bluuebunny/crossref_metadata_2025_split huggingface dataset:
Phosphorus adsorbed by HCl desorption–activated red mud adsorbents: a molecular dynamics studys
Authors: Li Longjiang , Yang Zhiwen , Qiu Yueqin , Wang Yalan 
Published: April 2024

Abstract
In this study, modified red mud after phosphorus adsorption was used as the adsorbent, and HCl and deionized water were used as desorbents to desorb phosphorus. The components in the adsorbent were optimized based on density functional theory, and adsorbent and desorbent models were established. Molecular dynamics simulation was performed to determine the phosphorus concentration before and after desorption, interaction energies, radial distribution function(RDF), mean-square displacement(MSD), and diffusion coefficient. The Monte Carlo method was used to simulate the desorption isotherm, desorption site, heat of desorption, and desorption energy. Simulation results showed that deionized water could only desorb phosphorus on the adsorbent surface, and the stability of the system deteriorated upon adding HCl. HCl destroyed the ionic and hydrogen bonding between the O atoms in H2PO4
− and reactive metal and oxygen atoms in the activated red mud particles. Moreover, the van der Waals force decreased considerably. The ionic and hydrogen bonds between H2PO4
− and the surface of activated red mud particles were broken by HCl, which accelerated the desorption of phosphorus from the adsorbent surface. The interaction between HCl and phosphorus accelerated the diffusion, which decreased the adsorption capacity. Moreover, the desorption capacity increased with increasing temperature.
Free vibration analysis of jute/kenaf/banana hybrid biocomposites: effects of various stacking sequences
Authors: Jeyaguru Sangilimuthukumar , Thiagamani Senthil Muthu Kumar , Mahmoud Mohamed H , Fouad Hassan 
Published: April 2024

Abstract
This work provides an insight of the free vibration behaviour based on experimental modal analysis of hybrid natural fibre reinforced polymer composites fabricated via compression moulding method. This study aims to study the impact of different polymer matrices (vinyl ester and epoxy) and stacking order of different natural fibres (Jute, Kenaf and Banana) on the composite’s free vibration characteristics. Jute-Kenaf-Banana-Epoxy (JKBE) matrix composites exhibited enhanced free vibration properties compared to single fiber reinforcements, except the banana/epoxy composites. Additionally, Jute-Kenaf-Banana-Vinyl ester (JKBV) composites demonstrated improved free vibration properties in comparison to single fiber reinforcements, with the exception of kenaf/vinyl ester composites. Kenaf-Banana-Jute-Jute-Banana-Kenaf-Epoxy (KBJJBKE) and Kenaf-Banana-Jute-Jute-Banana-Kenaf-Vinyl ester (KBJJBKV) hybrid composites showed the highest natural frequencies of 68.36 Hz and 97.66 Hz, respectively. However, there was no significant improvements in the natural frequencies of Jute-Kenaf-Banana-Kenaf-Banana-Jute-Epoxy (JKBKBJE) and Jute-Kenaf-Banana-Kenaf-Banana-Jute-Vinyl ester (JKBKBJV) hybrid composites. The highest damping factor was observed for JKBKBJE (0.153) and JKBKBJV (0.224) hybrid composites. These hybrid composites with better free vibration properties shall be a potential candidate in the automobile interior applications.
Investigation of the thermodynamic, structural, electronic, mechanical and phonon properties of D0c Ru-based intermetallic alloys: an ab-initio study
Authors: Mnisi B O , Benecha E M , Tibane M M 
Published: April 2024

Abstract
We present the structural, elastic, electronic, magnetic, and phonon properties of D0c X3Ru (X = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, and Zn) alloys in their respective ground-states at zero pressure using first-principles density functional theory (DFT). The calculated heat of formation for Sc3Ru, Ti3Ru, V3Ru, Mn3Ru, and Zn3Ru are negative, signifying their thermodynamic stability. Meanwhile, we find that Sc3Ru, V3Ru, Mn3Ru, Co3Ru, Ni3Ru, Cu3Ru and Zn3Ru alloys are mechanically stable. The electronic properties indicate a metallic nature in all the X3Ru alloys due to valence-conduction band overlap at the Fermi energy. Additionally, the phonon dispersion curves suggest that Cr3Ru, Fe3Ru, Ni3Ru, Cu3Ru, and Zn3Ru are dynamically stable. These results provide a comprehensive overview of the stability, electronic, and mechanical properties of D0c Zn3Ru structures, suggesting their suitability for engineering novel alloys in high-temperature structural applications.
Spatially modulated ablation driven by chaotic attractors in human lung epithelial cancer cells
Authors: Martines-Arano Hilario , Valdivia-Flores Alejandra , Castillo-Cruz Juan , García-Pérez Blanca Estela , Torres-Torres Carlos 
Published: April 2024

