The project proposes to establish a research agenda on urban climate adaptation taking first stock of knowledge and practices in Geneva and Cairo around the concept of Agile Urban Climate Spaces—public spaces and infrastructures designed or retrofitted to adapt to extreme climate events, particularly heatwaves, while fostering long-term urban resilience. These spaces integrate green (vegetation), blue (water), and grey (infrastructure) systems into flexible urban units that provide cooling, optimize water use, strengthen social inclusion, and enhance community health. They can be rapidly reconfigured and scaled, bridging formal and informal urban areas.

Geneva and Cairo, despite their contrasting contexts, both face growing climate challenges. Geneva offers experience in resource-intensive coordination and system-based transformations, while Cairo contributes inclusive, community-driven solutions developed under resource constraints. The project leverages these complementarities to generate adaptable, transferable strategies.

Activities include two flagship workshops: Geneva (Feb 2026), focusing on Swiss experiences and global policy frameworks, and Cairo (March 2026), emphasizing informal settlements and community practices. Both workshops will involve site visits, case studies, and peer learning across academia, municipalities, and NGOs. Outcomes will be documented through recorded talks and interviews, leading to a free bilingual e-learning course (“Fundamentals of Extreme Heat Adaptation in Neighborhoods”).

Key deliverables include:

• A 5,000-word bilingual policy brief synthesizing findings, to be presented at the World Urban Forum 13 and disseminated through networks such as ICLEI and UCCRN as a basis to a urban climate research consortium.

• A side event at the 2026 Forum of Mayors in Geneva, scaling results of data to a wider community of urban leaders.

• A permanent e-learning course hosted on the cityresiliencetraining.com platform.

• A follow-up UHPH regional research workshop (2026) to frame strategies for wider research and application in MENA cities.

Long-term, the partners will establish a Geneva–Cairo–GCC/MENA Urban Resilience Hub, foster researcher and student mobility and co-supervised theses, and organize regional dialogues in GCC and North Africa. By linking academic excellence with practical know-how and policy engagement, the initiative aims to create an enduring ecosystem that advances inclusive, resilient, and sustainable cities.

Lebanon’s agricultural sector is trapped in a perfect storm: climate volatility, economic collapse, and institutional fragility. Smallholder farmers, vital to national food security, face rising temperatures, declining rainfall, and soaring input costs. Although climate-smart agriculture (CSA) practices such as drip irrigation and drought-tolerant crops are proven, adoption remains critically low. This project addresses a vital gap: how farmers in fragile settings make decisions under stress. While CSA in Lebanon has been widely studied through technical and economic lenses, behavioral factors have been largely overlooked. This initiative is the first in Lebanon to systematically examine how six behavioral constructs (present bias, loss aversion, optimism bias, social norms, bounded rationality, and mental accounting) interact with structural barriers to shape farmer decisions on CSA practices.

Led by the School of Agricultural, Forest, and Food Sciences at Bern University of Applied Sciences (BFH-HAFL) and the Environment and Sustainable Development Unit at the American University of Beirut (ESDU-AUB), the project uses a streamlined mixed-methods approach. Activities include surveys, behavioral experiments, participatory mapping, and stakeholder interviews in the climate-vulnerable regions of Bekaa and Akkar. Rather than just diagnosing adoption gaps, the project builds a translational pathway: from behavioral insight ⇒ to experimental design ⇒ to field-tested interventions ⇒ to practical tools for policy and practice.

This initiative brings behavioral science, a high-leverage and low-cost lens, into CSA programming in fragile contexts. It fills a regional evidence gap and offers a replicable model for behavioral agriculture research across the MENA region. Outputs include a behaviorally informed CSA adoption toolkit (Arabic and English), a policy brief, behavioral risk maps, a peer-reviewed article, and a Swiss–Lebanese co-design workshop. Findings will support NGOs, extension agents, and decision-makers working in climate-stressed farming systems. The project seeds future impact through follow-on research, training, and funding aimed at scaling in Lebanon and the region. It strengthens the BFH–AUB collaboration as a platform for equitable, interdisciplinary, and applied research. Aligned with SDGs 2, 13, and 17, the FAO CSA Framework, and IPCC recommendations, this 12-month initiative delivers science with actionable outcomes for resilient agriculture.

Healthcare professionals in Gaza deliver care under conditions of chronic scarcity, infrastructural fragility, and recurrent political and military crises—yet continue to practice despite pervasive clinical uncertainty. In such a setting, clinical decisions are made without complete diagnostic information, standard treatment options, or reliable prognostic indicators. While existing research has examined epidemiological and operational challenges in Gaza’s health system, little is known about how clinicians live with and respond to uncertainty.

