bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Challenging and Hindering Potential of Time Pressure: Qualitative Job Demands as Suppressor Variables
Authors: Schilbach Miriam , Haun Verena C. , Baethge Anja , Rigotti Thomas 
Published: September 2022

AbstractThis study aims at resolving the inconsistent findings regarding the effects of time pressure on work engagement and personal resources by considering time pressure’s qualitative sources. Specifically, using the notion of statistical suppression, we argue that qualitative challenge and hindrance demands operate as suppressor variables and thus determine whether time pressure itself exerts a challenging or hindering potential. To test our assumptions, we conducted a daily diary study over the course of one workweek in a sample of 396 employees. We tested our hypotheses at the day-level. Results of multilevel structural equation modeling revealed that when controlling for qualitative challenge demands, time pressure positively related to exhaustion, but negatively related to work engagement and self-esteem. Suppression was significant. In contrast, when controlling for qualitative hindrance demands, time pressure was unrelated to work engagement, negatively related to self-esteem, and positively related to exhaustion, whereby qualitative hindrance demands did not act as a suppressor variable at the day-level. Additional analyses revealed that qualitative challenge and hindrance demands operated as suppressor variables at the person-level. In summary, when qualitative challenge demands were controlled for, time pressure operated as a hindrance demand. Yet, when qualitative hindrance demands were controlled for, time pressure operated as a challenge demand at the person-level. Our findings outline the need to account for the quality of work when assessing time pressure’s effects and further highlight the relevance of suppressor variables within the field of occupational health psychology.
A Vehicle-Assisted Computation Offloading Algorithm Based on Proximal Policy Optimization in Vehicle Edge Networks
Authors: Chen Geng , Xu Xianjie , Zeng Qingtian , Zhang Yu-Dong 
Published: September 2022

AbstractWith the continuous development of the Internet of Vehicles(IoV), Vehicle Edge Computing(VEC) has become a key technology for computational resource scheduling, but more and more smart devices are connected to the internet, which makes it difficult for traditional Vehicle Edge Networks(VEN) to deal with tasks in time. In this paper, in order to cope with the challenges of the large number of devices accessing the internet, we propose a vehicle-assisted computation offloading algorithm based on proximal policy optimization(VCOPPO) for User Equipment(UE) tasks, and it combines dynamic parked vehicles incentives mechanism and computational resource allocation strategy by using road vehicles and parked vehicles as edge servers. Firstly, a non-convex optimization problem combining VEN utility and task processing delay is formulated, subject to the constraints of the residual energy and the transmission rate of the task. Secondly, the proposed VCOPPO is used to solve the formulated non-convex optimization problem, and we use stochastic policy to obtain the optimal computation offloading decisions and resource allocation schemes. Finally, the experimental results have shown that the proposed VCOPPO has an excellent performance in network reward and task processing delay respectively, and it can effectively schedule and allocate computational resources. Compared with using Dueling Deep Q Network(Dueling DQN), Deep Q Network(DQN) and Q-learning methods, the proposed VCOPPO improves the network reward by 31%, 18% and 91%, reduces the delay in task processing by 78%, 63% and 74%, respectively.
Solution of ill-posed problems with Chebfun
Authors: Alqahtani A. , Mach T. , Reichel L. 
Published: September 2022

AbstractThe analysis of linear ill-posed problems often is carried out in function spaces using tools from functional analysis. However, the numerical solution of these problems typically is computed by first discretizing the problem and then applying tools from finite-dimensional linear algebra. The present paper explores the feasibility of applying the Chebfun package to solve ill-posed problems with a regularize-first approach numerically. This allows a user to work with functions instead of vectors and with integral operators instead of matrices. The solution process therefore is much closer to the analysis of ill-posed problems than standard linear algebra-based solution methods. Furthermore, the difficult process of explicitly choosing a suitable discretization is not required.
Market orientation (once again): Challenges and a suggested solution
Authors: Rokkan Aksel I. 
Published: September 2022

