bluuebunny/crossref_metadata_2025_split huggingface dataset:
Non-invasive pulse arrival time as a surrogate for oscillometric systolic blood pressure changes during non-pharmacological intervention
Authors: Hametner Bernhard , Maurer Severin , Sehnert Alina , Bachler Martin , Orter Stefan , Zechner Olivia , Müllner-Rieder Markus , Penkler Michael , Wassertheurer Siegfried , Sehnert Walter , Mengden Thomas , Mayer Christopher C 
Published: April 2024

Abstract

Background. Non-invasive continuous blood pressure (BP) monitoring is of longstanding interest in various cardiovascular scenarios. In this context, pulse arrival time (PAT), i.e., a surrogate parameter for systolic BP (change), became very popular recently, especially in the context of cuffless BP measurement and dedicated lifestyle interventions. Nevertheless, there is also understandable doubt on its reliability in uncontrolled and mobile settings. Objective. The aim of this work is therefore the investigation whether PAT follows oscillometric systolic BP readings during moderate interventions by physical or mental activity using a medical grade handheld device for non-invasive PAT assessment. Approach. A study was conducted featuring an experimental group performing a physical and a mental task, and a control group. Oscillometric BP and PAT were assessed at baseline and after each intervention. Interventions were selected randomly but then performed sequentially in a counterbalanced order. Multivariate analyses of variance were used to test within-subject and between-subject effects for the dependent variables, followed by univariate analyses for post-hoc testing. Furthermore, correlation analysis was performed to assess the association of intervention effects between BP and PAT. Main
results. The study included 51 subjects (31 females). Multivariate analysis of variances showed that effects in BP, heart rate, PAT and pulse wave parameters were consistent and significantly different between experimental and control groups. After physical activity, heart rate and systolic BP increased significantly whereas PAT decreased significantly. Mental activity leads to a decrease in systolic BP at stable heart rate. Pulse wave parameters follow accordingly by an increase of PAT and mainly unchanged pulse wave analysis features due to constant heart rate. Finally, also the control group behaviour was accurately registered by the PAT method compared to oscillometric cuff. Correlation analyses revealed significant negative associations between changes of systolic BP and changes of PAT from baseline to the physical task (−0.33 [−0.63, 0.01], p < 0.048), and from physical to mental task (−0.51 [−0.77, −0.14], p = 0.001), but not for baseline to mental task (−0.12 [−0,43,0,20], p = 0.50) in the experimental group. Significance. PAT and the used digital, handheld device proved to register changes in BP and heart rate reliably compared to oscillometric measurements during intervention. Therefore, it might add benefit to future mobile health solutions to support BP management by tracking relative, not absolute, BP changes during non-pharmacological interventions.
Comment on ‘The influence of plasma evolution on a kinetic scenario of collisional relaxation of a magnetized plasma’
Authors: Dong Chao , Li Ding 
Published: April 2024

Abstract
The collision term of a magnetized plasma is re-derived. It is found that the results of Rostoker (1960 Phys. Fluids
3 922) and Hassan and Watson (1977 Plasma Phys.
19 237) are correct while the result of Erofeev (2023 Plasma Phys. Control. Fusion
65 085014) is not.
Trapped flux in a small crystal of CaKFe4As4 at ambient pressure and in a diamond anvil pressure cell
Authors: Bud’ko Sergey L , Huyan Shuyuan , Xu Mingyu , Canfield Paul C 
Published: April 2024

Abstract
In an extension of our previous work, (Bud’ko et al 2023 Supercond. Sci. Technol.
36 115001) the measurements of temperature dependent magnetization associated with trapped magnetic flux in a small single crystal of CaKFe4As4, using zero—field—cooled and field—cooled protocols were performed, on the same crystal, at ambient pressure without a pressure cell and at 2.2 GPa in a commercial diamond anvil cell (DAC), showing comparable results. The data show that with a proper care and understanding, trapped flux measurements in superconductors indeed can be performed on samples in DACs under pressure, as was done on superhydrides (Minkov et al 2023 Nat. Phys.
19 1293).
Synergistic photocatalytic removal of moxifloxacin from aqueous solutions using ZnO-Fe3O4-chitosan composites
Authors: Quy B M , Nguyen V D , Hoa N T H , Linh N T N , Tung V Q , Thapphanya S , Phuong N N , Ha L T , Nghia N T 
Published: April 2024

