bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dislocation and strain mapping in metamorphic parabolic-graded InGaAs buffers on GaAs
Authors: Stephen Nicholas , Kumar Praveen , Gocalinska Agnieszka , Mura Enrica , Kepaptsoglou Demie , Ramasse Quentin , Pelucchi Emanuele , Arredondo Miryam 
Published: June 2023

AbstractWe investigate different architectures for parabolic-graded InGaAs metamorphic buffers grown on GaAs using transmission electron microscopy techniques. The different architectures include InGaP and AlInGaAs/InGaP superlattices with different GaAs substrate misorientations and the inclusion of a strain balancing layer. Our results correlate: (i) the density and distribution of dislocations in the metamorphic buffer and (ii) the strain in the next layer preceding the metamorphic buffer, which varies for each type of architecture. Our findings indicate that the dislocation density in the lower region of the metamorphic layer ranges between 108 and 1010 cm−2, with AlInGaAs/InGaP superlattice samples exhibiting higher values compared to samples with InGaP films. We have identified two waves of dislocations, with threading dislocations typically located lower in the metamorphic buffer (~ 200–300 nm) in comparison to misfit dislocations. The measured localised strain values are in good agreement with theoretical predications. Overall, our results provide a systematic insight into the strain relaxation across different architectures, highlighting the various approaches that can be used to tailor strain in the active region of a metamorphic laser.

Graphical abstract
Serum neurofilament light and white matter characteristics in the general population: a longitudinal analysis
Authors: Hermesdorf Marco , Wulms Niklas , Maceski Aleksandra , Leppert David , Benkert Pascal , Wiendl Heinz , Kuhle Jens , Berger Klaus 
Published: June 2023

AbstractNeurofilament light polypeptide (NfL) is a component of the neuronal cytoskeleton and particularly abundant in large-caliber axons. When axonal injury occurs, NfL is released and reaches the cerebrospinal fluid and the blood. Associations between NfL and white matter alterations have previously been observed in studies based on patients with neurological diseases. The current study aimed to explore the relationship between serum NfL (sNfL) and white matter characteristics in a population-based sample. The cross-sectional associations between sNfL as dependent variable, fractional anisotropy (FA), and white matter lesion (WML) volume were analyzed with linear regression models in 307 community-dwelling adults aged between 35 and 65 years. These analyses were repeated with additional adjustment for the potential confounders age, sex, and body mass index (BMI). Longitudinal associations over a mean follow-up of 5.39 years were analyzed with linear mixed models. The unadjusted cross-sectional models yielded significant associations between sNfL, WML volume, and FA, respectively. However, after the adjustment for confounders, these associations did not reach significance. In the longitudinal analyses, the findings corroborated the baseline findings showing no significant associations between sNfL and white matter macrostructure and microstructure beyond the effects of age. In synopsis with previous studies in patients with acute neurological diseases showing a significant association of sNfL with white matter changes beyond the effects of age, the present results based on a sample from the general population suggest the perspective that changes in sNfL reflect age-related effects that also manifest in altered white matter macrostructure and microstructure.
Pliocene Geotourism: Innovative Projects for Valorizing the Paleontological Heritage of Three Different-Staged Quarries of Tuscany (Central Italy)
Authors: Merella Marco , Collareta Alberto , Casati Simone , Di Cencio Andrea , Bianucci Giovanni 
Published: June 2023

