Miracle-dz/UniFAR-FTUs huggingface dataset:
Acceptance of climate-oriented policy measures under the COVID-19 crisis: an empirical analysis for Germany

ABSTRACT Based on data from a representative survey among citizens in Germany during the first peak of the COVID-19 crisis, this paper empirically examines the acceptance of climate-oriented economic stimulus programmes and several further individual climate policy measures. On the basis of our descriptive analysis, we find no evidence that support for such policy measures in Germany is very different during the COVID-19 crisis, compared to before the crisis. However, our econometric analysis points to the relevance of economic concerns. For example, a perceived deterioration of the general economic situation due to the COVID-19 crisis (and thus sociotropic concerns) has a significantly negative effect on the acceptance of climate-oriented economic stimulus programmes. Our econometric analysis also reveals that ‘pocketbook’ or egotropic concerns are even more relevant since individuals with concerns about their personal economic situation due to the COVID-19 crisis are significantly less supportive of several individual climate policy measures, especially if these lead to higher costs in daily life and are thus directly financially unfavourable. In addition, an identification with socially oriented policy has a significantly positive effect on support for climate policy measures that are financially beneficial for socially underprivileged groups and a significantly negative effect on support for policy measures that are financially unfavourable for them. Our estimation results thus point to the importance of social aspects for the acceptance of climate-oriented policy measures. We also discuss several climate policy implications. For example, our estimation results suggest that individually perceived economic and financial concerns due to the COVID-19 crisis should be addressed and ideally reduced to increase public support of climate-oriented policy measures. Key policy insights We find no evidence that the acceptance of climate policy measures in Germany is very different during the COVID-19 crisis, compared to before the crisis. Our empirical results are in line with the idea that economic downturns or crises can decrease the acceptance of climate-oriented policy measures among individuals with concerns about their personal economic situation. Our empirical analysis also points to the relevance of social aspects for support for climate policy measures. Therefore, general economic concerns due to the COVID-19 crisis should be addressed and ideally reduced to increase public support for climate-oriented policy measures. In addition, successful climate policy should be socially oriented and should consider distribution effects.
Adaptive Computing Scheduling for Edge-Assisted Autonomous Driving

This paper investigates computing resource scheduling for real-time applications in autonomous driving, such as localization and obstacle avoidance. In our considered scenario, autonomous vehicles periodically sense the environment, offload sensor data to an edge server for processing, and receive computing results from the server. Due to mobility and computing latency, a vehicle travels some distance in the duration between the instant of offloading its sensor data and the instant of receiving the computing result. Our objective is finding a scheduling scheme for the edge sever to minimize the above traveled distance of vehicles. The approach is to determine the processing order according to individual vehicle mobility and computing capability of the edge server. We formulate a restless multi-arm bandit (RMAB) problem, design a Whittle index based stochastic scheduling scheme, and determine the index using a deep reinforcement learning (DRL) method. The proposed scheduling scheme avoids the time-consuming policy exploration common in DRL scheduling approaches and makes effectual decisions with low complexity. Extensive simulation results demonstrate that the proposed indexed-based scheme can deliver computing results to the vehicles promptly while adapting to time-variant vehicle mobility.
Distinguishing three effects of time pressure on risk taking: Choice consistency, risk preference, and strategy selection

Quick decision making under risk is ubiquitous in modern times, yet its consequences are not fully understood. Time pressure might change people's risk preferences, lead to less consistent choices, or change people's decision strategy. With the present work, we make the novel contribution of testing all hypotheses against each other in a unifying hierarchical Bayesian model. In two studies, participants decided repeatedly between two risky gambles either with or without high time pressure. We found a significant increase in risky choices under time pressure. With modeling, we show that time pressure decreased choice consistency but did not systematically affect people's risk preferences. In addition, the number of participants using simple, noncompensatory strategies increased slightly under time pressure. Finally, participants did not systematically choose easier gambles more often under time pressure. Thus, a reliable analysis of the effect of time pressure on preferential choice requires a model framework that allows for the distinction between the various effects time pressure can have.
Cytotoxic Effect of Cord Blood Derived Natural Killer Cells on Breast Cancer Cells

METHODS CB mononuclear cells were isolated by Ficoll-Paque and NK cells were selected by with magnetic activated cell sorting (MACS) technique. CB-NK cell’s surface markers were quantified by flow cytometry analysis. Water-soluble tetrazolium salt cytotoxicity assay was used to measure cell viability; Annexin V/7-amino actinomycin D assay was used to measure apoptosis and necrosis. Enzyme-linked immunosorbent assay was used to determine perforin and Granzyme B activity of CB-NK cells.
Learning Analytics and Education Data Mining in Higher Education

