bluuebunny/crossref_metadata_2025_split huggingface dataset:
Microscopic and nanoscale mechanical properties of tonkin cane bamboo measured by advanced AFM methods
Authors: Nguyen Cam-Phu Thi , Schoenherr Peggy , Seidel Jan 
Published: January 2024

AbstractBamboo is a natural composite material with a high strength-to-weight ratio traditionally used in various consumer products as well as in building and modern advanced and sustainable manufacturing technology. While its macroscopic mechanical properties are well known, nanoscale studies of the mechanical properties of the hierarchical structure of bamboo at the level of individual cells and cell walls are lacking. Here we use different AFM-based methods to analyse the mechanical properties of individual bamboo fibres that are the foundation of the hierarchical structure at the nanoscale. The elastic modulus of the fiber in the outer region varies from 7.5 to 8.9 GPa, while that of the inner fiber ranges between 4.1 and 6.1 GPa. The results demonstrate that the fibre stiffness varies in nanoscale regions between fibre interior and the fibre wall and strongly depends on the position of the individual bamboo fibre within the culm. Outer fibres with high cellulose fibril density and low lignin level show low adhesion interaction force (13.5%) in the interface between cellulose and lignin/hemicellulose matrix, hence, resulting in low plastic deformation energy release during mechanical deformation. The implications of the interrelation of plasticity, lignin level, and adhesion force are discussed for the application of bamboo as a high-performance, renewable, and sustainable material.
Adaptive Bernstein change detector for high-dimensional data streams
Authors: Heyden Marco , Fouché Edouard , Arzamasov Vadim , Fenn Tanja , Kalinke Florian , Böhm Klemens 
Published: January 2024

AbstractChange detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring and prediction systems to react, e.g., by issuing an alarm or by updating a learning algorithm. However, detecting changes is challenging when observations are high-dimensional. In high-dimensional data, change detectors should not only be able to identify when changes happen, but also in which subspace they occur. Ideally, one should also quantify how severe they are. Our approach, ABCD, has these properties. ABCD learns an encoder-decoder model and monitors its accuracy over a window of adaptive size. ABCD derives a change score based on Bernstein’s inequality to detect deviations in terms of accuracy, which indicate changes. Our experiments demonstrate that ABCD outperforms its best competitor by up to 20% in F1-score on average. It can also accurately estimate changes’ subspace, together with a severity measure that correlates with the ground truth.
Flash floods in Mediterranean catchments: a meta-model decision support system based on Bayesian networks
Authors: Ropero Rosa F. , Flores M. Julia , Rumí Rafael 
Published: January 2024

AbstractNatural disasters, especially those related to water—like storms and floods—have increased over the last decades both in number and intensity. Under the current Climate Change framework, several reports predict an increase in the intensity and duration of these extreme climatic events, where the Mediterranean area would be one of the most affected. This paper develops a decision support system based on Bayesian inference able to predict a flood alert in Andalusian Mediterranean catchments. The key point is that, using simple weather forecasts and live measurements of river level, we can get a flood-alert several hours before it happens. A set of models based on Bayesian networks was learnt for each of the catchments included in the study area, and joined together into a more complex model based on a rule system. This final meta-model was validated using data from both non-extreme and extreme storm events. Results show that the methodology proposed provides an accurate forecast of the flood situation of the greatest catchment areas of Andalusia.
Bundle generation for the vehicle routing problem with occasional drivers and time windows
Authors: Mancini Simona , Gansterer Margaretha 
Published: January 2024