Abstract
A significant modification in photoinduced energy transfer in cancer cells is reported by the assistance of a dynamic modulation of the beam size of laser irradiation. Human lung epithelial cancer cells in monolayer form were studied. In contrast to the quantum and thermal ablation effect promoted by a standard focused Gaussian beam, a spatially modulated beam can caused around 15% of decrease in the ablation threshold and formation of a ring-shaped distribution of the photothermal transfer effect. Optical irradiation was conducted in A549 cells by a 532 nm single-beam emerging from a Nd:YVO4 system. Ablation effects derived from spatially modulated convergent waves were controlled by an electrically focus-tunable lens. The proposed chaotic behavior of the spatial modulation followed an Arneodo chaotic oscillator. Fractional dynamic thermal transport was analyzed in order to describe photoenergy in propagation through the samples. Immediate applications of chaos theory for developing phototechnology devices driving biological functions or phototherapy treatments can be considered.
Ultrasound imaging based recognition of prenatal anomalies: a systematic clinical engineering review
Authors: Sriraam Natarajan , Chinta Babu , Suresh Seshadhri , Sudharshan Suresh 
Published: April 2024

Abstract
For prenatal screening, ultrasound (US) imaging allows for real-time observation of developing fetal anatomy. Understanding normal and aberrant forms through extensive fetal structural assessment enables for early detection and intervention. However, the reliability of anomaly diagnosis varies depending on operator expertise and device limits. First trimester scans in conjunction with circulating biochemical markers are critical in identifying high-risk pregnancies, but they also pose technical challenges. Recent engineering advancements in automated diagnosis, such as artificial intelligence (AI)-based US image processing and multimodal data fusion, are developing to improve screening efficiency, accuracy, and consistency. Still, creating trust in these data-driven solutions is necessary for integration and acceptability in clinical settings. Transparency can be promoted by explainable AI (XAI) technologies that provide visual interpretations and illustrate the underlying diagnostic decision making process. An explanatory framework based on deep learning is suggested to construct charts depicting anomaly screening results from US video feeds. AI modelling can then be applied to these charts to connect defects with probable deformations. Overall, engineering approaches that increase imaging, automation, and interpretability hold enormous promise for altering traditional workflows and expanding diagnostic capabilities for better prenatal care.
Neural networks as effective surrogate models of radio-frequency quadrupole particle accelerator simulations
Authors: Villarreal Joshua , Winklehner Daniel , Koser Daniel , Conrad Janet M 
Published: April 2024

Abstract
Radio-frequency quadrupoles (RFQs) are multi-purpose linear particle accelerators that simultaneously bunch and accelerate charged particle beams. They are ubiquitous in accelerator physics, especially as injectors to higher-energy machines, owing to their impressive efficiency. The design and optimization of these devices can be lengthy due to the need to repeatedly perform high-fidelity simulations. Several recent papers have demonstrated that machine learning can be used to build surrogate models (fast-executing replacements of computationally costly beam simulations) for order-of-magnitude computing time speedups. However, while these pilot studies are encouraging, there is room to improve their predictive accuracy. Particularly, beam summary statistics such as emittances (an important figure of merit in particle accelerator physics) have historically been challenging to predict. For the first time, we present a surrogate model trained on 200 000 samples that yields

<

6% mean average percent error for the predictions of all relevant beam output parameters from defining RFQ design parameters, solving the problem of poor emittance predictions by identifying and including hidden variables which were not accounted for previously. These surrogate models were made possible by using the Julia language and GPU computing; we briefly discuss both. We demonstrate the utility of surrogate modeling by performing a multi-objective optimization using our best model as a callback in the objective function to select an optimal RFQ design. We consider trade-offs in RFQ performance for various choices of Pareto-optimal design variables—common issues for any multi-objective optimization scheme. Lastly, we make recommendations for input data preparation, selection, and neural network architectures that pave the way for future development of production-capable surrogate models for RFQs and other particle accelerators.
GWAK: gravitational-wave anomalous knowledge with recurrent autoencoders
Authors: Raikman Ryan , Moreno Eric A , Govorkova Ekaterina , Marx Ethan J , Gunny Alec , Benoit William , Chatterjee Deep , Omer Rafia , Saleem Muhammed , Rankin Dylan S , Coughlin Michael W , Harris Philip C , Katsavounidis Erik 
Published: April 2024