In this chapter, the authors propose an overview on the use of learning analytics (LA) and educational data mining (EDM) in addressing issues related to its uses and applications in higher education. They aim to provide meaningful and substantial answers to how both LA and EDM can advance higher education from a large scale, big data educational research perspective. They present various tasks and applications that already exist in the field of EDM and LA in higher education. They categorize them based on their purposes, their uses, and their impact on various stakeholders. They conclude the chapter by critically analyzing various forecasts regarding the impact that EDM will have on future educational setting, especially in light of the current situation that shifted education worldwide into some form of eLearning models. They also discuss and raise issues regarding fundamentals consideration on ethics and privacy in using EDM and LA in higher education.
Protein LC-MS Using Slip Flow Chromatography

Zhang, Ximo. Ph.D., Purdue University, August 2016. Protein LC-MS Using Slip Flow Chromatography. Major Professor: Mary J. Wirth. Histones are essential chromosomal proteins with large numbers of variants and post-translational modifications (PTMs). PTM levels of histones are known to correlate with different stages of cancer. Due to the lack of resolution on intact histone separations, conventional methods for histone analysis require time-consuming digestion, which often leads to the loss of PTM information. Slip flow chromatography with orderly packed nonporous silica particles has been shown to greatly increase the efficiency of protein separation by reducing eddy diffusion and resistance to mass transfer. In this work, higher resolution for intact histones separation was achieved by using a 5 cm capillary with 470 nm particle size and C18 bonded phase. The levels of histone phosphorylation and other PTMs were verified by deconvoluted MS spectra. This methodology was also applied to the separation of a complex mixture of protein digests. A peak capacity of 500 was achieved by using a 30 min gradient elution. Other bonded phases such as C4 and TDP were also developed to separate different protein samples. This dissertation also includes 1) separation of intact monoclonal antibodies by RPLC; 2) characterization of reduced monoclonal antibodies by nano-RPLC-MS; 3) analysis of ubiquitin and ubiquitin
Stakeholder Engagement: A Conceptual Framework and Initial Validation to Foster Sustainability Development in a Maritime Cluster

Sustainability development, as a concept, is becoming an important topic for discussion within the maritime industry due to its environmental impact and intensification of the compliance framework. Achieving sustainable development will require involvement from the industry diverse stakeholders. Yet the ongoing discussions and research studies on promoting sustainability within the maritime industry gave no attention to engaging the various stakeholders existing and/or operating within the industrial or professional cluster, whose absence from the decision-making processes may fail to address sustainability issues within the industry. This study argues in support of actively engaging the maritime industry diverse stakeholders to foster the implementation of sustainable development. Based on desk research and validation from subject experts, we propose a conceptual engagement framework that may be adopted by shipping entities to correctly identify and engage the industry key stakeholders towards promoting sustainable development.
Big homoclinic orbit bifurcation underlying post-inhibitory rebound spike and a novel threshold curve of a neuron

Post-inhibitory rebound (PIR) spike induced by the negative stimulation, which plays important roles and presents counterintuitive nonlinear phenomenon in the nervous system, is mainly related to the Hopf bifurcation and hyperpolarization-active caution ( \begin{document}$ I_h $\end{document} ) current. In the present paper, the emerging condition for the PIR spike is extended to the bifurcation of the big homoclinic (BHom) orbit in a model without \begin{document}$ I_h $\end{document} current. The threshold curve for a spike evoked from a mono-stable or coexisting steady state surrounds the steady state from left, to below, and to right, because the BHom orbit is big enough to surround the steady state. The right part of the threshold curve coincides with the stable manifold of the saddle and acts the threshold for the spike induced by the positive stimulation, resembling that of the saddle-node bifurcation on an invariant cycle, and the left part acts the threshold for the PIR spike, resembling that of the Hopf bifurcation. The bifurcation curve and a codimension-2 bifurcation point related to the BHom orbit are acquired in the two-parameter plane. The results present a comprehensive viewpoint to the dynamics near the BHom orbit bifurcation, which presents a novel threshold curve and extends the conditions for the PIR spike.
Improving text comprehension in ESL learners: a multichannel approach

