bluuebunny/crossref_metadata_2025_split huggingface dataset:
Superlithiation Performance of Pyridinium Polymerized Ionic Liquids with Fast Li+ Diffusion Kinetics as Anode Materials for Lithium‐Ion Battery
Authors: Wang Yeji , Yang Gege , Wang Gaolei , Min Yuxin , Zhou Le , Yang Chaofan , Huang Junjie , Dai Guoliang 
Published: May 2023

AbstractPolymerized ionic liquids (PILs) with super ion diffusion kinetics have aroused considerable attention in rechargeable batteries, which are very promising to solve the problem of the slow ion diffusion kinetics in organic electrode materials. Theoretically, PILs incorporated redox groups are very suitable as anode materials to realize “superlithiation” performance, achieving high lithium storage capacity. In this study, redox pyridinium‐based PILs (PILs‐Py‐400) have been synthesized through trimerization reactions by pyridinium ionic liquids with cyano groups under an appropriate temperature (400 °C). The positively charged skeleton, extended conjugated system, abundant micropores, and amorphous structure for PILs‐Py‐400 can boost the utilization efficiency of redox sites. A high capacity of 1643 mAh g−1 at 0.1 A g−1 (96.7% of the theoretical capacity) has been obtained, indicating intriguing 13 Li+ redox reactions in per repeating unit of one pyridinium ring, one triazine ring, and one methylene. Moreover, PILs‐Py‐400 exhibit excellent cycling stability with a capacity of around 1100 mAh g−1 at 1.0 A g−1 after 500 cycles, and the capacity retention is 92.2%.
Shape‐Dictated Self‐Assembly of Photoresponsive Hybrid Colloids
Authors: Zhang Haiyang , Feng Xuan , Xia Xiuyang , Zhu Jiao , Wang Huaguang , Ni Ran , Zhang Zexin 
Published: May 2023

AbstractThe shape‐dictated self‐assembly of hybrid colloids induced by chemical concentration gradients generated by photocatalytic reactions of the colloids is studied. Different shapes enable the formation of assemblies with distinct lattice structures including hexagons, distorted hexagons, and squares, which are corroborated by computer simulations. Furthermore, assemblies change from lattices to chains when increasing the attraction between the colloids. The results show that photoresponsive hybrid colloids possess a unique capability for shape‐dependent self‐assembly, offering a practical and versatile approach to manipulate self‐assembly at the microscale.
Testing of precast recycled aggregate concrete shear wall with pressed sleeve connection subjected to cyclic loading
Authors: Yuan Chang‐Min , Cai Jian , Wang Fangying , He An , He Bingquan , Chen Qing‐Jun , Zuo Zhiliang 
Published: May 2023

AbstractThe pressed sleeve connection is a new type of connection technique reported in China recently. To explore the possibility of combining the advantages of pressed sleeve connections and recycled aggregate concrete (RAC) in precast concrete, the seismic performance of precast shear walls with pressed sleeves and recycled fine aggregate (RFA) concrete was investigated through a thorough experimental programme. A total of seven precast shear wall specimens and one cast‐in‐situ specimen were fabricated and tested under lateral cyclic loading, considering the effects of the aspect ratio, the axial compression ratio, and the RFA content. The failure modes, hysteretic behavior, bearing capacity, energy dissipation, stiffness and shear distortion of the specimens, as well as the strains of the steels, were reported and discussed. The test results demonstrated that the pressed sleeve connections were capable of transmitting both tensile and compressive forces between reinforcements, and the precast shear walls with pressed sleeve connections exhibited the same hysteresis behavior, strengths, ductility coefficient and energy dissipation capacity as the cast‐in‐situ counterpart. Moreover, the seismic behavior of the precast specimens with the RFA content of 30% was almost the same as those with natural aggregate concrete (NAC). The increase in the axial compression ratio and aspect ratio led to higher peak loads of the precast shear walls. Finally, existing design methods of ordinary reinforced concrete shear walls were evaluated for their application to the design of precast RFA concrete shear walls with pressed sleeves. Overall, the evaluation results revealed that the examined design methods offer generally accurate strength predictions for the proposed shear walls.
Mechanical modeling of dowel action and the influence of small amounts of shear reinforcement on the shear‐transfer actions in RC beams
Authors: Autrup Frederik , Jørgensen Henrik Brøner , Ruiz Miguel Fernández , Hoang Linh Cao 
Published: May 2023

