bluuebunny/crossref_metadata_2025_split huggingface dataset:
Finite and high-temperature series expansion via many-body perturbation theory: application to Heisenberg spin-1/2 XXZ chain
Authors: Tag M A , Boudiar A , Mansour M E H , Hafdallah A , Bendjeroudib C , Zaidi Beddiaf 
Published: May 2024

Abstract
We present a new algorithm to evaluate the grand potential at high and finite temperatures using many-body perturbation theory. This algorithm enables us to calculate the contribution of any Hugenholtz or Feynman vacuum diagrams and formulate the results as a sum of divided differences. Additionally, the proposed method is applicable to any interaction in any dimension, allowing us to calculate thermodynamic quantities efficiently at any given temperature, particularly at high temperatures.Furthermore, we apply this algorithm to the Heisenberg spin-1/2 XXZ chain. We obtain all coefficients of the high-temperature expansion of the free energy and susceptibility per site of this model up to the sixth order.
Isotactic-polypropylene (iPP) wettability modified by surface DBD plasma
Authors: Bedrouni F , Ouchabane M , Almabouada F , Saidi-Amroun N 
Published: May 2024

Abstract
In this paper, atmospheric pressure plasma of ambient air was generated by a surface dielectric barrier discharge (SDBD) device for the purpose of modifying the surface of isotactic-polypropylene (iPP). The effect of SDBD treatment time on the chemical and physical properties of iPP was studied using various analytical techniques including, water contact angle (WCA), attenuated total reflection (ATR), Raman spectroscopy, x-ray diffraction (XRD), and atomic force microscopy (AFM). The results indicate that the hydrophilicity and the C=O bonds of the treated iPP were improved as observed, respectively, through WCA and ATR analysis. The crystal structure was evaluated by Raman spectroscopy and XRD. It was found that the iPP chain was under microscopic stress, which affected its crystallinity degree depending on the duration of the treatment. Furthermore, the AFM analysis revealed that the surface roughness was substantially modified.
Development of pulsed plasma operation scenario and required conditions in JA DEMO
Authors: Sugiyama S. , Aiba N. , Asakura N. , Hayashi N. , Sakamoto Y. 
Published: May 2024

Abstract
We have developed the pulsed plasma operation scenarios for JA DEMO, a design concept of the steady-state tokamak demonstration reactor, to clarify controls of the current profile and power required for the operation. We compare the scenarios when injecting electron cyclotron waves only and both neutral beam and electron cyclotron waves for external heating and current drive. We demonstrate current profile control that maintains the minimum value of the safety factor above one and avoids creating the local minima in the safety factor profile and power control by argon seeding that maintains the fusion power constant at the desired value and reduces the heat load on the divertor, performing long-time integrated modeling simulations. We clarify the conditions of the heating and current drive system and impurity injection system required for such control. The dependence of power control on argon anomalous transport coefficients is investigated. We have the prospect of maintaining the fusion power of 1 GW for more than two hours, i.e. obtaining the required plasma performance determined using a systems code.
The use of supramolecular systems in biomedical applications for antimicrobial properties, biocompatibility, and drug delivery
Authors: Li Yijie , Vrana Nihal Engin , Letellier Baptiste , Lavalle Philippe , Guilbaud-Chéreau Chloé 
Published: May 2024

Abstract
Supramolecular chemistry is versatile for developing stimuli-responsive, dynamic and multifunctional structures. In the context of biomedical engineering applications, supramolecular assemblies are particularly useful as coatings for they can closely mimic the natural structure and organisation of the extracellular matrix (ECM), they can also fabricate other complex systems like drug delivery systems and bioinks. In the current context of growing medical device-associated complications and the developments in the controlled drug delivery and regenerative medicine fields, supramolecular assemblies are becoming an indispensable part of the biomedical engineering arsenal. This review covers the different supramolecular assemblies in different biomedical applications with a specific focus on antimicrobial coatings, coatings that enhance biocompatibility, surface modifications on implantable medical devices, systems that promote therapeutic efficiency in cancer therapy, and the development of bioinks. The introduced supramolecular systems include multilayer coating by polyelectrolytes, polymers incorporated with nanoparticles, coating simulation of ECM, and drug delivery systems. A perspective on the application of supramolecular systems is also included.
High-speed spinning of collagen microfibers comprising aligned fibrils for creating artificial tendons
Authors: Yunoki Shunji , Kishimoto Masanori , Mandai Yoshinobu , Hiraoka Yosuke , Kondo Eiji 
Published: May 2024