ABSTRACT Purpose This paper is a proposal for improving ESL text comprehension by merging a multimodal perspective on language (Kress et al. [2005]. Urban Classrooms, Subject English: Multimodal Perspectives on Teaching and Learning. London: Routledge Falmer.) with an integrated psychological (or ‘metacognitive’) approach to language learning and awareness (Fairclough [1992]. Critical Language Awareness. London: Longman, 1) . Design A multichannel approach is presented, based on two strategies: a main ‘multichannel’ strategy for reading the text, which focuses on the assessment of the visual, auditory, cognitive and kinaesthetic/emotional channels and relative functions, and a corollary one, specifically dedicated to implementing the cognitive function. Methodology/approach The approach has been extensively applied in university-level English classes (Reggio Emilia, Parma, Bologna University) using a ‘multichannel test’ based on students’ feedback to explore the effectiveness of the method. In this paper, a case study is offered: data have been collected with specific reference to the University of Bologna, A. Y. 2017/18 and 2018/2019. Originality/value Evidence suggests that further and diversified applications promise an empowerment of the approach and of its impact on ELT as well as on students’ awareness and satisfaction.
Preventive justice: Exploring the coercive power of community protection notices to tackle anti-social behaviour

Community Protection Notices (CPNs) are civil preventive orders used in England and Wales to prevent and/or require specific behaviour by an individual or organisation, where existing conduct has a ‘detrimental impact on the quality of life of those in the locality’. Breach of the notice results in a £100 fine under a Fixed Penalty Notice or a possible criminal conviction. To date, CPNs have tackled an array of perceived anti-social behaviours, ranging from rough sleeping to overgrown gardens. Using Ashworth and Zedner’s preventive justice as an analytical framework, our research qualitatively explores recipients’ experiences of this new tool for the first time. The findings highlight how the operationalisation of CPNs extends the coercive power of the state, with a range of negative consequences relating to the concepts of disproportionality, due process and accountability. We also offer three empirically-grounded recommendations for reforming CPN practices.
FULE—Functionality, Usability, Look-and-Feel and Evaluation Novel User-Centered Product Design Methodology—Illustrated in the Case of an Autonomous Medical Device

The overall goal of the novel Functionality, Usability, Look-and-Feel, and Evaluation (FULE) user-centered methodology for product design proposed in this paper is to develop usable and aesthetic products. Comprising several product design methods, this novel methodology we devised focuses on the product designer’s role and responsibility. Following the first three formative assessment phases that define the product’s functionality, usability, and look-and-feel, the summative evaluation phase not only assesses the product, but also provide guidelines to its implementation, marketing, and support. A case study devoted to the design of an autonomous medical device illustrates how the FULE methodology can provide the designer with tools to better select among design alternatives and contribute to reducing bias and subjective decisions.
The solid-state physicochemical properties and biogas production of the anaerobic digestion of corn straw pretreated by microwave irradiation

The effect of different temperatures used in microwave pretreatment on enhancing methane production of corn straw was comparatively studied in this paper through the analysis of the physicochemical properties of the pretreated materials and the methane yield during anaerobic digestion. Analytic methods such as scanning electron microscopy, Fourier transform infrared spectroscopy, and X-ray diffraction were performed to detect the surface chemistry of the pretreated corn straw. The results indicated that microwave pretreatment could effectively disrupt the lignocellulosic structure to release cellulose, hemicellulose, and related derivatives and make them available for the process of anaerobic digestion. The outcome of the methanogenic assay demonstrated that methane production could be significantly improved by 73.08% concerning the variation of the temperatures in microwave pretreatment. This study provides technical support for pretreatment methods of lignocellulose materials and deems that microwave pretreatment boosts methane yield efficiently during the process of anaerobic digestion of lignocellulosic materials.
Internal Transcribed Spacer (ITS) Fails Barcoding of the Genus Neotinea Rchb.f. (Orchidaceae)

Internal Transcribed Spacer (ITS) is one of the most used barcoding regions for the molecular phylogenetics and barcoding of orchids. Our aim in this study is to test the reliability of ITS on barcoding of closely related Neotinea spp. , including Neotinea tridentata, Neotinea ustulata subsp. ustulata and Neotinea ustulata subsp. aestivalis, by comparing it to the accD-psaI intergenic spacer of the plastid DNA. Both ITS and accD-psaI regions were amplified by specific primer sets and sequenced. Phylogenetic trees were regenerated by using Maximum Parsimony approach. The results showed that ITS separated some N. tridentata samples of Turkish, Greek, Hungarian and Croatian samples from the others on the phylogenetic trees due to the incomplete lineage sorting. In contrast to ITS, the accD-psaI marker could successfully separate N. tridentata and N. ustulata samples according to a priori species classification. Our findings refer to a hybridisation story between some N. tridentata and N. ustulata. We propose not to use ITS sequences directly as a barcode and to reconstruct the phylogeny of the Neotinea group. Instead, the inclusion of other nuclear regions such as LFY, ADH, etc., or utilisation of whole genome sequencing could give better barcoding results.
Robust Current Control of Voice Coil Motor in Tip-Tilt Mirror Based on Disturbance Observer Framework

