Warning of a global "learning crisis" in education, the World Bank recently claimed that without learning, education will fail to deliver on its promise to eliminate extreme poverty and create opportunity and prosperity for all. In response, Artificial Intelligence (AI) has been proposed as having the potential to accelerate the process of achieving global education goals by reducing barriers to access education, automating management processes, and optimising methods for improvement of learning outcomes. In order to realise this vision, public and private partnerships (P3s) are being established where artificial intelligence in education (AIEd) initiatives are being rolled out in developing countries in an effort to spur innovative digital transformations in education. However, while AI in and for education is claimed to bring many benefits, it also potentially brings as many challenges, including social, political, economic, and ethical consequences. While the AI solution has considerable merit, a significant problem facing policy-makers, practitioners, industry and other relevant stakeholders is that there are no tangible indications that AI in education will promote the desired shifts or evidence of the impact that introducing such systems might have on the social life of Global South school communities. In light of this, the Fair-AIEd project will examine the impact of P3 initiatives on the use of AI in education in two African contexts as examples of emerging market economies (Ghana and South Africa). As well as the implications that AI systems might have for teaching and learning, the project will investigate potential benefits, harms, and risks associated with the leadership roles corporations play in design and use of AI within the educational practices of developing nations. A key successful outcome of this project will lie in establishing a baseline for further research which seeks to understand the impact of AI in education in the Global South. The project is guided by the following questions: RQ1: What are the social, political, economic, pedagogical, and ethical implications of embedding AI systems into international education and development contexts? RQ2: How can P3 partnerships most effectively channel machine learning to drive fair-AI innovation for international education and development? RQ3: How can governments facilitate the creation of ethical AIEd policies for development goals? Potential impacts will be explored using ethnographic case-studies. Informed by data obtained in the field, stakeholders from industry, government, academia, and civic groups will co-design an Algorithmic Impact Assessment tool that can be responsive to diverse populations. Using fair-ML (Machine Learning) as a point of reference, the framework will identify key cross-cultural values and social issues against which the implications of AI in education can be identified and evaluated. Design of the tool will incorporate a local adjustment resource to accommodate cultural, religious, or other sources of value differences that emerge from field work. The Impact Assessment will be piloted in six K-12 schools across Ghana and South Africa to determine the effectiveness of the tool. The impacts of AI technologies will be listed, mapped, and analysed to illustrate key issues and concerns in this emerging landscape and to identify potential for positive educational change. Upon identification of benefits, harms and risks as they apply to AIEd, stakeholders will then co-design and develop a Fair-AIEd Trust Mark. The Mark will be useful for the educational leadership of developing nations as they choose corporate partners and technical systems for their schools. Development and use of this tool will also provide guidance to companies who are willing to commit to the ethical principles upon which their educational technology proposals will be judged in terms of localising software and understanding regulatory compliance.

