bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bilateral comparison of 1.018 V and 10 V standards between the NSAI (Ireland) and the BIPM, May to June 2024 (part of the ongoing BIPM key comparison BIPM.EM-K11.a and b)
Authors: Solve S , Chayramy R , Stock M , Power O , Prendergast S 
Published: December 2024

Main text
As part of the ongoing BIPM key comparison BIPM.EM-K11.a and b, a comparison of the 1.018 V and 10 V voltage reference standards of the BIPM and the National Standards Authority of Ireland, National Metrology Laboratory (NSAI-NML), Dublin, Ireland, was carried out from May to June 2024. Two BIPM Zener diode-based travelling standards (Fluke 732B), BIPM_B (ZB) and BIPM_C (ZC), were transported by freight to NSAI-NML and back to BIPM. At the NSAI-NML, the reference standard for DC voltage is a Programmable Josephson Voltage Standard (PJVS). The output electromotive force (EMF) of each travelling standard was measured by direct comparison with the primary standard.
At the BIPM, the output EMF of each travelling standard was calibrated before and after the measurements at the NSAI-NML against the PJVS developed at the BIPM around a PTB programmable SNS Josephson junctions (Superconductor/Normal Metal/Superconductor) array.
Results of all measurements were corrected by the BIPM for the dependence of the output voltages of the Zener standards on internal temperature and ambient atmospheric pressure.
The final result of the comparison is presented as the difference between the values assigned to DC voltage standards by NSAI-NML, at the level of 1.018 V and 10 V, at NSAI-NML, UNSAI-NML, and those assigned by the BIPM, at the BIPM, UBIPM, at the reference date of the 28th of May 2024.
UNSAI-NML - UBIPM = -0.20 V;	uc = 0.08 V, at 1.018 V
UNSAI-NML - UBIPM = -0.02 V;	uc = 0.13 V, at 10 V
where uc is the combined standard uncertainty associated with the measured difference, including the uncertainty of the realization of the volt at the BIPM and at NSAI-NML, based on KJ, and the uncertainty related to the comparison.
To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.
The final report has been peer-reviewed and approved for publication by the CCEM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
Gyroscopic gravitational memory from quasi-circular binary systems
Authors: Faye Guillaume , Seraj Ali 
Published: December 2024

Abstract
Gravitational waves cause freely falling spinning objects to precess, resulting in a net orientation change called gyroscopic memory. In this paper, we will consider isolated gravitational sources in the post-Newtonian (PN) framework and compute the gyroscopic precession and memory at leading PN orders. We compare two competing contributions: the spin memory and the nonlinear helicity flux. At the level of the precession rate, the former is a 2PN oscillatory effect, while the latter is a 4PN adiabatic effect. However, the gyroscopic memory involves a time integration, which enhances subleading adiabatic effects by the fifth power of the velocity of light, leading to a 1.5PN memory effect. We explicitly compute the leading effects for a quasi-circular binary system and obtain the angular dependence of the memory on the celestial sphere.
Fabrication of strain-sensing fibers with silver nanoparticles and reduced graphene oxide via wet spinning
Authors: Liu Xiaoyuan , Xu Qiuyu , Zhang Xuepeng , Jiang Wei , Li Junmei , Pan Runan , Song Ruochen , Liu Lifang 
Published: December 2024

