bluuebunny/crossref_metadata_2025_split huggingface dataset:
Proposal for New Development Environment Based on Digital Materials and a Case Study of Gasoline Particulate Filter
Authors: Yamamoto K 
Published: August 2023

Abstract
Carbon dioxide emissions must be reduced as a countermeasure against global warming. A gasoline direct injection (GDI) engine is known for its high fuel economy, but there are more particulate emissions. To trap these particulates, a gasoline particulate filter (GPF) is needed. In this study, numerical simulations of the flow field in the GPF have been performed using the lattice Boltzmann method (LBM). Three types of filters were tested by our proposed approach, named as a digital material technique, where virtual materials are created by the simulation. Focusing on the pore characteristics of the filters, we used filters with the same pore size but different porosity. For comparison, the real internal structures of the actual GPF were detected using an X-ray CT method. The simulated flow field and the pressure drop across the filter wall were investigated, comparing with those of the actual GPF. As a result, it was found that the larger the porosity, the smaller the pressure drop. By setting the pore size and porosity to the same values as the actual GPF, a similar flow field was obtained, and the value of the pressure drop was close to that of the actual GPF.
Space Relative Position and Attitude Measurement Method Based on Solid State Lidar and High Precision Cooperative Target
Authors: Yuan Gao , Yifeng Yang , Di Wu , Lihui Jiang , Xuwei Zhang 
Published: August 2023

Abstract
With the continuous advancement and upgrading of human space exploration, the complexity and difficulty of space missions are increasing. Space manipulation, rendezvous and docking, and situation awareness of non cooperative targets have become important components of space missions. Relative position and attitude navigation system is an important part of space rendezvous and docking and space manipulation system. At present, the method of matching with cooperative target is mainly used for high-precision relative position and attitude measurement.
This paper introduces a method of matching solid-state lidar with high-precision cooperative target for attitude determination. First, a design method of three-dimensional cooperative target is carried out, and then a mathematical model of target recognition is constructed. Three criteria for recognition and matching are proposed. In the third part of the article, the specific process of this method is introduced, and experimental verification is carried out. The experiment shows that the method can achieve the position measurement accuracy of 0.006m and the attitude measurement accuracy of 0.15 ° , and has important application value in various high-precision space missions.
An Ultrasensitive Photoluminescent Immunosensing of Lipovitellin by Using N-doped Graphene Quantum Dots
Authors: Wang H D , Yang A L , Qi C , Zhang Z Z , Wang J , Ru S G 
Published: August 2023

Abstract
The increasing levels of environmental estrogens (EEs) have caused harmful effects on wildlife and human beings. Constructing a sensitive detection technique for EEs is meaningful. For intrinsic high specificity and sensitivity, immunosensing technique is a good choose for estrogeneous chemicals test in complex environment. As the main cleavage composition of vitellogenin (Vtg), a biomarker of EEs, lipovitellin (Lv) was proved to have same immunogenicity as Vtg, while better thermal stability than Vtg, thus Lv is more suitable than Vtg to be used as a standard matter to establish standard test curve. Upon the fluorescence resonance energy transfer (FRET) between N-doped graphene quantum dots (N-GQDs) coupled with anti-lipovitellin monoclonal antibody (anti-Lv-mAb) and reduced graphene oxide (rGO), a free-labelled photoluminescent immunosensing of Lv was developed and validated to quantify Lv of Paralichthys olivaceus. The assay obtained a large linear measuring range of 1 pg/mL- 2 µg/mL, high sensitivity of 9910.67 CPS/(ng/mL), low limit of detection (LOD) of 9.4 pg/mL and relative standard deviation of 1.19%. For 1 ng/mL Lv dispersed in drink water and sea water, the error of the recovery ratio is 1.65% and 10. 27% respectively. Our results demonstrated that the N-GQDs prepared by ultrasonic-assisted hydrothermal method could be effectively used to construct a nano photoluminescent immunosensor, which possesses the advantages of simplicity, rapidity, high specificity and anti-interference.
A Spatiotemporal Traffic Speed Prediction Method Combining Attention and Multivariate Graph Convolution Fusion
Authors: Chen Hongwei , Han Hui , Liu Donghua 
Published: August 2023

