bluuebunny/crossref_metadata_2025_split huggingface dataset:
Plasma-assisted pyrolysis for converting oil palm fronds into reduced graphite oxide
Authors: Purawiardi R I , Dimyati A , Handayani A H , Kismanto A , Sari Y W , Maddu A , Elbersen H W 
Published: March 2024

Abstract
One of the oil palm tree’s solid waste is oil palm fronds. Due to its lignocellulosic composition, it has the potency to convert it into carbon. The common heat treatment method to convert oil palm fronds into carbon-based material is pyrolysis. However, this process has some disadvantages, such as being time-consuming and just producing amorphous carbon. Different from common pyrolysis temperatures, in this study we use a higher temperature (4000 °C) generated by DC current arc plasma. This process is faster than the common pyrolysis process (less than 10 minutes) and produces crystalline material. This product is then characterized by Raman spectroscopy, SAED-TEM, XRD, FT-IR, and SEM/EDX. Based on Raman spectroscopy, this crystalline material shows the characteristics of semiconductor carbon. Based on the SAED-TEM, there is a (002) plane of hexagonal crystal structure detected (graphite phase characteristics). An XRD analysis shows the characteristics of the trigonal crystal structure (P3) constructed by O and H atoms. An FT-IR characterization shows that there are C=C, C-H, and O-H bonds, while the EDX analysis result shows a carbon-to-oxygen ratio of about 4.23. Therefore, based on the whole interpretation, the plasma-assisted pyrolysis treatment is successfully used to convert oil palm fronds into reduced graphite oxide (rGO).
Fault-tolerant fusing of repeater graph states and its application
Authors: Xu Shuang , Gong Wei-Jiang , Shen H Z , Yi X X 
Published: March 2024

Abstract
A repeater graph state (RGS) is a class of multipartite entangled states with favourable features for quantum communication, particularly as the enabler of all-photonic quantum repeaters. In this paper, based on an alternative formalism, we show that two RGSs can be fused via a Bell measurement in a fault-tolerant manner. The fusing of multiple RGSs can thus be carried out simultaneously and flexibly, which enables a 2-step rapid generation of an arbitrarily large RGS from relatively small building blocks using heralded but non-deterministic photonic Bell measurements. It then makes possible the distribution of a large multipartite entangled state from multiple sources to many parties via high-loss channels, and hence also a satellite-based entanglement distribution scheme. In such a scheme, stationary memories are only required for ground stations, the growth of memory noise with its scale can be effectively suppressed, and recipients of entangled pairs of qubits may remain anonymous.
Influence of temperature perturbation on moisture dynamics
Authors: Abawari Admasu , Elfaged Yitagesu 
Published: March 2024

Abstract
We analyzed the perturbation effect of temperature on the moisture content in the atmosphere [1], considering the saturation phenomenon. The air parcels of a saturated atmosphere are made of dry air, vapor, cloud, and rain water that flow on a rotating Earth, with a temperature perturbations induced at

9
°

1

′

48

″

N,

38
°

44

′

24

″

E

and 6.324 km above the Earth’s surface. The fundamental atmospheric parameter of this saturated moist air was treated as temperature-dependent, and the pressure force exerted on it by the precipitation and cloud water is not considered in the numerical computation. In this proposed research, the initial value of wind speed along zonal, meridional, and vertical directions varies with elevation, and the interaction of the atmosphere with the Earth’s surface is not neglected. The result of the investigation revealed that a single tropospheric disturbance of an atmospheric variable is a major factor in the spread of different long period waves patterns (350 hours) and this atmospheric oscillations play a crucial role in transferring energy, momentum, and mass across different parts of the atmosphere, impacting global climate systems.
Assessing rainfall erosivity changes over China through a Bayesian averaged ensemble of high-resolution climate models
Authors: Weng Xuerou , Zhu Jinxin , Wang Dagang , Zhong Ming , Luo Ming , Mei Yiwen , Tang Guoping 
Published: March 2024