AbstractDowel action of the longitudinal reinforcement in RC beams without and with small amounts of shear reinforcement is typically considered a constant shear contribution determined from the splitting strength of the concrete cover. However, in a recent experimental investigation by the authors, it was shown that the shear force transferred by dowel action for beams without shear reinforcement should be determined from the dowel displacement and a linear elastic model and a rigid plastic dowel model. This article is aimed at extending this model to also cover members with small amounts of shear reinforcement. To that aim, a novel approach to calculate the shear force carried by dowel action of the longitudinal reinforcement in both beams with and without shear reinforcement is presented. The model is derived by establishing an equilibrium of work between the internal stored elastic or dissipated plastic energy and the external work performed by the shear force in the dowel. Additionally, a method to determine the displacement of the dowel from DIC measurements is presented. For the remaining shear‐transfer actions, reasonable constitutive models from the literature are adapted. On the basis of DIC measurements, the shear force carried by each of the shear‐transfer actions is calculated for 16 shear tests of beams without and with small amounts of shear reinforcement. The sum of shear force carried by each of the shear‐transfer actions is shown to predict the applied shear force fairly well, from the development of the critical shear crack until failure. Additionally, it is shown that for beams with shear reinforcement below the minimum requirements according to the current design standards, the shear capacity is governed by aggregate interlock, residual tensile stresses, and the inclination of the compression chord. While for beams with shear reinforcement above the minimum requirements, the shear capacity is governed by the shear reinforcement and dowel action.
Global Operator Calculus on Spin Groups
Authors: Cerejeiras P. , Ferreira M. , Kähler U. , Wirth J. 
Published: May 2023

AbstractIn this paper, we use the representation theory of the group $$\textrm{Spin}(m)$$

Spin
(
m
)

to develop aspects of the global symbolic calculus of pseudo-differential operators on $$\textrm{Spin}(3)$$

Spin
(
3
)

and $$\textrm{Spin}(4)$$

Spin
(
4
)

in the sense of Ruzhansky–Turunen–Wirth. A detailed study of $$\textrm{Spin}(3)$$

Spin
(
3
)

and $$\textrm{Spin}(4)$$

Spin
(
4
)

-representations is made including recurrence relations and natural differential operators acting on matrix coefficients. We establish the calculus of left-invariant differential operators and of difference operators on the group $$\textrm{Spin}(4)$$

Spin
(
4
)

and apply this to give criteria for the subellipticity and the global hypoellipticity of pseudo-differential operators in terms of their matrix-valued full symbols. Several examples of first and second order globally hypoelliptic differential operators are given, including some that are locally neither invertible nor hypoelliptic. The paper presents a particular case study for higher dimensional spin groups.
Algorithmic discrimination in the credit domain: what do we know about it?
Authors: Garcia Ana Cristina Bicharra , Garcia Marcio Gomes Pinto , Rigobon Roberto 
Published: May 2023