AbstractIn this paper, we address the vehicle routing problem (VRP) with occasional drivers (ODs) and time windows (TWs). The problem (VRP-OD-TW) is an extension of the VRP-OD, where ODs serve customers within given TWs. Differently from the basic version of VRP-OD-TW, we assume that ODs not only accept single requests, but they can also serve bundles of requests. To deal with the bundle-to-driver assignment problem, an auction-based system has been designed; a company offers a set of bundles to the ODs, who bid for all the bundles they consider attractive. There is no limit on the number of bids a driver can place, but at most one bid per OD can be assigned to avoid infeasible workloads. This system could yield a large cost reduction for the company, but its success is strongly related to the bundles offered. Hence, determining bundles which are attractive for ODs and profitable for the company, becomes a crucial issue. We propose two different bundling strategies, which make use of a spatial-temporal representation of customers in a three-dimensional (3D) space. The former is based on the generation of 3D corridors, while the latter relies on 3D clustering techniques. Through extensive computational results, we show that the former technique outperforms the latter in terms of both solution quality and computational times and that both the approaches strongly outperform bundle generation techniques that neglect the temporal dimension and rely only on spatial information.
Genetic comparisons of the invasive pond mussel Sinanodonta woodiana from wild and pet shop populations in Germany
Authors: Benedict Alia , Kuehn Ralph , Stoeckle Bernhard C. , Geist Juergen 
Published: January 2024

AbstractSuccessful aquatic biological invasions are often dependent on human vectors. The Chinese pond mussel Sinanodonta woodiana is a fast-growing, high-filtering, and highly fecund unionid bivalve that has expanded rapidly throughout Europe in the last several decades. Human-mediated vectors such as fish aquaculture and pet shop trade are believed to be primary drivers of the mussel’s spread, but the specific dispersal pathways of S. woodiana remain unclear. We analyzed eleven microsatellites in 245 S. woodiana individuals from seven wild populations and three pet shops to assess genetic relatedness potential sources, and dispersal pathways of S. woodiana in Bavaria, Germany. Wild populations shared a single mitochondrial haplotype present in all European populations and shop-sourced individuals were genetically similar to wild populations. Only one wild population was structurally different than all other populations, which may be explained by its dispersal trajectory. Our results provide the first genetically informed insights into S. woodiana dispersal in the context of human activity. We underscore the value of genetic tools in invasive species management, which can uncover patterns of dispersal even on local scales.
Application of Hamilton–Jacobi–Bellman Equation/Pontryagin’s Principle for Constrained Optimal Control
Authors: Weston Jerome , Tolić Domagoj , Palunko Ivana 
Published: January 2024

AbstractThis article applies novel results for infinite- and finite-horizon optimal control problems with nonlinear dynamics and constraints. We use the Valentine transformation to convert a constrained optimal control problem into an unconstrained one and show uniqueness of the value function to the corresponding Hamilton–Jacobi–Bellman (HJB) equation. From there, we show how to approximate the solution of the initial (in)finite-horizon problem with a family of solutions that is$$\varGamma $$Γ-convergent. Optimal solutions are efficiently obtained via a solver based on Pontryagin’s Principle (PP). The proposed methodology is demonstrated on the path planning problem using the full nonlinear dynamics of an unmanned aerial vehicle (UAV) and autonomous underwater vehicle (AUV) involving state constraints in 3D environments with obstacles.
Development and applications of accelerator mass spectrometry methods for measurement of 14C, 10Be and 26Al in the CENTA laboratory
Authors: Povinec Pavel P. , Kontuľ Ivan , Ješkovský Miroslav , Kaizer Jakub , Kvasniak Jakub , Pánik Ján , Zeman Jakub 
Published: January 2024

AbstractTerrestrial and extraterrestrial radioisotope research has been strongly dependent on the development of analytical methods which would enable to trace radioisotopes at low concentrations in subgram samples (e.g., in tree rings, ice cores, meteorites, etc.). Accelerator mass spectrometry (AMS) has become the most sensitive technique for ultralow-level analysis of long-lived radioisotopes, such as 14C, 10Be and 26Al. We review developments and applications carried out in the CENTA laboratory, and describe a recently installed fully equipped AMS line, designed for analysis of long-lived radioisotopes from tritium to curium.
Towards a re-determination of the 42Ar half-life
Authors: Schumann D. , Maugeri E. , Dai Y. , Dressler R. , Köster U. 
Published: January 2024

AbstractWe report on a new measurement of the half-life of 42Ar by following its decay over a time period of more than 13 years using γ-ray spectrometry. The obtained value of (39.5 ± 3.6) y confirms the only other, previously measured value of (33 ± 2) y. However, since partial outgassing of the accumulated Ar from the sample cannot be excluded, this value should be understood as a lower limit. The sample has now been stored in a gas-tight quartz ampoule to enable further measurements excluding the outgas effect in order to confirm our finding.
Structural causal models reveal confounder bias in linear program modelling
Authors: Zečević Matej , Dhami Devendra Singh , Kersting Kristian 
Published: January 2024