Abstract
Spatiotemporal variation in rainfall erosivity resulting from changes in rainfall characteristics due to climate change has implications for soil erosion in developing countries. To promote soil and water conservation planning, it is essential to understand past and future changes in rainfall erosivity and their implications on a national scale. In this study, we present an approach that uses a Bayesian model averaging (BMA) method to merge multiple regional climate models (RCMs), thereby improving the reliability of climate-induced rainfall erosivity projections. Our multi-climate model and multi-emission scenario approach utilize five RCMs and two Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios for the baseline period (1986–2005) and future periods (2071–2090) to characterize the spatiotemporal projection of rainfall erosivity and assess variations in China. Our results indicate that the two models outperform other models in reproducing the spatial distribution and annual cycle of rainfall erosivity in China. Moreover, we found an increasing trend in the annual rainfall erosivity from the baseline climate up to the RCMs for all models, with an average change in erosivity of approximately 10.9% and 14.6% under RCP4.5 and RCP8.5, respectively. Our BMA results showed an increase in the absolute value of rainfall erosivity by 463.3 and 677.0 MJ·mm·hm−2·h−1, respectively, in the South China red soil region and the Southwest China karst region under the RCP8.5 scenario. This increase indicates that climate warming will significantly enhance the potential erosion capacity of rainfall in these regions. Additionally, our study revealed that the Southwest China karst region and the Northwest China Loess Plateau region are more sensitive to radiation forcing. To mitigate the risk of soil erosion caused by climate change, it is necessary to consider changes in rainfall erosivity, local soil conditions, vegetation coverage, and other factors in different regions and take appropriate soil and water conservation measures.
Efficient concentration of trace analyte with ordered hotspot construction for a robust and sensitive SERS platform
Authors: Hu Youdi , Hu Yanlei , Wang Zhenyu , Yong Jiale , Xiong Wei , Wu Dong , Xu Shixiang 
Published: March 2024

Abstract
Surface-enhanced Raman scattering (SERS) platform, which enables trace analyte detection, has important application prospects. By structuring/modifying the surface of the SERS substrate, analyte in highly diluted solutions can be concentrated into localized active areas for highly sensitive detection. However, subject to the difficulty of the fabrication process, it remains challenging to balance hot-spot construction and the concentration capacity of analyte simultaneously. Therefore, preparing SERS substrates with densely ordered hot spots and efficient concentration capacity is of great significance for highly sensitive detection. Herein, we propose an Ag and fluoroalkyl-modified hierarchical armour substrate (Ag/F-HA), which has a double-layer stacking design to combine analyte concentration with hotspot construction. The microarmour structure is fabricated by femtosecond-laser processing to serve as a superhydrophobic and low-adhesive surface to concentrate analyte, while the anodic aluminium oxide (AAO) template creates a nanopillar array serving as dense and ordered hot spots. Under the synergistic action of hot spots and analyte concentration, Ag/F-HA achieves a detection limit down to 10−7 M doxorubicin (DOX) molecules with a RSD of 7.69%. Additionally, Ag/F-HA exhibits excellent robustness to resist external disturbances such as liquid splash or abrasion. Based on our strategy, SERS substrates with directional analyte concentrations are further explored by patterning microcone arrays with defects. This work opens a way to the realistic implementation of SERS in diverse scenarios.
A novel three-dimensional orthogonal star honeycomb structure with negative Poisson’s ratio
Authors: He Baofeng , Huang Qian , Shi Zhaoyao 
Published: March 2024

Abstract
A novel three-dimensional(3D) orthogonal star honeycomb structure with increased strength and negative Poisson’s ratio(NPR) performance that is not subject to angle constraints was proposed, and the calculation model of Young’s modulus and Poisson’s ratio of the cell structure under axial load was derived in this study. Through the investigation of the deformation mode and the finite element (FE) research results of the structure, it is found that the longitudinal length parameter

l

2

and angle parameter

θ

2

are important concave axes that affect the mechanical properties of materials. The quasi-static compression experimental results shows that the constraints of adjacent elements can enhance the strength of the structure to a certain extent, and the theoretical analysis results and the FE simulation results were in good agreement with the experimental results. In addition, the results are also concluded that the prediction of Young’s modulus and Poisson’s ratio and the designability of mechanical properties of 3D orthogonal star honeycomb structures could be achieved by the theoretical calculation formula derived in this study.
Optimizing bearing health condition monitoring: exploring correlation feature selection algorithm
Authors: Sharma Anju , Priya Taruv Harshita , Naidu VPS 
Published: March 2024

Abstract
Vibration signals are a critical source of information for detecting and diagnosing bearing faults, making this research particularly relevant to the condition monitoring of industrial machinery, particularly bearings using vibration signals. This study delves into how feature selection can be done using Pearson’s Correlation Co-efficient within the context of monitoring bearing health conditions, utilizing two distinct approaches. Approach-1 involves feature selection without considering labels, while Approach-2 incorporates labels for feature selection. Comparative analysis is conducted against outcomes obtained when all features are selected. The research scrutinizes the impact of feature selection on classifier performance, accuracy, and execution times, utilizing various machine learning algorithms such as Decision Tree (DT), K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naïve Bayes (NB). The findings underscore that feature selection significantly enhances classifier accuracy while reducing execution times. Specifically, only DT and KNN with 50 neighbors achieved 100% accuracy when all features were considered. However, with feature selection using Approach-1 (without labels), DT, KNN, SVM (excluding 100 neighbors), and NB (with Normal/Gaussian kernel) attained 100% accuracy. Employing Approach-2 (with labeled features), DT with 0.7 and 0.9 thresholds, SVM-G with all thresholds (0.6, 0.7, and 0.9), KNN with all thresholds (except 100 neighbors), and NB-n (with all thresholds) achieved 100% accuracy. The study emphasizes the pivotal role of feature selection using Pearson’s Correlation Coefficient in enhancing machine learning classifier performance, offering promising avenues for future research and practical applications across diverse domains.
Finite element approximation for uniformly elliptic linear PDE of second order in nondivergence form
Authors: Tran Ngoc Tien 
Published: March 2024