Abstract
The escalating contamination of water bodies with antibiotic residues is an urgent environmental and public health issue. This study aimed to fabricate an innovative photocatalytic composite (CMZ) by combining chitosan, magnetic iron oxide (Fe3O4), and zinc oxide (ZnO) for efficiently removing antibiotic moxifloxacin (MFX) water. Comprehensive characterization of the fabricated CMZ was performed using various techniques such as X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), Raman spectroscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), ultraviolet–visible diffuse reflectance spectroscopy (UV–Vis DRS), photoluminescence spectroscopy (PL) and nitrogen adsorption/desorption isotherm analysis. The synergistic incorporation of ZnO, Fe3O4, and chitosan in the CMZ composite altered the structural properties of ZnO and chitosan The band gap energy of CMZ was 2.58 eV, significantly boosting its photocatalytic effectiveness under visible light exposure. The CMZ composites exhibited a high efficiency in catalyzing MFX degradation in aqueous environments. The optimal conditions for MFX degradation were established, including a neutral pH level of 7, a 90 min exposure to irradiation, and employing 0.1 g of the CMZ catalyst. The degradation process obeyed closely to the first-order kinetic model. The CMZ material showed consistently high performance in degrading MFX across four consecutive reuse cycles, emphasizing its practical applicability for mitigating antibiotic pollution.
Abnormal transition of the electron energy distribution with excitation of the second harmonic in low-pressure radio-frequency capacitively coupled plasmas
Authors: YU 余 Leyi 乐怡 , LU 陆 Wenqi 文琪 , ZHANG 张 Lina 丽娜 
Published: April 2024

Abstract
The self-excited second harmonic in radio-frequency capacitively coupled plasma was significantly enhanced by adjusting the external variable capacitor. At a lower pressure of 3 Pa, the excitation of the second harmonic caused an abnormal transition of the electron energy probability function, resulting in abrupt changes in the electron density and temperature. Such changes in the electron energy probability function as well as the electron density and temperature were not observed at the higher pressure of 16 Pa under similar harmonic changes. The phenomena are related to the influence of the second harmonic on stochastic heating, which is determined by both amplitude and the relative phase of the harmonics. The results suggest that the self-excited high-order harmonics must be considered in practical applications of low-pressure radio-frequency capacitively coupled plasmas.
Heat wave time of emergence patterns: a matter of definition
Authors: Padalino Christine , Lickley Megan , Solomon Susan 
Published: April 2024

Abstract
Hot extremes, such as heatwaves, have been associated with health, economic, and ecosystem-wide impacts. The timing of emergence of changes in extremes due to anthropogenic climate change is a topic of broad scientific and societal importance. While various studies have estimated the timing and impacts of heatwaves, the definitional aspect of a heatwave in determining the relative time of emergence has not been addressed. We adopt two commonly used definitions of heatwave employed in different reports of the Intergovernmental Panel on Climate Change (IPCC) to evaluate the time at which the frequency of heatwaves becomes detectably different from the historical baseline using an ensemble of 10 GCMS from the CMIP6 archives forced by the SSP2–4.5 concentration pathway. For a heatwave definition of sustained temperatures exceeding 5 °C warmer above the historical climatology, time of emergence is earliest in high latitudes over land and displays correlation with the signal (amount of warming) and noise (variability). In contrast, for a heatwave definition of sustained temperatures exceeding the 90th percentile of historical climatology, time of emergence is earliest in low latitude regions and is correlated with the signal to noise ratio. This work underscores the importance of metric choice in estimating the timing of new climate regimes and that metric selection for informing adaptation timing should thus be tailored to the regional context.
Strategies to develop stable alkali metal anodes for rechargeable batteries
Authors: Sunny Sanjay , Suriyakumar Shruti , Sajeevan Aswadh S , Shaijumon Manikoth M 
Published: April 2024