AbstractThe recent years have been marked by extended research on adversarial attacks, especially on deep neural networks. With this work we intend on posing and investigating the question of whether the phenomenon might be more general in nature, that is, adversarial-style attacks outside classical classification tasks. Specifically, we investigate optimization problems as they constitute a fundamental part of modern AI research. To this end, we consider the base class of optimizers namely Linear Programs (LPs). On our initial attempt of a naïve mapping between the formalism of adversarial examples and LPs, we quickly identify the key ingredients missing for making sense of a reasonable notion of adversarial examples for LPs. Intriguingly, the formalism of Pearl’s notion to causality allows for the right description of adversarial like examples for LPs. Characteristically, we show the direct influence of the Structural Causal Model (SCM) onto the subsequent LP optimization, which ultimately exposes a notion of confounding in LPs (inherited by said SCM) that allows for adversarial-style attacks. We provide both the general proof formally alongside existential proofs of such intriguing LP-parameterizations based on SCM for three combinatorial problems, namely Linear Assignment, Shortest Path and a real world problem of energy systems.
Measuring a Critical Component of Contraceptive Decision Making: The Contraceptive Concerns and Beliefs Scale
Authors: Rocca Corinne H. , Muñoz Isabel , Rao Lavanya , Levin Sara , Tzvieli Ori , Harper Cynthia C. 
Published: January 2024

Abstract
Introduction
Concerns about safety and side effects from contraceptives are widespread and related to reluctance to use them. Measuring these concerns is an essential component of understanding contraceptive decision-making and guiding contraceptive and interpregnancy clinical care.

Methods
We used qualitative research and item response theory to develop and test a psychometric instrument to measure contraceptive concerns and beliefs. We developed 55 candidate scale items and tested them among 572 adolescents and adults across nine California healthcare facilities in 2019–2020. We derived a 6-item scale and assessed differences by age and social determinants of health with multivariable regression.

Results
In qualitative data, participants voiced both concerns and positive beliefs about contraception. Quantitative survey respondents were aged 21 years on average, and 24% were parous. Over half (54%) worried contraception has dangerous side effects, and 39% worried it is unnatural. The mean Contraceptive Concerns score, increasing with higher concerns, was 1.85 (SD: 1.00, range 0–4, α = 0.81). Items fit a partial credit item response model and met prespecified criteria for internal structure validity. Contraceptive use declined with increasing Concerns score (adjusted prevalence ratio [aPR] = 0.81 [0.72–0.92]). Scores were elevated among Black (mean: 2.06; aβ = 0.34 [0.09, 0.59]) and Multiracial or other race (2.11; aβ = 0.34 [0.02, 0.66]) respondents vs. White (1.66), but not Latinx respondents (1.81; aβ = 0.11 [− 0.11, 0.33]). Scores were also elevated among participants with lower maternal education (high school/Associate’s 1.89 versus college 1.60; aβ = 0.28 [0.04, 0.53]).

Discussion
The psychometrically robust Concerns instrument can be used in research to measure autonomous contraceptive decision-making and to design person-centered care.
Expectation and judgment: towards a phenomenology of discrimination
Authors: Hedges Tris 
Published: January 2024

