Damage Detection from SAR Imagery: Application to the 2003 Algeria and 2007 Peru Earthquakes

This paper is focused on the improvement and further validation of a recently proposed approach for the joint use of radar satellite imagery of an area affected by a major disaster and ancillary data. The study was carried out at different sites on imagery of two different earthquakes occurred one in the Mediterranean coast of Algeria on May 21st, 2003, which severely affected the city of Boumerdes, and one in the Pacific Coast of Peru on August, 15th, 2007. The combination of different radar-extracted features results in very fuzzy classification of the damage patterns, far less detailed than what available using optical imagery. However, focused results using the above-mentioned ancillary data provide enough detail and precision to be comparable with them. In particular, quantized damage level at the block level is achieved at enough detail using ALOS/PALSAR data and thus validates the original idea

special section guest editorial airborne hyperspectral remote sensing of urban environments

University of Pavia, Department of Electrical, Computer and Biomedical Engineering, ItalyRemote sensing is a very useful tool in retrieving urban information in a timely, detailed, andcost-effective manner to assist various planning and management activities. Hyperspectralremote sensing has been of great interest to the scientific community since its emergence inthe 1980s, due to its very high spectral resolution providing the potential of finer material detec-tion, classification, identification, and quantification, compared to the traditional multispectralremote sensing. With the advance of computing facilities and more airborne high-spatial-reso-lution hyperspectral image data becoming available, many investigations on its real applicationsare taking place. In particular, urban environments are characterized by heterogeneous surfacecovers with significant spatial and spectral variations, and airborne hyperspectral imagery withhigh spatial and spectral resolutions offers an effective tool to analyze complex urban scenes.The objectiveof this special section of the Journal of Applied Remote Sensing is to provide asnapshot of status, potentials, and challenges of high-spatial-resolution hyperspectral imagery inurban feature extraction and land use interpretation in support of urban monitoring and man-agement decisions. This section includes twelve papers that cover four major topics: urban landuse and land cover classification, impervious surface mapping, built-up land analysis, and urbansurface water mapping.There are nine papers about urban land use and land cover classification. "Hyperspectralimage classification with improved local-region filters" by Ran et al. proposes two local-regionfilters, i.e., spatial adaptive weighted filter and collaborative-representation-based filter, for spa-tial feature extraction, thereby improving classification of urban hyperspectral imagery. "Edge-constrained Markov random field classification by integrating hyperspectral image with LiDARdata over urban areas" by Ni et al. adopts an edge-constrained Markov random field method foraccurate land cover classification over urban areas with hyperspectral image and LiDAR data."Combining data mining algorithm and object-based image analysis for detailed urban mappingof hyperspectral images" by Hamedianfar et al. explores the combined performance of a datamining algorithm and object-based image analysis, which can produce high accuracy of urbansurfacemapping."Dynamicclassifierselectionusingspectral-spatial information forhyperspec-tralimageclassification"bySuetal.proposestheintegrationofspectralfeatureswithvolumetrictextural features to improve the classification performance for urban hyperspectral images."Representation-based classifications with Markov random field model for hyperspectralurban data" by Xiong et al. improves representation-based classification by considering spa-tial-contextualinformationderivedfromaMarkovrandomfield."Classificationofhyperspectralurban data using adaptivesimultaneous orthogonal matching pursuit" by Zou et al. improves theclassification performance of a joint sparsity model, i.e., simultaneous orthogonal matching pur-suit, by using a priori segmentation map.Othertechniques,suchaslinearunmixinganddimensionalityreduction,arealsoinvestigatedin conjunction with urban surface mapping.Among the nine papersonclassification,twopapersconsider linear unmixing, which are "Unsupervised classification strategy utilizing an endmem-ber extraction technique for airborne hyperspectral remotely sensed imagery" by Xu et al., and"Endmembernumberestimationforhyperspectralimagerybasedonvertexcomponentanalysis"by Liu et al. One paper studies the impact of dimensionality reduction (through band selection)on classification accuracy, which is "Ant colony optimization-based supervised and unsuper-vised band selections for hyperspectral urban data classification" by Gao et al

R&D and market size: who benefits from orphan drug regulation?

