Friends United

Biological invasions drive biodiversity loss and result in significant economic costs. The pond slider (Trachemys scripta) is classified as an invasive alien species (IAS) in the European Union. Non-native terrapins have become widespread in Germany, and genetic evidence confirms the reproduction of three species and their establishment in the wild (Tietz et al., 2023). However, the ecological impact of these terrapins on freshwater ecosystems remains poorly quantified, leaving authorities without a robust basis for prioritising management. The AquaSchild project brings together scientists, authorities, and society to compile ecological and social evidence on non-native terrapins in water bodies of varying ecological value, assess the risks associated with different habitats, and co-develop prevention-first management strategies. In Baden-Württemberg, we are implementing an evidence sprint that combines standardised terrapin surveys in two contrasting water bodies with DNA metabarcoding of diets, parasites, and pathogens. This will enable us to characterise trophic and pathogen-mediated impacts between two water bodies with differing conservation values, and develop a habitat-risk ranking that highlights habitat types where the establishment of invasive terrapins is of particular concern. In parallel, a survey of stakeholders, including official authorities, planning offices, NGOs, the pet trade and citizen science coordinators, will assess their knowledge and perceived feasibility of management measures, and reveal implementation barriers. Subsequently, we will synthesise the ecological and social findings during co-design workshops. This will result in a practical management decision tree and associated standard operating procedures for the removal and handling of non-native terrapins. Targeted communication tools will be produced for the relevant authorities, the pet trade, and citizen scientists, together with a policy brief on management needs in Germany that will inform the Standing IAS Committee and embed these tools in national governance. Collectively, these outputs will provide a blueprint for the prevention-focused management of non-native terrapins, before they become invasive, which could be extended to other vertebrate species under the EU IAS Regulation. This will contribute to the implementation of the Kunming-Montreal Global Biodiversity Framework (GBF) and other biodiversity strategies.

Amidst global policy reforms including the 2024 amendments to the International Health Regulations (IHR) and the ongoing Pandemic Agreement negotiations, there is a renewed emphasis on sustainability, equity, and multisectoral coordination in global health security. However, the operationalization of sustainability in core assessment tools-such as the WHO Joint External Evaluation (JEE) and the WOAH Performance of Veterinary Services (PVS)-remains poorly defined and underdeveloped, particularly as they relate to low- and middle-income countries (LMICs). We conducted a qualitative study involving semi-structured interviews with 29 global experts across human, animal and environment health from both high and low/middle income countries affiliated with the One Health High-Level Expert Panel, the World Bank Pandemic Fund Technical Advisory Panel, and technical focal points from Quadripartite institutions. Using thematic analysis grounded in the Social Construction Framework and an adapted Schell et al. sustainability model, we explored how sustainability is conceptualized and measured across human, animal, and environmental health domains. Participants critiqued existing tools for framing sustainability narrowly, including their focus on short-term processes and compliance, rather than long-term outcomes, equity, or resilience. Sectoral and contextual differences emerged: human health experts emphasized workforce and financing; animal health experts stressed economic and institutional continuity; environmental health experts highlighted ecosystem resilience and intergenerational equity. LMIC respondents underscored the impact of donor dependency, weak local ownership, and limited transition planning. Several determinants of sustainability-such as financing, governance, workforce retention, and community engagement-are already measured in existing reporting tools but are not explicitly or coherently framed as sustainability indicators. We recommend that future iterations of JEE and PVS incorporate a clear definition of sustainability and explicitly integrate sustainability metrics, aligned with the revised IHR, the One Health Joint Plan of Action, relevant SDG targets and national planning cycles. Tools must also reflect sectoral and contextual nuances and integrate long-term monitoring frameworks that promote domestic accountability. In the field of One Health and GHS, strengthening the sustainability components of these tools is essential to build equitable and resilient health systems globally.

