bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hybridization effects on the magnetic ground state of ruthenium in double perovskite La2ZnRu1−xTixO6
Authors: Halder Shuvajit , Meneghini Carlo , Ray Sugata 
Published: January 2025

Abstract
An exotic quantum mechanical ground state, i.e. the non-magnetic J
eff = 0 state, has been predicted for higher transition metal

t

2
g

4

systems, due to the influence of strong spin–orbit coupling (SOC) or in other words, due to unquenched orbital moment contribution. However, previous attempts to experimentally realize such a state in 5d
4 systems had mostly been clouded by solid-state effects or the reduced strength of the renormalized SOC that might allow significant triplon condensation. Interestingly, a recent study on vacancy ordered double perovskite compound K2RuCl6 by Takahashi et al (2021 Phys. Rev. Lett.
127 227201) concluded that even within LS coupling regime the Ru4+ 4d
4 ions, within isolated RuCl6 octahedra, strongly accommodate J multiplets having J
eff = 0 as the ground state with weakly interacting J
eff = 1 excitation, due to large unquenced Ru orbital angular momentum in the system. In the present report, we show results from the double perovskite La2ZnRuO6, where Ru4+ ions form isolated RuO6 octahedra but unlike K2RuCl6, they remain chemically connected via corner-sharing with nonmagnetic ZnO6 octahedra. Next, we move on to separate out the RuO6 octahedra further by doping the Ru-site with Ti4+, in order to probe the character of the Ru4+ ions within a different structural background. We find that the system stabilizes in P2

1

/
n

space group with tilted octahedra without distortion as has been confirmed by the x-ray powder diffraction and x-ray absorption spectroscopic studies. Interestingly, the x-ray photoelectron spectroscopic valance band spectra indicated certain inhomogeneity around the half-doping region, while confirming insulating ground state for all. Moreover, unlike the vacancy ordered double perovskite cases, it is observed that here the Ru orbital angular momentum gets substantially quenched and only the Ru spin magnetic moments are realized.
Separation of sun gear crack signals based on homologous response fragment mining and common component reconstruction
Authors: Zhang Zhai , Li Hongkun 
Published: January 2025

Abstract
The operational state of the sun gear in the planetary gearbox significantly impacts the stability and reliability of the entire transmission system. This study introduces a novel method to enhance the precision of diagnosing faults in the sun gear of a planetary gearbox. By detecting the similarity and distribution patterns of specific length segments in vibration signals, we identify homologous response segments based on the variation of fault meshing points and sensor positions. We then utilize robust principal component analysis (Robust PCA) to reduce data dimensionality and extract low-dimensional information from these segments. Adaptive resonance sparse decomposition based on the particle swarm algorithm is employed to decouple and decompose the signals thereby enabling the separation of interference components from fault characteristic signals. This study introduces a novel method to enhance the precision of diagnosing faults in the sun gear of a planetary gearbox. By detecting the similarity and distribution patterns of specific length segments in vibration signals, we identify homologous response segments based on the variation of fault meshing points and sensor positions. We then utilize Robust PCA to reduce data dimensionality and extract low-dimensional information from these segments.
Path planning algorithm for articulated loader based on bidirectional Dubins curve
Authors: Liu Jinyue , Liu Chaojie , Han Ming , Wan Zhao , Li Tiejun , Jia Xiaohui 
Published: January 2025