This mixed-methods study investigates the prevalence, sources, and impacts of clinical uncertainty among healthcare providers in Gaza, and explores how they experience, interpret, and navigate it in daily practice, with particular attention to ethical challenges, adaptive strategies, decision-making, and resilience. The study employs a sequential design: a quantitative survey to assess patterns of uncertainty and perceived effects on patient care and provider well-being, with data analyzed through descriptive and inferential statistics; followed by qualitative interviews to generate narratives of lived experience, analyzed using a general inductive approach.

The study focuses on emergency medicine, surgery, internal medicine, and primary care, to reflect different dimensions of uncertainty: high-stakes decision-making (emergency medicine, surgery), management of chronic and complex conditions (internal medicine), and the uncertainties of first contact and continuity of care (primary care).

Participants include physicians, nurses, and allied health professionals directly involved in patient care. Eligibility requires at least two years of experience and consent. Using stratified sampling, 350–400 clinicians will be recruited for the survey, while 20–25 will be purposively selected for interviews to ensure diversity by specialty, gender, geography, and experience. Data collection combines digital and paper surveys, and in-depth interviews conducted in Arabic and translated into English for collaborative analysis.

Findings will map sources and impacts of clinical uncertainty, document clinicians’ strategies and resilience, and inform recommendations for clinical education, health policy, and humanitarian programming. By centering the lived realities of Gaza’s healthcare providers, the project will generate evidence with global relevance for strengthening decision-making and resilience in crisis-affected health systems.

While digital tools offer flexibility, they also place heavy demands on students’ attention and self-regulation, sometimes resulting in burnout or disengagement. Relatedly, hybrid (mix, remote and on-site) learning has introduced new levels of technostress, cognitive fatigue, and reduced student engagement in higher education. Recent evidence suggests that students’ attachment to hybrid modalities creates a digital strain which directly mediates and often dampens academic engagement, even when grades remain unaffected. Moreover, insufficient cognitive presence - the ability to meaningfully reflect and engage online - correlates with poorer motivation and learning outcomes (Daud, 2025). Despite these advancements in contemporary research, little is known on how cognitive and psychological stressors affect students in hybrid learning environments, nor how these experiences vary across national contexts (in our case Switzerland, Morocco, and Tunisia). In fact, existing studies focus mostly on fully online or professional settings, not sufficiently showing how technology can be both an enabler and a stressor. Consequently, this project addresses two questions: 1. What are the key cognitive and psychological stressors faced by students in hybrid classrooms? 2. How do institutional and cultural dynamics influence students’ engagement and self-regulatory strategies?

We will first review and select theoretical models and measures of student cognitive well-being. Then, conduct focus groups and interviews to elicit lived experiences of students. Finally, a cross-cultural framework of cognitive wellbeing of students in hybrid learning settings will be developed via qualitative and quantitative research methods. We expect our findings to support the development of evidence-based guidelines aimed at student support services and university governance in Switzerland, Morocco, and Tunisia. In these countries, digitalization and online teaching are increasingly reshaping students’ learning environments, thus prompting a rethinking of support (mental health) needs.

This project tackles one of the most pressing yet underexplored challenges in the age of generative AI (GenAI): how to cultivate students’ internal moral responsibility when using tools such as ChatGPT. Current debates around AI in education largely emphasize external controls—policies, detection systems, and assessment redesigns. Our approach is fundamentally different: we focus on students’ personal ethical decision-making, aiming to foster academic integrity and workplace readiness in an AI-driven future.

We begin by investigating why students use GenAI, exploring motivations ranging from curiosity and learning support to efficiency, shortcuts, and potential misconduct. Understanding this spectrum will help us capture not only the benefits but also the risks of AI use when moral responsibility is not explicitly developed.

A distinctive feature of the project is its co-design methodology. Students will be partners, not just subjects, collaborating to build an Ethical Reflection Custom GPT via the OpenAI developer platform. This tool will guide users through prompts and scenarios that encourage self-reflection on fairness, responsibility, and impact before they act on AI-generated content. By embedding reflection into AI use, the tool aims to shift students’ behavior from compliance with external rules to personal accountability.

The project combines research, behavioral innovation, and educational practice. It will deliver: (1) evidence on student motivations and ethical reasoning; (2) a user-friendly Custom GPT prototype; (3) policy and pedagogical recommendations; and (4) capacity-building resources for educators and students.

