D-glucose derivatives of dihydropyrido-[2,3-d:6,5-d′]-dipyrimidine-2, 4, 6, 8(1H,3H, 5H,7H)-tetraone (GPHs) have been synthesized and investigated as corrosion inhibitors for mild steel in 1M HCl solution using gravimetric, electrochemical, surface, quantum chemical calculations and Monte Carlo simulations methods. The order of inhibition efficiencies is GPH-3 > GPH-2 > GPH-1. The results further showed that the inhibitor molecules with electron releasing (-OH, -OCH3) substituents exhibit higher efficiency than the parent molecule without any substituents. Polarization study suggests that the studied compounds are mixed-type but exhibited predominantly cathodic inhibitive effect. The adsorption of these compounds on mild steel surface obeyed the Langmuir adsorption isotherm. SEM, EDX and AFM analyses were used to confirm the inhibitive actions of the molecules on mild steel surface. Quantum chemical (QC) calculations and Monte Carlo (MC) simulations studies were undertaken to further corroborate the experimental results.

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<p>There has been a recent increase in demand for climate data and insights on the potential impacts of climate change. This is particularly true in the finance sector - in the past 18 months financial regulators in the UK, Europe, USA, Canada and elsewhere globally have all stipulated that large and listed firms are legally required to understand their climate risk and do something to mitigate that risk. The finance sector is not well placed to generate these climate risk insights, motivating the rise of multiple climate risk data providers.</p> <p>Climate X is a private-sector provider of climate risk analytics and services. Our in-house science team makes use of a wealth of publicly available data in the science that underpins the services we provide; data such as climate models and remote sensing data. We provide science as a service and deliver our data in a way that is useful and used within the finance sector.</p> <p>I will briefly outline how we use publicly available data to derive climate risk information that is relevant to the finance sector, and how we deliver that data in away that is meaningful to our end users. I will discuss why the data in its raw form doesn’t address sector requirements, and feedback from the sector on how publicly available climate and remote sensing data is used. I will summarise lessons learned from our engagement with finance on how the public sector could provide data which is tailored to end user needs, and is more immediately relevant and useful for adaptation action in this industry.</p>

<p>The identification of assets susceptible to landslide-related damage is critical for planners, managers, and decision-makers in developing effective mitigation strategies. Recent applications of machine learning and data mining methods have demonstrated their use in geotechnical assessments including the spatial evaluation of landslide susceptibility.</p><p>At Climate X, we utilise tree-based machine learning techniques alongside geographic information system and remote sensing data to map landslide susceptibility across Great Britain. We compile several conditioning factors—including topographic, subsurface, land use, and climate-related data—and combine them with over 18,000 landslide instances, recorded in National Landslide Database. We evaluate the capabilities of several techniques including, decision tree, bagged tree, random forest, and balanced random forest (applies random undersampling of the majority, non-landslide class) for landslide susceptibility modelling. Several performance evaluation indices (area under receiver operator characteristic curve (AUC), precision, recall, F1 score) were used to assess and compare the performance of models. We show that the random forest is the most accurate of our models with an AUC of ​94.7%. Our results demonstrate that tree-based algorithms form a robust method to analyse regional landslide susceptibility and provide new insights into locations susceptible to landslide-related damage across Great Britain.</p>

<p>With ever increasing risks and impacts from climate change, there is an urgent need for adaptation information which is relevant and useful to policy makers, businesses and the general public. At Climate X we use an interdisciplinary, impacts-motivated approach to adaptation; combining multiple climate and hazard models to give a holistic view of risk, and engaging end-users at every stage. Our first version product can project the risks and impacts of climate change-related pluvial and fluvial flooding, extreme heat, landslides, subsidence, and sea level rise, all at street level UK-wide. We quantify these risks and the financial costs they could incur under low (RCP 2.6) and high (RCP 8.5) emissions scenarios out to 2080. We deliver risk and impact assessments via an easy-to-use interface, along with relevant and decision-able risk summaries. Aligning robust science at scale with user requirements and expectations is not without its challenges. I will outline our approach to multi-hazard climate risk modelling, and discuss some of the successes and challenges we have had in developing a tool which is aligned with the needs of stakeholders, businesses and other end users.</p>