AbstractQuarries are areas of pivotal importance for investigation, popularization, and educational purposes in the field of geosciences. Here, we focus on three quarries of Tuscany (La Serra, Arcille, and Certaldo) that have been home to significant finds of marine vertebrate fossils. The three selected localities are representative of the many active (La Serra), inactive (Arcille), and abandoned (Certaldo) Tuscan quarries where vertebrate-bearing Pliocene deposits are exposed. Given the richness and scientific value of their paleontological content (including holotypes as well as otherwise unique specimens), the abundance of exposed sedimentary structures, and the high potential for open-air musealization, the La Serra, Arcille, and Certaldo quarries should be regarded as geosites. These quarries are thoroughly described, and several suggestions for their preservation and valorization are proposed, focusing on their importance for geotourism, with the goal of reaching out to the broadest audience possible. Many innovative dissemination tools, including 3D technologies, are now available for pursuing such an aim.
Dynamic train dwell time forecasting: a hybrid approach to address the influence of passenger flow fluctuations
Authors: Pang Zishuai , Wang Liwen , Wang Shengjie , Li Li , Peng Qiyuan 
Published: June 2023

AbstractTrain timetables and operations are defined by the train running time in sections, dwell time at stations, and headways between trains. Accurate estimation of these factors is essential to decision-making for train delay reduction, train dispatching, and station capacity estimation. In the present study, we aim to propose a train dwell time model based on an averaging mechanism and dynamic updating to address the challenges in the train dwell time prediction problem (e.g., dynamics over time, heavy-tailed distribution of data, and spatiotemporal relationships of factors) for real-time train dispatching. The averaging mechanism in the present study is based on multiple state-of-the-art base predictors, enabling the proposed model to integrate the advantages of the base predictors in addressing the challenges in terms of data attributes and data distributions. Then, considering the influence of passenger flow on train dwell time, we use a dynamic updating method based on exponential smoothing to improve the performance of the proposed method by considering the real-time passenger amount fluctuations (e.g., passenger soars in peak hours or passenger plunges during regular periods). We conduct experiments with the train operation data and passenger flow data from the Chinese high-speed railway line. The results show that due to the advantages over the base predictors, the averaging mechanism can more accurately predict the dwell time at stations than its counterparts for different prediction horizons regarding predictive errors and variances. Further, the experimental results show that dynamic smoothing can significantly improve the accuracy of the proposed model during passenger amount changes, i.e., 15.4% and 15.5% corresponding to the mean absolute error and root mean square error, respectively. Based on the proposed predictor, a feature importance analysis shows that the planned dwell time and arrival delay are the two most important factors to dwell time. However, planned time has positive influences, whereas arrival delay has negative influences.
Heterogeneous differential evolution particle swarm optimization with local search
Authors: Lin Anping , Liu Dong , Li Zhongqi , Hasanien Hany M. , Shi Yaoting 
Published: June 2023

AbstractTo develop a high performance and widely applicable particle swarm optimization (PSO) algorithm, a heterogeneous differential evolution particle swarm optimization (HeDE-PSO) is proposed in this study. HeDE-PSO adopts two differential evolution (DE) mutants to construct different characteristics of learning exemplars for PSO, one DE mutant is for enhancing exploration and the other is for enhance exploitation. To further improve search accuracy in the late stage of optimization, the BFGS (Broyden–Fletcher–Goldfarb–Shanno) local search is employed. To assess the performance of HeDE-PSO, it is tested on the CEC2017 test suite and the industrial refrigeration system design problem. The test results are compared with seven recent PSO algorithms, JADE (adaptive differential evolution with optional external archive) and four meta-heuristics. The comparison results show that with two DE mutants to construct learning exemplars, HeDE-PSO can balance exploration and exploitation and obtains strong adaptability on different kinds of optimization problems. On 10-dimensional functions and 30-dimensional functions, HeDE-PSO is only outperformed by the most competitive PSO algorithm on seven and six functions, respectively. HeDE-PSO obtains the best performance on sixteen 10-dimensional functions and seventeen-30 dimensional functions. Moreover, HeDE-PSO outperforms other compared PSO algorithms on the industrial refrigeration system design problem.
A novel driver emotion recognition system based on deep ensemble classification
Authors: Zaman Khalid , Zhaoyun Sun , Shah Babar , Hussain Tariq , Shah Sayyed Mudassar , Ali Farman , Khan Umer Sadiq 
Published: June 2023

