Indoor scenario has emerged as one of the most congested, contested, and competitive wireless environments. With the need of resilient Internet of Everything (IoE) and Fourth Industrial Revolution (Industry 4.0) infrastructures, we expect to connect thousands of devices within a confined indoor environment, interfering to each other and contending for limited electromagnetic spectrum. While the growing demand for data traffic meets confined space and congested spectrum, it creates a clear and present technical challenge, and opportunities for innovation. The research objective of this proposal is to investigate new fundamental communication models and schemes, which dynamically program and customize indoor wireless propagation environments for enhanced wireless communication. This objective is attained by integrating the physics of wave-chaotic dynamics, the mathematics of random matrix theory, the engineering of reconfigurable electromagnetic surfaces, and the computing power of adiabatic quantum annealer. The proposed work consists of three components: (1) rigorous mathematical model for the statistical analysis of wave physics in complex confined indoor environment; (2) the configuration and control of wave chaos using reconfigurable intelligent surfaces; (3) Quantum-enabled, ultra-fast large-scale optimization of reconfigurable intelligent surface configuration.

In 1980, Farquhar et al. (Planta 149, 78-90) published a mechanistic photosynthesis model that successfully predicted rates of net carbon dioxide exchange in C3 plants. The impact of that model on carbon exchange research has been profound, with their 1980 paper having been cited over 1300 times since publication. Why was the paper so influential? One reason was the ability of the model to help researchers understand the underlying factors controlling rates of net photosynthesis. The other was that because of its simplicity, it could be readily incorporated into large scale applications (e.g. canopy photosynthesis and climate models). But having successfully modeled photosynthesis, most large scale models then dispense with about half of the assimilate in respiration without attempting to more accurately predict variations in respiratory flux. This failure to correctly model plant respiration has important consequences for the accuracy of large scale models, as plant respiration releases ten times more carbon dioxide (one of the greenhouse gases responsible for global warming) than does the burning of fossil fuels etc. Clearly, it is time that a mechanistic 'Farquhar-like' model of plant respiration be constructed. In this research project, we will use oxygen titration curves of plant respiration to construct a plant respiration-equivalent of the Farquhar et al. model. In collaboration with colleagues at the University of Illinois in the USA, we will use a state-of-the-art oxygen analyser (currently not available in the UK) to measure rates of leaf respiration over a broad range of oxygen concentrations. This data will then be used to test the effectiveness of a mathematical model that takes into account factors such as enzyme activity and the ability of individual enzymes to consume oxygen. In addition to being of high predictive value, such a model would also enable us to better understand what underlying factors regulate variations in respiratory flux, particularly in leaves exposed to two key environmental parameters associated with climate change: temperature and atmospheric carbon dioxide concentration. Having developed the model under moderate temperature conditions at current concentrations of atmospheric carbon dioxide, we will then subject leaves to high and low temperatures and assess the impact of the temperature treatments on the model parameters. Then, the model will be used to better understand why rates of leaf respiration often increase in leaves exposed to elevated atmospheric carbon dioxide. We will use soybean (Glycine max) for our experiments, as much is known about the regulation of respiration in this species and how photosynthetic and respiratory metabolism respond to elevated atmospheric carbon dioxide concentrations. Given that the University of Illinois will cover most plant growth/consumable costs, the proposal represents excellent value for money for NERC and an opportunity to achieve an outcome that is not possible within the UK.

Severe synoptic-scale windstorm events in the Northwest Atlantic are affecting the UK and western Central Europe in winter (DJF). Damages in the magnitude of ca. EUR 3,610 million were recorded for the last season in 2021/22. Those damaging, rare events are linked to the development of strong storm cyclones in the climate system of the North-Atlantic. This project will explore the opportunity to provide skilful and useful predictions of the winter storm season ahead of the season in November. Thus, it will explore and understand our ability to predict whether it will be e.g., an active season (number of severe events) or not, whether we can have confidence in the forecast at the time of it being issued and what the reasons for this confidence would be. Usability of predictions of the upcoming winter storm season depends a) on our understanding of the factors leading to the variability of storms, and b) on our understanding how a forecast for the next season will depend on these factors. This project will explore one potential critical factor and its role for the forecast skill of severe events leading to loss and damage. One crucial factor of steering the climatic conditions in the North-Atlantic and Europe is the forcing of the atmospheric conditions (and here especially its baroclinicity) from anomalous sea-surface temperature patterns. So called re-emerging (in autumn/winter) temperature anomalies (from summer) would provide a potential mechanism for memory transport (via slow-varying components of the climate system) from late summer/early autumn to winter and finally resulting in extreme storm activity. Recent developments in seasonal forecast suites to forecast those oceanic re-emerging events are existing and this project will explore their role in steering variability of the storm season in reality as well as to quantify their potential role in gaining forecast skill in the model domain. EX-Storms will apply a novel approach (UNSEEN, i.a. pioneered by the applicant) to use non-realised but physically consistent events from century long seasonal and decadal hindcast (multi-member ensembles) to explore this physical pathway influencing the winter storm activity level. For the first time, EX-Storms will explore how far our current abilities allow for a pre-season view on the upcoming risk of severe storms.