Abstract
Spatiotemporal prediction is widely used in the fields of neuroscience, climate, and transportation, and traffic speed prediction is one of the typical research areas. Since traffic networks are irregular grid structures with complex nonlinear spatiotemporal dependencies between nodes, traditional single-feature prediction methods are difficult to adapt to complex road conditions. In order to improve the prediction accuracy of traffic speed, this paper proposes a spatiotemporal traffic speed prediction method combining attention and multivariate graph convolution fusion (CAMGCF). It constructs a spatiotemporal model, specifically, the network uses multivariate graph convolution to fuse external factor features and traffic speed features and uses an attention mechanism to adaptively fuse feature information from different time series to achieve stability in long-term prediction. Finally, the framework is evaluated on three real-world large-scale road network traffic datasets, and the experimental results show that the prediction accuracy of this paper has more accurate results compared with the benchmark method.
Regional Soil Nutrient Content Prediction Model Based on Big Data
Authors: Cui Yaqi , Yue Qin 
Published: August 2023

Abstract
Soil nutrients play a decision-making role in environmental management, and the prediction of soil nutrients can be used to achieve precise fertilization and regulate production. In response to the problem of low accuracy of soil nutrient content prediction by traditional prediction models, this paper designs a prediction model of soil nutrient content based on big data statistics. Soil nutrient content data are collected using a spectral collector, and the data are smoothed, standardized, and orthogonalized to eliminate the data that affect the prediction accuracy. The processed spectrograms are analyzed to summarize the big data law of soil nutrients and clarify that different bands at the abrupt change of reflection curve correspond to different contents of soil nutrients. After repeating several times, the calibration of model accuracy is completed, and the design of the soil nutrient content prediction model based on big data statistics is realized. Through comparison experiments with the traditional model, it was verified that the designed model can improve the prediction accuracy by about 3 times and is more suitable for the prediction of soil nutrient content.
A Clustering Algorithm for Tunnel Boring Machine Data Based on Ridge Regression and Fuzzy C-Means
Authors: Li Yiyang , Pang Yong , Wang Yitang , Wang Suhang , Song Xueguan 
Published: August 2023

Abstract
A fuzzy clustering data partitioning method based on ridge regression is proposed to address the high correlation between the data attributes of the tunnel boring machine. The method utilizes the fuzzy partition matrix as the weight for ridge regression analysis and then employs the resulting regression equation as the clustering model to iteratively approach the target minimum value. This process enables data clustering based on the relevant characteristics of the data attributes. Experimental results using functional data sets demonstrate that this method achieves higher accuracy in clustering functional correlation data compared to traditional methods. Additionally, the proposed algorithm is evaluated using measured data from a bid section of a city subway. Results indicate that the algorithm effectively addresses the problem of data classification and key performance index prediction, with a misclassification rate and prediction error of 23.8% and 2.11%, respectively. These findings meet the engineering requirements and provide support for the subsequent data analysis of the tunnel boring machine.
A Fine-grained Chinese Short Text Classification Method Based on Capsule Networks
Authors: Xu Yangshuyi , Zhang Lin 
Published: August 2023

Abstract
Chinese short-text classification is an essential primary research direction in natural language processing. The main problem facing the current Chinese short text classification task is how to effectively extract critical semantic feature information and determine the text category within a short text length. Aiming at the problem of feature information extraction of Chinese short texts, this paper introduces a novel sparse attention mechanism. It proposes a Chinese short text classification model RGSC combined with capsule networks. After the model generates word vectors through the RoBERTa pre-training model, the Bi-GRU model initially extracts text feature information. The profound text feature extraction module SC, composed of sparse attention mechanism and capsule networks, is used to further extract critical semantic feature information. The results of ablation experiments on the TouTiao Chinese short text dataset and performance comparison experiments with various models show that the RGSC model can effectively extract the essential text feature information while effectively reducing the irrelevant noise information contained in the text features, and obtain fine-grained text feature information.
The Interval Estimation and Hypothesis Test of the Parameter from Inverse Topp-Leone Distribution under Type-II Censored Sample
Authors: Xi Changxin , Liu Hua 
Published: August 2023