Abstract
Bundles of engineered collagen microfibers are promising synthetic tendons as substitutes for autogenous grafts. The purpose of this study was to develop high-speed and continuous spinning of collagen microfibers that involves stretching of collagen stream. Our study revealed the ‘critical fibrillogenesis concentration (CFC)’ of neutralized collagen solutions, which is defined as the upper limit of the collagen concentration at which neutralized collagen molecules remain stable as long as they are cooled (

⩽

10 °C). Neutralized collagen solutions at collagen concentrations slightly below the CFC formed cord-like collagen gels comprising longitudinally aligned fibrils when extruded from nozzles into an ethanol bath. Dry collagen microfibers with a controlled diameter ranging from 122 ± 2–31.2 ± 1.7 μm can be spun from the cord-like gels using nozzles of various sizes. The spinning process was improved by including stretching of collagen stream to further reduce diameter and increase linear velocity. We extruded a collagen solution through a 182 μm diameter nozzle while simultaneously stretching it in an ethanol bath during gelation and fiber formation. This process resembles the stretching of a melted thermoplastic resin because it solidifies during melt spinning. The mechanical properties of the stretched collagen microfibers were comparable to the highest literature values obtained using microfluidic wet spinning, as they exhibited longitudinally aligned fibrils both on their surface and in their core. Previous wet spinning methods were unable to generate collagen microfibers with a consistent tendon-like fibrillar arrangement throughout the samples. Although the tangent modulus (137 ± 7 MPa) and stress at break of the swollen bundles of stretched microfibers (13.8 ± 1.9 MPa) were lower than those of human anterior cruciate ligament, they were within the same order of magnitude. We developed a spinning technique that produces narrow collagen microfibers with a tendon-like arrangement that can serve as artificial fiber units for collagen-based synthetic tendons.
Simultaneous influence of contact angle, capillary number, and contraction ratio on droplet dynamics in hydrophobic microchannel
Authors: Do Le Hung Toan , Nguyen Thanh Tung , Hoang Van Thanh , Lee Jyh-Wei 
Published: May 2024

Abstract
In droplet-based microfluidic systems, droplet motion and behavior largely depend on fluid properties, flow conditions, and microchannel geometry. This study presents a model to predict droplet dynamics within a contraction microchannel, considering various factors, such as contact angle (θ), capillary number (Ca), and contraction ratio (C), within defined ranges, through the employment of a three-dimensional numerical simulation method. Droplets may undergo trap, squeeze, or breakup as they traverse the contraction microchannel, contingent upon the aforementioned factors. The transition from trap to squeeze is determined by the critical value of capillary number (

C

a

I

). Within the squeezed regime, the contact angle influences droplet deformation and velocity within the contraction microchannel. However, when capillary number (

C

a

II

) in contraction microchannel increases to a specific threshold corresponding to the contraction ratio, the droplet no longer in contact with the wall and becomes independent of the contact angle, a phenomenon referred to as detachment. Moreover, the contact angle’s impact on the transition of droplets from squeeze to breakup is contingent upon the contraction ratio. This study provides a comprehensive overview of droplet behavior within microfluidic systems, offering valuable insights for the optimization and design of microsystems.
Advancements in P3HT:PCBM solar cells through experimental and simulated techniques
Authors: Chargui Taoufik , Lmai Fatima , Erraji Abdellah 
Published: May 2024

Abstract
Organic solar cells are a promising alternative in photovoltaics due to their low manufacturing cost and flexibility. In this study, we developed an organic solar cell of structure (ITO/PEDOT:PSS/P3HT:PCBM/Al) where the active layer is represented by P3HT:PCBM. Experimental studies revealed that this layer has an energy gap of 1.9 eV and a good absorption coefficient of

