The delegated authority model misused as a strategy of disengagement in the case of climate change

The characterisation of anthropogenic climate change as a violation of basic human rights is gaining wide recognition. Many people believe that tackling this problem is exclusively the job of governments and supranational institutions (especially the United Nations Framework Convention on Climate Change). This argument can be traced back to the delegated authority model, according to which the legitimacy of political institutions depends on their ability to solve problems that are difficult to address at the individual level. Since the institutions created to tackle climate change fail to do so, their legitimacy is under great pressure and can only be saved by considerations of feasibility. We argue that democratically elected representatives are able to claim that a more robust climate policy is unfeasible, but only because the mandate we as citizens grant them is very restrictive. Instead of shifting responsibility for the thoroughly inadequate response to climate change fully to political representatives, we should highlight the failure of the political community as a whole to fulfil its responsibility at the input-side of the delegation of authority. When individual voters fail to fulfil the minimal obligation to at least vote for parties that explicitly advocate robust climate policies, they cannot hide behind the delegated authority argument, but should accept their complicity in the massive violations of basic human rights caused by the failure to successfully tackle climate change

Training Working Memory in Adolescents Using Serious Game Elements: Pilot Randomized Controlled Trial

Working memory capacity has been found to be impaired in adolescents with various psychological problems, such as addictive behaviors. Training of working memory capacity can lead to significant behavioral improvements, but it is usually long and tedious, taxing participants' motivation to train. This study aimed to evaluate whether adding game elements to the training could help improve adolescents' motivation to train while improving cognition. A total of 84 high school students were allocated to a working memory capacity training, a gamified working memory capacity training, or a placebo condition. Working memory capacity, motivation to train, and drinking habits were assessed before and after training. Self-reported evaluations did not show a self-reported preference for the game, but participants in the gamified working memory capacity training condition did train significantly longer. The game successfully increased motivation to train, but this effect faded over time. Working memory capacity increased equally in all conditions but did not lead to significantly lower drinking, which may be due to low drinking levels at baseline. We recommend that future studies attempt to prolong this motivational effect, as it appeared to fade over time. [Abstract copyright: ©Wouter J Boendermaker, Thomas E Gladwin, Margot Peeters, Pier JM Prins, Reinout W Wiers. Originally published in JMIR Serious Games (http://games.jmir.org), 23.05.2018.

Overflow Tests on Grass-Covered Embankments at the Living Lab Hedwige-Prosperpolder: An Overview

In regions with a temperate climate, a well-maintained grass sod on a clay layer is considered a reliable protection for dams and dikes. In the Living Lab Hedwige-Prosperpolder, on the left bank of the Scheldt river straddling the border between Belgium and the Netherlands, a series of 27 overflow tests with a purpose-built overflow generator has been executed to determine the strength of the protective layer against erosion at various conditions. The goal of this paper is to inform on the executed test program and the initial results. From the results, it was concluded that in general, a high-quality grass cover on the landside dike slope can withstand high overflow discharges well for 12 to 30 h, without severe erosion damage. Anomalies, such as the presence of animal burrows, reed vegetation, and already present deformations can strongly reduce the resistance of the cover layer and may lead to failure within a couple of hours

Актуальность и основные аспекты антикризисного менеджмента предприятием

Целью данной статьи является выявление значимости и основных аспектов антикризисного менеджмента

Specific N-terminal attachment of TMTHSI linkers to native peptides and proteins for strain-promoted azide alkyne cycloaddition

The site specific attachment of the reactive TMTHSI-click handle to the N-terminus of peptides and proteins is described. The resulting molecular constructs can be used in strain-promoted azide alkyne cycloaddition (SPAAC) for reaction with azide containing proteins e.g., antibodies, peptides, nanoparticles, fluorescent dyes, chelators for radioactive isotopes and SPR-chips etc

Enhanced plastic recycling using RGB+depth fusion with massFaster and massMask R-CNN