Abstract
The maximum likelihood estimation of the parameters of inverse Topp-Leone distribution under Type-II Censored Sample is studied, the pivot value of the parameter is constructed, the accurate confidence interval of the parameter and the rejection region of the hypothesis test are obtained, and the statistical analysis is carried out by numerical simulation.
Investigation of bionic composite laminates inspired by the natural impact-resistant helicoidal structure in the mandibles of trap-jaw ants
Authors: Zhao Shicai , Zhang Deyuan , Yan Yupu , Yin Xiaoming 
Published: August 2023

Abstract
Many living organisms exhibit exceptional capabilities and have evolved effective strategies to synthesize impact-resistant and damage-tolerant structures. One such example can be observed in the rapid mandible strikes of Odontomachus monticola, a species of trap-jaw ants from the ponerine subfamily. During trap-jaw strikes, the mandibles can achieve peak speeds of 35.42 m s−1, and the maximum acceleration can reach 71 729 g within an average duration of 0.18 ms. The extreme acceleration results in instantaneous mandible strike forces that can exceed 330 times the ant’s body weight, withstanding thousands of impacts. A natural impact-resistant fibrous helicoidal structure is found in the mandibles of trap-jaw ants. This microstructure is characterized by periodic modulus oscillations that increase energy absorption and improve stress redistribution, offering added protection against damage from impact loading. A carbon fiber reinforced helicoidal composite is fabricated based on the microstructure of the trap-jaw ant’s mandibles. The results show that the helicoidal composite with a 12° helical-fiber exhibits higher residual strength, making it more capable of withstanding strong collisions. The catastrophic propagation of damage along the thickness direction is prevented by in-plane spreading and redirection of cracks. This research provides useful references for fabricating bionic impact-resistant composites.
Assessing non-synthetic crosslinkers in biomaterial inks based on polymers of marine origin to increase the shape fidelity in 3D extrusion printing
Authors: Carvalho Duarte Nuno , Dani Sophie , Sotelo Carmen G , Pérez-Martín Ricardo I , Reis Rui L , Silva Tiago H , Gelinsky Michael 
Published: August 2023

Abstract
In the past decade, there has been significant progress in 3D printing research for tissue engineering (TE) using biomaterial inks made from natural and synthetic compounds. These constructs can aid in the regeneration process after tissue loss or injury, but achieving high shape fidelity is a challenge as it affects the construct’s physical and biological performance with cells. In parallel with the growth of 3D bioprinting approaches, some marine-origin polymers have been studied due to their biocompatibility, biodegradability, low immunogenicity, and similarities to human extracellular matrix components, making them an excellent alternative to land mammal-origin polymers with reduced disease transmission risk and ethical concerns. In this research, collagen from shark skin, chitosan from squid pens, and fucoidan from brown algae were effectively blended for the manufacturing of an adequate biomaterial ink to achieve a printable, reproducible material with a high shape fidelity and reticulated using four different approaches (phosphate-buffered saline, cell culture medium, 6% CaCl2, and 5 mM Genipin). Materials characterization was composed by filament collapse, fusion behavior, swelling behavior, and rheological and compressive tests, which demonstrated favorable shape fidelity resulting in a stable structure without deformations, and interesting shear recovery properties around the 80% mark. Additionally, live/dead assays were conducted in order to assess the cell viability of an immortalized human mesenchymal stem cell line, seeded directly on the 3D printed constructs, which showed over 90% viable cells. Overall, the Roswell Park Memorial Institute cell culture medium promoted the adequate crosslinking of this biopolymer blend to serve the TE approach, taking advantage of its capacity to hamper pH decrease coming from the acidic biomaterial ink. While the crosslinking occurs, the pH can be easily monitored by the presence of the indicator phenol red in the cell culture medium, which reduces costs and time.
Factors influencing smallholder farmers’ climate change risk perceptions: A behavioral approach in semi-arid regions of Zimbabwe
Authors: Mugari Ephias , Vambe Arthur T , Tatsvarei Simbarashe , Nethengwe Nthaduleni Samuel , Mutambara Jackqeline 
Published: August 2023