3
×

10
5

c

m

−
1

. Using analytical and experimental methods based on (J–V–T) curves, we extracted the key parameters of our device, including parasitic resistances (

R
s

and

R

sh

), ideality factor (n), and Schottky potential barrier (

ϕ

B

)

at different temperatures. Also the charge transport mechanism in our junction is identified in this study. We then used simulation to evaluate the performance of our solar cell, obtaining an initial efficiency of 2.13%, in line with experimental results from other research. Our results confirm the feasibility of our theoretical approach, with performance consistent with previous work. However, by introducing an electron transport layer layer (PFN-Br or PDINO), we were able to improve efficiency up to 4.25%. These results highlight the importance of optimising intermediate layers to maximise the efficiency of organic solar cells.
Time distribution of stimulated transition probabilities
Authors: Pereyra Pedro 
Published: May 2024

Abstract
In recent years, an increasing number of experimental results have been published for a wide variety of systems in which high-precision, ultra-fast, and ultra-short laser pulses techniques have been used to study time-resolved dynamical processes. In the absence of a theoretical prediction of the time distribution of the transition probabilities, the lifetimes τ are generally extracted by fitting the decay time with an exponential, bi-exponential or three-exponential function. In fitting the data, the short-time behavior is generally neglected. The purpose of this study is to show that an explicit formula for the time distribution of the transition probability can be determined rigorously using time-dependent perturbation theory. A formula that perfectly fits an important subset of the experimental results from t = 0 to

t
=
∞

. We show that by following a route different from the usual procedure for deriving transition amplitudes and Einstein coefficients in time-dependent perturbation theory, one ends up with a time-dependent factor, that is, a temporal distribution function of the transition probabilities, and for the Einstein coefficients. The time distribution reported here looks in the time domain similar to the Planck distribution in the frequency domain. In fact, the behavior of the time distribution, with a maximum at

2
τ
ln
⁡
2

, and with τ playing the role of temperature, resembles the behavior of the Planck frequency distribution. We present several examples to demonstrate that the theoretical formula allows for easier fitting of the experimental results reported in the literature. We also show that the time distribution explains the difference in resonance heights between the experimental and theoretical blue laser spectra.
Next-day largest earthquake magnitude forecasting with the aid of Moon tidal force and sunspot data
Authors: Junqueira Saldanha Matheus Henrique , Hirata Yoshito 
Published: May 2024

Abstract
Seismicity is a complex phenomenon with a multitude of components involved. In order to perform forecasting, which has yet to be done sufficiently well, it is paramount to be in possession of information of all these components, and use this information effectively in a prediction model. In the literature, the influence of the Sun and the Moon in seismic activity on Earth has been discussed numerous times. In this paper we contribute to such discussion, giving continuity to a previous work. Most importantly, we instrument four earthquake catalogs from different regions, calculating the Moon tidal force at the region and time of each earthquake, which allows us to analyze the relation between the tidal forces and the earthquake magnitudes. At first, we find that the dynamical system governing Moon motion is unidirectionally coupled with seismic activity, indicating that the position of the Moon drives, to some extent, the earthquake generating process. Furthermore, we present an analysis that demonstrates a clear positive correlation between tidal force and earthquake magnitude. Finally, it is shown that the use of Moon tidal force data and sunspot number data can be used to improve next-day maximum magnitude forecasting, with the highest accuracy being achieved when using both kinds of data. We hope that our results encourage researchers to include data from Moon tidal forces and Sun activity in their earthquake forecasting models.
Incorporating background knowledge in symbolic regression using a computer algebra system
Authors: Fox Charles , Tran Neil D , Nacion F Nikki , Sharlin Samiha , Josephson Tyler R 
Published: May 2024

