bluuebunny/crossref_metadata_2025_split huggingface dataset:
Understanding the relationship between type-2 diabetes, MRI markers of neurodegeneration and small vessel disease, and dementia risk: a mediation analysis
Authors: Grasset Leslie , Frison Eric , Helmer Catherine , Catheline Gwénaëlle , Chêne Geneviève , Dufouil Carole 
Published: January 2024

AbstractTo explore to which extent neurodegeneration and cerebral small vessel disease (SVD) could mediate the association between type-2 diabetes and higher dementia risk. The analytical sample consisted in 2228 participants, out of the Three-City study, aged 65 and older, free of dementia at baseline who underwent brain MRI. Diabetes was defined by medication intake or fasting or non-fasting elevated glucose levels. Dementia status was assessed every 2 to 3 years, during up to 12 years of follow-up. Brain parenchymal fraction (BPF) and white matter hyperintensities volume (WMHV) were selected as markers of neurodegeneration and cerebral SVD respectively. We performed a mediation analysis of the effect of baseline BPF and WMHV (mediators) on the association between diabetes and dementia risk using linear and Cox models adjusted for age, sex, education level, hypertension, hypercholesterolemia, BMI, smoking and alcohol drinking status, APOE-ε4 status, and study site. At baseline, 8.8% of the participants had diabetes. Diabetes (yes vs. no) was associated with higher WMHV (βdiab = 0.193, 95% CI 0.040; 0.346) and lower BPF (βdiab = −0.342, 95% CI −0.474; −0.210), as well as with an increased risk of dementia over 12 years of follow-up (HRdiab = 1.65, 95% CI 1.04; 2.60). The association between diabetes status and dementia risk was statistically mediated by higher WMHV (HRdiab=1.05, 95% CI 1.01; 1.11, mediated part = 10.8%) and lower BPF (HRdiab = 1.12, 95% CI 1.05; 1.20, mediated part = 22.9%). This study showed that both neurodegeneration and cerebral SVD statistically explained almost 30% of the association between diabetes and dementia.
Circular economy and agricultural employment: a panel analysis of EU advanced and emerging economies
Authors: Erdiaw-Kwasie Michael Odei , Abunyewah Matthew , Owusu-Ansah Kofi Kusi , Baah Charles , Alam Khorshed , Basson Marita 
Published: January 2024

AbstractUsing data from 24 European advanced and emerging countries between 2000 and 2020, this paper examines the impacts of circular economy (CE) on agriculture employment (AE) and female employment patterns. Based on the availability of data, the study uses three CE indicators: generation of municipal waste (GMW) per capita, the recycling rate of municipal waste (RRMW), and patents related to recycling and secondary raw materials (PRSRM). To investigate the relationship between CE and AE, as well as CE and female AE, panel cointegration analysis and Granger causality tests are conducted. The results reveal that whereas RRMW, PRSRM and AE are in equilibrium in the long run, increasing waste lowers female attraction to the sector. The study therefore suggests that developing and expanding waste management initiatives and innovation are critical to the growth of agricultural employment and the closing of existing gender gaps. Despite concerns that rising recycling practices may negatively impact employment, our empirical results indicate that CE will create opportunities and increase employment levels. The evidence suggests that promoting CE in agriculture is economically, ecologically, and socially beneficial.
Extremes of locally-homogenous vector-valued Gaussian processes
Authors: Ievlev Pavel 
Published: January 2024

AbstractIn this paper, we study the asymptotical behaviour of high exceedence probabilities for centered continuous $$\mathbb {R}^n$$

R

n

-valued Gaussian random field $$\varvec{X}$$

X

with covariance matrix satisfying $$\Sigma - R ( t + s, t ) \sim \sum _{l = 1}^n B_l ( t ) \, | s_l |^{\alpha _l}$$

Σ
-
R

(
t
+
s
,
t
)

