bluuebunny/crossref_metadata_2025_split huggingface dataset:
Managing premature idleness in high-variety manufacturing
Authors: Bergenhenegouwen Thimo , Kasper T. A. Arno , Bokhorst Jos A. C. , Land Martin J. 
Published: November 2023

AbstractThis paper shows the effectiveness of labour transfers in addressing premature idleness caused by controlled order release. Controlled order release restricts order entry to the shop floor and is commonly employed in high-variety manufacturing where it results in benefits such as stable work-in-progress. However, it can increase waiting times when orders are blocked from release, while capacities are idling. This issue, known as premature idleness, negatively impacts delivery performance. Previous studies have primarily focused on addressing premature idleness through input control by releasing new orders to idling workstations. This approach overlooks the potential of output control during premature idleness, transferring labour to assist at other workstations in a dual resource constrained setting. Using simulation, this study demonstrates that output control significantly improves delivery performance—in terms of mean tardiness and percentage tardy—and reduces total and shop floor throughput times. Importantly, this result proves robust, even when the efficiency of the assisting worker is severely limited. Shop-level performance improves despite the efficiency loss of the worker. The impact of the where-rule is minimal, while the efficacy of the priority dispatching rule depends on the joint efficiency of collaborating workers. Finally, we show that combining input control and output control enhances performance, providing opportunities for further research on the role of both control approaches in high-variety manufacturing.
What is Post-normal Science? A Personal Encounter
Authors: Saltelli Andrea 
Published: November 2023

AbstractWhat is post-normal science? What are the reasons for, and consequences of, encountering it in one’s professional life? Here I share my own experience of readings, practices and discussions with the fathers, supporters and detractors of PNS. After a short description of PNS and of my own experience with it, I review some common criticism levelled to PNS from different authors and conclude reflecting on how PNS—difficult to explain and translate into formulae or checklists—provides its practitioners with useful keys to open relevant doors to understanding, and might be especially suited to face the present intersecting crises befalling the use of science for policy.
Evaluation of the aortic wall structural alteration following cryopreservation in 1 year follow-up period
Authors: Lőrinczy Dénes , Benkő László , Fazekas Gábor 
Published: November 2023

AbstractVascular graft infection is still a life-threatening complication of reconstructive surgery. Among other options, application of cryopreserved homografts can eventuate favorable outcome, if graft replacement is necessary. The preparation and storage of these allografts need special infrastructure and deep subzero (− 80 °C) temperature. However, the longer storage time can lead to inferior results after implantation, based upon clinicians’ experiences. The goal of our investigation was to circumscribe the optimal storage time interval with differential scanning calorimetry (DSC) and histological evaluation, using porcine aorta. All samples were deep-freezed using − 80 °C. Cryopreservated grafts were melted after 4, 6, 12, 16, 20, 24, 28 and 52 weeks; then, DSC and different types of histology were performed. Light microscopy analysis showed significant changes in the connective tissue fibers’ structure from the 16th week; while, DSC measurements confirmed systematic decrease in the thermal stability from the same week during the follow-up period. Our investigation suggested that cryopreservation can lead to significant and increasing microstructural damage of the fibers following the 12th week; thus, the homograft implantation can result in higher success rate inside this timeframe.
Predicting potential real-time donations in YouTube live streaming services via continuous-time dynamic graphs
Authors: Jin Ruidong , Liu Xin , Murata Tsuyoshi 
Published: November 2023

AbstractOnline live streaming platforms, such as YouTube Live and Twitch, have seen a surge in popularity in recent years. These platforms allow viewers to send real-time gifts to streamers, which can bring significant profits and fame. However, there has been little research on the donation system used on live streaming platforms. This paper aims to fill this gap by building a continuous-time dynamic graph to model the interactions among viewers based on real-time chat messages and predict the real-time donations on live streaming platforms. To achieve this, we propose a novel model called the Temporal Difference Graph Neural Network (TDGNN) that incorporates imbalanced learning strategies to identify potential donors during live streaming. Our model can predict the exact time when donations will appear. We conduct extensive experiments on three live streaming video datasets and demonstrate that our proposed model is more effective and robust than other baseline methods from other fields.
MIA3 promotes the degradation of GSH (glutathione) by binding to CHAC1, thereby promoting the progression of hepatocellular carcinoma
Authors: Wanbiao Zhou , Jing Man , Shi Zuo , Tengxiang Chen , Xueke Zhao , Haiyang Li 
Published: November 2023