AbstractThe widespread usage of machine learning systems and econometric methods in the credit domain has transformed the decision-making process for evaluating loan applications. Automated analysis of credit applications diminishes the subjectivity of the decision-making process. On the other hand, since machine learning is based on past decisions recorded in the financial institutions’ datasets, the process very often consolidates existing bias and prejudice against groups defined by race, sex, sexual orientation, and other attributes. Therefore, the interest in identifying, preventing, and mitigating algorithmic discrimination has grown exponentially in many areas, such as Computer Science, Economics, Law, and Social Science. We conducted a comprehensive systematic literature review to understand (1) the research settings, including the discrimination theory foundation, the legal framework, and the applicable fairness metric; (2) the addressed issues and solutions; and (3) the open challenges for potential future research. We explored five sources: ACM Digital Library, Google Scholar, IEEE Digital Library, Springer Link, and Scopus. Following inclusion and exclusion criteria, we selected 78 papers written in English and published between 2017 and 2022. According to the meta-analysis of this literature survey, algorithmic discrimination has been addressed mainly by looking at the CS, Law, and Economics perspectives. There has been great interest in this topic in the financial area, especially the discrimination in providing access to the mortgage market and differential treatment (different fees, number of parcels, and interest rates). Most attention has been devoted to the potential discrimination due to bias in the dataset. Researchers are still only dealing with direct discrimination, addressed by algorithmic fairness, while indirect discrimination (structural discrimination) has not received the same attention.
Laser-powder bed fusion molds without post-processing for micro-injection molding of mini/micro-products
Authors: Surace Rossella , Errico Vito , Valori Marcello , Fassi Irene , Campanelli Sabina Luisa 
Published: May 2023

AbstractThe use of additive manufacturing (AM) processes at the micro-scale helps to increase the development of micro-systems, thus enabling shorter tooling development to be exploited for other micro-technologies, such as micro-injection molding (µ-IM). In recent years, these process combinations have shown their capability of providing greater flexibility to micro-technologies and facilitating a high production rate, in particular allowing µ-IM to be economically feasible for low-volume production. Therefore, in order to assess the feasibility of micro-metal AM for µ-IM mold production, a set of mini- and micro-polymeric parts injected by using molds realized via the laser-powder bed fusion (L-PBF) process is discussed in this paper. The molds were manufactured in low-carbon steel and have been characterized and tested experimentally, without any post-process treatment. Two selected geometries, corresponding to a mini dogbone for tensile tests and a flexural hinge with features at the micro-scale, were designed and realized by the µ-IM process. These were replicated in order to assess the accuracy of the whole process. The obtained results of replication accuracy and mechanical tests confirm that the µ-IM process, performed with an L-PBF-made mold insert, is feasible and affordable for micro-production, although great accuracy, especially in mold design, fabrication, and assembly, is required.
Fast floating-point filters for robust predicates
Authors: Bartels Tinko , Fisikopoulos Vissarion , Weiser Martin 
Published: May 2023

AbstractGeometric predicates are at the core of many algorithms, such as the construction of Delaunay triangulations, mesh processing and spatial relation tests. These algorithms have applications in scientific computing, geographic information systems and computer-aided design. With floating-point arithmetic, these geometric predicates can incur round-off errors that may lead to incorrect results and inconsistencies, causing computations to fail. This issue has been addressed using a combination of exact arithmetic for robustness and floating-point filters to mitigate the computational cost of exact computations. The implementation of exact computations and floating-point filters can be a difficult task, and code generation tools have been proposed to address this. We present a new C++ meta-programming framework for the generation of fast, robust predicates for arbitrary geometric predicates based on polynomial expressions. We combine and extend different approaches to filtering, branch reduction, and overflow avoidance that have previously been proposed. We show examples of how this approach produces correct results for data sets that could lead to incorrect predicate results with naive implementations. Our benchmark results demonstrate that our implementation surpasses state-of-the-art implementations.
EEG Microstates as a Signature of Hemispheric Lateralization in Stroke
Authors: Rubega Maria , Facca Massimiliano , Curci Vittorio , Sparacino Giovanni , Molteni Franco , Guanziroli Eleonora , Masiero Stefano , Formaggio Emanuela , Del Felice Alessandra 
Published: May 2023

