bluuebunny/crossref_metadata_2025 huggingface dataset:
Genomic prediction for targeted populations of environments in oat (Avena sativa)
Authors: Sandro Pablo , Bhatta Madhav , Bower Alisha , Carlson Sarah , Jannink Jean-Luc , Waring David J. , Birkett Clay , Smith Kevin , Wiersma Jochum , Caffe Melanie , Kleinjan Jonathan , McMullen Michael S. , English Lydia , Gutierrez Lucia
Published: April 2024

Context Long-term multi-environment trials (METs) could improve genomic prediction models for plant breeding programs by better representing the target population of environments (TPE). However, METs are generally highly unbalanced because genotypes are routinely dropped from trials after a few years. Furthermore, in the presence of genotype × environment interaction (GEI), selection of the environments to include in a prediction set becomes critical to represent specific TPEs. Aims The goals of this study were to compare strategies for modelling GEI in genomic prediction, using large METs from oat (Avena sativa L.) breeding programs in the Midwest United States, and to develop a variety decision tool for farmers and plant breeders. Methods The performance of genotypes in TPEs was predicted by using different strategies for handling GEI in genomic prediction models including systematic and/or random GEI components. These strategies were also used to build the variety decision tool for farmers. Key results Genomic prediction for unknown genotypes, locations and years within TPEs had moderate to high predictive ability, accuracy and reliability. Modelling GEI was beneficial in small, but not in large, mega-environments. The latest 3 years were highly predictive of performance in an upcoming year for most years but not for years with unusual weather patterns. High predictive ability, accuracy and reliability were obtained when large datasets were used in TPEs. Conclusions Deployment of historical datasets can be accomplished through meaningful delineation and prediction for TPEs. Implications We have shown the performance of a simple modelling strategy for handling prediction for TPEs when deploying large historical datasets.
Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks
Authors: Del Tatto Vittorio , Fortunato Gianfranco , Bueti Domenica , Laio Alessandro
Published: April 2024

We introduce an approach which allows detecting causal relationships between variables for which the time evolution is available. Causality is assessed by a variational scheme based on the Information Imbalance of distance ranks, a statistical test capable of inferring the relative information content of different distance measures. We test whether the predictability of a putative driven system Y can be improved by incorporating information from a potential driver system X, without explicitly modeling the underlying dynamics and without the need to compute probability densities of the dynamic variables. This framework makes causality detection possible even between high-dimensional systems where only few of the variables are known or measured. Benchmark tests on coupled chaotic dynamical systems demonstrate that our approach outperforms other model-free causality detection methods, successfully handling both unidirectional and bidirectional couplings. We also show that the method can be used to robustly detect causality in human electroencephalography data.
Measuring affix rivalry as a gradient relationship
Authors: Salvadori Justine , Varvara Rossella , Huyghe Richard
Published: April 2024

In many languages, affixes can be used to derive
words with the same semantic types and can therefore compete in word
formation. This paper discusses how to quantitatively assess the
competition between derivational affixes based on their semantic
similarity. Two possible measures of affix rivalry drawn from
studies in ecology are examined: the Sørensen index, which considers
the proportion of shared functions between rival affixes; and the
Percentage similarity coefficient, which is based on the realization
frequency of functions. Two complementary measures (Balanced
richness and Balanced abundance) are also proposed to further
analyze the semantic dissimilarity between rival affixes. Using the
semantic competition between six French deverbal suffixes as a case
study, we show how these four measures suit the quantification of
affix rivalry and help capture different aspects of the
phenomenon.
A diachronic perspective on competition in denominal verb
formation in Italian
Authors: Iacobini Claudio , De Rosa Maria Pina
Published: April 2024