AbstractIn this paper, my aim is to develop a phenomenological understanding of discrimination from the perspective of the discriminator. Since early existential phenomenology, the phenomenon of discrimination has received a great deal of attention. While much of this work has focused on the experience of the discriminatee, recent scholarship has begun to reflect on the intentional structures on the side of the discriminator. In a contribution to this trend, I argue that our sense of what is (ab)normal plays a constitutively significant role in the reiteration and reinforcement of harmful discriminatory practices. More specifically, I argue that Husserl's distinction between two forms of normality, namely, concordance-normality [Einstimmigkeit] and optimal-normality [Optimalität], is an important tool for illuminating otherwise overlooked aspects of the discriminator's experience. I achieve this by demonstrating how these two notions of normality play distinct constitutive roles when comparing deliberate acts of discrimination committed with malintent, compared with more habitual and prereflective expectations which are already discriminatory in nucleo. I argue that at the heart of discriminatory practices there is a naïve, normalizing attempt to stabilize concordance at the expense of critical self-reflection, normative revisions, and enriched horizons of expectation. In doing so, this paper provides a novel and important contribution to philosophical discussions surrounding discrimination.
The role of microRNAs in brain metastasis
Authors: Hudson Kadie , Mondia Mark Willy , Zhang Ying , Saha Shekhar , Gibert Myron K. , Dube Collin , Sun Yunan , Marcinkiewicz Pawel , Fadul Camilo , Abounader Roger 
Published: January 2024

AbstractBrain metastasis (BM) is the most common type of brain tumor and frequently foreshadows disease progression and poor overall survival with patients having a median survival of 6 months. 70,000 new cases of BM are diagnosed each year in the United States (US) and the incidence rate for BM is increasing with improved detection. MicroRNAs (miRNAs) are small non-coding RNAs that serve as critical regulators of gene expression and can act as powerful oncogenes and tumor suppressors. MiRNAs have been heavily implicated in cancer and proposed as biomarkers or therapeutic targets or agents. In this review, we summarize an extensive body of scientific work investigating the role of microRNAs in BM. We discuss miRNA dysregulation, functions, targets, and mechanisms of action in BM and present the current standing of miRNAs as biomarkers and potential therapeutics for BM. We conclude with future directions of miRNA basic and clinical research in BM.
Fractional Derivative Description of the Bloch Space
Authors: Moreno Álvaro Miguel , Peláez José Ángel , de la Rosa Elena 
Published: January 2024

AbstractWe establish new characterizations of the Bloch space $$\mathcal {B}$$
B
which include descriptions in terms of classical fractional derivatives. Being precise, for an analytic function $$f(z)=\sum _{n=0}^\infty \widehat{f}(n) z^n$$

f

(
z
)

=

∑

n
=
0

∞

f
^

(
n
)

z
n

in the unit disc $$\mathbb {D}$$
D
, we define the fractional derivative $$ D^{\mu }(f)(z)=\sum \limits _{n=0}^{\infty } \frac{\widehat{f}(n)}{\mu _{2n+1}} z^n $$

D
μ

(
f
)

(
z
)

=

∑

n
=
0

∞

f
^

(
n
)

μ

2
n
+
1

z
n

induced by a radial weight $$\mu $$
μ
, where $$\mu _{2n+1}=\int _0^1 r^{2n+1}\mu (r)\,dr$$

μ

2
n
+
1

=

∫
0
1

r

2
n
+
1

μ

(
r
)

d
r

are the odd moments of $$\mu $$
μ
. Then, we consider the space $$ \mathcal {B}^\mu $$

B

μ

of analytic functions f in $$\mathbb {D}$$
D
such that $$\Vert f\Vert _{\mathcal {B}^\mu }=\sup _{z\in \mathbb {D}} \widehat{\mu }(z)|D^\mu (f)(z)|<\infty $$

‖
f
‖

B

μ

=

sup

z
∈
D

μ
^

(
z
)

|

D
μ

(
f
)

(
z
)

|

<
∞

, where $$\widehat{\mu }(z)=\int _{|z|}^1 \mu (s)\,ds$$

μ
^

(
z
)

=

∫

|
z
|

1

μ

(
s
)

d
s

. We prove that $$\mathcal {B}^\mu $$

B

μ

is continously embedded in $$\mathcal {B}$$
B
for any radial weight $$\mu $$
μ
, and $$\mathcal {B}=\mathcal {B}^\mu $$

B
=

B

μ

if and only if $$\mu \in \mathcal {D}=\widehat{\mathcal {D}}\cap \check{\mathcal {D}}$$

μ
∈
D
=

D
^

∩

D
ˇ

. A radial weight $$\mu \in \widehat{\mathcal {D}}$$

μ
∈

D
^

if $$\sup _{0\le r<1}\frac{\widehat{\mu }(r)}{\widehat{\mu }\left( \frac{1+r}{2}\right) }<\infty $$

sup

0
≤
r
<
1

μ
^

(
r
)