AbstractStroke recovery trajectories vary substantially. The need for tracking and prognostic biomarkers in stroke is utmost for prognostic and rehabilitative goals: electroencephalography (EEG) advanced signal analysis may provide useful tools toward this aim. EEG microstates quantify changes in configuration of neuronal generators of short-lasting periods of coordinated synchronized communication within large-scale brain networks: this feature is expected to be impaired in stroke. To characterize the spatio-temporal signatures of EEG microstates in stroke survivors in the acute/subacute phase, EEG microstate analysis was performed in 51 first-ever ischemic stroke survivors [(28–82) years, 24 with right hemisphere (RH) lesion] who underwent a resting-state EEG recording in the acute and subacute phase (from 48 h up to 42 days after the event). Microstates were characterized based on 4 parameters: global explained variance (GEV), mean duration, occurrences per second, and percentage of coverage. Wilcoxon Rank Sum tests were performed to compare features of each microstate across the two groups [i.e., left hemisphere (LH) and right hemisphere (RH) stroke survivors]. The canonical microstate map D, characterized by a mostly frontal topography, displayed greater GEV, occurrence per second, and percentage of coverage in LH than in RH stroke survivors (p < 0.05). The EEG microstate map B, with a left-frontal to right-posterior topography, and F, with an occipital-to-frontal topography, exhibited a greater GEV in RH than in LH stroke survivors (p = 0.015). EEG microstates identified specific topographic maps which characterize stroke survivors’ lesioned hemisphere in the acute and early subacute phase. Microstate features offer an additional tool to identify different neural reorganization.
Evaluating the viscoelastic shear properties of clear wood via off-axis compression testing and digital-image correlation
Authors: Bengtsson Rhodel , Bergeron Louis , Afshar Reza , Mousavi Mahmoud , Gamstedt E. Kristofer 
Published: May 2023

AbstractHighly anisotropic materials like wood and unidirectional polymer composite structures are sensitive to shear deformations, in particular close to fixed joints. Large wooden structures in buildings and, e.g. wind-turbine blades, are designed to last for decades, and hence are susceptible to unwanted creep deformations. For improved structural design, the shear-creep properties of the material are needed. These are rarely available in the literature, possibly because of technical difficulties to achieve a well-defined shear-stress state in test specimens. For cost-efficient testing, this goal of a pure stress state necessarily needs to be compromised. In the present study, we propose a simple test method based on uniaxial compression on wooden cubes, but is equally applicable for fibre composites. The viscoelastic shear properties of Norway spruce (Picea abies) under off-axis creep compression tests have been characterised in all three directions. The tests are performed in a controlled climate chamber and the creep strains are captured using digital-image correlation.
Accounting for doxastic agency: Mental action and self-awareness
Authors: Keeling Sophie 
Published: May 2023

AbstractUncontroversially, individuals exercise agency in acting; can we say the same about believing? This paper argues that subjects do indeed exercise agency over their beliefs and provides an account by which this is possible. On my picture, self-awareness is fundamental to the nature of doxastic agency. Drawing on work in the philosophy of action, I argue that subjects exercise agency in performing mental actions that form and sustain their beliefs, where they are aware of these actions as part of reasoning and exercising agency over their beliefs.
A mowing strategy for urban parks to support spring flowers in a mediterranean climate city in South Africa
Authors: Brom Peta D. , Underhill Leslie G. , Winter Kevin , Colville Jonathan F. 
Published: May 2023

AbstractConverting road verges and Public Open Space (POS) to floral-rich resources is an important strategy for supporting populations of urban pollinators. One of the ways this can be achieved is through adjusting mowing schedules, either with a “delayed start” after the end of winter, or a reduction in mowing frequency. In Mediterranean climate ecosystems with mild, wet winters, plant growth continues through the winter season and the definition of a “delayed start” is unclear. This study sought to identify a strategic period for the suspension of mowing activities in a city in South Africa with a Mediterranean climate. It estimated the duration of the flowering to seed-broadcast season of 20 species of native geophytes. Observations were made of phenological status from bud to seed broadcast in ten city parks in Cape Town during the austral spring flowering seasons of 2019 and 2020 (August – December). The Underhill and Zucchini (1988) Moult Model was employed to estimate the duration of the flowering season. Model results showed that mowing should stop in the second week of August and should not resume until the beginning of November in the study context. The results are discussed against the relative biodiversity contribution that different parks and verges make and the potential conflicts with the utility objectives of urban society.
Exploring the effects of a musical play intervention on young children’s self-regulation and metacognition
Authors: Zachariou Antonia , Bonneville-Roussy Arielle , Hargreaves David , Neokleous Rania 
Published: May 2023