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe rapid increase in waste generation from electrical and electronic equipment (WEEE) has created the need for more advanced sensor-based systems to sort this complex type of waste. Therefore, this study proposes a method for object detection, instance segmentation, and mass estimation of plastics and contaminants using the fusion of RGB and depth (D) images. The methodology is based on the Faster and Mask R-CNN with an extra head for the mass estimation. In addition, a pre-processing method to enhance the depth image (ED) is proposed. To evaluate the data fusion and pre-processing method, two data sets of plastics and impurities were created containing images with and without overlapping samples. The first data set contains 174 RGB images and depth (D) maps of 3146 samples, excluding their mass value, while the second data set contains 42 RGB and D images of 766 pieces together with their mass. The first and second data sets were used to evaluate the performance of Mask and Faster R-CNN. Further, the second data set was used to evaluate the network’s performance with the additional head for mass estimation.The proposed method achieved 0.75 R 2 , 1.39 RMSE, and 0.81 MAE with an IoU greater than 50% using the network Resnet50_FPN_RGBED. Hence, it can be concluded that the presented method can distinguish plastics from other materials with reasonable accuracy. Furthermore, the mass of each detected particle can be estimated individually, which is of great relevance for the recycling sector. Knowing the mass distribution and the percentage of contaminants in a waste stream of mixed plastics can be valuable for adjusting the parameters of upstream and downstream sorting processes.Peer ReviewedPostprint (author's final draft

Simultaneous mass estimation and class classification of scrap metals using deep learning

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksWhile deep learning has helped improve the performance of classification, object detection, and segmentation in recycling, its potential for mass prediction has not yet been explored. Therefore, this study proposes a system for mass prediction with and without feature extraction and selection, including principal component analysis (PCA). These feature extraction methods are evaluated on a combined Cast (C), Wrought (W) and Stainless Steel (SS) image dataset using state-of-the-art machine learning and deep learning algorithms for mass prediction. After that, the best mass prediction framework is combined with a DenseNet classifier, resulting in multiple outputs that perform both object classification and object mass prediction. The proposed architecture consists of a DenseNet neural network for classification and a backpropagation neural network (BPNN) for mass prediction, which uses up to 24 features extracted from depth images. The proposed method obtained 0.82 R2, 0.2 RMSE, and 0.28 MAE for the regression for mass prediction with a classification performance of 95% for the C&W test dataset using the DenseNet+BPNN+PCA model. The DenseNet+BPNN+None model without the selected feature (None) used for the CW&SS test data had a lower performance for both classification of 80% and the regression (0.71 R2, 0.31 RMSE, and 0.32 MAE). The presented method has the potential to improve the monitoring of the mass composition of waste streams and to optimize robotic and pneumatic sorting systems by providing a better understanding of the physical properties of the objects being sorted.Peer ReviewedPostprint (author's final draft

Epidemiology of traumatic brain injury in Europe

Background: Traumatic brain injury (TBI) is a critical public health and socio-economic problem throughout the world, making epidemiological monitoring of incidence, prevalence and outcome of TBI necessary. We aimed to describe the epidemiology of traumatic brain injury in Europe and to evaluate the methodology of incidence studies. Method: We performed a systematic review and meta-analyses of articles describing the epidemiology of TBI in European countries. A search was conducted in the PubMed electronic database using the terms: epidemiology, incidence, brain injur*, head injur* and Europe. Only articles published in English and reporting on data collected in Europe between 1990 and 2014 were included. Results: In total, 28 epidemiological studies on TBI from 16 European countries were identified in the literature. A great variation was found in case definitions and case ascertainment between studies. Falls and road traffic accidents (RTA) were the two most frequent causes of TBI, with falls being reported more frequently than RTA. In most of the studies a peak TBI incidence was seen in the oldest age groups. In the meta-analysis, an overall incidence rate of 262 per 100,000 for admitted TBI was derived. Conclusions: Interpretation of published epidemiologic studies is confounded by differences in inclusion criteria and case ascertainment. Nevertheless, changes in epidemiological patterns are found: falls are now the most common cause of TBI, most notably in elderly patients. Improvement of the quality of standardised data collection for TBI is mandatory for reliable monitoring of epidemiological trends and to inform appropriate targeting of prevention campaigns