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Competitiveness and carbon leakage issues have been some of the main concerns in the implementation and discussions of climate policies. This paper examines the macroeconomic and sectoral competitiveness and carbon leakage impacts associated with a range of stylised mitigation policy scenarios.

This paper presents a framework to include feedbacks from climate impacts on the economy in integrated assessment models. The proposed framework uses a production function approach, which links climate impacts to key variables and parameters used in the specification of economic activity. The paper pays particular attention to the challenges of distinguishing between damages and the costs of adapting to climate change.

This paper reviews the existing literature on modelling the macroeconomic consequences of the transition to a circular economy. It provides insights into the current state of the art on modelling policies to improve resource efficiency and the transition to a circular economy by examining 24 modelling-based assessments of a circular economy transition.

This report develops an analytical framework that assesses the macroeconomic, environmental and distributional consequences of energy subsidy reforms. The framework is applied to the case of Indonesia to study the consequences in this country of a gradual phase out of all energy consumption subsidies between 2012 and 2020.

She is fair, light-eyed and the perfect clothes horse. But on days when regular modelling assignments dry up, she doesn't mind changing 50 outfits and being photographed in a small studio against a plain white backdrop. Front, side, and back and you are done, says Anushree, 24, a Mumbai model.

The performance of automated model building in crystal structure determination usually decreases with the resolution of the experimental data, and may result in fragmented models and incorrect side-chain assignment. Presented here are new methods for machine-learning-based docking of main-chain fragments to the sequence and for their sequence-independent connection using a dedicated library of protein fragments. The combined use of these new methods noticeably increases sequence coverage and reduces fragmentation of the protein models automatically built with ARP/wARP.

The phase problem remains a major barrier to overcome in protein structure solution by X-ray crystallography. In recent years, new molecular-replacement approaches using ab initio models and ideal secondary-structure components have greatly contributed to the solution of novel structures in the absence of clear homologues in the PDB or experimental phasing information. This has been particularly successful for highly α-helical structures, and especially coiled-coils, in which the relatively rigid α-helices provide very useful molecular-replacement fragments. This has been seen within the program AMPLE, which uses clustered and truncated ensembles of numerous ab initio models in structure solution, and is already accomplished for α-helical and coiled-coil structures. Here, an expansion in the scope of coiled-coil structure solution by AMPLE is reported, which has been achieved through general improvements in the pipeline, the removal of tNCS correction in molecular replacement and two improved methods for ab initio modelling. Of the latter improvements, enforcing the modelling of elongated helices overcame the bias towards globular folds and provided a rapid method (equivalent to the time requirements of the existing modelling procedures in AMPLE) for enhanced solution. Further, the modelling of two-, three- and four-helical oligomeric coiled-coils, and the use of full/partial oligomers in molecular replacement, provided additional success in difficult and lower resolution cases. Together, these approaches have enabled the solution of a number of parallel/antiparallel dimeric, trimeric and tetrameric coiled-coils at resolutions as low as 3.3 Å, and have thus overcome previous limitations in AMPLE and provided a new functionality in coiled-coil structure solution at lower resolutions. These new approaches have been incorporated into a new release of AMPLE in which automated elongated monomer and oligomer modelling may be activated by selecting `coiled-coil' mode.

Molybdenum oxides and sulfides on various low-cost high-surface-area supports are excellent catalysts for several industrially relevant reactions. The surface layer structure of these materials is, however, difficult to characterize due to small and disordered MoOx domains. Here, it is shown how X-ray total scattering can be applied to gain insights into the structure through differential pair distribution function (d-PDF) analysis, where the scattering signal from the support material is subtracted to obtain structural information on the supported structure. MoOx catalysts supported on alumina nanoparticles and on zeolites are investigated, and it is shown that the structure of the hydrated molybdenum oxide layer is closely related to that of disordered and polydisperse polyoxometalates. By analysing the PDFs with a large number of automatically generated cluster structures, which are constructed in an iterative manner from known polyoxometalate clusters, information is derived on the structural motifs in supported MoOx.

Building Information Modelling (BIM) can enhance the design of a building, reduce costs and save energy. However, little research has been carried out on its impact on sustainable practices. A US survey illustrates that many practitioners do not see sustainability as a primary application, suggesting that more effort is needed to encourage the integration of ‘green’ design and construction into BIM.