Abstract
Matched-filtering detection techniques for gravitational-wave (GW) signals in ground-based interferometers rely on having well-modeled templates of the GW emission. Such techniques have been traditionally used in searches for compact binary coalescences (CBCs), and have been employed in all known GW detections so far. However, interesting science cases aside from compact mergers do not yet have accurate enough modeling to make matched filtering possible, including core-collapse supernovae and sources where stochasticity may be involved. Therefore the development of techniques to identify sources of these types is of significant interest. In this paper, we present a method of anomaly detection based on deep recurrent autoencoders to enhance the search region to unmodeled transients. We use a semi-supervised strategy that we name ‘Gravitational Wave Anomalous Knowledge’ (GWAK). While the semi-supervised approach to this problem entails a potential reduction in accuracy compared to fully supervised methods, it offers a generalizability advantage by enhancing the reach of experimental sensitivity beyond the constraints of pre-defined signal templates. We construct a low-dimensional embedded space using the GWAK method, capturing the physical signatures of distinct signals on each axis of the space. By introducing signal priors that capture some of the salient features of GW signals, we allow for the recovery of sensitivity even when an unmodeled anomaly is encountered. We show that regions of the GWAK space can identify CBCs, detector glitches and also a variety of unmodeled astrophysical sources.
GRINN: a physics-informed neural network for solving hydrodynamic systems in the presence of self-gravity
Authors: Auddy Sayantan , Dey Ramit , Turner Neal J , Basu Shantanu 
Published: April 2024

Abstract
Modeling self-gravitating gas flows is essential to answering many fundamental questions in astrophysics. This spans many topics including planet-forming disks, star-forming clouds, galaxy formation, and the development of large-scale structures in the Universe. However, the nonlinear interaction between gravity and fluid dynamics offers a formidable challenge to solving the resulting time-dependent partial differential equations (PDEs) in three dimensions (3D). By leveraging the universal approximation capabilities of a neural network within a mesh-free framework, physics informed neural networks (PINNs) offer a new way of addressing this challenge. We introduce the gravity-informed neural network (GRINN), a PINN-based code, to simulate 3D self-gravitating hydrodynamic systems. Here, we specifically study gravitational instability and wave propagation in an isothermal gas. Our results match a linear analytic solution to within 1% in the linear regime and a conventional grid code solution to within 5% as the disturbance grows into the nonlinear regime. We find that the computation time of the GRINN does not scale with the number of dimensions. This is in contrast to the scaling of the grid-based code for the hydrodynamic and self-gravity calculations as the number of dimensions is increased. Our results show that the GRINN computation time is longer than the grid code in one- and two- dimensional calculations but is an order of magnitude lesser than the grid code in 3D with similar accuracy. Physics-informed neural networks like GRINN thus show promise for advancing our ability to model 3D astrophysical flows.
A revised interpretation of signal-to-noise ratio paradox and its application to constrain regional climate projections
Authors: Duan Yanan , Kumar Sanjiv 
Published: April 2024