ABSTRACT Aims: To examine the effects of Wii-based exercise program on individuals with ID. Methods: The study consisted of a case reports design involving six individuals with ID who attended a special needs school. The Wii-based exercise program for participants consisted of twenty sessions. The Motor Development Scale (MDS), the Beery-Buktenica Developmental Visual-Motor Integration (Beery VMI) test, and their additional tests (i.e., fine motor coordination and visual perception) were administered before and after the Wii-based exercise program; the researcher manually recorded games scores practiced in the program for further analysis. Results: The Wii games' results showed that participants significantly improved their performances during twenty sessions. Regarding the results of the applied tests, supplementary test results for motor coordination of the Beery VMI showed that the participants' performances improved significantly before and after the Wii-based exercise program. However, there were no significant changes in the results of the Beery VMI visual-motor integration, visual perception, and EDM before and after the Wii-based exercise program. Conclusions: Active videogame is a method of physical exercise which the people with ID can practice and develop motor skills (performance and acceleration) and maintain physical exercise routines, in order to promote health to this population. Keywords: Intellectual disability. Exercise. Development. Motor skills.

Purpose Cross-border e-commerce has rapidly grown, with many companies adopting omni-channel strategies to unify online and offline shopping experiences. However, in multi-echelon distribution networks, firms face high inventory pressure due to cost differences between domestic and overseas warehouses, logistics uncertainty and demand fluctuations. Unpredictable replenishment cycles often lead to overstock or stockout. Thus, optimizing inventory allocation to adapt to dynamic demand while balancing cost and uncertainty is a critical challenge for cross-border e-commerce companies. Design/methodology/approach This study presents a cross-border multi-echelon inventory optimization model to address challenges in cross-border e-commerce supply chains. The model is solved using the Adaptive Genetic Basin Hopping (GABH) algorithm, which accounts for uncertainties in logistics, dynamic demand fluctuations and cost differences across distribution nodes. This approach efficiently balances inventory levels, demand and costs, providing an effective inventory management strategy. Findings The comparison with the SAGA algorithm shows that, under the same parameters, the GABH algorithm uses less computation time and achieves a lower optimal cost, demonstrating higher computational efficiency. Sensitivity analysis reveals that demand fluctuations, replenishment cycles and inventory configuration significantly impact costs. Increasing safety stock reduces stockout risks and replenishment costs, although it raises holding costs, still ultimately lowers overall costs. Originality/value The study shows that the GABH algorithm improves inventory optimization by reducing costs and boosting computational efficiency. It identifies how demand volatility, replenishment frequency and safety stock affect outcomes, offering practical guidance for responsive, cost-effective strategies in complex cross-border supply chains and supporting strategic inventory planning under uncertainty.

Abstract Creative aging programs are essential for promoting cognitive health, social engagement, and emotional well-being among older adults. They provide opportunities for self-expression, help to combat isolation, and enhance memory. By fostering creativity through music, and storytelling, these programs empower older adults to stay active, connected and fulfilled. This presentation shares results from a mixed methods evaluation of MusicWorks, a program that uses music and music-related activities to create opportunities for residents of low-income buildings to socialize and interact with neighbors, thereby reducing isolation and loneliness. Through the integration of music-related activities (e.g. singing, dancing, music bingo), the program also seeks to promote cognitive stimulation and physical health. Data was collected via 9 focus groups (4 in Spanish) at 7 program sites around Boston (n = 92). Staff from 6 sites were also interviewed. Attendance records from 6 of the sites revealed that for most sites, there were first-time participants in attendance as well as those who attend regularly. There was some variation in terms of which sites had more frequent participation compared to others and this trend seemed to align with the frequency of programming (e.g., sites with monthly programming had less regular attendance compared to those with weekly programming). Results from the qualitative analysis suggest that music programming offers an outlet for connection as well as positive physical and cognitive outcomes. Deep social connections and endearment to the program comes with repeat participation, suggesting that depth is achieved by creating group dynamics. Facilitators and barriers to these outcomes will be discussed.