AbstractDriver emotion classification is an important topic that can raise awareness of driving habits because many drivers are overconfident and unaware of their bad driving habits. Drivers will acquire insight into their poor driving behaviors and be better able to avoid future accidents if their behavior is automatically identified. In this paper, we use different models such as convolutional neural networks, recurrent neural networks, and multi-layer perceptron classification models to construct an ensemble convolutional neural network-based enhanced driver facial expression recognition model. First, the faces of the drivers are discovered using the faster region-based convolutional neural network (R-CNN) model, which can recognize faces in real-time and offline video reliably and effectively. The feature-fusing technique is utilized to integrate the features extracted from three CNN models, and the fused features are then used to train the suggested ensemble classification model. To increase the accuracy and efficiency of face detection, a new convolutional neural network block (InceptionV3) replaces the improved Faster R-CNN feature-learning block. To evaluate the proposed face detection and driver facial expression recognition (DFER) datasets, we achieved an accuracy of 98.01%, 99.53%, 99.27%, 96.81%, and 99.90% on the JAFFE, CK+, FER-2013, AffectNet, and custom-developed datasets, respectively. The custom-developed dataset has been recorded as the best among all under the simulation environment.
Novel Bilayer-Shelled N, O-Doped Hollow Porous Carbon Microspheres as High Performance Anode for Potassium-Ion Hybrid Capacitors
Authors: Pan Zhen , Qian Yong , Li Yang , Xie Xiaoning , Lin Ning , Qian Yitai 
Published: June 2023

AbstractWith the advantages of high energy/power density, long cycling life and low cost, dual-carbon potassium ion hybrid capacitors (PIHCs) have great potential in the field of energy storage. Here, a novel bilayer-shelled N, O-doped hollow porous carbon microspheres (NOHPC) anode has been prepared by a self-template method, which is consisted of a dense thin shell and a hollow porous spherical core. Excitingly, the NOHPC anode possesses a high K-storage capacity of 325.9 mA h g−1 at 0.1 A g−1 and a capacity of 201.1 mAh g−1 at 5 A g−1 after 6000 cycles. In combination with ex situ characterizations and density functional theory calculations, the high reversible capacity has been demonstrated to be attributed to the co-doping of N/O heteroatoms and porous structure improved K+ adsorption and intercalation capabilities, and the stable long-cycling performance originating from the bilayer-shelled hollow porous carbon sphere structure. Meanwhile, the hollow porous activated carbon microspheres (HPAC) cathode with a high specific surface area (1472.65 m2 g−1) deriving from etching NOHPC with KOH, contributing to a high electrochemical adsorption capacity of 71.2 mAh g−1 at 1 A g−1. Notably, the NOHPC//HPAC PIHC delivers a high energy density of 90.1 Wh kg−1 at a power density of 939.6 W kg−1 after 6000 consecutive charge–discharge cycles.
Cubic Vertex-Transitive Graphs Admitting Automorphisms of Large Order
Authors: Potočnik Primož , Toledo Micael 
Published: June 2023

AbstractA connected graph of order n admitting a semiregular automorphism of order n/k is called a k-multicirculant. Highly symmetric multicirculants of small valency have been extensively studied, and several classification results exist for cubic vertex- and arc-transitive multicirculants. In this paper, we study the broader class of cubic vertex-transitive graphs of order n admitting an automorphism of order n/3 or larger that may not be semiregular. In particular, we show that any such graph is either a k-multicirculant for some $$k \le 3$$

k
≤
3

, or it belongs to an infinite family of graphs of girth 6.
Shear Strength of Single and Multi-layer Soil–Geosynthetic and Geosynthetic–Geosynthetic Interfaces Using Large Direct Shear Testing
Authors: Muluti Shade Sitwala , Kalumba Denis , Sobhee-Beetul Laxmee , Chebet Faridah 
Published: June 2023