AbstractMIA3 (melanoma inhibitory active protein 3)/TANGO1 (Golgi transporter component protein) plays an important role in the initiation, development, and metabolism of cancer. We aimed to explore the role and underlying molecular mechanisms of MIA3/TANGO1 in the growth and migration of hepatoma cells. According to the analysis of The Cancer Genome Atlas (TCGA) database, MIA3 is expressed at higher levels in hepatocellular carcinoma (HCC) tissues than in normal tissues. Real-time quantitative polymerase chain reaction (qRT-PCR), immunohistochemistry, and western blotting were used to detect mRNA and protein expression in HCC tissues and cells. The in vitro function of MIA3 in HCC cells was evaluated using Cell Counting Kit-8 (CCK-8), colony formation, cell migration and invasion, and flow cytometry assays. Hep-G2 cells with MIA3 overexpression were subjected to RNA-seq, and the downstream target gene CHAC1 (glutathione-specific γ-glutamyl cyclotransferase 1) was selected according to the results of the volcano map of gene enrichment. The relationship between MIA3 and CHAC1 was revealed by coimmunoprecipitation and confocal microscopy. MIA3 expression was upregulated in HCC organizations and HCC samples in the TCGA dataset. Knocking out MIA3 inhibited the proliferation, migration, and invasion of Hep-G2 cells and promoted the apoptosis of Hep-G2 cells. Overexpression of MIA3 in Huh7 cells promoted the proliferation, migration, and invasion and suppressed the apoptosis of Huh7 cells. Overexpression of MIA3 promoted the expression of CHAC1 and the degradation of glutathione (GSH), thereby promoting the growth and metastasis of HCC cells. Knocking out MIA3 inhibited the expression of CHAC1 and slowed the degradation of glutathione, thereby inhibiting the growth and metastasis of HCC cells. MIA3 further promotes the growth, metastasis, and invasion of hepatoma cells by binding to the CHAC1 protein and promoting GSH degradation.
Second-order Rosenbrock-exponential (ROSEXP) methods for partitioned differential equations
Authors: Dallerit Valentin , Buvoli Tommaso , Tokman Mayya , Gaudreault Stéphane 
Published: November 2023

AbstractIn this paper, we introduce a new framework for deriving partitioned implicit-exponential integrators for stiff systems of ordinary differential equations and construct several time integrators of this type. The new approach is suited for solving systems of equations where the forcing term is comprised of several additive nonlinear terms. We analyze the stability, convergence, and efficiency of the new integrators and compare their performance with existing schemes for such systems using several numerical examples. We also propose a novel approach to visualizing the linear stability of the partitioned schemes, which provides a more intuitive way to understand and compare the stability properties of various schemes. Our new integrators are A-stable, second-order methods that require only one call to the linear system solver and one exponential-like matrix function evaluation per time step.
Income Inequality, Human Capital and Institutional Quality in Sub-Saharan Africa
Authors: Adeleye Bosede Ngozi 
Published: November 2023