Three derivational processes compete in the
formation of Italian denominal verbs: suffixation, conversion and
parasynthesis. The rivalry between these processes goes back to the
early history of the Italian language. Based on the analysis of an
original dataset, this chapter aims to offer a comprehensive
overview of denominal verb formation in Italian from a diachronic
perspective. The results of a macro-level analysis concern the
availability of nouns to form verbs, the distribution of the
competing verbalising processes over different diachronic stages, a
comparative overview of the meanings encoded by each process, and
the competition between synonymous verbs formed by means of
different processes from a same noun. A micro-level analysis
outlines the productivity of the specific derivational means
expressing each process. The data suggest a higher differentiation
of the processes over time, along with their enduring vitality, a
decreased abundance of verbs derived from the same noun, and a
recent growth in the employment of suffixation, which, however, does
not coincide with a significant decline in the resort to either of
the other two processes.
The shock wave evolution of the laser-induced breakdown under non-spherical symmetry using a 1064 nm nanosecond laser
Authors: Liu Qingxi , Tian Ye , Song Wenhua , Jia Ziwen , Zhong Qiang , Yao Zhifeng , Zheng Ronger , Yuan Guang
Published: April 2024

In acoustics-related interdisciplinary areas, the shock wave of laser-induced breakdown has garnered significant attention. However, research on the propagation of shock waves in non-spherical symmetry is insufficient in both theoretical and experimental aspects. This paper aims to thoroughly study the evolution of underwater shock wave directivity by employing the method of shadowgraph. The shock wave front is determined by the dark fringes in the shadowgraph image and the normal propagation speed of the shock wave is calculated using Huygens principle. Subsequently, normal propagation speed is converted to pressure in different directions by employing the equation of state of the medium. It has been found that the spherical plasma produces an isotropic shock wave, whereas filamentary plasma generates a highly anisotropic one. To evaluate the anisotropic property of the shock wave, we introduce pressure directivity, which is defined as the pressure at any direction normalized by the maximum value. The temporal evolution of shock wave pressure directivity is obtained based on the shadowgraph images. In the case of filamentary plasma, there is a sudden transition of the pressure directivity in the axial from 40 ns to 165 ns, after which the pressure directivity is consistent with the hydrophone measurement. Based on the moving breakdown model of the plasma and the superposition principle, we propose a theoretical model to explain the experimental result of the pressure directivity. The outcome of our model exhibits considerable consistency with the experiment.
Flexible piezoelectric pressure sensor based on BaTiO3-CNT/RTV composite film with filter paper microstructures for monitoring hand motions
Authors: Dong Yukun , Xu Ce , Chen Shenghua , Wang Zhihua
Published: April 2024

Flexible piezoelectric sensors are widely used in various applications such as physiological signal monitoring and human-computer interaction. The present study introduces a BaTiO3-CNT/RTV piezoelectric sensor fabricated using a filter paper template. It incorporates micro-scale fiber stacking and a 1% CNT doping in the microstructure, resulting in a notable enhancement of sensor sensitivity, increasing it from 0.07 V N−1 to 0.69 V N−1, representing an almost tenfold improvement. Furthermore, the study investigates the influence of affecting factors like the flexible substrate of the sensing film, thickness, and mass fractions of various materials on the output voltage. The sensor exhibits superior characteristics such as good repeatability under 5000 cyclic loads, high elongation at break, fast response (80 ms) and recovery times (90 ms), and good linearity. It also demonstrates outstanding sensitivity (12 mV/10°) when monitoring different finger bending states, enabling real-time, sensitive, and reliable hand motion tracking. This sensor holds promising prospects for future developments in the fields of intelligent grasping and sign language translation.
In-situ measurement of subway train-induced vibration and noise of steel spring floating slab with MEMS-based sensing units
Authors: Liu Xiao-Zhou , Wang Yuan , Yin Ziren , Gao Tian-Ci , Luo Qin
Published: April 2024