This paper proposes a novel technique for the approximation of strong solutions

u
∈
C
(

Ω
¯

)
∩

W

l
o
c

2
,
n

(
Ω
)

u \in C(\overline {\Omega }) \cap W^{2,n}_\mathrm {loc}(\Omega )

to uniformly elliptic linear PDE of second order in nondivergence form with continuous leading coefficient in nonsmooth domains by finite element methods (FEM). These solutions satisfy the Alexandrov-Bakelman-Pucci (ABP) maximum principle, which provides an a posteriori error control for

C
1

C^1

conforming approximations. By minimizing this residual, we obtain an approximation to the solution

u
u

in the

L
∞

L^\infty

norm. Although discontinuous functions do not satisfy the ABP maximum principle, this approach extends to nonconforming FEM as well thanks to well-established enrichment operators. Convergence of the proposed FEM is established for uniform mesh-refinements. The built-in a posteriori error control (even for inexact solve) can be utilized in adaptive computations for the approximation of singular solutions, which performs superiorly in the numerical benchmarks in comparison to the uniform mesh-refining algorithm.
Loss
Authors: Deaves Richard 
Published: March 2024

Abstract
This chapter focuses on behavioral risk preference as formulated by prospect theory. The prospect theory utility function is based on changes in wealth (rather than levels). Another key innovation is its incorporation of loss aversion, which means that the pain of a loss is more impactful than the pleasure of a gain of the same magnitude. Additional needed refinements are decision weights based on transformed probabilities, the calculation of the utility of a prospect, integration vs. segregation, and rank-dependent utility. Determinants of risk preference coming from psychological tendencies are also described, with one notable finding being that affect-rich environments lead to greater departures from expected utility theory.
Performers
Authors: Deaves Richard 
Published: March 2024

Abstract
This chapter focuses on the investment performance of individual investors. If someone has the right skill set, there is nothing wrong with the pursuit of active investment management. Unfortunately, reality is otherwise for most investors. The evidence makes clear that on average their performance is very much wanting at the level of the portfolio. This seems to stem in large part from excessive trading induced by overconfidence. Overconfidence operates by making people think they know more than actually do know, leading them to discount the consensus view of other investors inherent in market prices. Another problem well documented at the level of the portfolio is insufficient diversification.
Lambda3: homology search for protein, nucleotide, and bisulfite-converted sequences
Authors: Hauswedell Hannes , Hetzel Sara , Gottlieb Simon G , Kretzmer Helene , Meissner Alexander , Reinert Knut 
Published: March 2024

Abstract

Motivation
Local alignments of query sequences in large databases represent a core part of metagenomic studies and facilitate homology search. Following the development of NCBI Blast, many applications aimed to provide faster and equally sensitive local alignment frameworks. Most applications focus on protein alignments, while only few also facilitate DNA-based searches. None of the established programs allow searching DNA sequences from bisulfite sequencing experiments commonly used for DNA methylation profiling, for which specific alignment strategies need to be implemented.

Results
Here, we introduce Lambda3, a new version of the local alignment application Lambda. Lambda3 is the first solution that enables the search of protein, nucleotide as well as bisulfite-converted nucleotide query sequences. Its protein mode achieves comparable performance to that of the highly optimized protein alignment application Diamond, while the nucleotide mode consistently outperforms established local nucleotide aligners. Combined, Lambda3 presents a universal local alignment framework that enables fast and sensitive homology searches for a wide range of use-cases.