Integrated environmental modelling (IEM) is an organised approach to streamlining the movement of scientific information from its research sources to its application in problem solving, according to a study that envisions a global-scale IEM community. The researchers present a roadmap for the future of IEM, describing issues that could be addressed to develop its potential even further, such as how best to integrate diverse stakeholder perspectives and appropriate guidelines for ‘problem statements’.

A new model, named OUTOPIE could help design more effective agri-environmental schemes. The model links the farm, field and landscape levels to allow a more accurate assessment of the costs of enrolling specific fields in conservation schemes. Using the model, the researchers were able to assess the cost-effectiveness of different policies for the conservation of the Little Bustard bird (Tetrax tetrax) in France.

The effects of climate change on the distribution of species can be predicted more accurately by considering the genetic differences between different groups of the same species, a new study suggests. The researchers found that a computer model which incorporated genetic information on different groups of a US tree species was up to 12 times more accurate in predicting tree locations than a non-genetically informed model.

The emissions of two perfluoroalkyl acids (PFAAs) into the Danube River Basin have been estimated in a test of four different hypotheses regarding the factors affecting those emissions. The results were used to simulate water concentrations for comparison with measured data. The researchers found that incorporating wastewater treatment information and wealth distribution alongside population data can improve the accuracy of emissions estimates.

Chemicals that persist in the environment can harm humans and wildlife. This study describes a computer modelling-based approach to predict which chemical compounds are likely to be persistent. The models were correctly able to predict persistence for 11 of 12 chemicals tested and could provide a cost-effective alternative to laboratory testing.

A new tool used to investigate exhaust emissions of marine vessels has been developed and applied to shipping in the Baltic Sea.

Models that predict how nitrogen from the air is deposited in the sea could be useful in predicting algal blooms. Based on the knowledge that excess nitrogen increases algal growth rates, researchers simulated nitrogen deposition in the North Sea and suggested that, using predicted weather data, it might be possible to adapt this approach to predict algal blooms.

A recent study develops a framework for implementing IAMs using the Lombardy region of Italy as a case study. Researchers have run an uncertainty and sensitivity analysis with an environmental model, specifically with an Integrated Assessment Model (IAM) for air quality, demonstrating how model components are sources of uncertainty in the output of an integrated assessment. Policy responses should therefore consider uncertainty and sensitivity when developing measures to improve air quality.

Another look at 3D modelling suitable for IE10 and IE11 without using 'transform-style: preserve-3d;'

3D modelling with 360 º animation suitable for IE10 and IE11 without using 'transform-style: preserve-3d;'

3D modelling with 360 º animation using 'transform-style: preserve-3d;' so NOT for IE10 or IE11

Low lying areas are swamped, millions of people have hours to evacuate and destruction on a mass scale is predicted by scientists who have mapped the worst-case scenarios for how Queensland's coastline would be impacted if a one-in-10,000-year tsunami hit.

A Chinese mining giant is being accused of underestimating the impact a proposed open cut mine will have on groundwater on the New South Wales Liverpool Plains.

There's growing consensus among weather forecasting models that Australia could be in for a wet winter. But what do farmers think?

1 April 2020

To imagine earth without greenhouse gases in its atmosphere is to turn the familiar blue marble into a barren lump of rock and ice on which the average surface temperature hovers around -18ºC. Such a planet would not receive less of the sunlight which is the ultimate source of all Earth's warmth. But when the energy it absorbed from the sunlight was re-emitted as infrared radiation, as the laws of physics require, it would head unimpeded back out into space.

1 April 2020

1 April 2020

"A special challenge of hard rock exploration is to identify targets of interest within complex geological settings. Interpretation of the geology can be made from direct geological observations and knowledge of the area, and from 2D or 3D seismic surveys. These interpretations can be developed into 3D geological models that provide the basis for predictions as to likely targets for drilling and/or mining. To verify these predictions we need to simulate 3D seismic wave propagation in the proposed geological models and compare the simulation results to seismic survey data. To achieve this we convert geological surfaces created in an interpretation software package into discretised block models representing the different lithostratigraphic units, and segment these into discrete volumes to which appropriate density and seismic velocity values are assigned. This approach allows us to scale models appropriately for desired wave propagation parameters and to go from local to global geological models and vice versa. Then we use these digital models with forward modelling codes to undertake numerous 3D acoustic wave simulations. Simulations are performed with single shot and with exploding reflector (located on extracted geological surface) configurations" -- Summary.

Flávio B. Gonçalves, Bárbara C. C. Dias.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 782--800.

