Noise elimination by piecewise cross correlation of photometer outputs

A piecewise cross correlation technique has been developed to analyze the outputs of remote detection devices. The purpose of this technique is to eliminate the noise from optical background fluctuations, from transmission fluctuations and from detectors by calculating the instantaneous product of the detector output and a reference signal. Each noise component causes positive and negative oscillations of the instantaneous product and may thus be cancelled by an integration of the instantaneous product. The resultant product mean values will then contain the desired information on the spatial and temporal variation of emission, absorption and scattering processes in the atmosphere

A marine biodiversity observation network for genetic monitoring of hard-bottom communities (ARMS-MBON)

Marine hard-bottom communities are undergoing severe change under the influence of multiple drivers, notably climate change, extraction of natural resources, pollution and eutrophication, habitat degradation, and invasive species. Monitoring marine biodiversity in such habitats is, however, challenging as it typically involves expensive, non-standardized, and often destructive sampling methods that limit its scalability. Differences in monitoring approaches furthermore hinders inter-comparison among monitoring programs. Here, we announce a Marine Biodiversity Observation Network (MBON) consisting of Autonomous Reef Monitoring Structures (ARMS) with the aim to assess the status and changes in benthic fauna with genomic-based methods, notably DNA metabarcoding, in combination with image-based identifications. This article presents the results of a 30-month pilot phase in which we established an operational and geographically expansive ARMS-MBON. The network currently consists of 20 observatories distributed across European coastal waters and the polar regions, in which 134 ARMS have been deployed to date. Sampling takes place annually, either as short-term deployments during the summer or as long-term deployments starting in spring. The pilot phase was used to establish a common set of standards for field sampling, genetic analysis, data management, and legal compliance, which are presented here. We also tested the potential of ARMS for combining genetic and image-based identification methods in comparative studies of benthic diversity, as well as for detecting non-indigenous species. Results show that ARMS are suitable for monitoring hard-bottom environments as they provide genetic data that can be continuously enriched, re-analyzed, and integrated with conventional data to document benthic community composition and detect non-indigenous species. Finally, we provide guidelines to expand the network and present a sustainability plan as part of the European Marine Biological Resource Centre (www.embrc.eu).Peer reviewe

Hostile Corporate Takeovers: History and Overview

This Article covers the history of corporate anti-takeover from pre-Williams Act proxy fights, through the Williams Act changes, to first- and second-generation state anti-takeover legislation. The Article also describes and compares anti-takeover legislation with special emphasis on the Indiana statute and the new Delaware statute

A Divide & Concur Approach to Collaborative Goal Modeling with Merge in Early-RE: Supplemental Material

Supplemental material for the paper: A Divide & Concur Approach to Collaborative Goal Modeling with Merge in Early-RE This paper proposes a formal approach to the problem of merging the attributes of intentions and actors, once these elements have been matched

A Divide & Concur Approach to Collaborative Goal Modeling with Merge in Early-RE

Goal modeling enables the elicitation of stakeholders’ intentionality in the earlier stages of a project. Often, approaches are limited by the effort required to create an initial goal model. In this paper, we investigate the problem of model merging for Tropos goal models. Specifically, we propose a formal approach to the problem of automatically merging the attributes of intentions and actors, once these elements have been matched. Additionally, recent approaches have investigated answering questions about future evolutions of stakeholders’ projects with goal models. In this work we consider both static models, as well as those with timing information, using the principles of gullibility, contradiction, and consensus. We study our implementation and validate the merge operation on a variety of models from the literature