Abstract
Wheel loaders are often used for material transportation in areas such as mines and construction sites, and their articulated motion characteristics bring great challenges to their path planning and trajectory tracking. Aiming at the problems of long planning path and low tracking accuracy in the path planning of articulated loaders, this paper proposes an improved hybrid A* algorithm based on bidirectional Dubins curve to realize the path planning and trajectory tracking in the process of automatic material transportation of loaders. Firstly, the kinematics model is constructed based on the vehicle size, and the bidirectional Dubins curve combined with hybrid A* algorithm is introduced to improve the forward and backward switching mechanism of the loader, and the path is smoothed by factor graph. Then, the kinematics model is analyzed, and the mapping relationship between navigation point speed and vehicle speed and articulation angle is obtained, and the vehicle state is obtained by sensor feedback. Finally, the vehicle speed and articulation angle are solved by graph optimization. The simulation and field test verify its performance, and the path length is shortened by 17.73% compared with that before optimization. The research results show that the proposed algorithm can accurately control the wheel loader from the initial position to the target position, realize the stable work of the whole vehicle in the work area, and provide technical support for the loader’s autonomous operation.
Fourier energy spectrum centroid: a robust and efficient approach for shear wave speed estimation in ω–k space
Authors: Zhang Xi , Dong Jinping , Lee Wei-Ning 
Published: January 2025

Abstract

Objective. The propagation speed of a shear wave, whether externally or internally induced, in biological tissues is directly linked to the tissue’s stiffness. The group shear wave speed (SWS) can be estimated using a class of time-of-flight (TOF) methods in the time-domain or phase speed-based methods in the frequency domain. However, these methods suffer from biased estimations or time-consuming computations, and they are especially prone to wave distortions in in vivo cases. In this work, we present a parameter-free, robust, and efficient group SWS estimation method coined as Fourier energy spectrum centroid (FESC). Approach. The proposed FESC method is based on the center of mass in

ω
−
k

space. It was evaluated on data from computer simulations with additive Gaussian noise, a commercial elasticity phantom, an ex vivo pig liver, and in vivo biceps brachii muscles of three young healthy male subjects. The FESC method was compared with two 2D frequency-domain methods: Max-fre, which considers phase SWS at the peak of k-space, and Fre-regre, which applies linear regression of phase SWS within a fixed bandwidth. Two additional benchmarks included time-domain methods based on cross-correlation (X-Corr) and radon sum transformation (RD). Main results. Statistical results showed that our FESC method and the RD method had comparable accuracy and robustness, outperforming the other benchmark methods. In the simulation and phantom studies, when the signal-to-noise ratio was higher than 25 dB, our FESC showed higher accuracy than RD. In the in vivo study, our FESC method had better repeatability than RD. Furthermore, the proposed FESC method was 100 times faster than the runner-up method, X-Corr, and 3,000 times faster than the least efficient method, RD. Significance. All results indicated that our proposed Fourier-based method shows promise in reliably and efficiently providing reference values for group SWS in homogeneous bulk media.
Integrable systems on multiplicative quiver varieties from cyclic quivers
Authors: Fairon Maxime 
Published: January 2025

Abstract
We consider a class of complex manifolds constructed as multiplicative quiver varieties associated with a cyclic quiver extended by an arbitrary number of arrows starting at a new vertex. Such varieties admit a Poisson structure, which is obtained by quasi-Hamiltonian reduction. We construct several families of Poisson subalgebras inside the coordinate ring of these spaces, which we use to obtain degenerately integrable systems. We also extend the Poisson center of these algebras to maximal abelian Poisson algebras, hence defining Liouville integrable systems. By considering a suitable set of local coordinates on the multiplicative quiver varieties, we can derive the local Poisson structure explicitly. This allows us to interpret the integrable systems that we have constructed as new generalizations of the spin Ruijsenaars–Schneider system with several types of spin variables.
Systematic development and bioprinting of novel nanostructured multi-material bioinks for bone tissue engineering
Authors: Korkeamäki Jannika T , Rashad Ahmad , Ojansivu Miina , Karvinen Jennika , Koivisto Janne T , Syverud Kristin , Kellomäki Minna , Miettinen Susanna , Mustafa Kamal 
Published: January 2025