The concrete floating slab, as a widely-used measure to reduce the subway-induced vibration and noise, can deteriorate with the accumulation of trainload. To monitor the dynamic performance of the floating slab during operation, this study develops a lightweight sensing unit based on microelectromechanical systems (MEMS). This sensing unit, which can be easily mounted on the concrete slab, enables synchronous monitoring of vibration and noise at different sampling frequencies with a very low power consumption. With the advantage of easy installation, the sensing units are mounted on various concrete slabs and tunnel walls to collect massive vibration and noise data. With the monitoring data, the dynamic performance and vibration reduction capability of the floating slab can be assessed. A case study is conducted on two floating slab sections: one section has a floating slab with a potential defect on its shear hinge and the other has a normal floating slab. The monitoring results show that: (1) the train condition has a significant effect on the amplitude of the track-side vibration; (2) the train-induced noise data last longer than vibration data during the passage of trains; (3) for the floating slab with shear hinge defect, the vibration levels at edge points are much higher than that in the middle. Through the monitoring test, the performance of the MEMS-based sensing unit and its adaptability in subway circumstances are verified.
A lightweight dynamic dual-damped wavelet-based convolutional neural network for interpretable bearing fault diagnosis
Authors: Zhao Lijuan , Mao Yongfang , Qin Yi
Published: April 2024

Wavelet-based convolutional neural networks (CNNS) have attracted widespread attention because they can improve the interpretability of intelligent fault diagnosis methods. However, the fault feature representation capability of typical wavelet-based convolution kernel frameworks must be strengthened to improve the diagnostic accuracy of complex faults. In the meantime, the large number of network parameters leads to high computational costs. To address these issues, a lightweight wavelet-based dynamic CNN, which comprises a dual-damping wavelet-based dynamic CNN (DWDC) block and a discrete wavelet transformation (DWT) enhancement (DWTE) block, is put forward. In the DWDC block, a wavelet convolution layer is initially designed, where a dual-damping wavelet is used as the kernel function to improve the match of the convolution kernel with fault impulses. Subsequently, a dynamic convolution layer with multiple parallel small-size convolutional kernels is designed to screen the fault features instead of a multilayer network structure, thereby greatly reducing the number of network parameters. Finally, the DWTE block is constructed by combining the DWT and residual dense block, and it can mine more fault information from the previously extracted features. The experiments on the variable speed bearing dataset, locomotive bearing dataset with constant speed and the Case Western Reserve University dataset prove that the proposed approach outperforms five classical CNN models and six advanced wavelet-based CNN models. In addition, it can effectively solve the issue of data imbalance because of its powerful feature extraction capability.
Emergence of rapid solidification microstructure in additive manufacturing of a Magnesium alloy
Authors: Tourret Damien , Tavakoli Rouhollah , Boccardo Adrian D , Boukellal Ahmed K , Li Muzi , Molina-Aldareguia Jon
Published: April 2024

Bioresorbable Mg-based alloys with low density, low elastic modulus, and excellent biocompatibility are outstanding candidates for temporary orthopedic implants. Coincidentally, metal additive manufacturing (AM) is disrupting the biomedical sector by providing fast access to patient-customized implants. Due to the high cooling rates associated with fusion-based AM techniques, they are often described as rapid solidification processes. However, conclusive observations of rapid solidification in metal AM—attested by drastic microstructural changes induced by solute trapping, kinetic undercooling, or morphological transitions of the solid-liquid interface—are scarce. Here we study the formation of banded microstructures during laser powder-bed fusion (LPBF) of a biomedical-grade Magnesium-rare earth alloy, combining advanced characterization and state-of-the-art thermal and phase-field modeling. Our experiments unambiguously identify microstructures as the result of an oscillatory banding instability known from other rapid solidification processes. Our simulations confirm that LPBF-relevant solidification conditions strongly promote the development of banded microstructures in a Mg–Nd alloy. Simulations also allow us to peer into the sub-micrometer nanosecond-scale details of the solid–liquid interface evolution giving rise to the distinctive banded patterns. Since rapidly solidified Mg alloys may exhibit significantly different mechanical and corrosion response compared to their cast counterparts, the ability to predict the emergence of rapid solidification microstructures (and to correlate them with local solidification conditions) may open new pathways for the design of bioresorbable orthopedic implants, not only fitted geometrically to each patient, but also optimized with locally-tuned mechanical and corrosion properties.
Exploring the interplay between the TGF-β pathway and SLN-mediated transfection: implications for gene delivery efficiency in prostate cancer and non-cancer cells
Authors: Moraes-Lacerda Thaís , Garcia-Fossa Fernanda , de Jesus Marcelo Bispo
Published: April 2024