AbstractThis paper aligns with Sustainable Development Goal 10 to investigate the role of quality institutions in the human capital-income inequality nexus. It uses an unbalanced panel data on the Gini index (measure of income inequality), human capital index and institutional quality index on 46 sub-Saharan African countries from 2010 to 2019. The Driscoll and Kraay (Rev Econ Stat 80:549–560, 1998) panel spatial correlation consistent (PSCC) and Firpo et al. (Unconditional quantile regressions. National Bureau of Economic Research Working Paper, No. 339, pp 1–54, 2007. https://doi.org/10.3982/ECTA6822) bootstrap unconditional quantile regression (UQR) techniques are deployed. Among others, findings reveal that: (1) human capital and institutions aggravate inequality; (2) the interaction of human capital and institution reduce inequality; (3) UQR reveals that the interaction effect is negative at lower quantiles of 0.10, 0.25 and 0.50; and (4) results across the sub-regions are mixed. In addition, evidence from the margin plots reveals that the conditional effect of human capital on income inequality is negative as institutions are strengthened. The downward trend of the plot within the 95% confidence interval shows that institutional quality enhances the impact of human capital on income inequality. These are novel contributions to the literature as it suggests that quality institutions matter in the drive towards reducing the menace of income inequality. Policy recommendations include strengthening institutions and making basic education affordable.
Small-Scale Upflows in a Coronal Hole – Tracked from the Photosphere to the Corona
Authors: Schwanitz Conrad , Harra Louise , Barczynski Krzysztof , Mandrini Cristina H. , Orozco Suárez David , Moreno Vacas Alejandro , Raouafi Nour E. 
Published: November 2023

AbstractCoronal transients are known as sources of coronal upflows. With the commissioning of Solar Orbiter, it became apparent that coronal small-scale features are even more frequent than previously estimated. It was found that even small coronal features seen by Solar Orbiter can produce visible upflows. Therefore, it is important to study the plasma flows on small scales better and understand their atmospheric driving mechanisms.In this article, we present the results from a two-week coordinated multi-spacecraft observation campaign with Hinode, IRIS, and the GREGOR telescope. We identify a small region of coronal upflows with Doppler velocities of up to 16.5 km s−1. The upflows are located north of a coronal bright point in a coronal hole. We study the corona, the transition region, the chromosphere and the photospheric magnetic field to find evidence of underlying mechanisms for the coronal upflow. We find a complex photospheric magnetic field with several small mixed polarities that are the footpoints of different loops. Flux emergence and cancellation are observed at the constantly changing footpoints of the coronal loops. Reconnection of loops can be identified as the driver of the coronal upflow. Furthermore, the impact of the coronal activity triggers plasma flows in the underlying layers. This work highlights that frequent small coronal features can cause considerable atmospheric response and ubiquitously produce plasma upflows that potentially feed into the solar wind.
Lévy Langevin Monte Carlo
Authors: Oechsler David 
Published: November 2023

AbstractAnalogously to the well-known Langevin Monte Carlo method, in this article we provide a method to sample from a target distribution $$\varvec{\pi }$$

π

by simulating a solution of a stochastic differential equation. Hereby, the stochastic differential equation is driven by a general Lévy process which—unlike the case of Langevin Monte Carlo—allows for non-smooth targets. Our method will be fully explored in the particular setting of target distributions supported on the half-line $$(0,\infty )$$

(
0
,
∞
)

and a compound Poisson driving noise. Several illustrative examples conclude the article.
Tyrosinase enzyme purification and immobilization from Pseudomonas sp. EG22 using cellulose coated magnetic nanoparticles: characterization and application in melanin production
Authors: El-Aziz Salim Mohamed Abd , Faraag Ahmed Hassan Ibrahim , Ibrahim Ayman Meselhi , Albrakati Ashraf , Bakkar Marwa Reda 
Published: November 2023

AbstractMelanin is a brown-black pigment with significant roles in various biological processes. The tyrosinase enzyme catalyzes the conversion of tyrosine to melanin and has promising uses in the pharmaceutical and biotechnology sectors. This research aims to purify and immobilize the tyrosinase enzyme from Pseudomonas sp.EG22 using cellulose-coated magnetic nanoparticles. Various techniques were utilized to examine the synthesized nanoparticles, which exhibited a spherical shape with an average diameter of 12 nm and a negative surface potential of − 55.7 mV with a polydispersity index (PDI) of 0.260. Comparing the immobilized magnetic tyrosinase enzyme with the free enzyme, the study’s findings showed that the immobilized tyrosinase enzyme had optimal activity at a pH of 6 and a temperature of 35 °C, and its activity increased as the concentration of tyrosine increased. The study investigated the antibacterial and anticancer bioactivity of the enzyme’s melanin product and found that it exhibited potential antibacterial activity against a multi-drug resistant strain including S. aureus and E. coli. The produced melanin also demonstrated the potential to decrease cell survival and induce apoptosis in initiation cells.
Experimental and Computational Approaches to Measure Telomere Length: Recent Advances and Future Directions
Authors: Ferrer Alejandro , Stephens Zachary D. , Kocher Jean-Pierre A. 
Published: November 2023