Warning of a global "learning crisis" in education, the World Bank recently claimed that without learning, education will fail to deliver on its promise to eliminate extreme poverty and create opportunity and prosperity for all. In response, Artificial Intelligence (AI) has been proposed as having the potential to accelerate the process of achieving global education goals by reducing barriers to access education, automating management processes, and optimising methods for improvement of learning outcomes. In order to realise this vision, public and private partnerships (P3s) are being established where artificial intelligence in education (AIEd) initiatives are being rolled out in developing countries in an effort to spur innovative digital transformations in education. However, while AI in and for education is claimed to bring many benefits, it also potentially brings as many challenges, including social, political, economic, and ethical consequences. While the AI solution has considerable merit, a significant problem facing policy-makers, practitioners, industry and other relevant stakeholders is that there are no tangible indications that AI in education will promote the desired shifts or evidence of the impact that introducing such systems might have on the social life of Global South school communities. In light of this, the Fair-AIEd project will examine the impact of P3 initiatives on the use of AI in education in two African contexts as examples of emerging market economies (Ghana and South Africa). As well as the implications that AI systems might have for teaching and learning, the project will investigate potential benefits, harms, and risks associated with the leadership roles corporations play in design and use of AI within the educational practices of developing nations. A key successful outcome of this project will lie in establishing a baseline for further research which seeks to understand the impact of AI in education in the Global South. The project is guided by the following questions: RQ1: What are the social, political, economic, pedagogical, and ethical implications of embedding AI systems into international education and development contexts? RQ2: How can P3 partnerships most effectively channel machine learning to drive fair-AI innovation for international education and development? RQ3: How can governments facilitate the creation of ethical AIEd policies for development goals? Potential impacts will be explored using ethnographic case-studies. Informed by data obtained in the field, stakeholders from industry, government, academia, and civic groups will co-design an Algorithmic Impact Assessment tool that can be responsive to diverse populations. Using fair-ML (Machine Learning) as a point of reference, the framework will identify key cross-cultural values and social issues against which the implications of AI in education can be identified and evaluated. Design of the tool will incorporate a local adjustment resource to accommodate cultural, religious, or other sources of value differences that emerge from field work. The Impact Assessment will be piloted in six K-12 schools across Ghana and South Africa to determine the effectiveness of the tool. The impacts of AI technologies will be listed, mapped, and analysed to illustrate key issues and concerns in this emerging landscape and to identify potential for positive educational change. Upon identification of benefits, harms and risks as they apply to AIEd, stakeholders will then co-design and develop a Fair-AIEd Trust Mark. The Mark will be useful for the educational leadership of developing nations as they choose corporate partners and technical systems for their schools. Development and use of this tool will also provide guidance to companies who are willing to commit to the ethical principles upon which their educational technology proposals will be judged in terms of localising software and understanding regulatory compliance.

Molecular communication (MC) provides a way for nano/microdevices to communicate information over distance via chemical signals in nanometer to micrometer scale environments. The successful realization of MC will allow its future main applications, including drug delivery and environmental monitoring. The main hindrance for the MC application stands in the lack of nano/micro-devices capable of processing the time-varying chemical concentration signals in the biochemical environment. One promising solution is to design and implement programmable digital and analog building blocks, as they are fundamental building blocks for the signal processing at MC transceivers. With two existing approaches in realizing these building blocks, namely, biological circuits and chemical circuits, synthesizing biological circuits faces challenges such as slow speed, unreliability, and non-scalability, which motivates us to design novel chemical circuits-based functions for rapid prototyping and testing communication systems. Conventional chemical circuits designs are mainly based on chemical reaction networks (CRNs) to achieve various concentration transformation during the steady state from the input to the output with all chemical reactions occurring in same "point" location. This kind of design does not fit for the time-varying signals in communication system due to that the temporal information can be invisible to even state-of-the-art molecular sensors with high chemical specificity that respond only to the total amount of the signaling molecules. Thus, this project aims to design the chemical reaction-based microfluidic MC prototypes with time-varying chemical signal processing functionalities, including modulation and demodulation, encoding and decoding, emission and detection. This also facilitates the microfluidic drug delivery prototype design and cancer cell on chip testing under time-varying drug concentration signal. This project has the ambitious vision to develop novel time-varying chemical concentration signal processing methodology for microfluidic MC and microfluidic drug delivery. In the long run, 1) our microfluidic MC results will enable the implementation of MC functionality into nanoscale machines, by downsizing the proposed components through the utilization of nanomaterials with fluidic properties, and by translating the functional chemistry into biological circuit designs; 2) our microfluidic drug delivery results will revolutionize the conventional drug delivery testing approach by enabling ICT technologies for novel in-vitro microfluidics for drug delivery, allowing rapid measurement of therapeutic effect, toxicology, to reduce development costs and minimize the use of animal models.