Solid lipid nanoparticles (SLN) are widely recognized for their biocompatibility, scalability, and long-term stability, making them versatile formulations for drug and gene delivery. Cellular interactions, governed by complex endocytic and signaling pathways, are pivotal for successfully applying SLN as a therapeutic agent. This study aims to enhance our understanding of the intricate interplay between SLN and cells by investigating the influence of specific endocytic and cell signaling pathways, with a focus on the impact of the TGF-β pathway on SLN-mediated cell transfection in both cancerous and non-cancerous prostate cells. Here, we systematically explored the intricate mechanisms governing the interactions between solid lipid nanoparticles and cells. By pharmacologically manipulating endocytic and signaling pathways, we analyzed alterations in SLNplex internalization, intracellular traffic, and cell transfection dynamics. Our findings highlight the significant role of macropinocytosis in the internalization and transfection processes of SLNplex in both cancer and non-cancer prostate cells. Moreover, we demonstrated that the TGF-β pathway is an important factor influencing endosomal release, potentially impacting gene expression and modulating cell transfection efficiency. This study provides novel insights into the dynamic mechanisms governing the interaction between cells and SLN, emphasizing the pivotal role of TGF-β signaling in SLN-mediated transfection, affecting internalization, intracellular transport, and release of the genetic cargo. These findings provide valuable insight for the optimization of SLN-based therapeutic strategies in prostate-related applications.
Reconfigurable stochastic neurons based on strain engineered low barrier nanomagnets
Authors: Rahman Rahnuma , Ganguly Samiran , Bandyopadhyay Supriyo
Published: April 2024

Stochastic neurons are efficient hardware accelerators for solving a large variety of combinatorial optimization problems. ‘Binary’ stochastic neurons (BSN) are those whose states fluctuate randomly between two levels +1 and −1, with the probability of being in either level determined by an external bias. ‘Analog’ stochastic neurons (ASNs), in contrast, can assume any state between the two levels randomly (hence ‘analog’) and can perform analog signal processing. They may be leveraged for such tasks as temporal sequence learning, processing and prediction. Both BSNs and ASNs can be used to build efficient and scalable neural networks. Both can be implemented with low (potential energy) barrier nanomagnets (LBMs) whose random magnetization orientations encode the binary or analog state variables. The difference between them is that the potential energy barrier in a BSN LBM, albeit low, is much higher than that in an ASN LBM. As a result, a BSN LBM has a clear double well potential profile, which makes its magnetization orientation assume one of two orientations at any time, resulting in the binary behavior. ASN nanomagnets, on the other hand, hardly have any energy barrier at all and hence lack the double well feature. That makes their magnetizations fluctuate in an analog fashion. Hence, one can reconfigure an ASN to a BSN, and vice-versa, by simply raising and lowering the energy barrier. If the LBM is magnetostrictive, then this can be done with local (electrically generated) strain. Such a reconfiguration capability heralds a powerful field programmable architecture for a p-computer whereby hardware for very different functionalities such as combinatorial optimization and temporal sequence learning can be integrated in the same substrate in the same processing run. This is somewhat reminiscent of heterogeneous integration, except this is integration of functionalities or computational fabrics rather than components. The energy cost of reconfiguration is miniscule. There are also other applications of strain mediated barrier control that do not involve reconfiguring a BSN to an ASN or vice versa, e.g. adaptive annealing in energy minimization computing (Boltzmann or Ising machines), emulating memory hierarchy in a dynamically reconfigurable fashion, and control over belief uncertainty in analog stochastic neurons. Here, we present a study of strain engineered barrier control in unconventional computing.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
Recent advances of metal fluoride compounds cathode materials for lithium ion batteries: a review
Authors: Gao Yanshen , Li Jiaxin , Hua Yumeng , Yang Qingshan , Holze Rudof , Mijowska Ewa , Chu Paul K , Chen Xuecheng
Published: April 2024