Availability and implementation
Lambda3 is free and open-source software publicly available at https://github.com/seqan/lambda/.
A similarity-based assortativity measure for complex networks
Authors: Fierens Pablo I , Chaves Rêgo Leandro 
Published: March 2024

Abstract
There are several metrics that have been proposed to quantify the tendency of nodes to link with similar nodes in complex networks. Among them, the assortativity coefficient put forth by M.E.J. Newman has been successfully used in many cases with either categorical or scalar attributes of network nodes. Unfortunately, the assortativity coefficient cannot deal with vectorial attributes. Furthermore, we show that, in certain cases, it may not be able to capture the similarity of neighbors. In this work, we introduce a new metric that, without being much more complex to calculate, solves those problems. Moreover, we show that the proposed metric includes the categorical assortativity coefficient as a particular case. We also study the behavior of the new metric with a few illustrative real-world examples.
Toward Patient Centricity: Why Do Patients With Inflammatory Bowel Disease Participate in Pharmaceutical Clinical Trials? A Mixed-Methods Exploration of Study Participants
Authors: Solitano Virginia , Prins Heather , Archer Meagan , Guizzetti Leonardo , Jairath Vipul 
Published: March 2024

Abstract

Background
A better understanding of motivations to participate as well as recommendations to reduce barriers to enrollment may assist in design of future clinical trials.

Methods
We developed a 32-item electronic questionnaire to explore motivations, experiences, and recommendations of inflammatory bowel disease patients, who had participated in pharmaceutical clinical trials in a tertiary center in Canada over the last decade. We employed a mixed-methods approach that integrates both quantitative and qualitative research methods.

Results
We distributed a total of 69 e-mails with surveys and received 46 responses (66.6% response rate). Study participants were mostly male (27/46, 58.7%), non-Hispanic White (43/46, 93.5%), with a mean age of 45.5 years (SD 10.9). Most decided to participate in a clinical trial to benefit future patients (29/46, 63.0%). Half of the participants (23/46, 50.0%) reported they were worried about the possibility of receiving placebo, although the majority (29/46, 63.0%) understood they could improve on placebo. The most challenging aspect reported was the number and length of questionnaires (15/46, 32.6%), as well as the number of colonoscopies (14/46, 30.4%). Strategies recommended to increase enrollment were reduction of the chance of receiving placebo (20/46, 43.5%), facilitating inclusion of patients who have failed multiple therapies (20/46, 43.5%), allowing virtual visits (18/46, 39.1%), including subtypes of disease traditionally excluded from trials (16/46, 34.8%) and improving outreach to underrepresented populations (13/46, 28.3%). The vast majority (37/46, 80.4%) reported their experience of participation to be better than expected.

Conclusions
These results should help inform the design of future clinical trials with a focus on patient-centricity.
Breaking the Narrative Pattern: Motherhood Tropes in Norwegian Migrant Literature
Authors: Barkve Marit Ann 
Published: March 2024

Abstract
This article focuses on three motherhood tropes, which became narrative convention in Norwegian migration literature in the period 1986–2010. I categorize these motherhood tropes as: (1) the cultured mother; (2) the creative mother; and (3) the appropriation of Henrik Ibsen’s infamous character Nora Helmer. Illustrating these tropes shows a critique of motherhood found in some of Norway’s migration literature. However, Mala Naveen’s novel, Desiland (Desi Land), published in 2010, incorporates and challenges these three motherhood tropes by breaking with convention. I argue that Naveen performs disidentification, or an identity in difference, and calls for a renegotiation or remapping of the current paradigm of Norwegian migration literature, which limits minority women’s artistic representation.
Machine learning–based analysis of non-invasive measurements for predicting intracardiac pressures
Authors: van Ravensberg Annemiek E , Scholte Niels T B , Omar Khader Aaram , Brugts Jasper J , Bruining Nico , van der Boon Robert M A 
Published: March 2024

Abstract

Aims
Early detection of congestion has demonstrated to improve outcomes in heart failure (HF) patients. However, there is limited access to invasively haemodynamic parameters to guide treatment. This study aims to develop a model to estimate the invasively measured pulmonary capillary wedge pressure (PCWP) using non-invasive measurements with both traditional statistics and machine learning (ML) techniques.

Methods and results
The study involved patients undergoing right-sided heart catheterization at Erasmus MC, Rotterdam, from 2017 to 2022. Invasively measured PCWP served as outcomes. Model features included non-invasive measurements of arterial blood pressure, saturation, heart rate (variability), weight, and temperature. Various traditional and ML techniques were used, and performance was assessed using R2 and area under the curve (AUC) for regression and classification models, respectively. A total of 853 procedures were included, of which 31% had HF as primary diagnosis and 49% had a PCWP of 12 mmHg or higher. The mean age of the cohort was 59 ± 14 years, and 52% were male. The heart rate variability had the highest correlation with the PCWP with a correlation of 0.16. All the regression models resulted in low R2 values of up to 0.04, and the classification models resulted in AUC values of up to 0.59.

Conclusion
In this study, non-invasive methods, both traditional and ML-based, showed limited correlation to PCWP. This highlights the weak correlation between traditional HF monitoring and haemodynamic parameters, also emphasizing the limitations of single non-invasive measurements. Future research should explore trend analysis and additional features to improve non-invasive haemodynamic monitoring, as there is a clear demand for further advancements in this field.