bluuebunny/crossref_metadata_2025_split huggingface dataset:
Process optimisation and robustness analysis for HFQ process
Authors: Mohamed M , Szegda D , Swift J , Gaines O , Ling D , Sonntag M , Güner A 
Published: June 2023

Abstract
Lightweight materials and lightweight technologies have gained significant attention from all EV vehicle manufacturers. Aluminium has been commonly used as a lightweight replacement for steel in the automotive industry for many years. High-strength aluminium alloys have high specific strength, and their formability increases significantly at elevated temperatures. New manufacturing technologies incorporating elevated temperature forming have emerged recently, allowing forming of high and ultrahigh-strength aluminium alloys. One such technology is Hot Form Quench (HFQ)® which is a process that combines high formability with virtually no springback for (ultra)-high-strength aluminium alloys. The thermal expansion of aluminium alloys is approximately twice that of steel, leading to significant material contraction during in-die quenching. This is especially pronounced for large or long components. Therefore, robustness analysis of the process parameters can be used to optimise the process parameters and enable a capable production of parts meeting dimensional specification requirements. Such analysis can be performed using AutoForm-Sigma solver and this paper presents a simulation methodology developed in AutoForm and its use to verify the accuracy of the thermal distortion prediction for one of the safety cell structure components. A sensitivity analysis has been implemented to optimise the process parameters to improve manufacturing robustness.
Optimization of the physical scaling approach through the adaption of the inverted membrane stresses
Authors: Zimmermann P , Birkert A , Nowack M , Kraft T 
Published: June 2023

Abstract
Dimensional deviations of stamped car body components caused by elastic springback still represent a significant problem for the development of the stamping tools. First, the springback causes deviations of the part in vertical direction to the part surface due to bending stresses. Second, the surface area of the part contracts (and also bends) due to elastic membrane stresses. The precise manifestation of the contraction depends both on the part stiffness and on the – usually heterogeneous – distribution of the membrane stresses. Usually, the resulting deformations cannot adequately be compensated by globally homogeneous scaling approaches. In order to carry out a locally correct compensation of the stamping tools, Birkert et al. have recently presented a physical scaling approach based on the inverted membrane stresses gained from the part in the closed die to compensate the active die surfaces. This approach delivered significantly better results but left potential for improvement. It is shown in the present paper how the existing approach and thus the scaling values can further be improved by adapting the inverted membrane stresses in an appropriate way. This is done by comparing the amount of strain changes during spring-back with those of the scaling process in a first step and adjusting the compensation stresses, in a second step, in such a way that the considered strain amounts are approximately identical.
Pressure Distributions in Deep Drawing with a Passive Cardanic Blank Holder
Authors: Breunig A , Groche P 
Published: June 2023

Abstract
Deep drawing processes are strongly influenced by the blank holder forces, as well as their local distribution. In current deep drawing tools, this pressure distribution is dominated by the compliance of the specific machine the tool is mounted on. Therefore, each tool must be manually polished to enable the desired distribution of the blank holder pressure. This regularly causes problems when transferring tools from one machine to another, for example from a try-out press to a regular press line. As both machines have unique compliances, the pressure distribution on the flange of the drawn part changes. Additional manual spot grinding is needed to achieve the desired pressure distribution on the flange. To counteract this problem, the use of a passive cardanic blank holder is proposed in this paper. The cardanic blank holder can freely tilt around two axes, which leads to an auto-leveling effect of the blank holder on the sheet, resulting in a more uniform blank holder pressure. This effect is demonstrated experimentally using pressure sensitive film on the same tool with two different blank holders, one equipped with a conventional blank holder, the other with a cardanic blank holder. To demonstrate transferability, the experiments are repeated on multiple single acting presses. The contribution will present and discuss the resulting pressure distributions.
Novel tool steel for application in hot stamping
Authors: Knippenberg J , Niederhofer P , Krull H-G , Richert S 
Published: June 2023