∼

∑

l
=
1

n

B
l

(
t
)

|

s
l

|

α
l

as $$s \downarrow 0$$

s
↓
0

. Such processes occur naturally as time transformations of homogenous random fields, and we present two asymptotic results of this nature as applications of our findings. The technical novelty of our proof consists in showing that the Slepian-Gordon inequality technique, essential in the univariate case, can also be successfully applied in the multivariate setup. This is noteworthy because this technique was previously believed to be inaccessible in this particular context.
Astyanax caucanus: microsatellite loci development and population genetics in the Cauca River, Colombia
Authors: Velandia Rubén A. , Campo-Nieto Omer , Márquez Edna J. 
Published: January 2024

AbstractAstyanax caucanus is an endemic fish species to the Magdalena-Cauca basin in Colombia. It is considered a Least Concern species by the International Union for Conservation of Nature, and currently, it is not a fishery resource. Its fertilized eggs may drift up to 4–5 days before hatching and can be carried up to 340 km given the water velocity of the river. Although A. caucanus is listed as short -migratory species (< 50 km), this study hypothesized that it exhibits gene flow along the middle and lower section of the Cauca River due to the great potential for larval dispersal. To test this hypothesis, we developed a set of species-specific microsatellite primers suitable for population genetic studies. Genetic structure analyses with 193 samples evidenced two genetic stocks that coexist, comigrate, and exhibit gene flow along the study area. Both stocks show high genetic diversity indices (Na and HE) and effective population sizes (Ne > 1000), but also show evidence of bottlenecked populations and high values of the inbreeding coefficient (FIS). Finally, these results are useful to understand the effects of other anthropic activities, besides fishing pressure on population bottlenecks found for other fish species cohabiting the area.
A Systematic Review of Virtual Reality Features for Skill Training
Authors: Tusher Hasan Mahbub , Mallam Steven , Nazir Salman 
Published: January 2024

AbstractThe evolving complexity of Virtual Reality (VR) technologies necessitates an in-depth investigation of the VR features and their specific utility. Although VR is utilized across various skill-training applications, its successful deployment depends on both technical maturity and context-specific suitability. A comprehensive understanding of advanced VR features, both technical and experiential, their prospective impact on designated learning outcomes, and the application of appropriate assessment methodologies is essential for the effective utilization of VR technologies. This systematic literature review explored the inherent associations between various VR features employed in professional training environments and their impact on learning outcomes. Furthermore, this review scrutinizes the assessment techniques employed to gauge the effects of VR applications in various learning scenarios. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was used to systematically select 50 empirical VR studies sourced from three (03) academic databases. The analysis of these articles revealed complex, context-dependent relationships between VR features and their impact on professional training, with a pronounced emphasis on skill-based learning outcomes over cognitive and affective ones. This review also highlights the predominantly subjective nature of the assessment methods used to measure the effects of VR training. Additionally, the findings call for further empirical exploration in novel skill training contexts encompassing cognitive and affective learning outcomes, as well as other potential external factors that may influence learning outcomes in VR.
FTIR-ATR chemometric analysis on pelagic Sargassum reveals chemical composition changes induced by cold sample transportation and sunlight radiation
Authors: Peniche-Pavía Héctor A. , Tzuc-Naveda José D. , Rosado-Espinosa Luis A. , Collí-Dulá Reyna Cristina 
Published: January 2024

AbstractThe massive arrival of Sargassum biomass on the Caribbean coast is a potential raw material source that needs an assessment of its quality and preservation state. Thus, the present study aimed to investigate how pelagic Sargassum changes its chemical composition due to sample transportation, morphotype (S. natans I, S. natans VIII, and S. fluitans III), and exposure to open-air conditions during two months of outdoor conditions using Fourier Transform Infra-Red (IR) spectroscopy and chemometric analysis. The results demonstrate that cold transportation to the lab before flash-freezing caused sample degradation, characterized by mannitol consumption and the formation of anaerobic metabolism products. Multivariate analyses showed that the IR spectral zone with differences between S. natans and S. fluitans were in the same IR spectral regions related to sample degradation. In the two flash-freezing treatments, S. fluitans had the highest IR peak absorbance of mannitol and a lower absorbance for the carboxylic acids IR peak. Between S. natans morphotypes, S. natans I had the highest modification caused by the cold transportation to the lab. The decomposition under prolonged time (up to eight weeks) in open-air conditions demonstrated an increased absorbance on the IR bands of carboxylic acids in the first four weeks. In the sixth and eighth weeks, the IR signals of calcium carbonate increased more than those from organic matter. This study provides a better understanding of the importance of preserving the collected samples and how the decomposition dynamics of Sargassum species may impact the extraction of key compounds, e.g., alginate and mannitol.
Learning de-biased regression trees and forests from complex samples
Authors: Nalenz Malte , Rodemann Julian , Augustin Thomas 
Published: January 2024