AbstractLandfill liners are critical components of waste containment systems that are designed to prevent the migration of pollutants into the environment. Accurate measurement of the shear strength of soil–geosynthetic and geosynthetic–geosynthetic interfaces is essential for designing safe and cost-effective landfill liners. This paper presents a comparative study of the shear strength parameters of single and multi-layer interfaces using a Large Direct Shear Apparatus (LDSA). The study aimed to investigate the effects of using different testing configurations on the Peak and Large Displacement (LD) strengths of the interfaces and to identify the test configuration that provides the most critical shear strength results. A “305 × 305 mm” LDSA was used to perform interface shear tests in saturated conditions with applied normal stresses ranging from 50 to 400 kPa. The results showed good agreement between strength envelopes derived from single and multi-layer interface tests for the materials tested. However, the peak and LD strengths were generally 9% and 24% lower, respectively, for single interface tests than for multi-layer interface tests across the range of normal stresses considered. This conservative estimate may be attributed to the rigid clamping of the geosynthetics, resulting in some tensile strains that reduce the peak and LD shear strengths. Moreover, it was observed in multi-layer interface tests that transfer of shear stresses within the system could have occurred, which could have led to higher overall peak and LD shear strengths. Higher displacements along the critical interface in single interface tests than in multi-layer interface tests may also contribute to this outcome. Overall, these findings have important implications for designing landfill liner systems and performing shear strength tests. Specifically, multi-layer interface tests provide a better simulation of field conditions and a more accurate representation of the shear strength characteristics of composite liner systems. However, performing multiple single interface tests may still be necessary to fully understand the shear strength of individual interfaces. Further research is needed to explore the implications of these findings for other materials and testing conditions.
Comparison of an Optimised Multiresolution Segmentation Approach with Deep Neural Networks for Delineating Agricultural Fields from Sentinel-2 Images
Authors: Tetteh Gideon Okpoti , Schwieder Marcel , Erasmi Stefan , Conrad Christopher , Gocht Alexander 
Published: June 2023

AbstractEffective monitoring of agricultural lands requires accurate spatial information about the locations and boundaries of agricultural fields. Through satellite imagery, such information can be mapped on a large scale at a high temporal frequency. Various methods exist in the literature for segmenting agricultural fields from satellite images. Edge-based, region-based, or hybrid segmentation methods are traditional methods that have widely been used for segmenting agricultural fields. Lately, the use of deep neural networks (DNNs) for various tasks in remote sensing has been gaining traction. Therefore, to identify the optimal method for segmenting agricultural fields from satellite images, we evaluated three state-of-the-art DNNs, namely Mask R-CNN, U-Net, and FracTAL ResUNet against the multiresolution segmentation (MRS) algorithm, which is a region-based and a more traditional segmentation method. Given that the DNNs are supervised methods, we used an optimised version of the MRS algorithm based on supervised Bayesian optimisation. Monotemporal Sentinel-2 (S2) images acquired in Lower Saxony, Germany were used in this study. Based on the agricultural parcels declared by farmers within the European Common Agricultural Policy (CAP) framework, the segmentation results of each method were evaluated using the F-score and intersection over union (IoU) metrics. The respective average F-score and IoU obtained by each method are 0.682 and 0.524 for Mask R-CNN, 0.781 and 0.646 for U-Net, 0.808 and 0.683 for FracTAL ResUNet, and 0.805 and 0.678 for the optimised MRS approach. This study shows that DNNs, particularly FracTAL ResUNet, can be effectively used for large-scale segmentation of agricultural fields from satellite images.
Praktizieren – Benennen – Erzählen
Authors: Jacob Katharina 
Published: June 2023