Abstract
Hot stamping is a commonly used procedure to manufacture components from high strength steels, which are used for parts of car bodies. Against the background of production efficiency and costs, there is a number of factors which determine the performance of tools used in hot stamping, e.g. cycle time in production and lifetime of the tools. Main requirement is a high wear resistance, while additional properties such as high thermal conductivity can be considered being beneficial. Many steel grades, which are currently applied in hot stamping tools (e.g. hot work tool steel 1.2367) contain high amounts of alloying elements which gives them good mechanical properties, particularly at high temperatures. However, these steels in turn suffer from a rather low thermal conductivity and only moderate wear resistance. Optimization of the balance of mechanical properties, wear resistance, and thermal conductivity can lead to a better general performance of hot working tool steels used in hot stamping applications. In this study, a novel tool steel is characterized with special focus on properties relevant for hot stamping tools. Results are compared to those of reference hot work tool steels. Wear tests that were conducted in the framework of this paper include laboratory setups like ASTM G65 / G75 and high-temperature strip-drawing test setups to simulate the conditions of the process closer to reality. Results show that the novel tool steel is characterized by an increased wear resistance in combination with a higher thermal conductivity compared with common hot work tool steels. Furthermore it is approved that a wide range of desired properties can be adjusted in this alloy via heat treatment. The favourable combination of properties makes the novel tool steel suitable for application in hot stamping tools.
Experimental investigation of the effect of lubricant change on automotive component
Authors: Carvalho L A , Zsolt L 
Published: June 2023

Abstract
Manufacturing problems resulting from lubricant supplier changes are relatively common in automotive press shops. Simulations using computer-aided design (process and tool) detect a significant proportion of manufacturing problems before tools are physically produced. In modern forming codes, an enhanced Coulomb model is used to numerically model the friction phenomena associated with lubrication. In the laboratory condition, determining model parameters through physical measurement is challenging. As a result, it became necessary to design a device that could be utilized in industrial environments. In this study, we present the prediction of a manufacturing problem resulting from a change in the lubricant of an industrial sheet metal part. The measuring device has been developed based on the parameters of the friction model of the lubricant materials. The developed measuring device may be used to determine the pressure and velocity dependent friction model parameters. The measuring device can only measure a limited range of pressures and velocities, so the results are extended to a wider range using a mathematical method. The results are used to demonstrate the effect of lubricant material changes on the forming process using the AutoForm code.
Structural, spectroscopic and morphology studies on green synthesized ZnO nanoparticles
Authors: Sarkar Toton , Kundu Sani , Ghorai Gurupada , Sahoo Pratap Kumar , Bhattacharjee Ashis 
Published: June 2023

Abstract
Zinc oxide nanoparticles (ZnO NPs) were synthesised using Tabernaemontana divaricata flower extract (TFE) in different weight percentages by facile, eco-friendly and cost-effective green synthesis method. Formation and structure of the ZnO NPs were studied by powder XRD, FT−IR, Raman and TEM studies. The crystals formed are of hexagonal wurtzite structure with biological functional groups attached. Average crystallite size of the ZnO NPs (17.5−23.3 nm) was obtained from the analysis of powder XRD data which increased with increase of TFE amount while the estimated values of dislocation density and micro-strain exhibited an opposite behaviour. The optical (direct and indirect) energy band gap values estimated using UV–vis DRS spectral data decreased with increasing amount of TFE. The photoluminescence spectra for the ZnO NPs exhibited multiple peaks spread over the visible region with one peak in the NIR region indicating the existence of various defect levels of Zn and O. Position of these defect levels within the band gap was assigned which is significantly modulated by TFE. TFE amount-dependent peak shift and/or peak broadening were observed in the Raman spectra of the ZnO NPs which were correlated with the growing disorder in the crystals induced by the extract molecules. FESEM study showed the agglomerated NPs with quasi-spherical morphology. Particle size of the ZnO NPs was estimated from FESEM images. EDX study indicated that increased presence of TFE in ZnO decreased the oxygen content in the synthesised material. HRTEM study revealed the agglomeration of nanoparticles with single crystalline nature. Present study convincingly established that flower extract used for the green synthesis efficiently modified the structure and optical property, defect levels and morphology of the potentially useful ZnO nanoparticles.
Impact of nitrogen annealing on the electrical properties of HgCdTe epitaxial films
Authors: Jin Dapeng , Zhou Songmin , Chen Lu , Lin Chun , He Li 
Published: June 2023