Abstract
Purpose of Review
The length of telomeres, protective structures at the chromosome ends, is a well-established biomarker for pathological conditions including multisystemic syndromes called telomere biology disorders. Approaches to measure telomere length (TL) differ on whether they estimate average, distribution, or chromosome-specific TL, and each presents their own advantages and limitations.

Recent Findings
The development of long-read sequencing and publication of the telomere-to-telomere human genome reference has allowed for scalable and high-resolution TL estimation in pre-existing sequencing datasets but is still impractical as a dedicated TL test. As sequencing costs continue to fall and strategies for selectively enriching telomere regions prior to sequencing improve, these approaches may become a promising alternative to classic methods.

Summary
Measurement methods rely on probe hybridization, qPCR or more recently, computational methods using sequencing data. Refinements of existing techniques and new approaches have been recently developed but a test that is accurate, simple, and scalable is still lacking.
Frameproof codes, separable codes and $$B_2$$ codes: Bounds and constructions
Authors: Fernandez Marcel , Livieratos John , Martín Sebastià 
Published: November 2023

AbstractIn this paper, constructions of frameproof codes, separable codes, and $$B_2$$

B
2

codes are obtained. For each family of codes, the Lovász Local Lemmais used to establish lower bounds for the codes. The obtained bounds match all best known bounds in the literature. Our strategy allows us to present constructions of the aforementioned codes, by using the variable framework for the Lovász Local Lemma.
Artificial Intelligence for Risk Assessment on Primary Prevention of Coronary Artery Disease
Authors: Chen Shang-Fu , Loguercio Salvatore , Chen Kai-Yu , Lee Sang Eun , Park Jun-Bean , Liu Shuchen , Sadaei Hossein Javedani , Torkamani Ali 
Published: November 2023

Abstract
Purpose of Review
Coronary artery disease (CAD) is a common and etiologically complex disease worldwide. Current guidelines for primary prevention, or the prevention of a first acute event, include relatively simple risk assessment and leave substantial room for improvement both for risk ascertainment and selection of prevention strategies. Here, we review how advances in big data and predictive modeling foreshadow a promising future of improved risk assessment and precision medicine for CAD.

Recent Findings
Artificial intelligence (AI) has improved the utility of high dimensional data, providing an opportunity to better understand the interplay between numerous CAD risk factors. Beyond applications of AI in cardiac imaging, the vanguard application of AI in healthcare, recent translational research is also revealing a promising path for AI in multi-modal risk prediction using standard biomarkers, genetic and other omics technologies, a variety of biosensors, and unstructured data from electronic health records (EHRs). However, gaps remain in clinical validation of AI models, most notably in the actionability of complex risk prediction for more precise therapeutic interventions.

Summary
The recent availability of nation-scale biobank datasets has provided a tremendous opportunity to richly characterize longitudinal health trajectories using health data collected at home, at laboratories, and through clinic visits. The ever-growing availability of deep genotype-phenotype data is poised to drive a transition from simple risk prediction algorithms to complex, “data-hungry,” AI models in clinical decision-making. While AI models provide the means to incorporate essentially all risk factors into comprehensive risk prediction frameworks, there remains a need to wrap these predictions in interpretable frameworks that map to our understanding of underlying biological mechanisms and associated personalized intervention. This review explores recent advances in the role of machine learning and AI in CAD primary prevention and highlights current strengths as well as limitations mediating potential future applications.
Organic fluorescent probes for live-cell super-resolution imaging
Authors: Duan Xinxin , Zhang Meng , Zhang Yu-Hui 
Published: November 2023