AbstractPrevious research shows almost unequivocally that market orientation (MO) has positive performance consequences for companies. However, research conceptualizes MO as (potentially) too abstract and broad. Consequently, detecting negative performance implications from it is difficult. In this article, I propose to deconstruct MO. First, rather than operating MO as an integrated construct, I suggest treating MO as a class of orientations that share certain properties but differ on important others. I detail the competitive logic of and main risks related to alternative MOs and, drawing from literature on marketing, economics, and strategy, suggest contingencies for alternative MOs to perform well. Second, I propose measuring each individual MO (e.g., customer-centric) as a product of the two forms of orientations (strategy/plan and information) embedded within it. This suggested measurement approach enables further scrutiny of the association between MO and company performance. I conclude with a discussion of the potential implications for research and marketing management which could result from treating MO as a construct class.
Subsurface structure of the proposed Sirente meteorite crater: insights from ERT synthetic modelling
Authors: Torrese P. 
Published: September 2022

AbstractThe Sirente main crater is a ≈ 130 m wide, in plan view droplet-shaped depression with an elevated rim, surrounded by 30 smaller depressions. It was proposed to be of meteorite impact origin. Given the age of formation in the 3rd to 5th centuries A.D., the inferred catastrophic origin was related to the celestial sign (“Chi Rho”) said to have been seen by Emperor Constantine in 312 A.D. and suggested to have changed the course of both Roman and Christian history. However, the meteoritic origin is not yet confirmed. This paper presents new results from synthetic modelling of Electric Resistivity Tomography field data collected at the Sirente main crater which provide further clues around the controversy of its origin. This study arises from the need to validate the observed structural features which include possible upturned strata (i.e., overturning of strata below impact crater rims) and compaction-fissure-like features below and just outside the crater rim, well-developed “breccia lens”, as well as an ejecta layer, and provide key indicators for objective and quantitative interpretation of the measured resistivity pattern. The results from this study are consistent with the hypothesis of a small impact crater in a low-strength target, with a relatively shallow apparent crater and do not support other proposed mechanisms of formation such as karst, mud volcano or merely anthropogenic origin.
Moving-Target Intelligent Tutoring System for Marksmanship Training
Authors: Zotov Vladimir , Kramkowski Eric 
Published: September 2022

AbstractIntelligent tutoring systems (ITSs) may augment military training systems and mitigate existing limitations in training personnel and resources. A study was conducted to investigate the effectiveness of an embedded rifle marksmanship ITS for Moving Targets (MT-ITS). MT-ITS has two main components: (1) a Smart Sight System that provides a perceptual cue to help trainees adjust their point of aim to account for a target’s speed, direction of movement, and distance, and (2) a performance-based algorithm that delivers shooting performance feedback to trainees.The MT-ITS was tested in an experiment where participants engaged moving targets in a virtual shooting range. Moving targets were presented at different speeds, direction of movement, and distances. Two types of marksmanship training were compared: with ITS and without ITS (a standard training). The ITS training group produced better hit rate and aiming accuracy scores than the standard training group, requiring less practice to achieve asymptotic results. Implications for the design of embedded trainers with ITS for marksmanship specifically and for training motor skills in general are discussed in the context of future research directions.
Three Algorithms for Grouping Students: A Bridge Between Personalized Tutoring System Data and Classroom Pedagogy
Authors: Lechuga Christopher G. , Doroudi Shayan 
Published: September 2022