AbstractRegression trees and forests are widely used due to their flexibility and predictive accuracy. Whereas typical tree induction assumes independently identically distributed (i.i.d.) data, in many applications the training sample follows a complex sampling structure. This includes unequal probability sampling, which is often found in survey data. Then, a ‘naive estimation’ that simply ignores the sampling weights may be substantially biased. This article analyzes the bias arising from a naive estimation of regression trees or forests under complex sample designs and proposes ways of de-biasing. This is achieved by bridging tree learning to survey statistics, due to the correspondence of the mean-squared-error criterion in regression trees and variance estimation. Transferring population variance estimation approaches from survey statistics to tree induction, indeed considerably reduces the bias in the resulting trees, both in predictions and the tree structure. The latter is particularly crucial if the trees are to be interpreted. Our methodology is extended to random forests, where we show on simulated data and a housing dataset that correcting for complex sample designs leads to overall much better predictive accuracy and more trustworthy interpretation. Interestingly, corrected forests can surpass forests learned on i.i.d. samples in terms of accuracy, which also has important implications for adaptive data collection approaches.
Fast deep mixtures of Gaussian process experts
Authors: Etienam Clement , Law Kody J. H. , Wade Sara , Zankin Vitaly 
Published: January 2024

AbstractMixtures of experts have become an indispensable tool for flexible modelling in a supervised learning context, allowing not only the mean function but the entire density of the output to change with the inputs. Sparse Gaussian processes (GP) have shown promise as a leading candidate for the experts in such models, and in this article, we propose to design the gating network for selecting the experts from such mixtures of sparse GPs using a deep neural network (DNN). Furthermore, a fast one pass algorithm called Cluster–Classify–Regress (CCR) is leveraged to approximate the maximum a posteriori (MAP) estimator extremely quickly. This powerful combination of model and algorithm together delivers a novel method which is flexible, robust, and extremely efficient. In particular, the method is able to outperform competing methods in terms of accuracy and uncertainty quantification. The cost is competitive on low-dimensional and small data sets, but issignificantly lower for higher-dimensional and big data sets. Iteratively maximizing the distribution of experts given allocations and allocations given experts does not provide significant improvement, which indicates that the algorithm achieves a good approximation to the local MAP estimator very fast. This insight can be useful also in the context of other mixture of experts models.
Towards vibration-based damage detection of civil engineering structures: overview, challenges, and future prospects
Authors: Zar Ali , Hussain Zahoor , Akbar Muhammad , Rabczuk Timon , Lin Zhibin , Li Shuang , Ahmed Bilal 
Published: January 2024

AbstractIn this paper, we delve into the evolving landscape of vibration-based structural damage detection (SDD) methodologies, emphasizing the pivotal role civil structures play in society's wellbeing and progress. While the significance of monitoring the resilience, durability, and overall health of these structures remains paramount, the methodology employed is continually evolving. Our focus encompasses not just the transformation brought by the advent of artificial intelligence but also the nuanced challenges and future directions that emerge from this integration. We shed light on the inherent nonlinearities civil engineering structures face, the limitations of current validation metrics, and the conundrums introduced by inverse analysis. Highlighting machine learning's (ML) transformative role, we discuss how techniques such as artificial neural networks and support vector machine's have expanded the SDD's scope. Deep learning's (DL) contributions, especially the innovative capabilities of convolutional neural network in raw data feature extraction, are elaborated upon, juxtaposed with the potential pitfalls, like data overfitting. We propose future avenues for the field, such as blending undamaged real-world data with simulated damage scenarios and a tilt towards unsupervised algorithms. By synthesizing these insights, our review offers an updated perspective on the amalgamation of traditional SDD techniques with ML and DL, underlining their potential in fostering more robust civil infrastructures.
Equivariant localization and holography
Authors: Martelli Dario , Zaffaroni Alberto 
Published: January 2024

