Projecting land use changes and its consequences for hydrological response in the New Capital City of Indonesia

The Indonesian government intends to build a New Capital City in East Kalimantan Province, with a total development area of 56.000 ha located in existing forest and palm oil plantations. This study will look at the fast changes in land use throughout the urbanization process from 2020 to 2045. They will impact surface runoff and increase flood risk in the future. However, this study primarily focuses on modelling to simulate and predict land use changes in the landscape of the primary river basin in the New Capital City. Land use maps for the years 2013 to 2020 were generated using image classification from Landsat images. The CLUE-S semi-empirical model was used for simulations. The modelling effort included a land use/cover change with three scenarios. The simulation revealed that mainland use/cover types will rapidly increase from 2020 to 2045, particularly in converting forest, fishpond, and plantation areas into urban areas and open fields. According to the New Capital scenario model, the built-up area would continue to expand dramatically with a 742.46 % change. In a future study, a surface runoff will be estimated using the HEC-HMS and SCS-CN models.

Modular Model Composition for Rapid Implementations of Embedded Economic Model Predictive Control in Microgrids

Economic model predictive control in microgrids combined with dynamic pricing of grid electricity is a promising technique to make the power system more flexible. However, to date, each individual microgrid requires major efforts for the mathematical modelling, the implementation on embedded devices, and the qualification of the control. In this work, a field-suitable generalised linear microgrid model is presented. This scalable model is instantiated on field-typical hardware and in a modular way, so that a class of various microgrids can be easily controlled. This significantly reduces the modelling effort during commissioning, decreases the necessary qualification of commissioning staff, and allows for the easy integration of additional microgrid devices during operation. An exemplary model, derived from an existing production facility microgrid, is instantiated, and the characteristics of the results are analysed.

Investigating the internal structure of the Antarctic ice sheet: the utility of isochrones for spatiotemporal ice-sheet model calibration

Ice-sheet models are a powerful tool to project the evolution of the Greenland and Antarctic ice sheets and thus their future contribution to global sea-level changes. Testing the ability of ice-sheet models to reproduce the ongoing and past evolution of the ice cover in Greenland and Antarctica is a fundamental part of every modelling effort. However, benchmarking ice-sheet model results against real-world observations is a non-trivial process as observational data come with spatiotemporal gaps in coverage. Here, we present a new approach to assess the accuracy of ice-sheet models which makes use of the internal layering of the Antarctic ice sheet. We calculate isochrone elevations from simulated Antarctic geometries and velocities via passive Lagrangian tracers, highlighting that a good fit of the model to two-dimensional datasets such as surface velocity and ice thickness does not guarantee a good match against the 3D architecture of the ice sheet and thus correct evolution over time. We show that palaeoclimate forcing schemes derived from ice-core records and climate models commonly used to drive ice-sheet models work well to constrain the 3D structure of ice flow and age in the interior of the East Antarctic ice sheet and especially along ice divides but fail towards the ice-sheet margin. The comparison to isochronal horizons attempted here reveals that simple heuristics of basal drag can lead to an overestimation of the vertical interior ice-sheet flow especially over subglacial basins. Our model observation intercomparison approach opens a new avenue for the improvement and tuning of current ice-sheet models via a more rigid constraint on model parameterisations and climate forcing, which will benefit model-based estimates of future and past ice-sheet changes.

Mobile Telephony in Emerging Markets: The Importance of Dual-SIM Phones

A substantial share of customers in emerging markets use dual-SIM phones and subscribe to two mobile networks. A primary motive for so called multi-simming is to take advantage of cheap on-net services from both networks. In our modelling effort, we augment the seminal model of competing telephone networks á la Laffont, Rey and Tirole (1998b) by a segment of flexible price hunters that may choose to multi-sim. According to our findings, in equilibrium, the networks set a high off-net price in the linear tariffs to achieve segmentation. This induces the price hunters to multi-sim. We show that increased deployment of dual-SIM phones may induce a mixing equilibrium with high expected on-net prices. Thus, somewhat paradoxically, deployment of a technology that increases substitutability, and thereby competition, may end up raising prices.

The pitfalls of modelling the effects of COVID-19 on gender-based violence: lessons learnt and ways forward

Since early 2020, global stakeholders have highlighted the significant gendered consequences of the COVID-19 pandemic, including increases in the risk of gender-based violence (GBV). Researchers have sought to inform the pandemic response through a diverse set of methodologies, including early efforts modelling anticipated increases in GBV. For example, in April 2020, a highly cited modelling effort by the United Nations Population Fund (UNFPA) and partners projected headline global figures of 31 million additional cases of intimate partner violence due to 6 months of lockdown, and an additional 13 million child marriages by 2030. In this paper, we discuss the rationale for using modelling to make projections about GBV, and use the projections released by UNFPA to draw attention to the assumptions and biases underlying model-based projections. We raise five key critiques: (1) reducing complex issues to simplified, linear cause-effect relationships, (2) reliance on a small number of studies to generate global estimates, (3) assuming that the pandemic results in the complete service disruption for existing interventions, (4) lack of clarity in indicators used and sources of estimates, and (5) failure to account for margins of uncertainty. We argue that there is a need to consider the motivations and consequences of using modelling data as a planning tool for complex issues like GBV, and conclude by suggesting key considerations for policymakers and practitioners in using and commissioning such projections.