AbstractComputer-assisted instructional programs such as intelligent tutoring systems are often used to support blended learning practices in K-12 education, as they aim to meet individual student needs with personalized instruction. While these systems have been shown to be effective under certain conditions, they can be difficult to integrate into pedagogical practices. In this paper, we introduce three group formation algorithms that leverage learning data from the adaptive intelligent tutoring system ALEKS to support pedagogical and collaborative learning practices with ALEKS. Each grouping method was devised for different use cases, but they all utilize a fine-grained multidimensional view of student ability measured across several hundred skills in an academic course. As such, the grouping algorithms not only identify groups of students, but they also determine what areas of ALEKS content each group should focus on. We then evaluate each of the three methods against two alternative baseline methods, which were chosen for their plausibility of being used in practice—one that groups students randomly and one that groups students based on a unidimensional course score. To evaluate these methods, we establish a set of practical metrics based on what we anticipate teachers would care about in practice. Evaluations were performed by simulating mock groupings of students at different time periods for real ALEKS algebra classes that occurred between 2017 and 2019. We show that each devised method obtains more favorable results on the specified metrics than the alternative methods under each use-case. Moreover, we highlight examples where our methods lead to more nuanced groupings than grouping based on a unidimensional measure of ability.
Causal Inference of Social Experiments Using Orthogonal Designs
Authors: Heckman James J. , Pinto Rodrigo 
Published: September 2022

AbstractOrthogonal arrays are a powerful class of experimental designs that has been widely used to determine efficient arrangements of treatment factors in randomized controlled trials. Despite its popularity, the method is seldom used in social sciences. Social experiments must cope with randomization compromises such as noncompliance that often prevent the use of elaborate designs. We present a novel application of orthogonal designs that addresses the particular challenges arising in social experiments. We characterize the identification of counterfactual variables as a finite mixture problem in which choice incentives, rather than treatment factors, are randomly assigned. We show that the causal inference generated by an orthogonal array of incentives greatly outperforms a traditional design.
Modelling of free bubble growth with Interface Capturing Computational Fluid Dynamics
Authors: Giustini Giovanni , Issa Raad I. 
Published: September 2022

AbstractThis paper presents simulations of the growth of stationary and rising vapour bubbles in an extend pool of liquid using an Interface Capturing Computational Fluid Dynamics (CFD) methodology coupled with a method for simulating interfacial mass transfer at the vapour-liquid interface. The model enables mechanistic prediction of the local rate of phase change at the vapour-liquid interface and is applicable to realistic cases involving two-phase mixtures with large density ratios. The simulation methodology is based on the Volume of Fluid (VOF) representation of the flow, whereby an interfacial region in which mass transfer occurs is implicitly identified by a phase indicator, in this case the volume fraction of liquid, which varies from the value pertaining to the “bulk” liquid to the value of the bulk vapour. The novel methodology proposed here has been implemented using the Finite Volume framework and solution methods typical of “industrial” CFD practice embedded in the OpenFOAM CFD toolbox. Simulations are validated via comparison against experimental observations of spherical bubble growth in zero gravity and of the growth of a rising bubble in normal gravity. The validation cases represent a severe test for Interface Capturing methodologies due to large density ratios, the presence of strong interfacial evaporation and upward bubble rise motion. Agreement of simulation results with measurements available in the literature demonstrates that the methodology detailed herein is applicable to modelling bubble growth driven by phase-change in real fluids.
Evaluation of the impact of electron beam welding parameters on the mechanical properties and microstructure of the resulting joint for 39CrMoV13 and M50NiL steel grades
Authors: Wencel Zbigniew , Wiewiórowska Sylwia , Wieczorek Paweł , Michalczyk Jacek , Gontarz Andrzej 
Published: September 2022

Abstract
The paper covers a study consisting on the implementation of the electron beam welding process of two steel grades: 39CrMoV13 and M50NiL. The welding process was carried out for two values of electron beam debunching, obtaining a joint having different widths. Then, for the samples, after each welding variant, heat treatment was carried out consisting of double tempering at 520 °C for 3 h, and single freezing at − 84 °C for 3 h, combined with single tempering at 520 °C for 3 h. The impact of the parameters of the electron beam welding process and the subsequent heat treatment process on the mechanical properties and microstructure of the resulting joint was analysed. The conducted tests allowed to select the optimum welding technology combined with the heat treatment process for the tested steel grades ensuring that a weld is obtained in which the level of mechanical properties was close to the level of values obtained for the base material.
Recursive quality optimization of a smart forming tool under the use of perception based hybrid datasets for training of a Deep Neural Network
Authors: Feldmann S. , Schmiedt M. , Schlosser J. M. , Rimkus W. , Stempfle T. , Rathmann C. 
Published: September 2022