Abstract Accidental falls threaten the health and well-being of older adults, especially those with mild cognitive impairment (MCI) due to cognition’s important role in preserving functional gait and postural stability. The STEADI (Stopping Elderly Accidents, Deaths & Injuries) Initiative by the CDC reduces fall risk and rates but traditionally requires extensive, ongoing implementation resources for impact. This study tested the feasibility of using an already-scaled conversational technology platform with tablet-based agent (pet companion avatar) in 21 cognitively-normal (CN) older adults at low fall risk (CN–, n = 6), CN older adults at fall risk (CN+, n = 8), and older adults with MCI (MCI+, n = 7). Fall risk and cognitive status were confirmed with standardized measures. Participants were majority female (76%), 75 ± 10 years old (range 54-95), and racially diverse (White, 62%; Black/African American, 24%; Asian 9%; multi-racial, 5%). All evaluated avatar-led assessments of gait, balance, and leg strength plus an exercise safety check. Users appreciated the avatar’s friendliness, natural conversational tone, nonthreatening design, patience, and engaging nature. Instructions were seen as clear, concise, and easy to follow, corroborating a 9.4 ± 0.8 clarity rating out of 10. System Usability (SU) was good overall (76 ± 13) with areas of improvement noted across groups. Design recommendations included faster response times, clarifying task transitions and starts, and customizing avatar settings such as voice and volume to the individual. The agent and platform are a promising tool to implement STEADI and prevent falls. Future studies will incorporate the Otago Exercise Program.

Cybercrime, characterized by anonymity and decentralized operations, presents major challenges in identifying suspects, uncovering hidden accomplice ties, and dismantling organizational structures. Traditional investigative methods often fail when explicit connections are absent, and early analytical approaches based on simple similarity metrics lead to significant information loss. To overcome these limitations, we propose a novel unsupervised learning framework centered on Graph Neural Networks (GNNs). Our approach begins by constructing a 3-layer heterogeneous graph that models entities as distinct node types: persons, cases, and identifiers (e.g., bank accounts, IP addresses), preserving the rich context of investigative data. The GNN then directly learns deep relational patterns from the graph's structure. This enables: (1) latent link prediction to uncover hidden relationships, (2) community detection on learned embeddings to identify criminal organizations, and (3) a comprehensive risk assessment that integrates network centrality with GNN-derived node importance scores. By effectively modeling the intrinsic complexity of the data, our framework provides investigators with objective, quantitative evidence to prioritize targets and enhance strategic decision-making.

Introduction & BackgroundThe study of loneliness and social networks has largely involved social media analysis for both social circle insights and loneliness predictions from language. While geospatial data have also been used to investigate loneliness, the approach has predominantly involved a traditional investigation of geolocations per se. Few studies have focused on the factors contributing to the success of human relationships and the associated loneliness reduction – which was the objective of this project. Objectives & ApproachThe data were obtained from B:friend – a wellbeing charity dedicated to tackling loneliness through befriending schemes for Older Adults – and joined with the Index of Multiple Deprivation (2019) for feature engineering. The objective was to identify features which contributed to a successful friendship. The dataset included neodemographic information: geospatial and demographic data for both sides of each pair (the Older Adult (Older Neighbour) and the paired Volunteer); as well as preference features for a potential pairing. Machine learning classification models (logistic regression, decision trees, random forest, support vector machines and xgboost) were used to determine which features contributed to successful short- and long-term befriending schemes. A total of 805 pairings were analysed. Relevance to Digital FootprintsThe project used geospatial information paired with demographic records collected by B:friend digitally to identify the features which contributed to a successful friendship. ResultsThe preliminary results offer insight into the features which contribute to short-and long-term friendships, with variables such as house suitability, third COVID-19 lockdown and income decile difference driving the predictions. On average, a third of the friendships appear to succeed. Additionally, we use a series of variable importance measures to rank order variables which contribute to successful befriending of the pairings. Conclusions & ImplicationsCombining demographic and geospatial information for machine learning helps identify important factors which contribute to short- and long-term relationships, and, by extension, to tackling loneliness. This novel approach can benefit the public, as well as professionals, by offering tailored suggestions for loneliness interventions, utilising factors contributing to the specific requirements of various individuals, as well as their preferences, thus further expanding on the pre-established findings such as homophily.