Assessing the Impact of Greenhouse Gas Emissions on Economic Profitability of Arable, Forestry, and Silvoarable Systems

This study assesses the greenhouse gas (GHG) emissions and sequestration of a silvoarable system with poplar trees and a crop rotation of wheat, barley, and oilseed rape and compares this with a rotation of the same arable crops and a poplar plantation. The Farm-SAFE model, a financial model of arable, forestry, and silvoarable systems, was modified to account for life-cycle greenhouse gas emissions. Greenhouse gas emissions from tree and crop management were determined from life-cycle inventories and carbon storage benefits from the Yield-SAFE model, which predicts crop and tree yields in arable, forestry, and silvoarable systems. An experimental site in Silsoe in southern England served as a case study. The results showed that the arable system was the most financially profitable system, followed by the silvoarable and then the forestry systems, with equivalent annual values of EUR 560, 450 and 140 ha−1, respectively. When the positive and negative externalities of GHG sequestration and emissions were converted into carbon equivalents and given an economic value, the profitability of the arable systems was altered relative to the forestry and silvoarable systems, although in the analysis, the exact impact depended on the value given to GHG emissions. Market values for carbon resulted in the arable system remaining the most profitable system, albeit at a reduced level. Time series values for carbon proposed by the UK government resulted in forestry being the most profitable system. Hence, the relative benefit of the three systems was highly sensitive to the value that carbon was given in the analysis. This in turn is dependent on the perspective that is given to the analysis.

Design of the Safe Model of Landfill Dikes for Developing Countries

This article is a dimensioning study of the landfill locker dike of the city of Casablanca, where the geotechnical parameters of waste have particular limitations, theses limitation are a common characteristic for most developing countries. Considering the very small available land area in general, the objective is to achieve an optimal dimensioning of the locker to maximize the volume to be buried, while respecting the requirements of stability of the structure; namely, the model whose safety factor will be greater than 1.5. The Factor of Safety (FoS) was calculated by the Finite Element Method (FEM) using “PLAXIS 2D” software. The results show that, for both cases (with and without final cover), FoS, as obtained from both the analysis, show a similar pattern, with the maximum FoS for low inclinations, especially those lower than 15.95°. The critical FoS (< 1), was obtained for slopes strictly greater than 21.80°. The study also demonstrated that the 3.5H 1V model could be considered as the optimal one that satisfies the structural stability requirements (FoS> 1.5) and maximizes the volume to be buried. Nevertheless, the validation of this model is conditioned by a geometric modification (weakening the lateral slope of the landfill, by moving the dikes by 3 m) and the improvement of the mechanical characteristics of the soil of the peripheral dike, through replacing the material with the compacted clay. This model was validated by PLAXIS, which showed that the FoS for the phase preceding the anchoring of the final cover is 1.577, which reaches 1.604 after anchoring.

En kritisk granskning av Oregonmodellen

The so-called Oregon model has been described as a more attractive and safer alternative for assisted dying than the controversial euthanasia laws in the Benelux countries. Many advocates of assisted dying believe that the Oregon model, which implies physician-assisted suicide, is better adapted to Swedish (Nordic) conditions. In order to be able to offer assisted dying according to the Oregon model, seven criteria must be met. In this chapter, we will analyse the criteria and examine whether the practices of the Oregon model meet them. Is it a safe model to help severely ill people from suffering during their very last stage of life? To what extent can the model guarantee security and justice for those involved? Finally, we want to investigate whether it can be a suitable model for the Nordic countries.

DeepInspect: A Black-box Trojan Detection and Mitigation Framework for Deep Neural Networks

Deep Neural Networks (DNNs) are vulnerable to Neural Trojan (NT) attacks where the adversary injects malicious behaviors during DNN training. This type of ‘backdoor’ attack is activated when the input is stamped with the trigger pattern specified by the attacker, resulting in an incorrect prediction of the model. Due to the wide application of DNNs in various critical fields, it is indispensable to inspect whether the pre-trained DNN has been trojaned before employing a model. Our goal in this paper is to address the security concern on unknown DNN to NT attacks and ensure safe model deployment. We propose DeepInspect, the first black-box Trojan detection solution with minimal prior knowledge of the model. DeepInspect learns the probability distribution of potential triggers from the queried model using a conditional generative model, thus retrieves the footprint of backdoor insertion. In addition to NT detection, we show that DeepInspect’s trigger generator enables effective Trojan mitigation by model patching. We corroborate the effectiveness, efficiency, and scalability of DeepInspect against the state-of-the-art NT attacks across various benchmarks. Extensive experiments show that DeepInspect offers superior detection performance and lower runtime overhead than the prior work.