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ispartof: pages:7-12 ispartof: Procedia CIRP vol:105 pages:7-12 ispartof: The 29th CIRP Conference on Life Cycle Engineering location:Leuven status: Published online

The aluminium Twitch fraction of a Belgian recycling facility could be further sorted by implementing Laser-Induced Breakdown Spectroscopy (LIBS). To achieve this goal, the presented research identifies commercially interesting output fractions and investigates machine learning methods to classify the post-consumer aluminium scrap samples based on the spectral data collected by the LIBS sensor for 834 aluminium scrap pieces. The classification performance is assessed with X-Ray Fluorescence (XRF) reference measurements of the investigated aluminium samples, and expressed in terms of accuracy, precision, recall, and f1 score. Finally, the influence of misclassifications on the composition of the desired output fractions is evaluated.

PurposeThis paper seeks to identify how intrapreneurial self-efficacy (ISE) grows in a group of graduate students during their internship. We investigate which agency and structure factors shape their experience and stabilize or help grow their ISE and how this evolves in the course of their internship.Design/methodology/approachWe conducted group interviews with 49 last year master students of a large Belgian university during their seven-month internship. We focused on those interns with low starter ISE to better understand which factors aid or hinder ISE development.FindingsOur results show that students who did not experience ISE growth were less aware of their own agency factors, lacked supportive colleagues and experienced a misfit with their supervisors. Students who did grow their ISE did so mostly because of an initial experimentation phase, which was structured by their supervisor. This created a positive spiral where they started feeling increasingly better and able to act intrapreneurially.Originality/valueWith this study, we contribute to the extant literature in two main ways. First, we use a graduate employability lens to study the genesis of ISE. As such, we are amongst the first to investigate how education can nurture intrapreneurship and which agency and structure factors are particularly important for this. Second, we take a qualitative process approach, rather than a static and quantitative focus of most entrepreneurial education studies. As such, we gain better knowledge to the drivers of ISE at students first steps and during their internship.

The Compensation Related Utilization of Neural Circuits Hypothesis (CRUNCH) proposes a framework for understanding task-related brain activity changes as a function of healthy aging and task complexity. Specifically, it affords the following predictions: (i) all adult age groups display more brain activation with increases in task complexity, (ii) older adults show more brain activation compared with younger adults at low task complexity levels, and (iii) disproportionately increase brain activation with increased task complexity, but (iv) show smaller (or no) increases in brain activation at the highest complexity levels. To test these hypotheses, performance on a bimanual tracking task at 4 complexity levels and associated brain activation were assessed in 3 age groups (20-40, 40-60, and 60-80 years, n = 99). All age groups showed decreased tracking accuracy and increased brain activation with increased task complexity, with larger performance decrements and activation increases in the older age groups. Older adults exhibited increased brain activation at a lower complexity level, but not the predicted failure to further increase brain activity at the highest complexity level. We conclude that older adults show more brain activation than younger adults and preserve the capacity to deploy increased neural resources as a function of task demand. This work was supported by the KU Leuven Research Fund (grant number C16/15/070); the Research Foundation Flanders (grant number G089818N); the Excellence of Science (EOS) grant from the Fonds Wetenschappelijk Onderzoek—Vlaanderen (FWO) and the Fonds de la Recherche Scientifique—FNRS under EOS Project No. (grant number EOS 30446199, MEMODYN), and a postdoctoral fellowship from FWO (grant number K174216N for SC). The authors would like to thank René Clerckx for his assistance in programming the task.