Abstract
To advance the industrialization of flexible strain sensors, an innovative flexible sensing fiber was developed through a sophisticated wet spinning process. Silver trifluoroacetate and graphene oxide (GO) were combined with thermoplastic polyurethane (TPU) to prepare the fibers via wet spinning. Ascorbic acid was used to in situ reduce the silver trifluoroacetate and graphene oxide within the polyurethane, causing the growth of silver nanoparticles to bond with reduced graphene oxide, forming a dual conductive pathway. This resulted in the creation of silver nanoparticles/reduced graphene oxide/polyurethane fibers (AgNPs-rGO-TPU sensing fibers). The tensile and sensing properties of AgNPs-rGO-TPU sensing fibers under different parameters were investigated. The results showed that with 25 wt% TPU as the matrix, 30 wt% silver trifluoroacetate, and 1 wt% graphene oxide, the fibers achieved an optimal balance of mechanical and sensing properties. The tensile strength was 7.69 MPa, the elongation at break was 370.75%, and the toughness modulus was 18.45 MJ m−3. The AgNPs-rGO-TPU sensing fibers effectively detect external stimuli, exhibiting high sensitivity over a wide strain range (gauge factor is 4.25 below 5% strain, 24.79 in the 5%–25% strain range, 23.06 in the 25%–80% strain range, and 21.32 in the 80%–110% strain range), with a conductivity of 163.17 ms·cm−1. They can stably recognize movements and physiological signals from various parts of the human body, showing good application prospects.
A triboelectric sensor with an inverted pendulum design for machine and human movement monitoring in freestanding mode
Authors: Wong Kwong Long , Goh Qi Lun , Tan Chun Hui , Lim Eng Hock , Chee Pei Song 
Published: December 2024

Abstract
The vast availability of ambient mechanical vibrations in the natural environments and our daily activities has spurred the advancement of triboelectric sensors for vibration sensing. However, the operation of the traditional triboelectric vibration sensors (VSs) is usually constrained to contact–separation mode, limiting their functionality to transverse vibrations and making them unsuitable for longitudinal and rotary movements. The inherent wear and tear associated with the contact–separation mode further restricts their practical application. To address these limitations, this study presents a new design for a VS that employs a spring-assisted inverted pendulum structure. The VS exhibits a 10 Hz resonant frequency and produces maximum triboelectric output at the vibration amplitude of 5 mm. As a proof of concept, the VS successfully detects balanced and unbalanced fan blades by analyzing the obtained data via fast Fourier transform analysis. Besides machine monitoring, the proposed VS can be integrated into a wearable device for tracking human wrist movements. Combined with a machine learning algorithm, the sensor has achieved an accuracy level of greater than 95% in recognizing four distinct wrist motions: bending, rotating, waving, and handshaking. These findings have proven that the proposed triboelectric sensor design can be effectively integrated into wearable technologies, smart factories, and virtual/augmented reality systems, significantly broadening the triboelectric sensor applications.
A constitutive artificial neural networks-based mechanical model of the pneumatic artificial muscles
Authors: Wang Shuopeng , Wang Rixin , Ma Binwu , Zhang Ying , Hao Lina 
Published: December 2024

Abstract
Pneumatic artificial muscles (PAMs), recognized as typical smart material actuators, have perennially presented a formidable challenge in the realm of precise mechanical modeling due to the hyperelasticity and nonlinearity. In order to construct the mechanical model of the PAM, we propose a constitutive artificial neural network-based mechanical model. Utilizing the constitutive artificial neural network (CANN), we have constructed a strain energy function for PAMs that satisfies symmetry, objectivity, and polyconvexity. Furthermore, by employing the principle of virtual work and considering the hyper-elastic material, the geometric constraints, and the deformation of the internal air chamber, we have derived the mechanical model of PAMs. To verify the accuracy of the proposed model, the finite element simulation is used to demonstrate the modeling accuracy under different load conditions for PAMs with different geometries and constitutive model conditions. Finally, the accuracy and generalization of the proposed model is validated through experiments on a PAM experimental platform.
Ion-molecule reactions in CO2 clusters induced by low-energy electron attachment
Authors: Fan Mengyuan , Liu Jiayi , Tang Guoqiang , Tian Shan Xi 
Published: December 2024

Abstract
Dissociative electron attachment (DEA) process in molecular clusters is inherently complex, and elucidating the underlying mechanisms presents significant challenges. This study builds upon our previous research by investigating DEA in the larger and more numerous CO2 clusters, and extending the electron attachment energy to 15 eV. This enhanced approach allows for the detection of anionic species such as O