Abstract
A functional bioink with potential in bone tissue engineering must be subjected to critical investigation throughout its intended lifespan. The aim of this study was to develop alginate–gelatin-based (Alg–Gel) multicomponent bioinks systematically and to assess the short- and long-term exposure responses of human bone marrow stromal cells (hBMSCs) printed within these bioinks with and without crosslinking. The first generation of bioinks was established by incorporating a range of cellulose nanofibrils (CNFs), to evaluate their effect on viscosity, printability and cell viability. Adding CNFs to Alg–Gel solution increased viscosity and printability without compromising cell viability. In the second generation of bioinks, the influence of nano-hydroxyapatite (nHA) on the performance of the optimized Alg–Gel-CNF formulation was investigated. The addition of nHA increased the viscosity and improved printability, and an adjustment in alginate concentration improved the stability of the structures in long-term culture. The third generation bioink incorporated RGD-functionalized alginate to support cell attachment and osteogenic differentiation. The optimized bioink composition exhibited improved printability, structural integrity in long-term culture and high hBMSC viability. In addition, the final bioink composition, RGD-Alg–Gel-CNF-nHA, showed osteogenic potential: production of the osteogenic marker proteins (Runx2, OCN), enzyme (ALP), and gene expression (Runx2, OCN). A further aim of the study was to evaluate the osteogenic functionality of cells released from the structures after bioprinting. Cells were printed in two bioinks with different viscosities and incubated at 37 °C in growth medium without additional CaCl2. This caused gelatin to dissolve, releasing the cells to attach to tissue culture plates. The results demonstrated differences in hBMSC osteogenic differentiation. Moreover, the osteogenic differentiation of the released cells was different from that of the embedded cells cultured in 3D. Thus, this systematic investigation into bioink development shows improved results through the generations and sheds light on the biological effects of the bioprinting process.
Universal state transition from soliton network to soliton-free incommensurate stacking in reconstructed van der Waals bilayers
Authors: Zhang Baidu , He Linghui , Zhang Sulin , Ni Yong 
Published: January 2025

Abstract
The atomic reconstruction and moiré patterns within moiré superlattices resulting from twist, stretch and vertical pressure have generated significant interest in the exploration of novel physical phenomena. In this study, we reveal a universal structural transition in moiré superlattices through comprehensive numerical simulations and theoretical analyses. This transition is characterized by a shift in the moiré pattern from a strain soliton network state at large moiré periods to a soliton-free state at smaller moiré periods. This transition is marked by the convergence of two branches of total energy solutions at a critical moiré period. We propose an intuitive size rule, highlighting that one third of the moiré periods smaller than the width of strain solitons serve as a universal trigger for this transformation. Additionally, our research shows that applying external vertical pressure can enhance interlayer van der Waals interactions, resulting in a decrease in the critical moiré period. We also demonstrate that during this state transition, the maximum elastic interaction occurs in proximity to the critical moiré period. Such structural transition regulated by the adjustment of parameters such as twist angle, in-plane stretch and pressure, provides opportunities for manipulating the exotic electronic properties of van der Waals materials.
Highly sensitive, stretchable, and transparent multidirectional wearable strain sensors based on patterned vertical graphene array
Authors: Hong Tianzeng , Li Haonan , Xue Jie , Wen Yahui , Zhu Zifu , Gao Xiaobo , Shi Yunli , Liu Dan , Zheng Qingbin 
Published: January 2025

Abstract
Since traditional uniaxial strain sensors constructed from isotropic materials mainly focus on identifying strain amplitude without the information of strain direction, multidimensional strain sensors are critical for capturing complex movements, such as human motions. Here, we propose a highly sensitive, stretchable, and transparent multidirectional strain sensor based on patterned vertical graphene array (PVGA) synthesized by plasma-enhanced chemical vapor deposition followed by selective etching and transferring. The obtained PVGA/Polydimethylsiloxane (PDMS) strain sensor exhibits outstanding anisotropic strain sensing performance, including high gauge factor (GF = 85.8 at 10% strain), excellent linearity (R
2 = 0.99), and outstanding selectivity (|▽| = 4.17). Cross-plied PVGA/PDMS strain sensors are also fabricated to demonstrate the application of detecting joint movements of the human body, as well as monitoring wrist motion for controlling a fruit-slicer game in the virtual environment. The sensor’s remarkable multi-dimensional sensing performance confirms their significant potential for diverse applications, notably in wearable electronics, such as personal health sensing, artificial intelligence, human-computer interaction, and soft robotics.
Financing grassroots initiatives in solid waste management: benchmarking the Tanzanian experience against international practices
Authors: Ilomo Mesia , Charles Goodluck 
Published: January 2025