Abstract
Alkali metal anodes are among the most promising candidates for next-generation high-capacity batteries like metal–air, metal–sulphur and all-solid-state metal batteries. The underlying interfacial mechanism of dendrite formation is not yet fully understood, preventing the practical implementation of metal batteries, particularly lithium, despite decades of research. Parallelly, there is an equal significance to the other alkali metal candidates viz sodium and potassium. The major challenges of alkali metal batteries, including dendrite formation, huge volume change, and unstable solid–electrolyte interface, are highlighted. Here, we also present an overview of the recent developments toward improving the anode interfaces. Given the enormous practical potential of alkali metal anodes as next-generation battery electrodes, we discuss some advanced probing techniques that enable a more complete understanding of the complex plating/stripping mechanism. Finally, perspectives and suggestions are provided on the remaining challenges and future directions in alkali metal battery research.
Multi-objective optimization of glass/carbon hybrid composites for small wind turbine blades using extreme mixture design response surface methodology
Authors: Mohammed Suhaib , Naik Raghuram L 
Published: April 2024

Abstract
Small wind turbines (SWTs) are a prominent renewable energy technology for decentralized power generation. Blade material and its profile are vital parameters for the aerodynamic performance of SWTs. Traditionally E-glass fiber-reinforced composites (FRCs) are the widely accepted material for developing SWT blades. However, its application is limited by moderate tensile and fatigue properties. Alternatively, other FRC materials such as carbon, basalt and natural fiber composites are proposed as future materials for SWT blades. However, individual materials are observed to satisfy the requirements partially. Therefore, the hybridization of these materials, particularly Glass/Carbon composites is foreseen as a prospective solution for developing cost-competitive and high-strength SWT blades. There are various studies performed to obtain optimized glass/carbon hybrid composites. However, overall material properties required for SWT blades such as low cost, lightweight, moderate flexural strength and higher tensile and fatigue strengths have not been considered simultaneously during the optimization process. This work presents multi-objective optimization of Glass/Carbon hybrid composites using extreme mixture design response surface methodology (RSM) for SWT applications. The weight percentages of glass and carbon fibers are optimized to achieve desired material properties for SWT blades. The experiments are planned using extreme mixture design RSM and the regression models for desired material properties are developed with a 95% confidence level. RSM-based desirability function is employed to perform multi-objective optimization. Maximum composite desirability of 93.5% is achieved with optimal proportions of 37.9% and 27.1% for glass and carbon fibers respectively. An adequate tensile, flexural and fatigue strengths of 486.02, 435.41 and 316.27 MPa respectively are obtained for optimized glass/carbon hybrid composite at an optimum cost of 2228.76 Rs Kg−1 and density of 3.39 g cm−3. The regression models and optimization results are validated through a confirmation experiment with an error of less than 6.1%.
Development of latex/zinc oxide compounds with antibacterial properties for applications in biomedical engineering
Authors: Durango-Giraldo G , Zapata-Hernandez C , Santa J F , Buitrago-Sierra R 
Published: April 2024

Abstract
Natural rubber latex (NRL)—a polymer extracted from the rubber tree (Hevea brasiliensis)—has been used in multiple biomedical applications but does not have antibacterial properties. In this work, ZnO nanoparticles with two different morphologies were synthesized and added to NRL at different concentrations in order to evaluate the antibacterial properties of the resulting compounds. The characterization results obtained by electron microscopy and x-ray diffraction showed nanoparticles with spherical (mean size 69 ± 17 nm) and sheet morphology (mean size 154 ± 46 nm) with Wurtzite crystalline phase for both nanomaterials, respectively. The results of antibacterial tests showed that both compounds are effective against E. coli, and the reduction in bacterial viability was 90.3% and 96.4% for sheets and spherical nanomaterials, respectively. In the case of S. aureus, bacterial viability was reduced in both cases. The greatest antibacterial activity was evidenced in the nanoparticles with spherical morphology.
Analysing the shape memory behaviour of MWCNT-enhanced nanocomposites: a comparative study between experimental and finite element analysis
Authors: Gupta Ritesh , Mittal Gaurav , Kumar Krishna , Pandel Upender 
Published: April 2024