Waste Electric and Electronic Equipment (WEEE) is a prominent and increasing waste stream for which the Commission of the European Union has put in place ambitious recycling targets. However, these targets can only be achieved by ensuring that both industry and governments develop adequate infrastructure and policies for recycling plastics in an economically and technically optimal manner. Unfortunately, a quantitative overview of WEEE plastics covering the composition of waste streams down to the product component level and describing polymer and additive concentrations, is currently lacking. This hinders policymakers and recyclers in making strategic decisions regarding WEEE plastics recycling. Therefore, a novel method is proposed in this paper combining experimental results with findings from prior literature in order to provide sound quantitative insights into the volume and characteristics of the plastics content of WEEE collected in the European Union. The provided overview was obtained through a combination of proprietary experimental data and a statistical data integration method. More specifically, over 3800 samples awere analysed through manual composition analysis, FTIR, and XRF. The obtained results were integrated with data from prior literature through a novel data integration methodology based on linear opinion pools. The obtained results confirm that distinct plastic types can be found in different product categories and that flame retardants are only found in high concentrations in specific waste streams or components thereof. Hence, the presented analysis provides a quantitative substantiation for the separate collection and treatment of specific waste streams in order to reduce the complexity of the mix of plastic types and allow for the more cost-efficient and higher quality recycling of plastics.

The aim of this study is to compare a qualitative and a quantitative assessment of brain diffusion-weighted imaging (DWI) in predicting outcome of comatose patients after cardiac arrest (CA). Two observers used a scoring template to analyze the DWI of 75 patients. A total of 13 regions were scored from 0 to 3 (0 = normal, 1 = probably normal, 2 = probably abnormal, 3 = definitely abnormal). The total cerebral cortex (TCC), the total deep grey nuclei (TDGN), the total brain stem, the total cerebellum, and the total brain score were calculated. Intra- and inter-observer variability were tested. The mean whole brain apparent diffusion coefficient (ADC) values and percentage of voxels below a specific ADC value cut-off were calculated. The data were correlated with clinical outcome (cerebral performance category score after 180 days, dichotomized in a score 1–2 with favorable outcome and score 3–5 with unfavorable outcome) using ROC analysis. Intra-observer variability was excellent for the TCC score (ICC 0.95 and 0.86) and the TDGN score (ICC 0.89 and 0.75). Inter-observer variability was good to excellent for total cerebral cortex score and total deep grey nuclei score in both the first (ICC 0.78 and 0.69) and third (ICC 0.86 and 0.83) image assessment. TCC and TDGN score show the best correlation with clinical outcome (highest AUC values 0.87 and 0.87). Quantitative parameters did not show good correlation with clinical outcome (AUC values 0.57 and 0.60). A qualitative assessment of brain DWI using a scoring template provides useful data regarding patient outcome while quantitative data appeared less reliable.

ispartof: Journal Of Industrial Ecology vol:27 issue:3 status: published

Due to shifting material use in several sectors, such as the automotive sector, the demand for wrought aluminium alloys is significantly increasing. Because of their low weight and desirable mechanical properties, wrought aluminium alloys find their use in many different applications. However, the primary production of aluminium is extremely energy intensive. Therefore, using secondary aluminium yields major environmental benefits. Hence, in order to avoid degradation of the aluminium quality during recycling, sorting aluminium alloys, based on their alloying elements, is necessary. Today, various non-ferrous metal fractions are either still sorted manually in unhealthy working conditions, resulting in either high labour costs, or the export of this waste stream to countries with a lower labour cost. With the emergence of novel spectrometric techniques, such as laser-induced breakdown spectrometry (LIBS) and deep learning computer vision techniques, the technical feasibility of classifying different aluminium alloys has been demonstrated. Therefore, the techno-economic viability of a robotic sorting process, that could be combined with such advanced classification systems, is presented. This study presents the development and evaluation of a robotic sorting system consisting of; a vision system, a conveyor, a SCARA robot and a pneumatic gripper. The vision system recognises the dimensions and positions of the objects on the conveyor and communicates with an innovative sequence planning algorithm. The use of experimental data enables to obtain realistic insights in the sorting efficiencies that can be obtained. The initial economic analysis illustrates the substantial potential of the proposed robotic sorting approach. To overcome saturation of the conveyor belt, two of the proposed systems are assumed to be capable of sorting 20.000 tons of aluminium annually each equipped with 6 robots creating a total added revenue up to 1,95 million euro per year.

