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    Data of physical and electrochemical characteristics of calendered NMC622 electrodes and lithium-ion cells at pilot-plant battery manufacturing

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    The data reported here was prepared to study the effects of calendering process on NMC622 cathodes using a 3-3-2 full factorial design of experiments. The data set consists of 18 unique combinations of calender roll temperature (85 °C, 120 °C, or 145 °C), electrode porosity (30%, 35%, or 40%), and electrode mass loading (120 g/m² or 180 g/m²). The reported physical characteristics of the electrodes include thickness, coating weight, maximum tensile strength, and density. The electrochemical performances of the electrodes were obtained by testing coin cells. In this context, 54 half-cells were produced, 3 per each calendering experiment to ensure repeatability and reliability of the results. The responses of interest included, charge energy capacity at C/2, C/5, discharge energy capacity at C/20, C/5, C/2, C, 2C, 5C, 10C, gravimetric capacity (charge at C/2, C/5, discharge at C/20, C/5, C/2, C, 2C, 5C, 10C), volumetric capacity (charge at C/2, C/5, discharge at C/20, C/5, C/2, C, 2C, 5C, 10C), rate performance (5C:0.2C), area specific impedance (at 10% to 90% state of charge (SoC) in 10 breakpoints), long-term cycling capacity (charge at C/5 for 50 cycles, discharge at C/2 for 50 cycles), long-term cycling degradation (at C/2 during 50 cycles of charge and discharge), and cycling columbic efficiency (50 cycles of C/2 charge and discharge). The details of the experimental design that has led to this data as well as comprehensive statistical analysis, and machine learning-based models can be found in the recently published manuscripts by Hidalgo et al. and Faraji-Niri et al. [1,2]

    Harmonisation of assessments of attention, social, emotional, and behaviour problems using the Child Behavior Checklist and the Strengths and Difficulties Questionnaire

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    Objectives: Retrospective harmonisation of data obtained through different instruments creates measurement error, even if the underlying concepts are assumed the same. We tested a novel method for item‐level data harmonisation of two widely used instruments that measure emotional and behavioural problems: the Child Behavior Checklist (CBCL) and the Strengths and Difficulties Questionnaire (SDQ). Methods: Item content of the CBCL and SDQ was mapped onto four dimensions: emotional problems, peer relationship problems, hyperactivity/inattention and conduct problems. A diverse test sample was drawn from four prospective longitudinal birth cohort studies in Australia and Europe who used one or both instruments. The pooled sample included 5188 data points assessing children and adolescents aged 6–13 years (N = 257–704 participants per cohort). Measurement invariance was assessed using latent variable multi‐group confirmatory factor analysis. Results: Fifteen items from the CBCL and SDQ were mapped onto four dimensions allowing for measurement invariance testing as part of a stepwise process. Partial strict invariance between CBCL and SDQ assessments was established for all four dimensions. Conclusions: The harmonised dimensions of emotional, peer relationship, hyperactivity/inattention and conduct problems are invariant across the CBCL and SDQ suggesting that these dimensions can be reliably compared with limited measurement error

    Spectrogram-based approach with convolutional neural network for human activity classification

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    Human activity recognition (HAR) is an expanding research field for analyzing holistic wellbeing trajectory, frailty detection and prevention of critical situations. With the increased availability of wearables and novel machine learning methods, the automatic recognition of human activities is exploited by real-time signals via Deep Learning techniques. This is due to their capability of learning contextual and localized patterns which give them a significant edge over traditional machine learning approaches. However, most of the state-of-the-art deep learning techniques have limitations due to limited number of features present in temporal dimension. In this regard, we propose Spectrogram-driven multilayer 2D-Convolutional Neural Network (2D-CNN) to classify among different types of human activities using triaxial accelerometer data obtained under MEDICON Scientific Challenge. The spectrogram has significant advantage over 1D time domain signals due to their capability to extract power spectrum in time as well as in frequency domain. The dataset consists of twelve activities of daily living and three types of simulated falls performed by subjects wearing a single accelerometer. In total, the dataset was composed by 468 instances. The spectrograms were determined by Short Time Fourier Transform (STFT) from the continuous signal obtained from X-, Y-, and Z-axis of the accelerometer signals. Experimental results show that our spectrogram driven 2D-CNN model reach an overall accuracy of 86.02% and an overall -score of 81.09% in classifying all the activity classes; significantly outperforming the deep learning architecture based on 1D time domain signal


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    Sampling as a bridge across levels of analysis

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    Effects of delayed testing on decisions to stop learning

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    This study explores whether people’s preference to restrict to-be-learned material is influenced by memory test timing. In Experiments 1a and 2a, participants studied word lists. For control groups, lists were displayed in their entirety, whereas participants in other groups could stop the lists early. We investigated whether participants decided to terminate learning when they expected their free-recall memory to be tested after a short (Experiment 1a) or long (Experiment 2a) delay. Experiments 1b and 2b tested participants’ theoretical assumptions about learning termination. Participants who terminated learning recalled fewer words than those who saw all to-be-remembered materials. When the memory test immediately followed the learning phase, more than half of the participants decided to stop learning. However, when there was any time delay between learning and testing, only around a quarter of them decided to stop. Delayed testing can effectively discourage a maladaptive learning strategy of learning termination

    Digging holes, excavating the present, mining the future : extractivism, time, and memory in Fiston Mwanza Mujila’s and Sammy Baloji’s works

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    This article explores the links between creative imagination and extraction in the Democratic Republic of the Congo (DRC). This question has an undeniable memorial dimension, for extraction, as a crucial point of entry into Congolese historical consciousness, allows for a multi-perspectivist examination of the way in which the memory of the past has been archived, experienced, and (mis)interpreted. As a key term to understand Congo’s geopolitical position since colonial times, extraction offers a rich array of tropes and ideas to assess culture from the DRC and the Congolese diaspora. First, I reflect on the notions of extraction and extractivism; secondly, I analyse how they form the basis of Sammy Baloji's multi-media work in Mémoire (2006) and Mémoire/Kolwezi (2014); then, I turn to La Danse du vilain (2020) and Tram 83 (2014) by Fiston Mwanza Mujila, first to assess how extraction is employed in these novels, then to conduct a reflection on ‘necropolitics’, and reveal little-known aspects of diamond digging during the Mobutu era. I will also show that Baloji’s and Mujila’s creative trajectories have been enriched by dialogues with Filip De Boeck, the Belgian social anthropologist and specialist of the DRC

    Ancient solutions and translators of Lagrangian mean curvature flow

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    Suppose that ℳ is an almost calibrated, exact, ancient solution of Lagrangian mean curvature flow in Cn. We show that if ℳ has a blow-down given by the static union of two Lagrangian subspaces with distinct Lagrangian angles that intersect along a line, then ℳ is a translator. In particular in C2, all almost calibrated, exact, ancient solutions of Lagrangian mean curvature flow with entropy less than 3 are special Lagrangian, a union of planes, or translators


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