Energy system analytics and good governance -U4RIA goals of Energy Modelling for Policy Support

Energy modelling is the process of using mathematical models to develop abstractions and then seek insights into future energy systems. It can be an abstract academic activity. Or, it can insert threads that influence our development. We argue therefore, that energy modelling that provides policy support (EMoPS) should not only be grounded in rigorous analytics, but also in good governance principles. As, together with other policy actions, it should be accountable. Almost all aspects of society and much of its impact on the environment are influenced by our use of energy. In this context, EMoPS can inspire, motivate, calibrate, and ‘post assess’ energy policy. But, such modeling is often undertaken by too few analysts under time and resource pressure. Building on the advances of ‘class leaders’, we propose that EMoPS should reach for practical goals — including engagement and accountability with the communities it involves, and those it will later affect. (We use the term Ubuntu, meaning ‘I am because you are’ to capture this interdependency). We argue that Ubuntu, together with retrievability, repeatability, reconstructability, interoperability and auditability (U4RIA) of EMoPS should be used to signal the beginnings of a new default practice. We demonstrate how the U4RIA principles can contribute in practice using recent modelling of aspirational energy futures by Costa Rica as a case study. This modelling effort includes community involvement and interfaces and integrates stakeholder involvement. It leaves a trail that allows for its auditing and accountability, while building capacity and sustainable institutional memory.

Investigating the internal structure of the Antarctic Ice Sheet: the utility of isochrones for spatio-temporal ice sheet model calibration

<p>Ice Sheet Models are a powerful tool to project the evolution of the Greenland and Antarctic Ice Sheets, and thus their future contribution to global sea-level changes. Probing the fitness of ice-sheet models to reproduce ongoing and past changes of the Greenland and Antarctic ice cover is a fundamental part of every modelling effort. However, benchmarking ice-sheet model data against real-world observations is a non-trivial process, as observational data comes with spatio-temporal gaps in coverage. Here, we present a new approach to assess the ability of ice-sheet models which makes use of the internal layering of the Antarctic Ice Sheet. We simulate observed isochrone elevations within the Antarctic Ice Sheet via passive Lagrangian tracers, highlighting that a good fit of the model to two dimensional datasets does not guarantee a good match against the three dimensional architecture of the ice-sheet and thus correct evolution over time. We show, that paleoclimate forcing schemes commonly used to drive ice-sheet models work well in the interior of the Antarctic Ice Sheet and especially along ice divides, but fail towards the ice-sheet margin. The comparison to isochronal horizons attempted here reveals, that simple heuristics of basal drag can lead to an overestimation of the vertical interior ice sheet flow especially over subglacial basins. Our model-observation intercomparison approach opens a new avenue to the improvement and tuning of current ice-sheet models via a more rigid constraint on model parameterisations and climate forcing which will benefit model-based estimates of future and past ice-sheet changes.</p>

Regression QSAR Models for Predicting HIV-1 Integrase Inhibitors

The Human Immunodeficiency Virus (HIV) infection is a global pandemic that has claimed 33 million lives to date. One of the most efficacious treatment for naïve or pre-treated HIV patients is with the HIV integrase strand transfer inhibitors (INSTIs). However, given that HIV treatment is life-long, the emergence of HIV-1 strains resistant to INSTIs is an imminent challenge. In this work, we showed two best regression QSAR models that were constructed using a boosted Random Forest algorithm and a boosted K* algorithm to predict the pIC50 values of INSTIs. Subsequently, the regression QSAR models were deployed against the Drugbank database for drug repositioning. The top ranked compounds were further evaluated for their target engagement activity using molecular docking studies and their potential as INSTIs evaluated from our literature search. Our study offers the first example of a large-scale regression QSAR modelling effort for discovering highly active INSTIs to combat HIV infection.

Regression QSAR Models for Predicting HIV-1 Integrase Inhibitors

The Human Immunodeficiency Virus (HIV) infection is a global pandemic that has claimed 33 million lives to date. One of the most efficacious treatment for naïve or pre-treated HIV patients is with the HIV integrase strand transfer inhibitors (INSTIs). However, given that HIV treatment is lifelong, the emergence of HIV-1 strains resistant to INSTIs is an imminent challenge. In this work, we showed two best regression QSAR models that were constructed using a boosted Random Forest algorithm (r2 = 0.998, q210CV = 0.721, q2external_test = 0.754) and a boosted K* algorithm (r2 = 0.987, q210CV = 0.721, q2external_test = 0.758) to predict the pIC50 values of INSTIs. Subsequently, the regression QSAR models were deployed against the Drugbank database for drug repositioning. The top ranked compounds were further evaluated for their target engagement activity using molecular docking studies and their potential as INSTIs evaluated from our literature search. Our study offers the first example of a large-scale regression QSAR modelling effort for discovering highly active INSTIs to combat HIV infection.

Granular-slurry rheology and asphalt compaction

Hot mixed asphalt (HMA) is a mixture of particles (coarse and fine aggregates) and interstitial fluid (asphalt binder) designed to compact and harden for long-lasting roads. In this study, we implement a two-scale approach to capture the compaction behaviour of hot asphalt mixtures using both a granular-slurry rheology (GSR) at a smaller scale and a discrete element method (DEM) simulation at the scale of a compactor. We show that this modelling effort captures the compaction of HMA with different binder viscosities modified by adding graphene nano-platelets (GNP). This research has the capacity to shed light on how the properties of mixture components can influence compaction efficiency and effectiveness.