ZusammenfassungIn dem Beitrag wird der Frage nachgegangen, was unter Semantiken und Narrative des Entscheidens linguistisch zu verstehen ist. Zielsetzung ist daher, ausgehend von der Untersuchung der Autorin zu einer »Linguistik des Entscheidens« (Jacob 2017), in der die kommunikative Praxis des Entscheidens in Bezug auf linguistische Theorien und Methoden in den Blick genommen wird, bestimmte Semantiken und Narrative des Entscheidens zu identifizieren und an konkreten Beispielen aus dem parlamentarischen Diskurs um erneuerbare Energien von 1983 bis 2013 zu veranschaulichen.
Understanding Evil Deeds in Human Terms: Empathy for the Perpetrators, the Dead Victims, and the Ethics of Being the Afterlife
Authors: Elgabsi Natan 
Published: June 2023

AbstractThis essay concerns what it means to historicize evil in an ethically responsible way: that is, what it means to think and narrate perpetrators and victims of evil through what is testified to and told about them. I show that a responsible gaze can only be recognized by allowing ourselves to be addressed by the dead victims. The argument consists in an existential critique of a set of common ideas in the human sciences, which suggest that we must attempt to empathize with the perpetrators in order to understand their deeds in human terms. This empathetic thought, I suggest, easily leads us either to aestheticize the victims or to think that the evils in our history should be addressed as a metaphysical susceptibility to evil internal to being human. By critically discussing these ideas, I show that empathy for the perpetrators in several respects does injustice to the dead victims, which marks that we as their afterlife continue to have an irresponsible relationship with the dead.
Limits to the Weighing of Goods. A Proposal for Determining the Scopes of Deontological and Consequentialist Ethics
Authors: Rothhaar Markus 
Published: June 2023

AbstractIt is a widespread notion that certain rights duties put categorical constraints on the actions of any rational agent, whereas other rights or duties that can be weighed against each other, based on the value of the goods affected. Both assumptions, however, seem to exclude each other in a theoretical perspective. In this article, I will nevertheless propose an approach, how to reconcile them. The starting point for this proposal is the idea that any action must be justifiable to the persons concerned. Against this backdrop, I try to show that submitting another person to a strategic calculus of weighing goods cannot be justified to her if the goods in question cannot be separated from that person, i.e. are in a certain perspective “identical” with her, as with the good of life. This also opens a perspective to reconcile deontological and consequentialist ethics.
Analysis of Different Electric Current Frequencies in Soil Apparent Conductivity
Authors: dos Santos Fernando Ferreira Lima , de Queiroz Daniel Marçal , Valente Domingos Sárvio Magalhães , Khorsandi Farzaneh , de Moura Araújo Guilherme 
Published: June 2023

Abstract
Purpose
The electrical resistivity method is commonly used to identify areas with similar soil characteristics by measuring the soil’s apparent electrical conductivity (ECa). However, the ideal frequency range to be used and the influence of the signal frequency on ECa values obtained by sensors is still uncertain. This study aimed to (1) evaluate the effect of electric current frequency on ECa values and (2) evaluate the correlation between ECa values obtained from different frequencies and soil attributes of agronomic interest, such as soil water content and pH. ECa determinations were obtained in four experimental areas using six different frequencies of electric current (1, 5, 10, 20, 30, and 40 Hz).

Methods
The influence of the electric current signal frequency on the value obtained by the sensor was analyzed by linear regressions in all experimental areas, followed by the F-test (α = 0.05). Pearson’s correlations (α = 0.05) were calculated to quantify the relationships between ECa and soil physical and chemical attributes, for each frequency of the electric current signal of the sensor.

Results
Statistical analyses indicated that the electric current frequency does not significantly affect the measured conductivity value. According to the chi-square test (p ≤ 0.05), the number of significant correlations between ECa and soil physical and chemical attributes was homogeneous for all frequencies. The F-test indicated that the frequency of the electric current had no significant effect on ECa in 90% of the experimental points.

Conclusion
It was shown that all frequencies in the range of 1 to 40 Hz can be used to correlate ECa values with chemical and physical soil attributes, such as soil water content (r = 0.73, p < 0.05) and exchangeable calcium content (r = 0.66, p < 0.05).