AbstractWe discuss the theory of equivariant localization focussing on applications relevant for holography. We consider geometries comprising compact and non-compact toric orbifolds, as well as more general non-compact toric Calabi–Yau singularities. A key object in our constructions is the equivariant volume, for which we describe two methods of evaluation: the Berline–Vergne fixed point formula and the Molien–Weyl formula, supplemented by the Jeffrey–Kirwan prescription. We present two applications in supersymmetric field theories. Firstly, we describe a method for integrating the anomaly polynomial of SCFTs on compact toric orbifolds. Secondly, we discuss equivariant orbifold indices that are expected to play a key role in the computation of supersymmetric partition functions. In the context of supergravity, we propose that the equivariant volume can be used to characterize universally the geometry of a large class of supersymmetric solutions. As an illustration, we employ equivariant localization to prove the factorization in gravitational blocks of various supergravity free energies, recovering previous results as well as obtaining generalizations.
Transport Cross Sections and Collision Integrals for O($$^{3}$$P)–O($$^{3}$$P) Interaction
Authors: Buchowiecki Marcin , Szabó Péter 
Published: January 2024

AbstractNew collision integrals and transport cross sections for O($$^{3}$$

3

P)–O($$^{3}$$

3

P) interaction are reported in the 300–30000 K range. Those values are based on a new set of potential energy curves (PECs) calculated with the multireference configuration interaction method. The results of the classical and semiclassical WKB (Wentzel–Kramers–Brillouin) methods are compared, excellent performance of the classical approach is shown (discrepancy much lower than 1% even at room temperature). In particular, the classical and WKB methods agree very well for the repulsive potentials effectively reducing overall uncertainty.
In-Situ FTIR and Laser Induced Fluorescence RONS Characterization of Atmospheric Pressure Nanosecond-Pulsed Surface DBD Plasma for Indirect Treatments of E. Coli
Authors: Ibba Lorenzo , Agus Rita , Avino Fabio , Furno Ivo , Ambrico Paolo F. 
Published: January 2024

AbstractWe study the bactericidal efficacy of surface dielectric barrier discharge low-temperature plasma treatments, powered by nanosecond high voltage pulses. We achieve $$\sim $$
∼
4-log reduction in Escherichia coli population, after 10 min treatments, at a distance of 1.5 cm from the plasma surface. To investigate the reactive oxygen and nitrogen species (RONS) responsible for the bactericidal effect, we employ in-situ fourier transform infrared (FTIR) spectroscopy to measure a selection of relevant species, such as O$$_3$$

3

, NO$$_2$$

2

, N$$_2$$

2

O and N$$_2$$

2

O$$_5$$

5

. The measurements are taken under various relative humidity conditions to replicate the bacteria treatment environment. While RONS originating from nitrogen chemistry are detected, nitric oxide (NO), a pivotal molecule in nitrate production, is absent due to the sensitivity limitations of FTIR detection. To overcome this limitation, we employ laser induced fluorescence utilizing a picosecond-pulsed laser to measure the kinetics of NO produced by the plasma. Our results show that the NO concentration is smaller than 1 ppm and primarily localized near the plasma surface, with concentrations increasing proportionally with relative humidity. Notably, at a distance of 1.5 cm from the plasma surface, at which the E. coli is treated, the concentration of NO falls below 50 ppb. Although NO is pivotal in generating secondary reactive species within the plasma, our results suggest that it does not directly contribute to the bacteria inactivation process. Instead, other molecules, such as O$$_3$$

3

, NO$$_2$$

2

, and N$$_2$$

2

O, which are found in higher concentrations, may be responsible for the bactericidal properties observed in indirect plasma treatments.
Thermochemistry of monocharged cation substitutions in ionic solids
Authors: Glasser Leslie , Liebman Joel F. 
Published: January 2024