2

−

and (CO2)nO

2

−

. We recorded the production efficiency curves and kinetic energy distributions of the fragment anions, revealing that the O

2

−

anions can be formed via the DEA process of CO2 monomers, consistent with previously reported observations. Additionally, O

2

−

may also be produced through a cascade reaction process involving ion-molecule/atom interactions within the clusters, even when the attachment energy exceeds the DEA threshold of O

2

−

production from CO2 monomers. Furthermore, the stabilization of these detected anions (CO2)nO

2

−

is attributed to energy dissipation via molecular evaporative cooling, as previously proposed. This study offers new insights into the underlying mechanisms of DEA in molecular clusters, thereby enhancing our understanding of electron-induced reactions in atmospheric and astrochemical contexts.
Analysis on sensitivity and imperfect interfaces on the propagation of inter-facial anti-plane waves on the finely coated piezomagnetic reinforced composite structure
Authors: Singh Abhishek Kumar , Singh Pratiksha 
Published: December 2024

Abstract
Surface acoustic waves are pivotal in modern technology due to their exceptional sensitivity and adaptability. The motivation for this study stems from the need to enhance the efficiency of surface wave sensors by advancing mass loading techniques and addressing the imperfections that can alter the magneto-mechanical properties, velocities, and propagation behavior of anti-plane waves in layered composite structures. The present work analyses the propagation behavior of Love-type and B-G-type waves in a piezomagnetic fiber-reinforced composite (PMFRC) structure underlying an Air medium (in Model-1) and under a coated thin filmed mass loading (in Model-2) in the presence of distinct inter-facial imperfections viz. Mechanically compliant magnetically weakly permeable (MCMWP), Mechanically compliant magnetically highly permeable (MCMHP), Low magnetic permeable (LMP), Magnetically grounded (MG), and welded. These inter-facial imperfections have been considered as five different submodels under both Model-1 and 2. At first, the micromechanical model of PMFRC has been developed, and closed-form expression for the material constants has been derived by harnessing the techniques of strength of materials and the rule of mixtures. Employing the suitable boundary conditions associated with distinct five inter-facial imperfections, velocity equations have been derived, validated, and graphically demonstrated for both magnetic cases (open (MO) and short (MS)) under various submodels of both models. Magneto-mechanical coupling, mass loading sensitivity, volume fraction, and imperfect interfaces affecting the phase velocity of Love-type and B-G type waves have been analyzed with a comparative approach under various models and submodels. The outcomes of the study can enhance detection capabilities in SAW devices, broadening their use in telecommunications, aerospace, and other industries.
Forchheimer model and generalized Fourier and Fick heat flux in water based Williamson hybrid nanofluid flow over stretched surface under Lorentz force
Authors: Shahzad Hassan , Sagheer M 
Published: December 2024

Abstract
This research provides a comprehensive understanding of water-based Williams-on MoS2 /H2O nanofluid and $MoS2 - Go/H2O hybrid nanofluid flow, considering various physical parameters. The study investigates a two-dimensional, time-independent boundary layer flow over a stretching sheet, addressing the critical challenge of optimizing heat transfer efficiency and stabilizing nanoparticle dynamics. These challenges are essential for applications in chemical processing, material coating, and advanced electronic cooling systems. Governing equations are modeled and analyzed, incorporating the generalized Fourier and Fick heat flux to account for heat transfer time delay, magnetic field effect, diffusion, Forchheimer flow, and chemical reaction parameters. The converted coupled system of ordinary differential equations is solved numerically using the shooting method. The findings reveal that the generalized Fourier and Fick heat flux, together with the magnetic field effects, significantly enhance the heat transfer, thereby improving the thermal diffusion. The diffusion and Forchheimer flow are observed to have a substantial impact on velocity, temperature, and concentration profiles and their rates of change. Heat transfer is observed to decrease by 4.32% in nanofluid and 3.42% in hybrid nanofluid with a 33.3% rise in the radiative heat parameter. Additionally, an initial transient behavior in nanoparticle concentration is observed near the surface which is reversed a bit away from the surface. Understanding this behavior is crucial for improving the performance and efficiency of thermal systems in diverse engineering applications.
Statistical inferences of reliability and order-restricted nano-droplet rebound by comparative kumaraswamy populations based on balanced joint progressive censoring
Authors: Panahi Hanieh 
Published: December 2024