A UAE–Switzerland partnership underpins this initiative, bringing together the UAE’s rapid AI adoption and ethics-driven national strategy with Switzerland’s strong educational traditions and world-class expertise in pedagogy and ethics. This collaboration ensures both international relevance and local grounding.

Expected outcomes include an open-access ethical prompt library, increased AI literacy and responsibility among students, cross-national policy recommendations, and a hybrid dissemination symposium in the UAE. Ultimately, the project will equip the next generation not just with AI skills, but with the moral compass to use them wisely.

Political determinants of health - such as power dynamics, governance, legislation and policymaking - shape access to healthcare, education, housing and environmental conditions and significantly impact health equity and outcomes (1). Despite their importance, the political determinants of health remain underexplored in public health practice and higher education, limiting the capacity of professionals to address systemic health challenges (2). In Switzerland (CH), our recent project (Cap4HP, 2024-2025) on capacity building in public health and health promotion brought together academics and decision-makers to establish a shared and systematic understanding of capacity building in health promotion in CH (3). The results show a heterogeneous and diversified educational context and reveal an urgent need for coherent national policies concerning capacity building in this field and highlight the pressing need for ongoing research on political determinants of health.

Building on this foundation, a new international collaboration is being established with Dr Marguerite Sendall, a global expert on political determinants of health. This collaborative project includes the Institute of Public Health at ZHAW Zurich University of Applied Sciences and the College of Health Sciences at Abu Dhabi University and aims to develop a competitive research proposal exploring how public health professionals and academics in different countries understand political determinants of health.

This collaborative project will include (1) a kickoff meeting for the project team, (2) sharing and discussing case studies from CH, UAE and Australia using a conceptual framework for political determinants of health, (3) a research visit in CH and an intercultural workshop with academics and practitioners from all three countries, and (4) proposal development and submission.

Through this collaborative project, a research proposal will be developed on political determinants of health as a central domain in public health. This research project will generate evidence-based recommendations for practice, higher education, and policy in public health. It will contribute to scholarly discourse, inform curriculum development, and support advocacy for health equity. Ultimately, this research will establish a foundation for a sustainable research agenda and position CH and the UAE as leaders in advancing the research agenda about the political determinants of health.

This project addresses a pressing challenge in global health and AI: how to adapt state-of-the-art (SOTA) models, originally trained on Western datasets, for equitable and trustworthy colorectal cancer detection in low-resource settings. Colorectal cancer is a leading cause of death worldwide, but early detection via colonoscopy can dramatically improve outcomes. AI tools have shown strong potential to support clinicians during procedures; however, their performance and usability in diverse clinical contexts remain largely untested. This project fills that gap.

We pursue two main objectives. First, we assess whether existing SOTA AI models can accurately detect and classify polyp malignancy during colonoscopies in the Egyptian healthcare system. Second, we evaluate the fairness and generalizability of these models by testing and fine-tuning them using newly collected and annotated clinical data from Egyptian hospitals. The goal is to adapt these tools to local clinical realities while preserving diagnostic quality and reducing potential bias.

Beyond accuracy, we investigate how Egyptian endoscopists interact with AI in real clinical settings: How do they interpret recommendations? When do they trust or override AI outputs? What shapes usability and trust? To explore these questions, we conduct a mixed-methods study using surveys, interviews, and in-procedure observations. These insights will inform best practices for integrating AI in medical workflows in resource-constrained contexts.

The project design prioritizes feasibility and impact. The Egyptian PI will lead clinical data collection and annotation across gastroenterology clinics, contributing substantial in-kind access and expertise. The Swiss team will lead the human–AI collaboration study, drawing on strengths in behavioral and organizational research. We also collaborate with Prof. Binod Bhattarai (University of Aberdeen, UK) to benchmark model performance and support domain-adaptation through subcontracting.

Expected outcomes include: (1) a novel, validated colonoscopy image dataset from Egypt; (2) fine-tuned, context-adapted AI models; and (3) new evidence on human–AI interaction in low-resource healthcare. Together, these contributions promote inclusive, explainable, and clinically useful AI. The project builds on strong ties within the Global Gastrointestinal AI Network and is well-positioned for long-term collaboration and global impact.

Cities in hot climates face a growing energy challenge: how to meet rising demand for electricity, especially for cooling, while transitioning to low-carbon systems. Hydrogen is increasingly viewed as a flexible solution, with the potential to store excess solar power and provide energy when it is most needed. While Morocco does not experience strong seasonal variation, its high solar potential and daily fluctuations in PV production versus energy demand create a need for medium-duration energy storage. Hydrogen offers an attractive complement to batteries, providing higher-density storage and the ability to serve multiple sectors including power, thermal loads, and potentially mobility. But its role in urban environments remains uncertain. Can hydrogen outperform other options like batteries or thermal storage? Under what conditions does it add value? And how can it be operated efficiently in real time?