Abstract
Self-regulation and metacognition are important for lifelong functioning and can be strengthened with intervention at a young age. Research proposes that musical play enables self-regulatory development, but lacks rigorous approaches to investigate whether a causal relationship between the two exists. We introduced a musical play intervention in a real-world classroom, and examined its impact on self-regulation and metacognition. We adopted a quasi-experimental, pre-test and post-test control-group design, with 98 children aged 6. The intervention group (N = 45) participated in 13 musical play sessions, while the control group (N = 53) had 13 music lessons following the usual music curriculum. Children’s self-regulation and metacognition, including metacognitive knowledge, metacognitive regulation and emotional/motivational regulation, was assessed before and after the intervention through three validated instruments: a) an observational assessment of children’s self-regulation and metacognition while completing a task, b) a metacognitive knowledge interview following the task, and c) a teacher-reported assessment of self-regulation and metacognition. The results were equivocal: according to the teacher ratings, the intervention group’s self-regulation and metacognition improved significantly more than controls following the intervention, but this result was not corroborated by assessment of children’s self-regulation on task. Despite this, from the three areas of self-regulation and metacognition, there was a statistically significant improvement in metacognitive knowledge in the intervention group compared to controls, a result indicated by the teacher-reported assessment and by the metacognitive knowledge interview, which revealed a significant effect on children’s metacognitive knowledge of strategies. This study can further the discussions on the use of different methodological approaches when exploring self-regulation, and can inform policy and practice in relation to music and play in schools.
Super-resolution of 2D ultrasound images and videos
Authors: Cammarasana Simone , Nicolardi Paolo , Patanè Giuseppe 
Published: May 2023

Abstract
This paper proposes a novel deep-learning framework for super-resolution ultrasound images and videos in terms of spatial resolution and line reconstruction. To this end, we up-sample the acquired low-resolution image through a vision-based interpolation method; then, we train a learning-based model to improve the quality of the up-sampling. We qualitatively and quantitatively test our model on different anatomical districts (e.g., cardiac, obstetric) images and with different up-sampling resolutions (i.e., 2X, 4X). Our method improves the PSNR median value with respect to SOTA methods of $$1.7\%$$

1.7
%

on obstetric 2X raw images, $$6.1\%$$

6.1
%

on cardiac 2X raw images, and $$4.4\%$$

4.4
%

on abdominal raw 4X images; it also improves the number of pixels with a low prediction error of $$9.0\%$$

9.0
%

on obstetric 4X raw images, $$5.2\%$$

5.2
%

on cardiac 4X raw images, and $$6.2\%$$

6.2
%

on abdominal 4X raw images. The proposed method is then applied to the spatial super-resolution of 2D videos, by optimising the sampling of lines acquired by the probe in terms of the acquisition frequency. Our method specialises trained networks to predict the high-resolution target through the design of the network architecture and the loss function, taking into account the anatomical district and the up-sampling factor and exploiting a large ultrasound data set. The use of deep learning on large data sets overcomes the limitations of vision-based algorithms that are general and do not encode the characteristics of the data. Furthermore, the data set can be enriched with images selected by medical experts to further specialise the individual networks. Through learning and high-performance computing, the proposed super-resolution is specialised to different anatomical districts by training multiple networks. Furthermore, the computational demand is shifted to centralised hardware resources with a real-time execution of the network’s prediction on local devices.

Graphical abstract