Abstract
The nitrogen annealing of HgCdTe materials grown by molecular beam epitaxy (MBE) was carried out to manipulate their electrical properties. The results show that the annealing temperature, annealing time and cooling process all have significant influences on the electrical properties of HgCdTe materials. Excessive annealing temperature or long annealing time can make voids emerge on the surface of the CdTe passivation layer. Carrier concentration and mobility vary exponentially with annealing time and they reach an equilibrium value determined by annealing temperature over a long annealing duration. Moreover, time constants are given and a longer time is needed for mobility to reach an equilibrium value than carrier concentration. The relationship between equilibrium carrier concentration and annealing temperature is given and the activation energy under nitrogen annealing is calculated as 0.63 eV. For a long cooling duration, Hg vacancies are annihilated by Hg atoms diffusion, which makes carrier concentration lower and mobility higher. In addition, some outlier data were found in this experiment and explained by the combination between Te antisites and Hg vacancies.
Towards control of the chirality sign at ultrathin metal films: Bi at 2Ni/Co
Authors: Gallego Silvia 
Published: June 2023

Abstract
Proximity effects can be used to introduce spin–orbit interactions in magnetic metallic layers in contact to a heavy metal (HM). This well known phenomenon has been exploited to induce chiral spin textures at Co ultrathin films, where the left- or right- handedness can be tuned by the HM layer position, based on the broken inversion symmetry of the film and the existence of an additive interface effect. Here we show that structural and chemical features introducing further symmetry reductions can be added to this scenario ultimately enabling control over the definition of a unique winding sense. We focus on 2Ni/Co heterostructures and Bi, a scarcely explored HM metal of large size, to combine a chemically inhomogeneous ferromagnetic stack along the normal to the surface with in-plane asymmetries. Our results are contrasted to 2Co layers combined with Ir.
Performance evaluation of a novel synchronously interdigitated/winded lithium-ion battery configuration enabled by 3D printing through numerical simulations
Authors: Li Yide , Li Jie , Liu Zhiyuan , Chen Zhangwei , Liu Changyong 
Published: June 2023

Abstract
Thick electrodes with higher energy density are highly desirable for lithium-ion batteries (LIBs). However, the sluggish transport of Li-ions in thick electrodes is a critical challenge. In this study, a novel synchronously interdigitated/winded battery configuration enabled by 3D printing is proposed. The cathode, separator, and anode are synchronously interdigitated in the core and synchronously winded in the outer-rings to form an integrated full battery. With this novel battery configuration, Li-ions can transport between neighboring cathode and anode, thereby significantly reduce the transport distance of Li-ions, and improve the electrochemical reaction kinetics. To evaluate the electrochemical performance of this battery configuration, this study investigates the effects of various parameters including the electronic conductivity, electrode porosity, electrode line width, separator thickness, and number of winded outer-rings on the electrochemical performance through numerical simulations. Results showed that electronic conductivity is the most crucial factor in determining the electrochemical performance. In combination with multi-material 3D printing, the battery configuration proposed in this study may be utilized to build LIBs with higher energy density.
Climatology and decadal changes of Arctic atmospheric rivers based on ERA5 and MERRA-2
Authors: Zhang Chen , Tung Wen-wen , Cleveland William S 
Published: June 2023