Abstract
The signal-to-noise ratio paradox is interpreted as the climate model’s ability to predict observations better than the model itself. This view is counterintuitive, given that climate models are simplified numerical representations of complex earth system dynamics. A revised interpretation is provided here: the signal-to-noise ratio paradox represents excessive noise in climate predictions and projections. Noise is potentially reducible, providing a scientific basis for improving the signal in regional climate projections. The signal-to-noise ratio paradox was assessed in long-term climate projections using single-model and multi-model large ensemble climate data. A null hypothesis was constructed by performing bootstrap resampling of climate model ensembles to test its ability to predict the 20th-century temperature and precipitation trends locally and compare it with the observations. The rejection of the null hypothesis indicates the existence of a paradox. The multi-model large ensemble does not reject the null hypothesis in most places globally. The rejection rate in the single-model large ensemble is related to the model’s fidelity to simulate internal climate variability rather than its ensemble size. For regions where the null hypothesis is rejected in the multi-model large ensemble, for example, India, the paradox is caused by a smaller signal strength in the climate model’s ensemble. The signal strength was improved by 100% through ensemble selection and based on past performance, which reduced uncertainty in India’s 30-year temperature projections by 25%. Consistent with previous studies, precipitation projections are noisier, leading to a paradox metric value 2–3 times higher than that of the temperature projections. The application of ensemble selection methodology significantly decreased uncertainty in precipitation projections for the United Kingdom, Western Australia, and Northeastern America by 47%, 36%, and 20%, respectively. Overall, this study makes a unique contribution by reducing uncertainty at the temporal scale, specifically in estimating trends using the signal-to-noise ratio paradox metric.
Monotonicity rules for the ratio of two function series and two integral transforms
Authors: Mao Zhong-Xuan , Tian Jing-Feng 
Published: April 2024

In this paper, we investigate the monotonicity of the functions

t
↦

∑

k
=
0

∞

a
k

w
k

(
t
)

∑

k
=
0

∞

b
k

w
k

(
t
)

t \mapsto \frac {\sum _{k=0}^\infty a_k w_k(t)}{\sum _{k=0}^\infty b_k w_k(t)}

and

x
↦

∫
α
β

f
(
t
)
w
(
t
,
x
)

d

t

∫
α
β

g
(
t
)
w
(
t
,
x
)

d

t

x \mapsto \frac {\int _\alpha ^\beta f(t) w(t,x) \mathrm {d} t}{\int _\alpha ^\beta g(t) w(t,x) \mathrm {d} t}

, focusing on case where the monotonicity of

a
k

/

b
k

a_k/b_k

and

f
(
t
)

/

g
(
t
)

f(t)/g(t)

change once. The results presented also provide insights into the monotonicity of the ratios of two power series, two

Z

\mathcal {Z}

-transforms, two discrete Laplace transforms, two discrete Mellin transforms, two Laplace transforms, and two Mellin transforms. Finally, we employ these monotonicity rules to present several applications in the realm of special functions and stochastic orders.
Small fibre neuropathy in Fabry disease: a human-derived neuronal in vitro disease model and pilot data
Authors: Klein Thomas , Grüner Julia , Breyer Maximilian , Schlegel Jan , Schottmann Nicole Michelle , Hofmann Lukas , Gauss Kevin , Mease Rebecca , Erbacher Christoph , Finke Laura , Klein Alexandra , Klug Katharina , Karl-Schöller Franziska , Vignolo Bettina , Reinhard Sebastian , Schneider Tamara , Günther Katharina , Fink Julian , Dudek Jan , Maack Christoph , Klopocki Eva , Seibel Jürgen , Edenhofer Frank , Wischmeyer Erhard , Sauer Markus , Üçeyler Nurcan 
Published: April 2024

Abstract
Acral burning pain triggered by fever, thermal hyposensitivity and skin denervation are hallmarks of small fibre neuropathy in Fabry disease, a life-threatening X-linked lysosomal storage disorder. Variants in the gene encoding alpha-galactosidase A may lead to impaired enzyme activity with cellular accumulation of globotriaosylceramide. To study the underlying pathomechanism of Fabry-associated small fibre neuropathy, we generated a neuronal in vitro disease model using patient-derived induced pluripotent stem cells from three Fabry patients and one healthy control. We further generated an isogenic control line via gene editing. We subjected induced pluripotent stem cells to targeted peripheral neuronal differentiation and observed intra-lysosomal globotriaosylceramide accumulations in somas and neurites of Fabry sensory neurons using super-resolution microscopy. At functional level, patch-clamp analysis revealed a hyperpolarizing shift of voltage-gated sodium channel steady-state inactivation kinetics in isogenic control neurons compared with healthy control neurons (P &lt; 0.001). Moreover, we demonstrate a drastic increase in Fabry sensory neuron calcium levels at 39°C mimicking clinical fever (P &lt; 0.001). This pathophysiological phenotype was accompanied by thinning of neurite calibres in sensory neurons differentiated from induced pluripotent stem cells derived from Fabry patients compared with healthy control cells (P &lt; 0.001). Linear–nonlinear cascade models fit to spiking responses revealed that Fabry cell lines exhibit altered single neuron encoding properties relative to control. We further observed mitochondrial aggregation at sphingolipid accumulations within Fabry sensory neurites utilizing a click chemistry approach together with mitochondrial dysmorphism compared with healthy control cells. We pioneer pilot insights into the cellular mechanisms contributing to pain, thermal hyposensitivity and denervation in Fabry small fibre neuropathy and pave the way for further mechanistic in vitro studies in Fabry disease and the development of novel treatment approaches.
An Increase in Vascular Stiffness Is Positively Associated With Mitochondrial Bioenergetics Impairment of Peripheral Blood Mononuclear Cells in the Older Adults
Authors: Attachaipanich Tanawat , Sriwichaiin Sirawit , Apaijai Nattayaporn , Kerdphoo Sasiwan , Thongmung Nisakron , Vathesatogkit Prin , Sritara Piyamitr , Chattipakorn Nipon , Kitiyakara Chagriya , Chattipakorn Siriporn C 
Published: April 2024