Abstract:
Educational assessment usually considers a contextual questionnaire to extract relevant information from the applicants. This may include items related to socio-economical profile as well as items to extract other characteristics potentially related to applicant’s performance in the test. A careful analysis of the questionnaires jointly with the test’s results may evidence important relations between profiles and test performance. The most coherent way to perform this task in a statistical context is to use the information from the questionnaire to help explain the variability of the abilities in a joint model-based approach. Nevertheless, the responses to the questionnaire typically present missing values which, in some cases, may be missing not at random. This paper proposes a statistical methodology to model the abilities in dichotomous IRT models using the information of the contextual questionnaires via linear regression. The proposed methodology models the missing data jointly with the all the observed data, which allows for the estimation of the former. The missing data modelling is flexible enough to allow the specification of missing not at random structures. Furthermore, even if those structures are not assumed a priori, they can be estimated from the posterior results when assuming missing (completely) at random structures a priori. Statistical inference is performed under the Bayesian paradigm via an efficient MCMC algorithm. Simulated and real examples are presented to investigate the efficiency and applicability of the proposed methodology.

Rodrigo Citton P. dos Reis, Enrico A. Colosimo, Gustavo L. Gilardoni.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 2, 374--396.

Abstract:
In the data analysis from multiple repairable systems, it is usual to observe both different truncation times and heterogeneity among the systems. Among other reasons, the latter is caused by different manufacturing lines and maintenance teams of the systems. In this paper, a hierarchical model is proposed for the statistical analysis of multiple repairable systems under different truncation times. A reparameterization of the power law process is proposed in order to obtain a quasi-conjugate bayesian analysis. An empirical Bayes approach is used to estimate model hyperparameters. The uncertainty in the estimate of these quantities are corrected by using a parametric bootstrap approach. The results are illustrated in a real data set of failure times of power transformers from an electric company in Brazil.

Methods for generating synthetic data have become of increasing importance to build large datasets required for Convolution Neural Networks (CNN) based deep learning techniques for a wide range of computer vision applications. In this work, we extend existing methodologies to show how 2D thermal facial data can be mapped to provide 3D facial models. For the proposed research work we have used tufts datasets for generating 3D varying face poses by using a single frontal face pose. The system works by refining the existing image quality by performing fusion based image preprocessing operations. The refined outputs have better contrast adjustments, decreased noise level and higher exposedness of the dark regions. It makes the facial landmarks and temperature patterns on the human face more discernible and visible when compared to original raw data. Different image quality metrics are used to compare the refined version of images with original images. In the next phase of the proposed study, the refined version of images is used to create 3D facial geometry structures by using Convolution Neural Networks (CNN). The generated outputs are then imported in blender software to finally extract the 3D thermal facial outputs of both males and females. The same technique is also used on our thermal face data acquired using prototype thermal camera (developed under Heliaus EU project) in an indoor lab environment which is then used for generating synthetic 3D face data along with varying yaw face angles and lastly facial depth map is generated.

Alex Diana, Eleni Matechou, Jim Griffin, Alison Johnston.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 473--493.

Abstract:
Environmental changes in recent years have been linked to phenological shifts which in turn are linked to the survival of species. The work in this paper is motivated by capture-recapture data on blackcaps collected by the British Trust for Ornithology as part of the Constant Effort Sites monitoring scheme. Blackcaps overwinter abroad and migrate to the UK annually for breeding purposes. We propose a novel Bayesian nonparametric approach for expressing the bivariate density of individual arrival and departure times at different sites across a number of years as a mixture model. The new model combines the ideas of the hierarchical and the dependent Dirichlet process, allowing the estimation of site-specific weights and year-specific mixture locations, which are modelled as functions of environmental covariates using a multivariate extension of the Gaussian process. The proposed modelling framework is extremely general and can be used in any context where multivariate density estimation is performed jointly across different groups and in the presence of a continuous covariate.

Y. X. Rachel Wang, Purnamrita Sarkar, Oana Ursu, Anshul Kundaje, Peter J. Bickel.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1511--1536.

Abstract:
Chromosome conformation capture experiments such as Hi-C are used to map the three-dimensional spatial organization of genomes. One specific feature of the 3D organization is known as topologically associating domains (TADs), which are densely interacting, contiguous chromatin regions playing important roles in regulating gene expression. A few algorithms have been proposed to detect TADs. In particular, the structure of Hi-C data naturally inspires application of community detection methods. However, one of the drawbacks of community detection is that most methods take exchangeability of the nodes in the network for granted; whereas the nodes in this case, that is, the positions on the chromosomes, are not exchangeable. We propose a network model for detecting TADs using Hi-C data that takes into account this nonexchangeability. In addition, our model explicitly makes use of cell-type specific CTCF binding sites as biological covariates and can be used to identify conserved TADs across multiple cell types. The model leads to a likelihood objective that can be efficiently optimized via relaxation. We also prove that when suitably initialized, this model finds the underlying TAD structure with high probability. Using simulated data, we show the advantages of our method and the caveats of popular community detection methods, such as spectral clustering, in this application. Applying our method to real Hi-C data, we demonstrate the domains identified have desirable epigenetic features and compare them across different cell types.