Abstract
We present the Arctic atmospheric river (AR) climatology based on twelve sets of labels derived from ERA5 and MERRA-2 reanalyses for 1980–2019. The ARs were identified and tracked in the 3-hourly reanalysis data with a multifactorial approach based on either atmospheric column-integrated water vapor (IWV) or integrated water vapor transport (IVT) exceeding one of the three climate thresholds (75th, 85th, and 95th percentiles). Time series analysis of the AR event counts from the AR labels showed overall upward trends from the mid-1990s to 2019. The 75th IVT- and IWV-based labels, as well as the 85th IWV-based labels, are likely more sensitive to Arctic surface warming, therefore, detected some broadening of AR-affected areas over time, while the rest of the labels did not. Spatial exploratory analysis of these labels revealed that the AR frequency of occurrence maxima shifted poleward from over-land in 1980–1999 to over the Arctic Ocean and its outlying Seas in 2000–2019. Regions across the Atlantic, the Arctic, to the Pacific Oceans trended higher AR occurrence, surface temperature, and column-integrated moisture. Meanwhile, ARs were increasingly responsible for the rising moisture transport into the Arctic. Even though the increase of Arctic AR occurrence was primarily associated with long-term Arctic surface warming and moistening, the effects of changing atmospheric circulation could stand out locally, such as on the Pacific side over the Chukchi Sea. The changing teleconnection patterns strongly modulated AR activities in time and space, with prominent anomalies in the Arctic-Pacific sector during the latest decade. Besides, the extreme events identified by the 95th-percentile labels displayed the most significant changes and were most influenced by the teleconnection patterns. The twelve Arctic AR labels and the detailed graphics in the atlas can help navigate the uncertainty of detecting and quantifying Arctic ARs and their associated effects in current and future studies.
Managing small populations—using genetic data and trial translocations to help inform suitable conservation measures for the alpine blue-sowthistle (Cicerbita alpina) in Scotland
Authors: Finger A , Smedley M , Halley D , Frachon N , Ahrends A 
Published: June 2023

Abstract
Habitat fragmentation is causing an increasing amount of species loss around the world and creates problems at the population level. Many species are left as only small and isolated populations, which are vulnerable to genetic erosion and inbreeding depression. Here we present a study on the alpine blue-sowthistle (Cicerbita alpina). Due to intensive grazing the species is very rare in Scotland, where it occurs at only four small, montane sites, has never been reported to reproduce and is in need of conservation interventions. As the species can grow clonally it is unknown how many individuals remain and whether populations are affected by genetic isolation. We (1) quantified genetic diversity, inbreeding and between-population differentiation in Scotland and Norway using 15 microsatellite loci, and (2) experimentally translocated plants to new sites. Genetic diversity in Scotland was low (H
E: 0.35; Allelic Richness: 1.84; 4 sites) compared to Norway (H
E: 0.52; Allelic Richness: 2.56; 5 sites). The transplants were able to grow at new sites and are therefore not restricted to steep, montane ledges. While grazing is likely to be the main factor preventing range expansion, long-term genetic isolation has possibly further lowered population viability. To avoid local extinction of this species, conservation translocations and genetic rescue might be appropriate conservation interventions, but this needs to be further tested in a controlled environment and away from wild sites to avoid potential risks of outbreeding depression.
A Closer Look at Practice Effects in Mild Cognitive Impairment and Alzheimer’s Disease
Authors: Duff Kevin , Dixon Ava , Embree Lindsay 
Published: June 2023

Abstract
Practice effects have become a potentially important variable regarding the diagnosis, prognosis, and treatment recommendations in mild cognitive impairment (MCI) and Alzheimer’s disease (AD). However, the understanding of these short-term changes in test scores remains unclear. The current observational study sought to examine variables that influence the magnitude of short-term practice effects in MCI and AD, including demographic information, cognitive performance, daily functioning, and medical comorbidities. One hundred sixty-six older adults classified as cognitively intact, amnestic MCI, or mild AD were tested twice across 1 week with a brief battery of neuropsychological tests. Correlational and regression analyses examined the relationship of practice effects with demographic and clinical variables. Results indicated that practice effects were minimally related to demographic variables and medical comorbidities, but they were significantly related to cognitive variables, depressive symptoms, and daily functioning. These findings expand our understanding of practice effects in MCI and AD, and they may allow a better appreciation of how they could affect clinical care and research.
Biologically informed variational autoencoders allow predictive modeling of genetic and drug-induced perturbations
Authors: Doncevic Daria , Herrmann Carl 
Published: June 2023