AbstractIn industrial metal forming processes, the generation of datasets for inline and optical quality assessment is expensive and time-consuming. Within the research project SimKI, conventional metal forming plants were digitalized under the use of perception-based 3D-sensors in combination with a completely redesigned forming tool. The integration of optical quality observation methods connected with a retrofitting approach of the press tool provides the opportunity to generate an information-feedback loop that predicts part defects before their occurrence. Additionally, the SimKI-method combines conventional statistical measurement methods with AI-based defect detection algorithms that are trained by generic datasets of a finite-element simulation, real component images of a 3D imaging device, and a combination of both. The generated datasets are used to accelerate the training of a DNN-based algorithm to identify the position and deviation from the agreed quality. The high degree of innovation is based on obtaining real-time component quality information under the use of AI-based optical quality assessment, which in turn provides information to the control algorithm of the smart forming tool.
How to Answer Dworkin’s Argument from Theoretical Disagreement Without Attributing Confusion or Disingenuity to Legal Officials
Authors: Watson Bill 
Published: September 2022

AbstractRonald Dworkin’s argument from theoretical disagreement remains a pressing challenge for legal positivists. In this paper, I show how positivists can answer Dworkin’s argument without having to attribute confusion or disingenuity to legal officials. I propose that the argument rests on two errors. The first is to assume that positivism requires legal officials to converge on precise grounds of law when convergence on more general grounds will do. The second is to construe judicial speech too literally. If we pay attention to the pragmatics of judicial speech, we see that judges do not disagree over what the grounds of law are; they at most disagree over how courts should proceed when agreed-upon, though imprecise, grounds of law underdetermine what the content of the law directs in the case at hand.
Characterising Dutch forests, wetlands and cultivated lands on the basis of phytolith assemblages
Authors: de Wolf Iris K. , McMichael Crystal N.H. , Philip Annemarie L. , Gosling William D. 
Published: September 2022

Abstract
Palaeoecological reconstructions in the Netherlands are commonly based on pollen and macrofossil analysis, but can be limited if the preservation of organic material is poor. Phytoliths, biogenic silica, do not have this limitation and preserve in settings where other macro- and microfossils do not. Little is known about how phytolith assemblages preserved in soils and sediments reflect the parent vegetation in north-western European systems, so it is currently difficult to contextualise past environments. Here, we characterise phytolith assemblages for soil samples recovered from three major vegetation types in the Netherlands to provide reference data for future reconstructions of past vegetation change. We collected 42 soil surface samples from forests, wetlands and agricultural fields across the Netherlands and characterised the phytolith assemblages they contained. We identified the different phytolith morphotypes and quantified the percentages and concentrations (#phytoliths/cm3 soil) in each sample. We used non-metric multidimensional scaling to assess the variation in phytolith assemblage composition within, and between, the three vegetation types. The phytolith assemblages analysed from the forests, wetlands and agricultural fields were clearly distinguishable from each other. Agricultural fields were dominated by four phytolith morphotypes of grass silica short cells (GSSCs): rondel (tabular), cross type 1 (>15 µm), rondel (elongated) and disturbance or crop phytoliths. Forests settings had significantly higher amounts of different arboreal phytoliths (large and small spheroid rugose) compared with other vegetation types. Wetlands could be identified by significantly higher amounts of Cyperaceae phytoliths (papillate) and other GSSCs (saddle and bilobates with thick castula). Phytolith assemblages could distinguish different subtypes of vegetation within forest and wetland areas, while differences between agricultural systems could not be identified. Our study demonstrates that phytoliths preserved in soils or sediments can be used to separate major vegetation types across the Netherlands. Thus, these results support the hypothesis that phytoliths can be used to infer past environmental conditions in palaeoecological reconstructions. We suggest that future work should: (1) focus on characterising which phytolith types are produced by the commonest tree, wetland, shrub and herb species in the Netherlands and (2) characterise phytolith assemblages across a wider array of vegetation types in north-western European systems to increase the capability for quantitative reconstructions using phytolith assemblages.
A pedagogical review of the vacuum retarded dipole model of pulsar spin down
Authors: Satherley J. C. , Gordon C. 
Published: September 2022

