central and eastern regions. These findings suggests that the historical failure of Neochetina weevils introduced to Rwandan water bodies in 2000 was not due to temperature extremes. Rather, other factors such as release numbers or water quality may have played a role. This study provides crucial information for future biocontrol efforts in Rwanda and similar temperate regions, highlighting the importance of pre-release thermal tolerance assessments and climate modelling to predict biocontrol agent establishment and efficacy.

I discuss and clarify the relationship between the recent wave of “intrinsic” coordinate-free approaches to Maxwell gravitation and the coordinate-based discussions of Saunders (2013) and Wallace (2020).

This study presents an innovative framework to improve the accessibility and usability of collaborative robot programming. Building on previous research that evaluated the feasibility of using a domain-specific language based on behaviour-driven development, this paper addresses the limitations of earlier work by integrating additional features like a drag-and-drop Blockly web interface. The system enables end users to define and execute robot actions with minimal technical knowledge, making it more adaptable and intuitive. Additionally, a gesture-recognition module facilitates multimodal interaction, allowing users to control robots through natural gestures. The system was evaluated through a user study involving participants with varying levels of professional experience and little to no programming background. Results indicate significant improvements in user satisfaction, with the system usability scale overall score increasing from 7.50 to 8.67 out of a maximum of 10 and integration ratings rising from 4.42 to 4.58 out of 5. Participants completed tasks using a manageable number of blocks (5 to 8) and reported low frustration levels (mean: 8.75 out of 100) alongside moderate mental demand (mean: 38.33 out of 100). These findings demonstrate the tool’s effectiveness in reducing cognitive load, enhancing user engagement and supporting intuitive, efficient programming of collaborative robots for industrial applications.

Traditional regression models typically estimate parameters for a factor F by designating one level as a reference (intercept) and calculating slopes for other levels of F. While this approach often aligns with our research question(s), it limits direct comparisons between all pairs of levels within F and requires additional procedures for generating these comparisons. Moreover, Frequentist methods often rely on corrections (e.g., Bonferroni or Tukey), which can reduce statistical power and inflate uncertainty by mechanically widening confidence intervals. This paper demonstrates how Bayesian hierarchical models provide a robust framework for parameter estimation in the context of multiple comparisons. By leveraging entire posterior distributions, these models produce estimates for all pairwise comparisons without requiring post hoc adjustments. The hierarchical structure, combined with the use of priors, naturally incorporates shrinkage, pulling extreme estimates toward the overall mean. This regularization improves the stability and reliability of estimates, particularly in the presence of sparse or noisy data, and leads to more conservative comparisons. Bayesian models also offer a flexible framework for addressing heteroscedasticity by directly modeling variance structures and incorporating them into the posterior distribution. The result is a coherent approach to exploring differences between levels of F, where parameter estimates reflect the full uncertainty of the data.