Evolution and external impact of MNE subsidiaries in emerging economies : case study evidence from German MNEs in China and India

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Stackelberg Max Closure with Multiple Followers

In a Stackelberg max closure game, we are given a digraph whose vertices correspond to projects from which firms can choose and whose arcs represent precedence constraints. Some projects are under the control of a leader who sets prices in the first stage of the game, while in the second stage, the firms choose a feasible subset of projects of maximum value. For a single follower, the leader’s problem of finding revenue-maximizing prices can be solved in strongly polynomial time. In this paper, we focus on the setting with multiple followers and distinguish two situations. In the case in which only one copy of each project is available (limited supply), we show that the two-follower problem is solvable in strongly polynomial time, whereas the problem with three or more followers is NP-hard. In the case of unlimited supply, that is, when sufficient copies of each project are available, we show that the two-follower problem is already APX-hard. As a side result, we prove that Stackelberg min vertex cover on bipartite graphs with a single follower is APX-hard

Draft Genome Sequence of Rheinheimera sp. Strain SA_1 Isolated from Iron Backwash Sludge in Germany

Rheinheimera sp. strain SA_1 is an iron-depositing bacterium for which we report a draft genome sequence. Strain SA_1 was isolated from iron backwash sludge of a waterworks in Germany. The Illumina MiSeq technique was used to sequence the genome of the strain.BMBF, 02WT1184, Verbundprojekt Mikrobielle Verockerung, Teilprojekt 1: Mikrobiologie, Wasserreinhaltung & Fluidsystemdynami

Decomposition of Random Errors Inherent to HOAPS-3.2 Near-Surface Humidity Estimates Using Multiple Triple Collocation Analysis

Latent heat fluxes (LHF) play an essential role in the global energy budget and are thus important for understanding the climate system. Satellite-based remote sensing permits a large-scale determination of LHF, which, amongst others, are based on near-surface specific humidity qa. However, the qa random retrieval error (Etot) remains unknown. Here, a novel approach is presented to quantify the error contributions to pixel-level qa of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite (HOAPS, version 3.2) dataset. The methodology makes use of multiple triple collocation (MTC) analysis between 1995-2008 over the global ice-free oceans. Apart from satellite records, these datasets include selected ship records extracted from the Seewetteramt Hamburg (SWA) archive and the International Comprehensive Ocean-Atmosphere Data Set (ICOADS), serving as the in-situ ground reference. The MTC approach permits the derivation of Etot as the sum of model uncertainty EM and sensor noise EN, while random uncertainties due to in-situ measurement errors (Eins) and collocation (EC) are isolated concurrently. Results show an Etot average of 1.1 ± 0.3 g kg-1, whereas the mean EC (Eins) is in the order of 0.5 ± 0.1 g kg-1 (0.5 ± 0.3 g kg-1). Regional analyses indicate a maximum of Etot exceeding 1.5 g kg-1 within humidity regimes of 12-17 g kg-1, associated with the single-parameter, multilinear qa retrieval applied in HOAPS. Multi-dimensional bias analysis reveals that global maxima are located off the Arabian Peninsula

HOAPS precipitation validation with ship-borne rain gauge measurements over the Baltic Sea

Global ocean precipitation is an important part of the water cycle in the climate system. A number of efforts have been undertaken to acquire reliable estimates of precipitation over the oceans based on remote sensing and reanalysis modelling. However, validation of these data is still a challenging task, mainly due to a lack of suitable in situ measurements of precipitation over the oceans. In this study, validation of the satellite-based Hamburg Ocean Atmosphere Parameters and fluxes from Satellite data (HOAPS) climatology was conducted with in situ measurements by ship rain gauges over the Baltic Sea from 1995 to 1997. The ship rain gauge data are point-to-area collocated against the HOAPS data. By choosing suitable collocation parameters, a detection rate of up to about 70% is achieved. Investigation of the influence of the synoptic situation on the detectability shows that HOAPS performs better for stratiform than for convective precipitation. The number of collocated data is not sufficient to validate precipitation rates. Thus, precipitation rates were analysed by applying an interpolation scheme based on the Kriging method to both data sets. It was found that HOAPS underestimates precipitation by about 10%, taking into account that precipitation rates below 0.3 mm h−1 cannot be detected from satellite information

Validation of HOAPS Rain Retrievals against OceanRAIN In-Situ Measurements over the Atlantic Ocean

The satellite-derived HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data) precipitation estimates have been validated against in-situ precipitation measurements from optical disdrometers, available from OceanRAIN (Ocean Rainfall And Ice-phase precipitation measurement Network) over the open-ocean by applying a statistical analysis for binary estimates. In addition to using directly collocated pairs of data, collocated data were merged within a certain temporal and spatial threshold into single events, according to the observation times. Although binary statistics do not show perfect agreement, simulations of areal estimates from the observations themselves indicate a reasonable performance of HOAPS to detect rain. However, there are deficits at low and mid-latitudes. Weaknesses also occur when analyzing the mean precipitation rates; HOAPS underperforms in the area of the intertropical convergence zone, where OceanRAIN observations show the highest mean precipitation rates. Histograms indicate that this is due to an underestimation of the frequency of moderate to high precipitation rates by HOAPS, which cannot be explained by areal averaging