Abstract

Motivation
Variational autoencoders (VAEs) have rapidly increased in popularity in biological applications and have already successfully been used on many omic datasets. Their latent space provides a low-dimensional representation of input data, and VAEs have been applied, e.g. for clustering of single-cell transcriptomic data. However, due to their non-linear nature, the patterns that VAEs learn in the latent space remain obscure. Hence, the lower-dimensional data embedding cannot directly be related to input features.

Results
To shed light on the inner workings of VAE and enable direct interpretability of the model through its structure, we designed a novel VAE, OntoVAE (Ontology guided VAE) that can incorporate any ontology in its latent space and decoder part and, thus, provide pathway or phenotype activities for the ontology terms. In this work, we demonstrate that OntoVAE can be applied in the context of predictive modeling and show its ability to predict the effects of genetic or drug-induced perturbations using different ontologies and both, bulk and single-cell transcriptomic datasets. Finally, we provide a flexible framework, which can be easily adapted to any ontology and dataset.

Availability and implementation
OntoVAE is available as a python package under https://github.com/hdsu-bioquant/onto-vae.
WarpSTR: determining tandem repeat lengths using raw nanopore signals
Authors: Sitarčík Jozef , Vinař Tomáš , Brejová Broňa , Krampl Werner , Budiš Jaroslav , Radvánszky Ján , Lucká Mária 
Published: June 2023

Abstract

Motivation
Short tandem repeats (STRs) are regions of a genome containing many consecutive copies of the same short motif, possibly with small variations. Analysis of STRs has many clinical uses but is limited by technology mainly due to STRs surpassing the used read length. Nanopore sequencing, as one of long-read sequencing technologies, produces very long reads, thus offering more possibilities to study and analyze STRs. Basecalling of nanopore reads is however particularly unreliable in repeating regions, and therefore direct analysis from raw nanopore data is required.

Results
Here, we present WarpSTR, a novel method for characterizing both simple and complex tandem repeats directly from raw nanopore signals using a finite-state automaton and a search algorithm analogous to dynamic time warping. By applying this approach to determine the lengths of 241 STRs, we demonstrate that our approach decreases the mean absolute error of the STR length estimate compared to basecalling and STRique.

Availability and implementation
WarpSTR is freely available at https://github.com/fmfi-compbio/warpstr
Interpretable deep learning architectures for improving drug response prediction performance: myth or reality?
Authors: Li Yihui , Hostallero David Earl , Emad Amin 
Published: June 2023

AbstractMotivationInterpretable deep learning (DL) models that can provide biological insights, in addition to accurate predictions, are of great interest to the biomedical community. Recently, interpretable DL models that incorporate signaling pathways have been proposed for drug response prediction (DRP). While these models improve interpretability, it is unclear whether this comes at the cost of less accurate DRPs, or a prediction improvement can also be obtained.ResultsWe comprehensively and systematically assessed four state-of-the-art interpretable DL models using three pathway collections to assess their ability in making accurate predictions on unseen samples from the same dataset, as well as their generalizability to an independent dataset. Our results showed that models that explicitly incorporate pathway information in the form of a latent layer perform worse compared to models that incorporate this information implicitly. However, in most evaluation setups, the best performance was achieved using a black-box multilayer perceptron, and the performance of a random forests baseline was comparable to those of the interpretable models. Replacing the signaling pathways with randomly generated pathways showed a comparable performance for the majority of the models. Finally, the performance of all models deteriorated when applied to an independent dataset. These results highlight the importance of systematic evaluation of newly proposed models using carefully selected baselines. We provide different evaluation setups and baseline models that can be used to achieve this goal.Availability and implementationImplemented models and datasets are provided at https://doi.org/10.5281/zenodo.7787178 and https://doi.org/10.5281/zenodo.7101665, respectively.