Robotic grasping is still challenging due to limitations in perception and control, especially when the CAD models of objects are unknown. Although some grasp planning approaches using computer vision have been proposed, these methods can be seen as open-loop grasp planning methods and are often not robust enough. In this paper, a novel grasp planning method combining CNN-based quality prediction and closed-loop control (CNNB-CL) is proposed for a vacuum gripper. A large-scale dataset is generated for CNN training, which contains more than 2.3 million synthetic grasps and their grasp qualities evaluated by grasp simulations with 3D models. Unlike other neural networks which predict grasp success by assigning a binary value or grasp quality level by assigning an integer value, the proposed CNN predicts the grasp quality via a linear regression architecture. Additionally, the method adjusts the grasp strategies and detects the optimal grasp based on feedback from a force-torque sensor. Various simulations and physical experiments prove that the CNNB-CL method is robust for random noise disturbance in observation and compatible with different depth cameras and vacuum grippers. The proposed method finds the optimal grasp from 2,000 candidates within 300 ms and achieves a 92.18% average success rate for different vacuum grippers, which outperforms the state-of-the-art methods regarding success rate and robustness.

With the rapid growth in the number of sequenced genomes, genome annotation efforts became almost exclusively reliant on automated pipelines. Despite their unquestionable utility, these methods have been shown to underestimate the true complexity of the studied genomes, with small open reading frames (sORFs; ORFs typically considered shorter than 300 nucleotides) and, in consequence, their protein products (sORF encoded polypeptides or SEPs) being the primary example of a poorly annotated and highly underexplored class of genomic elements. With the advent of advanced translatomics such as ribosome profiling, reannotation efforts have progressed a great deal in providing translation evidence for numerous, previously unannotated sORFs. However, proteomics validation of these riboproteogenomics discoveries remains challenging due to their short length and often highly variable physiochemical properties. In this work we evaluate and compare tailored, yet easily adaptable, protein extraction methodologies for their efficacy in the extraction and concomitantly proteomics detection of SEPs expressed in the prokaryotic model pathogen Salmonella typhimurium (S. typhimurium). Further, an optimized protocol for the enrichment and efficient detection of SEPs making use of the of amphipathic polymer amphipol A8-35 and relying on differential peptide vs. protein solubility was developed and compared with global extraction methods making use of chaotropic agents. Given the versatile biological functions SEPs have been shown to exert, this work provides an accessible protocol for proteomics exploration of this fascinating class of small proteins.

Bioactive peptides exhibit key roles in a wide variety of complex processes, such as regulation of body weight, learning, aging, and innate immune response. Next to the classical bioactive peptides, emerging from larger precursor proteins by specific proteolytic processing, a new class of peptides originating from small open reading frames (sORFs) have been recognized as important biological regulators. But their intrinsic properties, specific expression pattern and location on presumed non-coding regions have hindered the full characterization of the repertoire of bioactive peptides, despite their predominant role in various pathways. Although the development of peptidomics has offered the opportunity to study these peptides in vivo, it remains challenging to identify the full peptidome as the lack of cleavage enzyme specification and large search space complicates conventional database search approaches. In this study, we introduce a proteogenomics methodology using a new type of mass spectrometry instrument and the implementation of machine learning tools toward improved identification of potential bioactive peptides in the mouse brain. The application of trapped ion mobility spectrometry (tims) coupled to a time-of-flight mass analyzer (TOF) offers improved sensitivity, an enhanced peptide coverage, reduction in chemical noise and the reduced occurrence of chimeric spectra. Subsequent machine learning tools MS2PIP, predicting fragment ion intensities and DeepLC, predicting retention times, improve the database searching based on a large and comprehensive custom database containing both sORFs and alternative ORFs. Finally, the identification of peptides is further enhanced by applying the post-processing semi-supervised learning tool Percolator. Applying this workflow, the first peptidomics workflow combined with spectral intensity and retention time predictions, we identified a total of 167 predicted sORF-encoded peptides, of which 48 originating from presumed non-coding locations, next to 401 peptides from known neuropeptide precursors, linked to 66 annotated bioactive neuropeptides from within 22 different families. Additional PEAKS analysis expanded the pool of SEPs on presumed non-coding locations to 84, while an additional 204 peptides completed the list of peptides from neuropeptide precursors. Altogether, this study provides insights into a new robust pipeline that fuses technological advancements from different fields ensuring an improved coverage of the neuropeptidome in the mouse brain.