Abstract
The cardio-ankle vascular index (CAVI) is a noninvasive parameter reflecting vascular stiffness. CAVI correlates with the burden of atherosclerosis and future cardiovascular events. Mitochondria of peripheral blood mononuclear cells (PBMCs) have been identified as a noninvasive source for assessing systemic mitochondrial bioenergetics. This study aimed to investigate the relationship between CAVI values and mitochondrial bioenergetics of PBMCs in the older adults.. This cross-sectional study enrolled participants from the Electricity Generating Authority of Thailand between 2017 and 2018. A total of 1 640 participants with an ankle-brachial index greater than 0.9 were included in this study. All participants were stratified into 3 groups based on their CAVI values as high (CAVI ≥ 9), moderate (9 &gt; CAVI ≥ 8), and low (CAVI &lt; 8), in which each group comprised 702, 507, and 431 participants, respectively. The extracellular flux analyzer was used to measure mitochondrial respiration of isolated PBMCs. The mean age of the participants was 67.9 years, and 69.6% of them were male. After adjusted with potential confounders including age, sex, smoking status, body mass index, diabetes, dyslipidemia, hypertension, and creatinine clearance, participants with high CAVI values were independently associated with impaired mitochondrial bioenergetics, including decreased basal respiration, maximal respiration, and spare respiratory capacity, as well as increased mitochondrial reactive oxygen species. This study demonstrated that CAVI measurement reflects the underlying impairment of cellular mitochondrial bioenergetics in PBMCs. Further longitudinal studies are necessary to establish both a causal relationship between CAVI measurement and underlying cellular dysfunction.
Validation of a Rule-Based ICD-10-CM Algorithm to Detect Fall Injuries in Medicare Data
Authors: Ganz David A , Esserman Denise , Latham Nancy K , Kane Michael , Min Lillian C , Gill Thomas M , Reuben David B , Peduzzi Peter , Greene Erich J 
Published: April 2024

Abstract

Background
Diagnosis-code-based algorithms to identify fall injuries in Medicare data are useful for ascertaining outcomes in interventional and observational studies. However, these algorithms have not been validated against a fully external reference standard, in ICD-10-CM, or in Medicare Advantage (MA) data.

Methods
We linked self-reported fall injuries leading to medical attention (FIMA) from the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) trial (reference standard) to Medicare fee-for-service (FFS) and MA data from 2015–19. We measured the area under the receiver operating characteristic curve (AUC) based on sensitivity and specificity of a diagnosis-code-based algorithm against the reference standard for presence or absence of ≥1 FIMA within a specified window of dates, varying the window size to obtain points on the curve. We stratified results by source (FFS vs MA), trial arm (intervention vs control), and STRIDE’s 10 participating health care systems.

Results
Both reference standard data and Medicare data were available for 4 941 (of 5 451) participants. The reference standard and algorithm identified 2 054 and 2 067 FIMA, respectively. The algorithm had 45% sensitivity (95% confidence interval [CI]: 43%–47%) and 99% specificity (95% CI: 99%–99%) to identify reference standard FIMA within the same calendar month. The AUC was 0.79 (95% CI: 0.78–0.81) and was similar by FFS or MA data source and by trial arm but showed variation among STRIDE health care systems (AUC range by health care system, 0.71 to 0.84).

Conclusions
An ICD-10-CM algorithm to identify fall injuries demonstrated acceptable performance against an external reference standard, in both MA and FFS data.