This Swiss–Moroccan research collaboration addresses these questions by investigating the integration of hydrogen into urban energy systems in Morocco. The project is jointly led by Empa’s Urban Energy Systems Laboratory and Green Energy Park (GEP), and combines advanced modeling tools with real-world data from Moroccan testbeds.

Using a novel bi-level approach, the project links system design with real-time operation. A techno-economic model explores how hydrogen can be sized and integrated into local energy networks, while a predictive control layer simulates how the system would perform under dynamic conditions such as solar variability and cooling-driven peaks. The modeling includes the full hydrogen supply chain, from production via electrolysis to storage and end use.

The work is grounded in case studies from different Moroccan cities, selected for their climatic and urban diversity. These ensure that the insights are transferable to a range of real-world settings across the MENA region.

In addition to generating new knowledge, the project strengthens scientific cooperation between Switzerland and Morocco. Its outcomes will inform future research, support practical energy planning, and contribute to long-term strategies for decarbonizing cities in hot climates.

Intellectual disability (ID) is a neurodevelopmental disorder, defined by the DSM-V as significant limitations in intellectual, adaptive, and social functioning before adulthood [1]. Affecting 1_3% of the global population and ~1% in Switzerland, ID represents a major burden for families and health systems, particularly in regions with high rates of consanguinity, like Tunisia, where autosomal recessive ID (ARID) forms are prevalent yet underdiagnosed [2,3].

Switzerland excels in genomic technologies but lacks access to genetically informative, inbred populations critical for identifying novel ARID genes, limiting global diagnostic equity. Tunisia, with 38% consanguineous marriages and a high prevalence of familial ID, offers a unique opportunity to uncover new genetic causes of syndromic ID.

This 12-month Swiss-Tunisian project directly addresses this gap by leveraging Switzerland’s advanced genomic infrastructure and expertise at the Center for Integrative Genomics (CIG) and Tunisia’s unique cohort at the National Institute Mongi Ben Hamida of Neurology (INN). By studying a large cohort of 50 multiplex Tunisian families using next-generation sequencing (WES/WGS), AI-assisted variant prioritization and deep phenotyping, we aim to identify 3-5 novel ARID genes and establish genotype–phenotype correlations. Advanced in silico functional annotation methods, including predictive modeling of protein dysfunction and molecular docking simulations, will assess the biological significance of candidate genes. Ethical considerations, including informed consent and GDPR-compliant data protection, are embedded.

The project’s impact is multidimensional, enabling novel gene discoveries, enhanced diagnostics, and improved patient management and genetic counselling. It also supports cross-border skills transfer and institutional capacity building establishing a sustainable Swiss–Tunisian neurogenetics partnership. In addressing both unmet clinical needs and scientific frontiers, this project aligns with the LHMENA initiave’s goal of fostering equitable scientific partnerships.

How something is said often matters as much as what is said. General-purpose AI systems such as ChatGPT, also called large language models (LLMs), don’t just produce facts; they adopt styles, and those styles can steer readers. Research shows that even basic arithmetic LLMs rely on multiple heuristics rather than a single tidy procedure or algorithm [2]. In high-stakes areas like healthcare, finance, history, and politics, those heuristics can nudge outputs toward oversimplification, one-sided framing, or information overload, all styles that shape perception and can mislead [3–5]. Because this is about form rather than truth, the usual fact-checking tools often miss it.

We ask a simple question with big consequences: Which heuristics drive these deceptive styles, and where do they live inside a model? Our project aims at mapping those heuristics across languages (EN/AR) and trace how they are implemented at the circuit level [2,7]. We will adapt recent causal-interpretability techniques from arithmetic to natural language and design an objective panel of style metrics and a composite Deceptive Style Index (DSI) for cross-lingual comparison.

We also focus attention on the security implications. Adversaries can use prompt design to induce manipulative styles, turning phishing, one-sided propaganda, or high-volume “info dumps” into more persuasive outputs that remain hard to catch. We believe the risk is sharper in underrepresented languages (e.g., Arabic) where data imbalance and morphological complexity can push models toward shallower or less stable heuristics. By tracing style circuits, we make these risks visible and measurable, laying a grounded basis for future defenses.