As the most successful new energy storage device developed in recent decades, lithium-ion batteries (LIBs) are ubiquitous in the modern society. However, current commercial LIBs comprising mainly intercalated cathode materials are limited by the theoretical energy density which cannot meet the high storing energy demanded by renewable applications. Compared to intercalation-type cathode materials, low-cost conversion-type cathode materials with a high theoretical specific capacity are expected to boost the overall energy of LIBs. Among the different conversion cathode materials, metal fluorides have become a popular research subject for their environmental friendliness, low toxicity, wide voltage range, and high theoretical specific capacity. In this review, we compare the energy storage performance of intercalation and conversion cathode materials based on thermodynamic calculation and summarize the main challenges. The common conversion-type cathode materials are described and their respective reaction mechanisms are discussed. In particular, the structural flaws and corresponding solutions and strategies are described. Finally, we discussed the prospective of metal fluorides and other conversion cathode materials to guide further research in this important field.
Physical Computation
Authors: Anderson Neal G , Piccinini Gualtiero
Published: April 2024

This chapter is a primer on physical computation. It distinguishes between abstract and concrete computation. It introduces the notion of simulation of one physical system by another and the more specific notion of computational simulation of a physical system by a computing system. It introduces the problem of distinguishing between physical processes that count as computations and physical processes that don’t, as well as other desiderata of an adequate account of physical computation. It introduces pancomputationalism, which is the most liberal way of drawing a boundary. It introduces different accounts of concrete computation—mapping accounts, semantic accounts, and mechanistic accounts—and explains that the robust mapping account developed in subsequent chapters can improve upon and be integrated with the three families of accounts. Finally, it introduces the physical Church-Turing thesis, according to which any physically computable function is computable by Turing machines.
Computation and the Mind
Authors: Anderson Neal G , Piccinini Gualtiero
Published: April 2024

This chapter addresses the vexed question of whether mentation is computation. To get there, it expands on the ontological framework used in previous parts of this book. It introduces two types of natural property: physical qualities and causal powers. It introduces levels of composition and realization. It introduces mechanisms and the notions of multiple realizability and medium independence. It points out that physical computation is a medium-independent notion. It introduces living organisms and the teleological functions possessed by organismic traits and artifacts. Finally, it argues that cognition is largely a matter of computation but phenomenal consciousness most likely involves physical qualities, which are aspects of physical reality that are not reducible to computation.
Moral Realism and Its Burdens
Authors: Bengson John , Cuneo Terence , Shafer-Landau Russ
Published: April 2024

The animating idea of moral realism is that morality is objectively authoritative. This chapter develops a version of moral realism—realism without qualification—designed to fully capture this idea. Presenting six core theses to which realism is committed, the chapter eschews extant characterizations that focus on the bare minimum a view must endorse in order to qualify as realist to some extent. The chapter then defends its formulation of realism: it properly classifies paradigm realist and non-realist views, makes sense of disputes between the two positions, and distinguishes them in a way that is neither arbitrary nor stipulative, but philosophically substantive. The chapter concludes by discussing realism’s capacity to handle the core metaethical data identified in Chapter 1. Realism, it turns out, accommodates many of these data. But it does not explain any of them. As a result, realism incurs a series of theoretical burdens that it must discharge. The remainder of the book is dedicated to addressing these burdens.
Moral Conceptual Truths and Real Definitional Facts
Authors: Bengson John , Cuneo Terence , Shafer-Landau Russ
Published: April 2024

The previous chapter developed the view that there are moral fixed points. This chapter’s central idea is that such platitudes possess a special status: alethic moral fixed points are conceptual truths, being true fully in virtue of essence facts regarding their antecedent concepts, while factual moral fixed points are real definitional facts, holding fully in virtue of essence facts regarding their antecedent properties. After defending these claims, the chapter closes by arguing that viewing moral fixed points as conceptual truths and real definitional facts does not preclude, but may in fact shed light on, the possibility of rational doubt or denial regarding moral platitudes.