AbstractA rather straightforward synthetic procedure for the production of new materials is the substitution of principal ions in the crystal for one another, and some general principles relating to structure have been developed which guide the material designer as to which substitutions are likely to lead to stable materials. These are briefly reported. We here consider the thermochemical consequences, rather than the structural implications, of unit-charged cation substitutions, namely of NH4+, Li+, Na+, K+, Rb+, Cs+, Tl+, Ag+, and Cu+, among a large group of ionic solids. It is observed that the formation enthalpies hardly differ among these materials, while their absolute entropies, heat capacities, and formula unit volumes follow similar linear trends but with some relative displacements.
Stratospheric platform applications: reduced cognitive load for multimedia and education
Authors: Periola A. A. 
Published: January 2024

AbstractThe proliferation of computing solutions in future communication networks leads to the occurrence of cognitive visual overstimulation (CVO). CVO occurrence arises in multimedia content editing and provisioning of Science, Technology, Engineering, and Mathematics (STEM) education. The presented research proposes a high-altitude platform-based architecture that enable multimedia content editing related to space themes, and insertion in the edge of space i.e., the stratosphere (space horizon) environment. The use of the proposed architecture in content editing and STEM education contexts reduces the CVO by an average of (22–84.1) % and (13.6–31.7) %, respectively. Investigations show that the proposed architecture reduces the latency by an average of 30.3%, and 49.4% considering individual, and multiple content access, respectively. The use of the proposed architecture also reduces the power consumption associated with multimedia content editing by (8.05–42.9) % on average.
The digital leadership emerging construct: a multi-method approach
Authors: Tigre Fernanda Bethlem , Henriques Paulo Lopes , Curado Carla 
Published: January 2024

AbstractThis study presents new insights into the capabilities that a leader needs to thrive in the digital scenario. These insights come from a systematic literature review of 21-years of publications on leadership in the digital environment that supports a four-round Delphi study with a panel of 24 experts from diverse geographical backgrounds (six countries across 3 continents). The e-leadership concept has evolved to digital leadership by transitioning from an electronic way of communication to a broader context of how to lead effectively in a digital environment. Digital leadership is a way of thinking and behaving in a complex time. The findings show that digital leadership has two main aspects within four dimensions: the first aspect is related to business, and it is strategy focused and delivery related; the second one involves personal attributes, and it is interpersonal oriented. Moreover, we identify the associated leadership capabilities for each of these aspects.
In-vivo and in-silico studies to identify toxicity mechanisms of permethrin with the toxicity-reducing role of ginger
Authors: Himtaş Damla , Yalçin Emine , Çavuşoğlu Kültiğin , Acar Ali 
Published: January 2024

AbstractIn this study, the toxic effects of permethrin on Allium cepa L. and the protective role of Zingiber officinale rhizome extract (Zoex) were investigated. In this context, 6 different groups were formed. While the control group was treated with tap water, the groups II and III were treated with 10 µg/mL and 20 µg/mL Zoex, respectively, and the group IV was treated with 100 µg/L permethrin. The protective effect of Zoex against permethrin toxicity was studied as a function of dose, and groups V and VI formed for this purpose were treated with 10 µg/mL Zoex + 100 µg/L permethrin and 20 µg/mL Zoex + 100 µg/L permethrin, respectively. After 72 h of germination, cytogenetic, biochemical, physiological, and anatomical changes in meristematic cells of A. cepa were studied. As a result, permethrin application decreased the mitotic index (MI) and increased the frequency of micronuclei (MN), and chromosomal abnormalities. The increase in malondialdehyde (MDA), superoxide dismutase (SOD), and catalase (CAT) and the decrease in glutathione (GSH) indicate that permethrin causes oxidative damage. Compared to the control group, a 68.5% decrease in root elongation (p < 0.05) and an 81.8% decrease (p < 0.05) in weight gain were observed in the permethrin-treated group. It was found that the application of Zoex together with permethrin resulted in regression of all detected abnormalities, reduction in the incidence of anatomical damage, MN and chromosomal aberrations, and improvement in MI rates. The most significant improvement was observed in group VI treated with 20 µg/mL Zoex, and Zoex was also found to provide dose-dependent protection. The toxicity mechanism of permethrin was also elucidated by molecular docking and spectral studies. From the data obtained during the study, it was found that permethrin has toxic effects on A. cepa, a non-target organism, while Zoex plays a protective role by reducing these effects.