This project will conduct a feasibility study on the applicability of a digital-tool, in form of a plugin in design software like Autodesk REVIT, that will automate the pollution-analysis-process by automatically assessing air-pollution impact of a building during design stage and make recommendations for improvements. This will entail a literature review on the analysis of indoor air-quality including doors and recommend improvement that can potentially increase quality and performance of buildings for users/occupants. A sample model will be developed using geometry optimization algorithms (artificial intelligence), cloud computing, big-data-analytics and a recommender-system to carry out computations on building design parameters (e.g. geometry, doors and windows positioning, materials used, etc.) and data from air-pollution sensors.

Steady power-supply is a major challenge for all businesses in Nigeria, irrespective of type/class. Agriculture remains Nigeria's economy keystone (over 210million population), despite oil exportations. Agriculture employs 40% Nigeria's labour force and covers 21%GDP, representing major livelihood source for many (World-bank, 2018; United-Nations, 2018). Agriculturists exist in urban areas and rural areas and all lack power-supply and post-harvest storage systems/infrastructures. Fossil fuel powers their small-capacity storage units(refrigerators), usually in urban areas. Currently, 88% Nigeria's farmers are smallholders who produce 90% of domestic-output but are only able to sell 26% (Nigeria's Agric Minister, Ogbeh,2018). Truthfully, Nigeria loses over 60% of produce to post-harvest impediments like lack of cooling/storage infrastructures despite having food insecurity causing circa-$7bn yearly food importation (Central Bank of Nigeria,2017). For example, yearly tomato demand is over 2million-tons, but 700,000tons of the 1.5million-tons produced perish due to lack of preservation/storage (Nigeria Agriculture Ministry,2019). Similarly, fast-food chains complain of poultry scarcity while many smallholders lament poor poultry sales (Poultry-Worlds, 2018). While bulk-buyers struggle to find farm-produce locally and end-up importing, many smallholders cannot aggregate stock, locate/sell to these bulk-buyers. This issue is compounded by constant-failing-power-supply and costly fossil fuel to power low-capacity storages(refrigerators). Consequently, 72% of Nigeria's smallholders live below the US$1.9/day poverty line (United-Nations Nigeria,2019). Despite the ironic scenario, middlemen/agent-companies/wholesalers like LMD-Agro-Consultants have struggled in providing robust quantity to demanding bulk-buyers (e.g. food-factories, hotels,etc.) from smallholders' produce. Why? On one hand, urban smallholders consistently complain lack of power and aggregation software/system for cooling infrastructure/storage that can support aggregation to middlemen/agent-companies/wholesalers desiring large stocks for their bulk-buyers. This means NGOs' (e.g., Youth Sustainable Development Network(YSDN)) push for cooperative-system-aggregation has faced the problem of earlier stocks perishing before later stocks/addition readiness during aggregation. In contrast, bulk-buyers complain of low-quantity bulk-stock from smallholders. The inability of middlemen/agent-companies/wholesalers secure large quantity bulk-supply forces bulk-buyers to the more financially/logistically costly option of importation from large-farms abroad where large aggregation/preservation is possible due to good power supply. LMD-Agro-Consultants has teamed up with YSDN among others to provide a holistic solution(SO-COOL) to this market-pull situation. SO-COOL involves three key integrated elements: Solar-Powered cooling-storage-facilities close to clustered-farms for aggregating perishable farm produce. A clustered-blockchain-enabled-platform on which aggregated produce will be marketed and from which (bulk)buyers can make orders/reservations. It will use machine-learning to provide power-consumption forecast models and commodity guide prices. LMD would facilitate aggregation of produce from small-holders-groups to appropriate/cooling infrastructure systems for (bulk) buyers' purchases.

A report by cost consultants at Arcadis reveals that the average construction dispute value in the UK was £27.7m in 2020 (The Construction Index, 2021). 76% of disputes in construction contracts involve the contractor/subcontractor (Thomas Reuters, 2022). Gateley (2022), the first commercial law firm to list on London Stock Exchange, explained that subcontractors frequently face damaging contractual disputes with employers and main contractors. The main problem responsible for this, according to Gateley is that many clauses that are highly unfavourable for subcontractors are inserted into the contract simply because many of them do not review it before signing. Dispute of this nature can result in many subcontractors closing companies down or shedding skilled jobs to pay legal fees. UH will carry out a literature review and check the feasibility of using Natural Language Processing (NLP) AI to read the contracts and use the classification and extraction features to identify clauses of concern. J-Wadel Solutions Ltd will investigate the optimal deployment solutions for the AI model considering cost and performance. The models should do the job within a minute and can be offered for as low as £100-per-contract. By reducing conflicts between contractor/subcontractor could also save many jobs and enable many subcontractors to maintain their economic activity and be a significant source of local skilled tradesmen jobs.

This project (LINK) will develop a digital system to connect end-user buyers directly to salvage material owners (usually construction/renovation/demolition/deconstruction sites). This will minimize logistics costs and thereby reduce price and increase usage. LINK will include * Machine learning-enabled **Rapid listing mobile app (Rambo****)**: * **Extender:** digital extension that allows SC2M connect their customer bases directly to salvage construction material owners (SCMOs) * **Connector:** Online commercial platform that connects buyers directly SCMOs * Material reuse data to support green credentials**(G-cert)**

Air-pollution kills millions every year. Like a 'pandemic in slow motion', dirty air is a plague on our health, causing 7-million deaths and many preventable illnesses like stroke, heart disease, lung cancer and acute respiratory infections worldwide each year (WHO, 2021). Yearly, in the UK, it causes 36,000 premature deaths (Government's Committee on Medical Effects of Air-Pollutants COMEAP) and costs £20-billion. Although, pandemic-induced lockdowns caused largest drop in annual global emissions in 2020, lockdown easing has seen a surge to more than pre-pandemic levels. Despite various actions taken by many governments (e.g. enacting clean-air-zones), poor air quality is projected to continue into 2050 (OECD,2019). Scientists recommend dodging approach to pollution-vulnerable people like those with respiratory illness (e.g. asthma, bronchitis, etc.) who develop complications and sometimes die due to exposure to high pollution levels (European Public Health Alliance, 2020). Pollution levels can vary widely between many locations within a city/town and will vary from time to time for a location. Current solutions provide city-wide information and are thus ineffective for dodging approach as a vulnerable person is unable to decipher which location(s) within a city/town to use or avoid if they were out on walk/journey/exercise etc. A solution with pollution-data on a much smaller scale, e.g. at postcode-units level, can solve this problem. However, the UK does not, and probably cannot, have monitoring equipment for each of its approximately 1.7 million postcode-units (a city/town can have 100s of postcode-units). Nonetheless, the thousands of emission-sensors operational across UK (BBC, 2019) provide enough data to develop models for all postcode-units if the right tools are deployed. This project therefore aims to develop a system that can provide postcode-units-specific pollution data to users using machine learning algorithms, GIS data, telematics, weather data and big data analytics. The system will be available via web and mobile app and will include **Live-Pass:** will provide live pollution data for each UK postcode-unit to support users in deciding for or against an outdoor activity (e.g. journey, outdoor exercise etc.) in a specific location/postcode-unit. It will suggest cleaner alternatives. **Future-Pass:** will provide hourly 7-day future pollution forecast for each UK postcode-units to support planning/scheduling future outdoor events for a cleaner location/time. **City/town analytics dashboard (CAD):** provide data and insights on the different levels of pollution for all the postcode-units within a city/town (targeted at local authorities)