Concretely, we will: (i) build a controlled, multilingual dataset with and without style injections on topics relevant to misinformation [5,6]; (ii) discover and causally validate the minimal circuits that control each style by domain and language [2]; (iii) run focused crowd studies to elicit visible cues and potential heuristics for each circuit component; and (iv) test light-touch attenuation to see whether reducing activity in specific circuit components lowers the style without harming unrelated capabilities. 

All datasets, metrics, code, and documentation will be released openly to support reproducibility, responsible research, and a durable Swiss-UAE collaboration centered on multilingual, style-level safety.

Background and rationale: The Marine Isotope Stage (MIS) 5a to 4 transition (82,000–71,000 years ago) represents a critical paleoclimatic shift from interglacial to glacial conditions during the last Ice Age. This transition, identified through oxygen isotope variations in marine sediments reflecting global ice volume changes, was characterized by abrupt cooling, sea level fall (20–30 m), and enhanced Saharan dust emissions due to increased aridification (Lisiecki and Raymo, 2005; Shackleton et al., 2021; Paine et al., 2024). Southern Tunisia’s loess deposits – windblown dust accumulations preserving paleoenvironmental signals – provide an exceptional terrestrial archive of these changes. The Matmata Plateau sequences in particular record dust provenance shifts reflecting atmospheric circulation reorganization during this transition (Dearing et al., 2001). Understanding these dust-climate interactions is crucial for predicting how modern dust emissions may respond to climate change, given dust’s significant impacts on Earth’s radiation balance and ocean biogeochemistry (Mahowald et al., 2006).

Goal: Reconstruction of atmospheric dust circulation patterns over North Africa during MIS 5a–4 by tracing the origin (provenance) of Tunisian loess using luminescence fingerprinting, complemented by geochemical-mineralogical analyses.

Field and laboratory methods:

INRAP (Tunis): Sediment source characterization by X-ray fluorescence (XRF) for bulk geochemistry, X-ray diffraction (XRD) for crystallography and mineralogy, grain-size analysis and carbonate content (provenance).

University of Lausanne (UNIL): Luminescence fingerprinting of Tunisian loess, to identify its source ‎(Grand Erg Oriental vs. Western corridors)‎ by measuring their unique luminescence signatures.

Validation: against published regional isotopic (Sr-Nd) data and paleoclimate data (Bory et al., 2002; Grousset & Biscaye, 2005).

Relevance and impact:

1- Climate relevance: Deciphers how dust sources and wind regimes responded to climate shifts during the MIS 5a–4 transition

2- Feasibility: Focused 1-year workflow benefiting from INRAP’s sediment-analytical expertise and UNIL’s luminescence specialization

3- Partnership synergy: combines INRAP’s regional knowledge with UNIL’s provenance methods

4- Long-term value: Establishes a Swiss-Tunisian collaboration (UNIL-INRAP) with shared protocols, training early-career researchers in cutting-edge luminescence applications, regional geology and sedimentology

This project explores the use of citizen science to understand household food and plastic waste in Switzerland and Lebanon, two countries with contrasting waste infrastructures and consumption patterns. By engaging 100 households (50 per country), primarily students, educators, and their families, the project tests a hybrid waste monitoring approach that combines digital self-reporting with physical waste analysis to support system understanding and targeted interventions.

Participants will track their food and plastic waste over ten days using a digital diary. For food waste, they will record what was discarded, the reason (e.g. spoilage, over-preparation), and the disposal method (e.g. trash, compost, sewer, animal feed). For plastic waste, entries will include the item type (e.g. packaging, bag), origin (e.g. supermarket, take-away), reason for disposal, and disposal route (e.g. recycling, trash). Participants will also note if the plastic was avoidable, reusable, or recyclable.

A short behavioral survey will accompany the diary to gather information on shopping, food storage, cooking and eating habits, plastic use, and attitudes toward waste. These data will help contextualize disposal behaviors and inform interventions.

While the sample is not fully representative, focusing on students and teachers offers insights into food and plastic waste behaviors in educational and youth-influenced environments. It also leverages existing networks to ensure strong participation. Further, to address the limitations of self-reported data, 20% of households will also conduct physical waste sorting. Comparing this with diary data will help assess reporting biases and validate findings.

This mixed-method approach enhances data quality and provides behavioral insights across two contrasting settings. The project contributes to Sustainable Development Goal 12.3 by identifying avoidable food and plastic waste, raising awareness at the household level, and informing scalable citizen science approaches for waste reduction in Switzerland, Lebanon, and beyond.