Abstract
Pulsars are rapidly spinning highly magnetised neutron stars. Their spin period is observed to decrease with time. An early analytical model for this process was the vacuum retarded dipole (VRD) by Deutsch (1955, AnAp, 18). This model assumes an idealised star and it finds that the rotational energy is radiated away by the electromagnetic fields. This model has been superseded by more realistic numerical simulations that account for the non-vacuum like surroundings of the neutron star. However, the VRD still provides a reasonable approximation and is a useful limiting case that can provide some qualitative understanding. We provide detailed derivations of the spin down and related electromagnetic field equations of the VRD solution. We also correct typographical errors in the general field equations and boundary conditions used by Deutsch (1955, AnAp, 18).
The Theatre of Genet: A Sociological Study
Authors: Goldmann Lucien 
Published: September 2022

Until a few years ago, aestheticians, critics, and literary historians
accorded sociology only marginal status. However, the availability of Georg
Lukacs’ early work, the psychological and epistemological researches of Jean
Piaget, and the acceptance of the dialectic as genetic structuralism has
changed all that.
Traditional sociology —which still dominates university teaching — tried
to relate the content of a literary work to the
content of the collective unconscious: how
men think and act in daily life. Such criticism becomes more effective the
more mundane is the writer being studied, content merely to relate his
experiences without imaginatively transposing them. Structural sociology
begins from premises that exclude those of traditional sociology.
Transfer of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) and polychlorinated biphenyls (PCBs) from oral exposure into cow’s milk – Part I: state of knowledge and uncertainties
Authors: Krause Torsten , Moenning Jan-Louis , Lamp Julika , Maul Ronald , Schenkel Hans , Fürst Peter , Pieper Robert , Numata Jorge 
Published: September 2022

AbstractPolychlorinated dibenzo-para-dioxins (PCDDs) and dibenzofurans (PCDFs) (collectively and colloquially referred to as ‘dioxins’) as well as polychlorinated biphenyls (PCBs) are persistent and ubiquitous environmental contaminants that may unintentionally enter and accumulate along the food chain. Owing to their chronic toxic effects in humans and bioaccumulative properties, their presence in feed and food requires particular attention. One important exposure pathway for consumers is consumption of milk and dairy products. Their transfer from feed to milk has been studied for the past 50 years to quantify the uptake and elimination kinetics. We extracted transfer parameters (transfer rate, transfer factor, biotransfer factor and elimination half-lives) in a machine-readable format from seventy-six primary and twenty-nine secondary literature items. Kinetic data for some toxicologically relevant dioxin congeners and the elimination half-lives of dioxin-like PCBs are still not available. A well-defined selection of transfer parameters from literature was statistically analysed and shown to display high variability. To understand this variability, we discuss the data with an emphasis on influencing factors, such as experimental conditions, cow performance parameters and metabolic state. While no universal interpretation could be derived, a tendency for increased transfer into milk is apparently connected to an increase in milk yield and milk fat yield as well as during times of body fat mobilisation, for example during the negative energy balance after calving. Over the past decades, milk yield has increased to over 40 kg/d during high lactation, so more research is needed on how this impacts feed to food transfer for PCDD/Fs and PCBs.