Abstract
Shape memory polymers (SMPs) are known for their unique ability to withstand large deformations and revert to their original shape under specific external stimuli. However, their broader application in biomedical and structural applications is restricted by limited mechanical and thermal properties. Introducing multi-walled carbon nanotubes (MWCNTs) into SMPs has proven to significantly enhance these characteristics without affecting their inherent shape memory features. This study investigates shape memory nanocomposites (SMNCs) through dynamic and thermogravimetric analyses, along with tensile, flexural, and shape memory testing, and explores fracture interfaces using scanning electron microscopy. Findings indicate optimal shape memory, thermal, and mechanical properties with 0.6 wt% MWCNT content, showcasing a shape recovery ratio of 93.11%, storage modulus of 4127.63 MPa, tensile strength of 55 MPa, and flexural strength of 107.94 MPa. Moreover, incorporating MWCNTs into epoxy demonstrated a reduction in recovery times by up to 50% at 0.6 wt% concentration. Despite a slight decrease in shape fixity ratio from 98.77% to 92.11%, shape recoverability remained nearly consistent across all samples. The study also introduces a novel finite element (FE) method in ABAQUS for modeling the thermomechanical behavior of SMNCs, incorporating viscoelasticity, validated by matching experimental results with FE simulations, highlighting its accuracy and practical applicability in engineering.
Optimization of BLDC-based electric vehicles: Vehicle dynamics modelling through dual-motor approach and designing a novel augmented TLBO algorithm for PID control
Authors: Kumar Rajnish , Bera Chinmay , Kumar Amitesh 
Published: April 2024

Abstract
The work primarily focuses on increasing the efficiency of EV drive in electric two-wheeler by working on several aspects, such as modulating the vehicle’s design, optimizing the control strategy, and increasing the speed range using a dual-motor approach. The dynamics of electric two-wheeler have been discussed with a mathematical vehicle model and further tuning of several aspects. Besides, this paper also introduces a novel Augmented Teaching and Learning based Optimization (ATLBO) technique designed exclusively to control BLDC motors for the electric two-wheeler vehicle. Besides, the designed technique has been implemented for the widely used commercial e-bike of Hero Company. Most two-wheeler electric motors are mounted on the rear side of the wheel, which has a limited speed range. Therefore, an analysis has been performed to increase the vehicle’s speed range using a dual motor, from 45 km hr−1 to 62 km hr−1, proving to be a viable alternative to a single motor generally used in an electric bike. ATLBO technique has been designed against a conventional TLBO to optimize the proportional-integral-derivative (PID) controller for the speed control of a linear brushless DC (BLDC) motor. The proposed method has several advantages, including ease of implementation, a consistent convergence characteristic, and high computational efficiency. Furthermore, the literature has validated the merits of the presented novel control technique. The only disadvantage of using a dual motor is the initial cost, but the overall cost is moderated in the long-term usage for its augmented performance parameters. The performance parameters of the above technique are analyzed against other optimization techniques like conventional Teaching and Learning based optimization (TLBO), Particle Swarm Optimization (PSO). MATLAB/Simulink models the brushless DC motor and implements ATLBO, TLBO, and PSO algorithms. It has been found that the response obtained from ATLBO is comparatively much faster than other optimization techniques, which supports the motor for quick acceleration as well as more efficient in improving the step response characteristics such as rise time, settling time, and steady-state error in the speed control of a linear BLDC motor.
Study on circular scanning for cross-scale micro/nanoscratching machining
Authors: Lin Yu , Tan Rui , Chen Tingting , Gao Jian , Qian Linmao , Yu Bingjun 
Published: April 2024