Abstract
The rebounded nano-droplet (RND) is one of the important issues in creating defective coatings. The RND depends on many factors such as angle of droplet impact and velocity. Reliability model can be considered to determine the probability of surface defect under two impact angles in the RND data. In this paper, we have delved into the application of reliability and order-restricted (ORe) estimation methods for two Kumaraswamy populations in the RND data. The study kicks off by evaluating maximum likelihood and Bayesian estimates of the reliability parameter and order-restricted parameters in presence of balanced joint progressive censored data. Bayes estimations for reliability and ORe parameters under two different loss functions are investigated with the Metropolis-Hastings technique and Lindley’s approach. To evaluate order-restricted Bayesian estimates, ordered Beta-Gamma prior is considered. The asymptotic, Bootstrapping and Bayesian bounds are also computed. A thorough simulation analysis is demonstrated to assess the performance of the supplied approaches across various sample sizes. The usefulness of the techniques is illustrated using RND data to prove their versatility in practical applications.
Machine Learning Driven Design and Optimization of Broadband Metamaterial Absorber for Terahertz Applications
Authors: Chamundeswari G , Dornala Raghunadha , Kumar Sandeep , Jain Arpit , Kumar Parvathanani Rajendra , Pandey Vaibhav , Gupta Mansi , Bansal Shonak , Prakash Krishna 
Published: December 2024

Abstract
This article presents a novel broadband metamaterial absorber (MMA) that leverages machine learning to enhance performance in terahertz applications. The MMA consists of a vanadium dioxide (VO2) resonator placed above a dielectric spacer and a conductive ground layer. By altering the conductivity of VO2, the MMA modulates its absorption characteristics. At high temperatures, the absorber demonstrates over 80% absorption across the frequency range of 1.22 to 3.14 THz, achieving peak absorption levels of 99.99% at 4.84 THz and 99.90% at 5.82 THz. The study further explores how geometric adjustments influence the absorption characteristics. The MMA exhibits polarization independence and wide-angle absorption, further enhancing its adaptability. Machine learning models—k-nearest neighbor (KNN), LightGBM (LGBM), and Histogram-based Gradient Boosting (HGB)—are employed to predict absorption coefficients at intermediate frequency, periodic dimensions, and substrate thickness. These regression models are evaluated using root mean squared error (RMSE), mean absolute error (MAE), and R2 score across test sizes ranging from 0.4 to 0.6. The KNN model demonstrates outstanding performance, with R2 values of 0.9977 for feature ‘a’ and 0.9982 for feature ‘hs’ at a test size of 0.4. The regression analysis suggests that ML-based MMAs can reduce simulation time and resource usage by up to 60%. This work establishes a framework for broadband MMA design using machine learning techniques, offering promising applications in terahertz modulation, sensing, and tunable devices.
Robust assessment of the cortical encoding of word-level expectations using the temporal response function
Authors: Chalehchaleh Amirhossein , Winchester Martin M , Di Liberto Giovanni M 
Published: December 2024