μ
^

1
+
r

2

<
∞

and a radial weight $$\mu \in \check{\mathcal {D}}$$

μ
∈

D
ˇ

if there exist $$K=K(\mu )>1$$

K
=
K
(
μ
)
>
1

such that $$\inf _{0\le r<1}\frac{\widehat{\mu }(r)}{\widehat{\mu }\left( 1-\frac{1-r}{K}\right) }>1.$$

inf

0
≤
r
<
1

μ
^

(
r
)

μ
^

1
-

1
-
r

K

>
1
.
Digital strategies for nitrogen management in grain production systems: lessons from multi-method assessment using on-farm experimentation
Authors: Colaço A. F. , Whelan B. M. , Bramley R. G. V. , Richetti J. , Fajardo M. , McCarthy A. C. , Perry E. M. , Bender A. , Leo S. , Fitzgerald G. J. , Lawes R. A. 
Published: January 2024

AbstractDuring the past few decades, a range of digital strategies for Nitrogen (N) management using various types of input data and recommendation frameworks have been developed. Despite much research, the benefits accrued from such technology have been equivocal. In this work, thirteen methods for mid-season N recommendations in cereal production systems were evaluated simultaneously, ranging from simple mass balance through to non-mechanistic approaches based on machine learning. To achieve this, an extensive field research program was implemented, comprising twenty-one N strip trials implemented in wheat and barley fields across Australia over four cropping seasons. A moving window regression approach was used to generate crop response functions to applied N and calculate economically optimal N rates along the length of the strips. The N recommendations made using various methods were assessed based on the error against the optimal rate and expected profitability. The root mean squared error of the recommendations ranged from 15 to 57 kg/ha. The best performing method was a data-driven empirical strategy in which a multivariate input to characterise field and season conditions was abundantly available and used to predict optimal N rates using machine learning. This was the only approach with potential to substantially outperform the existing farmer management, reducing the recommendation error from 42 to 15 kg/ha and improving profitability by up to A$47/ha. Despite being reliant on extensive historical databases, such a framework shows a promising pathway to drive production systems closer towards season- and site-specific economically optimum recommendations. Automated on-farm experimentation is a key enabler for building the necessary crop response databases to run empirical data-driven decision tools.
Reconstructing Sunspot Number by Forward-Modelling Open Solar Flux
Authors: Owens Mathew J. , Lockwood Mike , Barnard Luke A. , Usoskin Ilya , Hayakawa Hisashi , Pope Benjamin J. S. , McCracken Ken 
Published: January 2024

AbstractThe open solar flux (OSF) is the integrated unsigned magnetic flux leaving the top of the solar atmosphere to form the heliospheric magnetic field. As the OSF modulates the intensity of galactic cosmic rays at Earth, the production rate of cosmogenic isotopes – such as 14C and 10Be stored in tree rings and ice sheets – is closely related to the OSF. Thus on the basis of cosmogenic isotope data, OSF can be reconstructed over millennia. As sunspots are related to the production of OSF, this provides the possibility of reconstructing sunspot number (SSN) and hence properties of the solar cycles prior to the first sunspot telescopic observations in 1610. However, while models exist for estimating OSF on the basis of SSN, the hysteresis present in OSF and the lack of a priori knowledge of the start/end dates of individual solar cycles means that directly inverting these models is not possible. We here describe a new method that uses a forward model of OSF to estimate SSN and solar cycle start/end dates through a Monte Carlo approach. The method is tested by application to geomagnetic reconstructions of OSF over the period 1845-present, and compared to the known SSN record for this period. There is a substantial improvement in reconstruction of both the SSN time series and the solar cycle start/end dates compared with existing OSF-SSN regression methods. This suggests that more accurate solar-cycle information can be extracted from cosmogenic isotope records by forward modelling, and also provides a means to assess the level of agreement between independent SSN and OSF reconstructions. We find the geomagnetic OSF and observed SSN agree very well after 1875, but do differ during the early part of the geomagnetic record, though still agree within the larger observational uncertainties.