Since the early 80s, orphan drug regulations have been introduced to stimulate R&D for rare diseases. We develop a theoretical model to study the heterogeneous impact on optimal R&D decisions of the incentives for diseases with different levels of prevalence. We show the mechanisms through which the type of incentives deployed by orphan drug regulations may stimulate R&D more for orphan diseases with comparatively high prevalence, thus increasing inequality within the class of orphan diseases. Using data from the Food and Drug Administration on the number of orphan designations, our empirical analysis shows that, while R&D has increased over time for all orphan diseases, the increase has been much greater for the less rare. According to our baseline specification, the difference between the predicted number of orphan designations for a disease belonging to the highest and the lowest class of prevalence is 5.6 times larger after 2008 than it was in 1983. Our findings support the idea that the type of incentives in place may be responsible for this increase in inequality within orphan diseases

Free-Riding in Pharmaceutical Price Regulation: Theory and Evidence

We present a model of the strategic interaction among authorities regulating pharmaceutical prices in different countries and the R&D investment decisions of pharmaceutical firms. Regulators’ decisions affect consumer surplus directly, via prices, and indirectly via firms’ profits and R&D investment policies, which in turn affect patient health. The positive externality of a price increase in one country provides an incentive for other countries to free-ride, and we show how country-level characteristics affect optimal pricing decisions and equilibria. Our theoretical predictions are tested using price data for a set of 70 cancer drugs in 25 OECD countries. We find evidence of behaviour that is consistent with the free-riding hypothesis and which, in line with the theoretical predictions, differs according to country-level characteristics. Countries with comparatively large market shares tend to react to increases in other countries’ prices by lowering their own prices; in countries with comparatively small market shares, regulators’ decisions are consistent with the objective of introducing the product at as low a price as possible. We discuss the policy implications of our results for incentivising global pharmaceutical R&D and the recent proposal to move towards a joint pharmaceutical procurement process at the European level

R&D and market size: who benefits from orphan drug regulation?

Since the early 80s, orphan drug regulations have been introduced to stimulate R&D for rare diseases. We develop a theoretical model to study the heterogeneous impact on optimal R&D decisions of the incentives for diseases with different levels of prevalence. We show the mechanisms through which the type of incentives deployed by orphan drug regulations may stimulate R&D more for orphan diseases with comparatively high prevalence, thus increasing inequality within the class of orphan diseases. Using data from the Food and Drug Administration on the number of orphan designations, our empirical analysis shows that, while R&D has increased over time for all orphan diseases, the increase has been much greater for the less rare. According to our baseline specification, the difference between the predicted number of orphan designations for a disease belonging to the highest and the lowest class of prevalence is 5.6 times larger after 2008 than it was in 1983. Our findings support the idea that the type of incentives in place may be responsible for this increase in inequality within orphan diseases

R&D and market size: who benefits from orphan drug legislation?

Since the early 80s, incentives have been introduced to stimulate R&D for rare diseases. We develop a theoretical model to study the impact of push and pull incentives on the intensive and extensive margin of optimal R&D investments. The model describes the mechanisms by which the type of incentives provided may favor R&D for orphan diseases with comparatively high prevalence. In our empirical analysis, we merge data on orphan drug designations by the Food and Drug Administration with Orphanet data on disease characteristics. In line with the theoretical results, we find evidence supporting the idea that the incentives adopted may have contributed substantially to widening the gap between more and less rare diseases classified as orphan. Our theoretical and empirical findings together suggest that, if providing some therapeutic option to patients with very rare diseases is a priority, a revision of the current system of incentives should be considered

Spillovers of Pharmaceutical Price Regulations: evidence from the AMNOG Reform in Germany

In years of growing pharmaceutical spending, the adoption of new health technologies faces several regulatory hurdles. Such policies are typically studied at the country level, even though there are explicit and implicit channels that link decisions made in different countries. This can be relevant in the EU, where external reference pricing is widely adopted. This work exploits the IMS pricing database of cancer drugs approved by the European Medicine Agency between 2007 and 2017 to assess the impact of a pharmaceutical pricing regulation change that occurred in Germany in 2011 (the AMNOG bill) on foreign pharmaceutical prices. We show that the impact on foreign prices depends on whether the foreign country adopts external reference pricing policies and whether it includes Germany in its basket of reference countries and, symmetrically, if it enters Germany’s reference set. In particular, our diff-in-diff approach shows that AMNOG led to a price reduction for products launched in countries that refer to Germany (indirect spillover effect), whereas products launched in countries referenced by Germany experienced a 5.48% price increase (strategic spillover effect)

R&D and market size: who benefits from orphan drug legislation?

Since the early 80s, incentives have been introduced to stimulate R&D for rare diseases. We develop a theoretical model to study the impact of push and pull incentives on the intensive and extensive margin of optimal R&D investments. The model describes the mechanisms by which the type of incentives provided may favor R&D for orphan diseases with comparatively high prevalence. In our empirical analysis, we merge data on orphan drug designations by the Food and Drug Administration with Orphanet data on disease characteristics. In line with the theoretical results, we find evidence supporting the idea that the incentives adopted may have contributed substantially to widening the gap between more and less rare diseases classified as orphan. Our theoretical and empirical findings together suggest that, if providing some therapeutic option to patients with very rare diseases is a priority, a revision of the current system of incentives should be considered

Efficient detection and decoding of q-ary LDPC coded signals over partial response channels

n/