Abstract

Objective. Speech comprehension involves detecting words and interpreting their meaning according to the preceding semantic context. This process is thought to be underpinned by a predictive neural system that uses that context to anticipate upcoming words. However, previous studies relied on evaluation metrics designed for continuous univariate sound features, overlooking the discrete and sparse nature of word-level features. This mismatch has limited effect sizes and hampered progress in understanding lexical prediction mechanisms in ecologically-valid experiments. Approach. We investigate these limitations by analyzing both simulated and actual electroencephalography (EEG) signals recorded during a speech comprehension task. We then introduce two novel assessment metrics tailored to capture the neural encoding of lexical surprise, improving upon traditional evaluation approaches. Main results. The proposed metrics demonstrated effect-sizes over 140% larger than those achieved with the conventional temporal response function (TRF) evaluation. These improvements were consistent across both simulated and real EEG datasets. Significance. Our findings substantially advance methods for evaluating lexical prediction in neural data, enabling more precise measurements and deeper insights into how the brain builds predictive representations during speech comprehension. These contributions open new avenues for research into predictive coding mechanisms in naturalistic language processing.
Available land for cellulosic biofuel production: a supply chain centered comparison
Authors: O’Neill E G , Lark T J , Xie Y , Kim S , Dale B E , Robertson G P , Basso B , Maravelias C T 
Published: December 2024

Abstract
The land that is potentially available to produce dedicated cellulosic bioenergy crops, often referred to as ‘marginal’ land, depends heavily on the underlying assumptions used to classify and identify it. In this study we compare three definitions and types of marginal land to identify the interactions between the bioenergy landscape and the logistics networks needed for the biofuel supply chain. Typical studies of the scale, cost, and greenhouse gas (GHG) mitigation potential of cellulosic biofuel take a land-centered approach which may neglect to account for the trade-offs between establishing bioenergy crops and the supply chain design decisions needed to allow those crops to be converted to liquid fuel. A mathematical programming approach is used to minimize the total annualized cost of a large-scale field-to-product system producing bioethanol in the USA midwest. Results show that a high concentration of marginal land leads to efficient systems and that the bioenergy landscape design becomes increasingly important with a higher emphasis on GHG mitigation. Additionally, targeted landscape design (including fertilization) with a focus on fields with high soil carbon sequestration potential can greatly reduce the system-wide GHG emissions for only a small increase in the unit cost of biofuel.
Global disparity in synergy of solar power and vegetation growth
Authors: Chen Shi , Wang Yuhan , Lu Xi , He Kebin , Hao Jiming 
Published: December 2024

Abstract
The impacts of large-scale solar photovoltaic (PV) power on local vegetation are pivotal in the context of widespread solar deployment for net-zero goals, amid escalating land fragility and use competition. Amid debates over positive or negative effects, a global pattern analysis post the past decade’s PV surge remains absent. This study quantified vegetation changes across 626 largest global PV stations and probed influencing factors using remote sensing data. Results suggest a positive effect of PV stations on local vegetation for more than 90% of stations in barren areas and 80% of stations in grasslands. But the positive effects only exert for less than 55% stations located in deserts and croplands. PV system design, especially lower installation density, along with favorable pre-existing attributes especially low original vegetation coverage, emerge as pivotal factors potentially improving PV positive impacts. These findings offer fundamental insights into cross-sectoral synergies in renewable development and vegetation growth.
Water Quality Analysis of the Manuhing Watershed (DAS) of Gunung Mas Regency, Central Kalimantan
Authors: Binoto P D , Gumiri S , Anwar M 
Published: December 2024

Abstract
The Watershed (DAS) of Gunung Mas Regency, Central Kalimantan, is used as a carrying capacity for the lives of the community in Manuhing and Manuhing Raya Districts as a natural resource from the forestry, plantation, and mining sectors. The rise in population around the watershed contributes to the ongoing depletion of natural resources, causing detrimental effects on the developing watershed ecosystem including degradation, water quality decline, erosion, landslides, and sedimentation. These issues can significantly disrupt the livelihoods of the people living in the area of the Manuhing watershed. This research seeks to evaluate the water quality of the Manuhing watershed by analyzing physical, chemical, and microbiological parameters. A cluster sampling (CS) method with a quantitative descriptive approach was applied in this study. Based on physical, chemical, and microbiological parameters detected during both wet and dry seasons, the research results illustrate the effect of climate changes on the water quality of the Manuhing Watershed (DAS). Deteriorations in water quality within the TSS, BOD5, COD, DO, fecal coliforms, and total coliforms parameters have a detrimental effect on the capacity of the Manuhing watershed ecosystem to live.