Abstract
Symbolic regression (SR) can generate interpretable, concise expressions that fit a given dataset, allowing for more human understanding of the structure than black-box approaches. The addition of background knowledge (in the form of symbolic mathematical constraints) allows for the generation of expressions that are meaningful with respect to theory while also being consistent with data. We specifically examine the addition of constraints to traditional genetic algorithm (GA) based SR (PySR) as well as a Markov-chain Monte Carlo (MCMC) based Bayesian SR architecture (Bayesian Machine Scientist), and apply these to rediscovering adsorption equations from experimental, historical datasets. We find that, while hard constraints prevent GA and MCMC SR from searching, soft constraints can lead to improved performance both in terms of search effectiveness and model meaningfulness, with computational costs increasing by about an order of magnitude. If the constraints do not correlate well with the dataset or expected models, they can hinder the search of expressions. We find incorporating these constraints in Bayesian SR (as the Bayesian prior) is better than by modifying the fitness function in the GA.
Towards XAI agnostic explainability to assess differential diagnosis for Meningitis diseases
Authors: Messai Aya , Drif Ahlem , Ouyahia Amel , Guechi Meriem , Rais Mounira , Kaderali Lars , Cherifi Hocine 
Published: May 2024

Abstract
Meningitis, characterized by meninges and cerebrospinal fluid inflammation, poses diagnostic challenges due to diverse clinical manifestations. This work introduces an explainable AI automatic medical decision methodology that determines critical features and their relevant values for the differential diagnosis of various meningitis cases. We proceed with knowledge acquisition to define the rules for this research. Currently, we have established the etiological diagnosis of Meningococcaemia, Meningococcal Meningitis, Tuberculous Meningitis, Aseptic Meningitis, Haemophilus influenzae Meningitis, and Pneumococcal Meningitis. The data preprocessing was conducted after collecting data from samples with meningitis diseases at Setif Hospital in Algeria. Tree-based ensemble methods were then applied to assess the model’s performance. Finally, we implement an XAI agnostic explainability approach based on the SHapley Additive exPlanations technique to attribute each feature’s contribution to the model’s output. Experiments were conducted on the collected dataset and the SINAN database, obtained from the Brazilian Government’s Health Information System on Notifiable Diseases, which comprises 6729 patients aged over 18 years. The Extreme Gradient Boosting model was chosen for its superior performance metrics (Accuracy: 0.90, AUROC: 0.94, and F1-score: 0.98). Setif’s hospital data revealed notable performance metrics (Accuracy: 0.7143, F1-Score: 0.7857). This study’s findings showcase each feature’s contribution to the model’s predictions and diagnosis. It also reveals critical biomarker ranges associated with distinct types of Meningitis. Significant diagnostic effect was found for Meningococcal Meningitis with elevated neutrophil levels (

>

40%) and balanced lymphocyte levels (40%–60%). Tuberculous Meningitis demonstrated low neutrophil levels (

<

60%) and elevated lymphocyte levels (

>

60%). H. influenzae meningitis exhibited a predominance of neutrophils (

>

80%), while Aseptic meningitis showed lower neutrophil levels (

<

40%) and lymphocyte levels within the range of 50%–60%. The majority of the AI automatic medical decision results are twinned with validation by our team of infectious disease experts, confirming the alignment of algorithmic diagnoses with clinical practices.
Kalai’s 3^{𝑑} conjecture for unconditional and locally anti-blocking polytopes
Authors: Sanyal Raman , Winter Martin 
Published: May 2024

Kalai’s

3
d

3^d

conjecture states that every centrally-symmetric

d
d

-polytope has at least

3
d

3^d

faces. We give short proofs for two special cases: if

P
P

is unconditional (that is, invariant w.r.t. reflection in any coordinate hyperplane), and more generally, if

P
P

is locally anti-blocking. In both cases we show that the minimum is attained exactly for the Hanner polytopes.
Microbial Abundances Retrieved from Sequencing data—automated NCBI Taxonomy (MARS): a pipeline to create relative microbial abundance data for the Microbiome Modelling Toolbox and utilizing homosynonyms for efficient mapping to resources
Authors: Hulshof Tim , Nap Bram , Martinelli Filippo , Thiele Ines 
Published: May 2024

Abstract

Motivation
Computational approaches to the functional characterization of the microbiome, such as the Microbiome Modelling Toolbox, require precise information on microbial composition and relative abundances. However, challenges arise from homosynonyms—different names referring to the same taxon, which can hinder the mapping process and lead to missed species mapping when using microbial metabolic reconstruction resources, such as AGORA and APOLLO.