Electricity infrastructure is key to sustain human and economic wellbeing since it supplies energy to industrial, commercial and financial sectors, critical services (health, traffic control, water supply), communication networks, and hence almost all activities in modern societies. Consequently, the effects of long electricity blackouts have demonstrated impacts on economic activities and social stability and security. A framework for disaster management and resilience of the power sector is needed, beyond the occurrence of "average" outages contemplated in current security standards. This framework should consider network management under the occurrence of natural hazards such as earthquakes and tsunamis that may cause major blackouts, and assess proper measures to manage the associated disasters. Developing and implementing such a framework will be crucial to increase the opportunities for Chile and other countries, especially developing and low-income ones located around the Pacific Ring of Fire which are particularly exposed to the risk of earthquakes and tsunamis. In this context, this project will undertake holistic risk analyses associated with natural hazards on electricity networks along with identification of mitigation and adaptation measures that can allow us to manage the arising disasters. This holistic perspective of disaster management and resilience will be supported by development of mathematical models to, firstly, assess risks related to high impact low probability events, such as earthquakes and tsunamis, on the electric power systems. These models will then serve to identify an optimal portfolio of preventive and corrective measures that can support mitigation of impacts and compare different adaptation strategies. In particular, besides classical infrastructure reinforcement, we will assess how operational measures for disaster management, for instance though distributed energy systems, e.g., based on communities and microgrids, can provide system resilience. Building on this last point, resilience can in fact also be built through citizens and communities and by how they prepare for, and respond to, power outages. Such preparedness could for instance be led by the electricity companies and targeted at the individual and community levels by sharing accountability for response across the official responders, local officials, community groups, individual citizens, and the electricity companies. The aim is for households to have response strategies that are complemented by resilience measures prepared for (and by) the community. Such shared responsibility is becoming the response culture in the UK (with the very recent recognition of spontaneous volunteers as a source of untrained, unknown support which converges at the time of an incident). In developing countries, where the capacity of official responders may be insufficient given the scale of the disaster, the reliance on community preparedness and spontaneous emergence of willing helpers is more acute to lessen the effects of an incident and quicken the return to normality. Thus, in addition to more technical features, the framework developed here will explicitly include community resilience as a way to lessen the impact of outages and manage disasters. By analysing several case studies in Chile based on both data from past experiences and simulations, we will propose a general framework for disaster management and network and community resilience which can be applicable to other developing and low-income countries. We will use the research findings to develop networks standards following disasters along with a standard on community resilience to power outages. These standards will include socio-economic and engineering indicators that can support monitoring of network resilience and readiness to withstand natural, catastrophic events as well as quantifying impacts of such events after they occur, enhancing quality of post-mortem analysing

The proposed multidisciplinary network for Decarbonizing Transport through Electrification (DTE) will bring together research expertise to address the challenges of interactions between energy networks, future electric vehicle charging infrastructure ( including roadside wireless charging, the shift to autonomous vehicles), electric and hybrid aircraft and electrification of the rail network. The DTE network will bring together industry, academia and the public sector to identify the challenges limiting current implementation of an electrified, integrated transport system across the automotive, aerospace and rail sectors. The network will develop and sustain an interdisciplinary team to solve these challenges, leveraging external funding from both public and private sectors, aiming to be become self sustainable in future and growing to establish an International Conference. The network will be inclusive, with a focus EDI and mechanisms to support colleagues such as early career researchers. The DTE network will address low-carbon transport modes (road, rail and airborne) alongside associated electricity infrastructures to support existing and deliver future mobility needs, treating these as an integrated system embedded within the electricity energy vector with the goal of decarbonising the transport sector. It will explore drivers for change within the transport system including technology innovation, individual mobility needs and economic requirements for change alongside environmental and social concerns for sustainability and consider the role, social acceptance and impact of policies and regulations to result in emissions reduction. The network has three key "Work Streams" focusing on: (i) vehicular technologies; (ii) charging infrastructure; (iii) energy systems. These will be underpinned by cross-cutting themes around large scale data analysis and human factors. The network also has a dedicated Work Stream on people-based activities to enable us to widen our dissemination and impact across other communities. The outcome of the DTE network is expected to transform current practices and research in the decarbonization of transport (considering a number of different perspectives).