Peter J. Diggle, Emanuele Giorgi, Julienne Atsame, Sylvie Ntsame Ella, Kisito Ogoussan, Katherine Gass.

Source: Statistical Science, Volume 35, Number 1, 42--50.

Abstract:
Vector-borne diseases have long presented major challenges to the health of rural communities in the wet tropical regions of the world, but especially in sub-Saharan Africa. In this paper, we describe the contribution that statistical modelling has made to the global elimination programme for one vector-borne disease, onchocerciasis. We explain why information on the spatial distribution of a second vector-borne disease, Loa loa, is needed before communities at high risk of onchocerciasis can be treated safely with mass distribution of ivermectin, an antifiarial medication. We show how a model-based geostatistical analysis of Loa loa prevalence survey data can be used to map the predictive probability that each location in the region of interest meets a WHO policy guideline for safe mass distribution of ivermectin and describe two applications: one is to data from Cameroon that assesses prevalence using traditional blood-smear microscopy; the other is to Africa-wide data that uses a low-cost questionnaire-based method. We describe how a recent technological development in image-based microscopy has resulted in a change of emphasis from prevalence alone to the bivariate spatial distribution of prevalence and the intensity of infection among infected individuals. We discuss how statistical modelling of the kind described here can contribute to health policy guidelines and decision-making in two ways. One is to ensure that, in a resource-limited setting, prevalence surveys are designed, and the resulting data analysed, as efficiently as possible. The other is to provide an honest quantification of the uncertainty attached to any binary decision by reporting predictive probabilities that a policy-defined condition for action is or is not met.

Bank of England Working Papers by Aydan Dogan and Ida Hjortsoe

It’s always interesting to know about some cool tricks that you can use when it comes to modelling within SOLIDWORKS. Well, within this blog we just happen to have one of those tricks for you. They always say a magician never

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Review of: Modelling Transitions: Virtues, Vices, Visions of the Future by Moallemi, Enayat A. and de Haan, Fjalar J. (Eds.), reviewed by Cesar Garcia-Diaz

This paper uses a 3D model for stability assessment of Zhujiabaobao waste dump with ground cracks. The study data were gathered via reconnaissance, geomorphological analysis and laboratory experiment. A 3D finite extended element method model that can consider cracks was then used to calculate the factor of safety (FOS) of the waste dump via the strength reduction technique. The simulation shows the dump to have an FOS of 1.22 and both the position and depth of penetration of cracks in the waste dump have a crucial impact on the stability of the slope. Because the study area is located in a seismically active area, simulation and analysis of the dynamic response of the waste dump under different magnitudes of seismic waves (peak acceleration is 0.05, 0.15, 0.25 and 0.45g) were performed via an explicit dynamic model. The simulation shows that high steps in the slope are particularly responsive to earthquakes. The approach used here for analysing stability under static and dynamic loads is useful for hazard prevention and mitigation.

The modelling predicts more than a quarter of a million jobs could be lost in Victoria due to the coronavirus pandemic in what Premier Daniel Andrews says is the perhaps the "biggest economic and employment challenge" in the state's history.

Apr 1, 2020

Today’s economic crisis leaves us with an unsettling and perplexing regret. Why weren’t financial portfolios already adjusted for risks that stem from health events such as pandemics? After all, financial portfolios are adjusted for liquidity risks, market risks, credit risks, and even operational and political risks.

Apr 1, 2020

Today’s economic crisis leaves us with an unsettling and perplexing regret. Why weren’t financial portfolios already adjusted for risks that stem from health events such as pandemics? After all, financial portfolios are adjusted for liquidity risks, market risks, credit risks, and even operational and political risks.

Apr 1, 2020

Today’s economic crisis leaves us with an unsettling and perplexing regret. Why weren’t financial portfolios already adjusted for risks that stem from health events such as pandemics? After all, financial portfolios are adjusted for liquidity risks, market risks, credit risks, and even operational and political risks.