Literacy is the foundation for an informed, skilled citizenry. But in East Africa, less than 1/3 of pupils possess basic literacy skills. Ugandan children perform the worst; only 44.5 percent pass basic literacy tests. Early Grade Reading Assessment (EGRA) data from a Research Triangle Institute survey in northern Uganda in 2009 indicated that 82% of P2 pupils could not read a single word in the local language, compared to 51% of P2 pupils in the central region. Similar to other African countries there are many problems in Uganda's education system, including undertrained teachers, lack of materials and quality methods for teaching literacy, non-existent systems for tracking pupil performance, and parents, communities and local officials that lack the know-how to support and advocate for their children's education. Despite strong mother tongue education policies, due to underdeveloped orthographies and a lack of materials in many languages, implementing successful mother tongue literacy programs poses a significant challenge for African countries, including Uganda. While many educational interventions and literacy programs have been implemented in Africa, impacts have been minimal overall; moving to scale has also proven problematic as program effects reduce further. Since 2010 Mango Tree, a private, locally owned educational tools company, has been piloting a successful early literacy project in one language community in northern Uganda. The main goals of the Mango Tree program include increasing literacy rates, enhancing education quality through improved, effective materials and teachers, and fostering a culture of reading among pupils, parents and communities within a cost-effective and scalable framework. Compelling evidence for the large benefits and cost-effectiveness of the intervention comes from a pilot randomized evaluation of the program conducted by University of Michigan researchers in 2013 and 2014. The Literacy Laboratory Project (LLP) will scale up and evaluate the Mango Tree literacy program, whose model delivers better-quality teacher instruction, access to relevant literacy materials, inclusive approaches to learner assessment, parental and community engagement in schools and strengthening literacy infrastructure so that reading and writing, especially in local languages, becomes a meaningful part of daily life in households and communities. This scale-up will test a piloted and improved model to evaluate its effectiveness and test the mode of program delivery. Under the LLP, researchers from the University of Michigan will conduct a rigorous randomized control trial of the program in the Lango Sub-region over 4 years to measure the effectiveness of the instructional model, teacher training and support supervision innovations and literacy materials and methods on Primary 1- Primary 3 pupils' literacy achievement and explore public-private avenues for scale-up. We will study 128 schools, which are randomly assigned to either the full LLP implemented by Mango Tree's field officers, a partial-program implemented by Government Teacher Tutors, or a control group. The study will also randomize instructional materials to evaluate their contribution to effective teaching. The study will collect a rich set of pupil, parent, teacher, classroom, and school-level longitudinal data. Learning will be measured principally in terms of improvements in EGRA and Early Grade Writing Assessment scores. Our goals are to: 1) demonstrate that big effects on learning are possible (as the 2013 pilot evaluation results point toward); 2) show that with the right combination of training, teaching and learning materials and correct support, teachers can be supported to effectively teach literacy - even in rural, under-resourced, overcrowded classrooms; and 3) to test and evaluate economic approaches to implementation at scale to determine value-for-money impacts on pupil learning and teacher performance in African schools.

A large amount of money goes into funding education, especially literacy, with the belief that there will be long-term, positive effects. However, literacy rates - especially in Sub-Saharan Africa - remain incredibly low. Very few of the hundreds of rigorously evaluated interventions show more than moderate gains in student learning; almost none provide longitudinal evidence on longer-term outcomes. In short, there is little evidence from longitudinal studies in Africa about the effects of early learning on later school or life outcomes, and whether a strong early foundation better supports transitions, paving the way for continuing education, life-long learning and post-primary success. In response to this gap, our project will provide some of the first rigorous evidence regarding how investments in the early years of schooling from a highly successful literacy program can translate into long-term academic success and life outcomes. The project builds upon a randomized evaluation of an early grade literacy program in Northern Uganda. The evaluation involved 128 schools and studied a teacher training and support program for mother-tongue literacy in grades one through three. After four years of the program, we found massive effects of the program: Grade 4 pupils tested in 2017 after being exposed to the program in grades 1-3 scored 0.92 standard deviations higher in mother tongue reading - equivalent to 6.3 grade levels - more than the control students. The effects on English oral reading fluency were almost just as large. While it was impressive that such large gains were possible, in a post-conflict low-resource setting, a new set of open questions emerged. Specifically, at the end of 2017, only 52% of our study respondents were found during school visits. Importantly, we found no differential attrition across study arms - in other words, despite the unprecedented learning gains from the program, there was no positive impact on keeping children in school suggesting that outside factors - such as barriers and marginalization - rather than learning, play an important role in education transitions. This study addresses the following new questions: 1. How do children and parents/guardians plan for, and navigate challenges to, successful school and life transitions and how does early grade literacy help this navigation and transitions? 2. What are the causal effects of solid foundations in early grade literacy on learning, life skills, and school and life transitions? 3. What are the factors that, in a resource-poor environment, affect children's ability to harness the potential returns of early literacy skills? This proposed study will extend the NULP longitudinal data by interviewing a sub-sample of children and their parents/caregivers as they transition into adolescence. We will collect three rounds of data from children, and one round of data from parents/caregivers, over two years to measure learning life skills, school and life transitions. The study will conduct innovative qualitative child journey mapping and quantitative experimental analyses to provide some of the first evidence on how investments in the early years from a highly successful literacy program can improve learning, school and life transitions.