Abstract
Large-scale scanning probe can facilitate fabricating cross-scale micro/nano structures. However, the processing of complex two-dimensional patterns usually encounters challenges including numerous machining feature points and probe jumps, which results in low efficiency, as well as poor machining quality. Therefore, a circular scanning method based on corner point extraction (CSCE) was proposed in this study for programming the probe path and reducing the number of probe jumps. Before the machining, the target structures or images were converted into raw data points through image processing, and then the data was refined by corner point extraction for obtaining the machining points. Subsequently, the machining points were arranged in the order of a circular scanning algorithm to get the probe path. Using CSCE, the probe jump was only 1 time for processing an H-shaped pattern. By comparison with the circular scanning method based on intersection point extraction (CSIE) for the H pattern, the number of machining points in CSCE was reduced from 82 to 12, and the machining time was shortened from 17.15 s to 3.17 s. Consequently, CSCE can enable efficient and high-quality fabrication of cross-scale micro/nanostructures.
A comprehensive research on measurement and evaluation of intelligent meter reading systems
Authors: Zhang Wei , Bai Lu , Zhang Fengyi , Zhao Feifan , Liu Zilong , Xiong Xingchuang 
Published: April 2024

Abstract
Based on the machine vision and artificial intelligence technologies, the reading and acquisition of meter values have become the mainstream technical solutions for real-time monitoring of production data in the current industrial field. At the same time, the number of intelligent instruments and meters is increasing, and the quality varies. There is a lack of scientifically measurement methods and quality trust for intelligent meter reading algorithms. To promote their reliable and widespread application in various fields, it is necessary to conduct in-depth research on the assessment system of artificial intelligence in intelligent meter reading systems. This paper comprehensively studies various aspects involved in the emerging measurement field of artificial intelligence evaluation at the current stage, providing scientific measurement and evaluation references for the quality trust of artificial intelligence in the field of meters.
Reliability assessment of bridge hangers based on fault tree with response surface
Authors: Lan Min-Li , Chu Tian-Yun , Li Jing-Jing , Ding Yang 
Published: April 2024

Abstract
Bridge hangers play a crucial role in transmitting forces and supporting bridge structures. The condition of these hangers directly impacts the overall safety of the bridge. However, they are susceptible to fatigue failure due to cyclic loads like wind and vehicle traffic. Therefore, evaluating the fatigue performance of bridge hangers is vital. Typically made up of multiple parallel steel wires, the failure of even one wire can affect the fatigue performance of the entire hanger. This underscores the importance of considering the individual components’ impact on the system as a whole. This study presents a time-dependent reliability assessment model for bridge hangers based on the S-N curve of steel wire. Additionally, a system-level reliability assessment model is developed using fault tree analysis, taking into account the influence of broken wires and stress redistribution on the hanger system. The model is solved using response surface methodology, and through five numerical simulations, the study examines the effect of key parameters, including the number of broken wires, equivalent structural stress, and total cycles on the system’s reliability.
A comprehensive machine learning-based investigation for the index-value prediction of 2G HTS coated conductor tapes
Authors: Bonab Shahin Alipour , Russo Giacomo , Morandi Antonio , Yazdani-Asrami Mohammad 
Published: April 2024

Abstract
Index-value, or so-called n-value prediction is of paramount importance for understanding the superconductors’ behaviour specially when modeling of superconductors is needed. This parameter is dependent on several physical quantities including temperature, the magnetic field’s density and orientation, and affects the behaviour of high-temperature superconducting devices made out of coated conductors in terms of losses and quench propagation. In this paper, a comprehensive analysis of many machine learning (ML) methods for estimating the n-value has been carried out. The results demonstrated that cascade forward neural network (CFNN) excels in this scope. Despite needing considerably higher training time when compared to the other attempted models, it performs at the highest accuracy, with 0.48 root mean squared error (RMSE) and 99.72% Pearson coefficient for goodness of fit (R-squared). In contrast, the rigid regression method had the worst predictions with 4.92 RMSE and 37.29% R-squared. Also, random forest, boosting methods, and simple feed forward neural network can be considered as a middle accuracy model with faster training time than CFNN. The findings of this study not only advance modeling of superconductors but also pave the way for applications and further research on ML plug-and-play codes for superconducting studies including modeling of superconducting devices.