The ambition to extend material recycling from end-of-life electric and electronic products seems to lead to an unavoidable trade-off between circularity maximisation and quality concerns. However, optimized pre-treatment can help to assure that both polymer fraction compatibility and imperfect separation with the available technologies are fully taken into account to assure maximum plastics recycling. A systematic approach to determine pre-sorting product and component clusters, that allow recycling with conventional separation techniques, is explained. Using statistical data sets for a case study of selected common WEEE products categories, the clusters in which to sort the products and dismantled components are documented and can support enhanced polymer recycling in an industrial setting.

Because of the low concentrations of Tantalum on printed wiring boards, today it is not economically viable to recycle Tantalum in pyro- and hydrometallurgical processes. However, in case the Tantalum can be up concentrated, the recycling of the Tantalum can become economically viable in the multi-stage process used to refine Tantalum from ores. Therefore, the presented research investigates the techno-economic potential of selectively removing Tantalum capacitors from PWBs prior to treating the complete PWBs in integrated precious metal smelting and refining installations. In the presented research the economic potential is analysed using return on investment analysis for automated selective dismantling and the viability of manual selective dismantling. These analyses are based on the measured metal concentration of Tantalum capacitors, the composition of a typical PWB and the results of lab experiments for the removal and detection of components using deep learning computer vision.

PurposeThe purpose of this paper is to investigate to what extent a consumer’s repair strategy impacts the annual costs of ownership of a washing machine and two types of vacuum cleaner.Design/methodology/approachThe annual cost of ownership is determined by calculating the annual life cycle cost (LCC) for the respective devices. The annual LCCs of the different scenarios allow a comparison of the different repair strategy options. A Monte Carlo simulation is run to introduce parameter variability. The device’s failure rate is estimated by a combination of data sets on the devices’ performance.FindingsResults demonstrate that the repair of the devices considered is a more favourable option over replacement. A consumer who aims for the lowest annual LCC should allow for a high number of repairs per device, without putting a maximum on the cost per repair. However, the consumer should become more cautious when a device approaches the end of its expected lifetime. Finally, the purchase of warranty can be interesting when the warranty covers a sufficiently long proportion of the device’s (expected) lifetime and when its cost does not exceed a threshold proportion of the initial purchase price.Research limitations/implicationsThe costs for repair might be overestimated. Future research can focus on the reduction of repair costs following self-repair.Practical implicationsThe results provide strong arguments in favour of repair instead of replacement of broken devices.Originality/valueThis is the first research to quantify the influence of consumer behaviour in the context of repair of devices on the ownership costs of these devices.

Repetition suppression (RS) reflects a neural attenuation during repeated stimulation. We used fMRI and the subsequent memory paradigm to test the predictive coding hypothesis for RS during visual memory processing by investigating the interaction between RS and differences due to memory in category-selective cortex (FFA, pSTS, PPA, and RSC). Fifty-six participants encoded face and house stimuli twice, followed by an immediate and delayed (48 h) recognition memory assessment. Linear Mixed Model analyses with repetition, subsequent recognition performance, and their interaction as fixed effects revealed that absolute RS during encoding interacts with probability of future remembrance in face-selective cortex. This effect was not observed for relative RS, i.e. when controlled for adapter-response. The findings also reveal an association between adapter response and RS, both for short and long term (48h) intervals, after controlling for the mathematical dependence between both measures. These combined findings are challenging for predictive coding models of visual memory and are more compatible with adapter-related and familiarity accounts., In order to examine underlying mechanisms of repetition suppression, Stam et al conducted an fMRI study in which participants were presented with face and house stimuli followed by an immediate and 48 h-delayed memory task. Their data demonstrated that adapter-related and familiarity account modelling are more likely to be more accurate for visual memory than previous predictive coding models.