Abstract
Although financing solid waste management (SWM) is commonly reported as a critical challenge facing waste management initiatives in most developing countries, few studies have attempted to investigate the options for financing SWM initiatives and the extent to which they are accessible by grassroots actors. To address this knowledge gap, this study explored the models for financing SWM initiatives in Tanzania and benchmarked them against international practices. In addition, the study investigated the advantages and disadvantages of the financing options available for grassroots initiatives. The findings from the systematic literature review and analysis of seven waste collection initiatives indicate that, while in advanced countries SWM activities are financed through carbon financing, and loans from the capital market, financial institutions and international agencies, in Tanzania, they are mostly financed through interest-free municipal loans, user charges (service contract), commercial loans, grants and members’ subscriptions. However, the service contract was the most common financing model in Tanzania, mainly because of the limited budget the government allocated for waste management activities. The financing options identified have some advantages and disadvantages, depending on their cost, flexibility, terms and conditions, accessibility and the inclusiveness of beneficiaries. Accordingly, we propose a hybrid model for financing SWM encompassing the public and private sector, especially in a context where government budgets allocated for SMW are limited.
Application of correlation-based recurrent neural network in porosity prediction for petroleum exploration
Authors: Mansoor Mustafa Ibrahim , Tuama Hadeel Mohammed , Humaidi Amjad Jaleel 
Published: January 2025

Abstract
Porosity prediction serves as a key metric to determine the characteristics of reservoirs and to reduce the exploration efforts, which in turn lead to enhancing the production planning and making economic vital decisions. In this sense, one can use low costly and low time-consumptive method by conducting experimental tests on various soil samples. Predicting porosity can be effectively utilized for identifying suitable drilling locations for petroleum exploration, thus reducing the need for extensive laboratory experiments beforehand. The seismic behavior has a direct impact on porosity features. This study works to find a mapping relationship between well-log porosity and seismic attributes. A Pearson Correlation Coefficient (PCC) method has been proposed based on diverse scales to determine the most significant seismic attributes and to reduce data dimensionality. The recurrent prediction neural network models have been proposed for learning and prediction. Three neural network models are introduced, represented by Nonlinear Autoregressive (NARX) network, the Long Short-Term Memory (LSTM) network, and the Gated Recurrent Unit (GRU) network. All these proposed models are trained to associate the nonlinear relationship between the selected attributes and porosity. The seismic attributes are assigned as the inputs to the neural models, while the porosity represents the target of neural structure. Moreover, the correlated and uncorrelated strategies are involved to test the effective attributes based on seven scenarios with the proposed neural structures. The results showed that the correlated NARX network consistently delivers better prediction accuracy and minimum errors as compared to other models for different correlation scales. Consequently, correlated NARX network is the best choice for porosity prediction.
A new approach utilizing DTCWT for feature extraction and ANN for classification to improve fingerprint recognition
Authors: N R Pradeep , D V Manjunatha , A Napolean , Rao Arjun Sunil 
Published: January 2025