AbstractThe development of super-resolution technology has made it possible to investigate the ultrastructure of intracellular organelles by fluorescence microscopy, which has greatly facilitated the development of life sciences and biomedicine. To realize super-resolution imaging of living cells, both advanced imaging systems and excellent fluorescent probes are required. Traditional fluorescent probes have good availability, but that is not the case for probes for live-cell super-resolution imaging. In this review, we first introduce the principles of various super-resolution technologies and their probe requirements, then summarize the existing designs and delivery strategies of super-resolution probes for live-cell imaging, and finally provide a brief conclusion and overview of the future.
Graphical Abstract
Type of Education Affects Individuals’ Adoption of Intentional Stance Towards Robots: An EEG Study
Authors: Roselli Cecilia , Navare Uma Prashant , Ciardo Francesca , Wykowska Agnieszka 
Published: November 2023

AbstractResearch has shown that, under certain circumstances, people can adopt the Intentional Stance towards robots and thus treat them as intentional agents. Previous evidence showed that there are factors at play in modulating the Intentional Stance, for example individuals’ years of education. In the present study, we aimed at investigating whether, given the same years of education, participants’ type of formal education- in terms of theoretical background- affected their adoption of the Intentional Stance.To do so, we recruited two samples of participants varying in their type of formal education, namely, a sample of participants comprised individuals with a background in robotics, whereas the other comprised individuals with a background in psychotherapy. To measure their likelihood of adopting the Intentional Stance, we asked them to complete the InStance Test (IST). To do it at the neural level, we recorded their neural activity during a resting state via electroencephalography (EEG).Results showed that therapists attributed higher IST scores of intentionality to the robot than roboticists, i.e., they were more likely to attribute Intentional Stance to explain robot’s behaviour.This result was mirrored by participants’ EEG neural activity during resting state, as we found higher power in the gamma frequency range (associated with mentalizing and the adoption of Intentional Stance) for therapists compared to roboticists.Therefore, we conclude that the type of education that promotes mentalizing skills increases the likelihood of attributing intentionality to robots.
Fast norm computation in smooth-degree Abelian number fields
Authors: Bernstein Daniel J. 
Published: November 2023

AbstractThis paper presents a fast method to compute algebraic norms of integral elements of smooth-degree cyclotomic fields, and, more generally, smooth-degree Galois number fields with commutative Galois groups. The typical scenario arising in S-unit searches (for, e.g., class-group computation) is computing a $$\Theta (n\log n)$$

Θ
(
n
log
n
)

-bit norm of an element of weight $$n^{1/2+o(1)}$$

n

1
/
2
+
o
(
1
)

in a degree-n field; this method then uses $$n(\log n)^{3+o(1)}$$

n

(
log
n
)

3
+
o
(
1
)

bit operations.An $$n(\log n)^{O(1)}$$

n

(
log
n
)

O
(
1
)

operation count was already known in two easier special cases: norms from power-of-2 cyclotomic fields via towers of power-of-2 cyclotomic subfields, and norms from multiquadratic fields via towers of multiquadratic subfields. This paper handles more general Abelian fields by identifying tower-compatible integral bases supporting fast multiplication; in particular, there is a synergy between tower-compatible Gauss-period integral bases and a fast-multiplication idea from Rader.As a baseline, this paper also analyzes various standard norm-computation techniques that apply to arbitrary number fields, concluding that all of these techniques use at least $$n^2(\log n)^{2+o(1)}$$

n
2

(
log
n
)

2
+
o
(
1
)

bit operations in the same scenario, even with fast subroutines for continued fractions and for complex FFTs. Compared to this baseline, algorithms dedicated to smooth-degree Abelian fields find each norm $$n/(\log n)^{1+o(1)}$$

n
/

(
log
n
)

1
+
o
(
1
)

times faster, and finish norm computations inside S-unit searches $$n^2/(\log n)^{1+o(1)}$$

n
2

/

(
log
n
)

1
+
o
(
1
)

times faster.