In this paper, a control methodology based on the disturbance observer (DOB) framework is proposed to improve the current dynamic characteristic and the robustness of the voice coil motor (VCM) in the tip-tilt mirror. It is shown that the open-loop current characteristic is a third-order plant, which is influenced obviously by both the electrical and mechanical uncertainties under different environment temperatures. Although the traditional PID control is helpful to improve the current dynamics, it is not able to provide enough robustness. Therefore, a new control methodology based on DOB is proposed. The plant model is simplified from third-order to first-order by considering the back-electromotive force (EMF) as the disturbance. The perturbation of the electrical parameters is compensated by the inner-loop of the DOB framework, and the perturbation of the mechanical parameters is compensated by suppressing the back-EMF. Moreover, new sufficient conditions for robust stability are presented in the control method based on ${H_\infty }$ algorithm. Compared with the traditional researches, the upper bound of the uncertainties is calculated directly in this paper. Therefore, the ranges of the electrical parameters are very clear according to the Q-filter. Based on the stability conditions, new design guidelines for the DOB-based robust current control for induction motor are also presented. Experiment results are given to show the design details and the superiority of the proposed control method.
An Technical Condition Evaluation Index Reduction Method Combining Rough Set and Genetic Algorithm

The evaluation of equipment technical condition involves lots of indexes and the relationship between different indexes is complex. It brings a huge workload to the collection of evaluation information, and the interference caused by the correlation between indicators also increases the uncertainty of the evaluation results. Aiming at the problems mentioned above, this paper proposes a reduction method of equipment technical condition evaluation index system based on rough set and genetic algorithm. The rough set and genetic algorithm are used to reduce the state evaluation index, the redundancy is removed, and the best indexes reflecting the change of equipment technical condition status are retained. A reduction case of a gas turbine technical condition status evaluation index system shows that this method can reduce a complex index system to a more concise and clear index system without losing its representativeness and systematicity. This method can reduce the difficulty of information collection, and improve the accuracy of the evaluation results.
[Paper] CFA Handling and Quality Analysis for Compressive Light Field Camera

A light ﬁeld can carry rich visual information of a real 3-D scene, leading to many attractive applications. However, the acquisition of a light ﬁeld is challenging due to the large amount of data. In our previous work, we proposed an eﬃcient method for this task using a coded-aperture camera with a convolutional neural network (CNN) which can computationally reconstruct a light ﬁeld from several images acquired with diﬀerent aperture patterns. In this work, we report two follow-up contributions to the previous work. First, we integrated a color ﬁlter array, which is common in RGB cameras, and the related color processing into the algorithm pipeline. This integration led to better reconstruction quality for color light ﬁelds. We then analyzed how the reconstruction quality obtained with our method was aﬀected by the complexity of light ﬁelds. We also showed the possibility of using this analysis to predict the reconstruction quality from the acquired images.
Genetic algorithm based cooling energy optimization of data centers

Purpose This study aims to present development of genetic algorithm (GA)-based framework aimed at minimizing data center cooling energy consumption by optimizing the cooling set-points while ensuring that thermal management criteria are satisfied. Design/methodology/approach Three key components of the developed framework include an artificial neural network-based model for rapid temperature prediction (Athavale et al., 2018a, 2019), a thermodynamic model for cooling energy estimation and GA-based optimization process. The static optimization framework informs the IT load distribution and cooling set-points in the data center room to simultaneously minimize cooling power consumption while maximizing IT load. The dynamic framework aims to minimize cooling power consumption in the data center during operation by determining most energy-efficient set-points for the cooling infrastructure while preventing temperature overshoots. Findings Results from static optimization framework indicate that among the three levels (room, rack and row) of IT load distribution granularity, Rack-level distribution consumes the least cooling power. A test case of 7.5 h implementing dynamic optimization demonstrated a reduction in cooling energy consumption between 21%–50% depending on current operation of data center. Research limitations/implications The temperature prediction model used being data-driven, is specific to the lab configuration considered in this study and cannot be directly applied to other scenarios. However, the overall framework can be generalized. Practical implications The developed framework can be implemented in data centers to optimize operation of cooling infrastructure and reduce energy consumption. Originality/value This paper presents a holistic framework for improving energy efficiency of data centers which is of critical value given the high (and increasing) energy consumption by these facilities.