Abstract
The authentication of biometric data using fingerprints is increasingly crucial in today’s security landscape. Despite advancements in fingerprint identification algorithms, challenges remain in matching incomplete prints. Fingerprints, known for their uniqueness and long-term durability, are widely used in biometric research, including this study. While existing literature presents successful strategies, there is still room for integrating these methods into more advanced algorithms. As technology and security demands evolve, the need for efficient, cost-effective, and reliable systems has driven researchers to develop robust algorithms. This study demonstrates a direct correlation between test and database images, with a particular focus on forensic fingerprint evidence submitted in the court of law, which must be supported by scientifically validated methods. Dual-Tree Complex Wavelet Transforms (DTCWT) is effective in capturing intricate fingerprint details by decomposing images into multiple levels while preserving directional information, making it highly suitable for fingerprint feature extraction. When combined with Artificial Neural Networks (ANNs), which excel in learning complex patterns and classifying data based on feature vectors, The identification process utilizes a combination of DTCWT for feature extraction and ANNs for classification, constructing a feature vector that forms the basis of a multiclass classifier. Using the National Institute of Standards and Technology, Special Database-4 (NIST SD-4) for evaluation, the proposed approach surpasses notable machine learning algorithms, such as K-Nearest Neighbor, Decision Tree, Random Forest, and Support Vector Machine, based on performance metrics. This approach has proven particularly effective in handling incomplete or low-quality fingerprints, addressing a major challenge in biometric authentication systems, Utilizing DTCWT and ANN in fingerprint recognition is considered a significant advancement, offering a more scalable and accurate solution for security applications, including forensic analysis and biometric identification systems. Impact Statement. The surge in information technology and the demand for heightened security have compelled researchers and scholars to seek efficient, cost-effective, accessible, and reliable methods for individual identification. Conventional fingerprint matching algorithms struggle to preserve feature information, hindering their ability to handle partial input data and perform multiple computational functions simultaneously. The adoption of Man and Machine Intelligent systems emerges as a potential solution, warranting further in-depth research. This investigation centers on deploying ANN and gauging its effectiveness using assessment metrics like F_Score, Specificity, Precision, Accuracy, and Sensitivity. It aims to compare these results with the currently prevailing machine learning algorithms and classifiers.
A comprehensive experimental investigation of different nanofluids effective thermal conductivity
Authors: Theegala Purushotham , Rachamalla Shyam Sunder 
Published: January 2025

Abstract
Nanofluids, renowned for their superior thermal conductivity relative to traditional fluids, have attracted considerable interest for their prospective applications in heat exchangers, cooling systems, and renewable energy technologies. Effective thermal conductivity has been determined for four distinct nanofluids such as silicon dioxide, cerium oxide, magnesium oxide and copper oxide at four different temperatures between 30 and 60 °C with intervals of 10 °C. Furthermore, four different volume percentages of nanoparticles have been chosen in the water-based base fluid, ranging from 0.5 to 2 percentages with increment of 0.5 percentages. There are various techniques to prepare the nanofluids and in the present study the ultrasonication technique has been adopted in the preparation of nanofluids. The aim of this study is to determine how the temperature and the volume percent variations of nanoparticles in the bass fluid influence the effective thermal conductivity. The effective thermal conductivity has been found to be increase with increase in the temperature and volume percentage of nanoparticles in the base fluid. The highest effective thermal conductivity at 60 °C was discovered when 0.2% volume percentage of nanoparticles have been added. The thermal conductivity of nanofluids increases with rising temperature (30 °C–60 °C) and nanoparticle volume fraction (0.5%–2%), as shown for CuO (13.1%–15.1%), MgO (18.2%–20.9%), SiO2 (5.2%–6.6%), and CeO2 (10%–12.9%) nanofluids. Numerous correlations have been adopted to compare with experimental effective thermal conductivity, such as Maxwell, Chandrasekar, and Corcine correlations. The Chandrasekar correlation consistently showed better agreement than the Maxwell and Corcine correlations when comparing the measured effective thermal conductivity to the effective thermal conductivity predicted by correlations at various temperatures and volume percentages. This present study is important as it offers insights into the optimization of nanofluids for improved thermal conductivity, which is advantageous for energy systems, cooling technologies, and industrial operations.