Results
We introduce the integrated MARS pipeline, a user-friendly Python-based solution that addresses these challenges. MARS automates the extraction of relative abundances from metagenomic reads, maps species and genera onto microbial metabolic reconstructions, and accounts for alternative taxonomic names. It normalizes microbial reads, provides an optional cut-off for low-abundance taxa, and produces relative abundance tables apt for integration with the Microbiome Modelling Toolbox. A sub-component of the pipeline automates the task of identifying homosynonyms, leveraging web scraping to find taxonomic IDs of given species, searching NCBI for alternative names, and cross-reference them with microbial reconstruction resources. Taken together, MARS streamlines the entire process from processed metagenomic reads to relative abundance, thereby significantly reducing time and effort when working with microbiome data.

Availability and implementation
MARS is implemented in Python. It can be found as an interactive application here: https://mars-pipeline.streamlit.app/along with a detailed documentation here: https://github.com/ThieleLab/mars-pipeline.
fastCCLasso: a fast and efficient algorithm for estimating correlation matrix from compositional data
Authors: Zhang Shen , Fang Huaying , Hu Tao 
Published: May 2024

Abstract

Motivation
The composition and structure of microbial communities on the body surface are closely related to human health. The interaction relationship among microbes can help us understand the formation of the microecological environment and the biological mechanism by which microorganisms influence host health. With the help of high-throughput sequencing technologies, microbial abundances in a natural environment can be directly measured without the isolation of microorganisms in culture. Sequencing experiments in microbiome studies can measure the relative abundance of microbes, which is called compositional data. Although there are already many methods for correlation analysis for compositional data, the computation time or accuracy still needs to be improved for current microbiome studies.

Results
We develop a fast and efficient algorithm, called fastCCLasso, based on a penalized weighted least squares for inferring the correlation structure of microbes from compositional data in microbiome studies. We perform a large number of numerical experiments and the simulation results show that fastCCLasso outperforms its competitors in edge detection for inferring the correlation network. We also apply fastCCLasso for estimating microbial networks in microbiome studies and fastCCLasso provides a conservative network with comparable false discovery counts that are derived from shuffled data.

Availability and implementation
FastCCLasso is open source and freely available from https://github.com/ShenZhang-Statistics/fastCCLasso under GNU LGPL v3.
Label-free, high-throughput holographic imaging to evaluate mammalian gametes and embryos
Authors: Wheeler Matthew B , Rabel R A Chanaka , Rubessa Marcello , Popescu Gabriel 
Published: May 2024

Abstract
Assisted reproduction is one of the significant tools to treat human infertility. Morphological assessment is the primary method to determine sperm and embryo viability during in vitro fertilization cycles. It has the advantage of being a quick, convenient, and inexpensive means of assessment. However, visual observation is of limited predictive value for early embryo morphology. It has led many to search for other imaging tools to assess the reproductive potential of a given embryo. The limitations of visual assessment apply to both humans and animals.
One recent innovation in assisted reproduction technology imaging is interferometric phase microscopy, also known as holographic microscopy. Interferometric phase microscopy/quantitative phase imaging is the next likely progression of analytical microscopes for the assisted reproduction laboratory. The interferometric phase microscopy system analyzes waves produced by the light as it passes through the specimen observed. The microscope collects the light waves produced and uses the algorithm to create a hologram of the specimen. Recently, interferometric phase microscopy has been combined with quantitative phase imaging, which joins phase contrast microscopy with holographic microscopy. These microscopes collect light waves produced and use the algorithm to create a hologram of the specimen. Unlike other systems, interferometric phase microscopy can provide a quantitative digital image, and it can make 2D and 3D images of the samples.
This review summarizes some newer and more promising quantitative phase imaging microscopy systems for evaluating gametes and embryos. Studies clearly show that quantitative phase imaging is superior to bright field microscopy-based evaluation methods when evaluating sperm and oocytes prior to IVF and embryos prior to transfer. However, further assessment of these systems for efficacy, reproducibility, cost-effectiveness, and embryo/gamete safety must take place before they are widely adopted.