Abstract
The resilience of the agricultural sector is increasingly threatened by climate change while rainfed agriculture has become more susceptible to climate and weather-related stressors. However, farmers’ perceptions of climate change risk which vary by niche, greatly influence their adaptation strategies. This study used a questionnaire survey to solicit factors that inform and shape climate change risk perceptions of smallholder farmers in semi-arid areas in Zimbabwe. A sample of 180 farming households was randomly selected from six wards of Beitbridge District, Matabeleland South Province. Descriptive statistics, chi-square tests, and the ordered probit regression model were used to assess the determinants of smallholder farmers’ perceptions of climate change risk. Results show that perceptions of climate change risk are significantly enhanced by educational level, participation in social groups, household experience in agriculture, access to climate information and extension services, as well as age, gender, and education level of the household head. However, resource endowments reduced the climate change risk perception of households. However, the weak influence of these factors points towards the existence of other issues that influence smallholder farmer climate risk perceptions that warrant additional research to understand how these inform and shape smallholder farmer decision-making. Nonetheless, there is a continuous need to improve smallholder farmers’ access to climate information and build their capacity to address emerging climate risks. This is critical to shaping their climate change risk perceptions and influencing their decisions to adopt resilience-building technologies.
Magnetocaloric properties in (La,R)(Fe,Mn,Si)13H (R = Ce and Pr)—toward a better alloy design that results in a reduction in volume of permanent magnets and the establishment of long-term reliability in cooling systems
Authors: Fujita Asaya , Imaizumi Kaoru 
Published: August 2023

Abstract
The magnetocaloric effect (MCE) in La1−z
R
z
(Fe0.89−x
Mn
x
Si0.11)13H
y

max (R = Ce and Pr) is verified in view of correlation between alloying recipes such as selection of doping elements and fundamental physics that governs MCE. The Ce-doped specimen with z = 0.3 & x = 0.017 exhibits a peaky isothermal entropy change ΔS
M profile with a maximum value of 20 J kg−1 K under a field change of 0.8 T at the Curie temperature of 285 K. In contrast, the enlarged field dependence of the Curie temperature and diminished hysteresis results in the adiabatic temperature change ΔT
ad of 2.7 K under a field change of 0.8 T at the Curie temperature of 289 K for the Pr-doped specimen.
Micromixing optimization of non-newtonian fluids with heterogeneous zeta potential
Authors: Bansal Anshul Kumar , Nhaichaniya Gajendra Kumar , Bhardwaj Mayank , Chitnis Swapnil 
Published: August 2023

Abstract
This paper aims to improve micromixing in T-shaped microchannels using heterogeneous zeta potential, which is essential for analyzing biochemical fluids in various industrial applications. We investigate the impact of different parameters, including zeta potential, applied electric field, and a number of heterogeneous surface patterns, on microchannel mixing performance. Our results demonstrate that increasing the zeta potential on the heterogeneous pair patches amplifies the recirculation zone, improving mixing performance within the microchannel. Therefore, we recommend increasing the value of heterogeneous zeta potential on the patches to enhance the mixing performance further. Additionally, we examine the non-Newtonian behavior of the fluid using the power law model and find that pseudoplastic fluids (n < 1) have lower mixing performance compared to dilatant fluids (n > 1) due to higher velocity. We evaluate the effects of various parameters on mixing performance to design an efficient micromixer that achieves rapid and homogeneous mixing of sample fluids. Finally, we optimize the micromixing efficiency using Taguchi to achieve the optimum value of mixing. The optimized value of micromixing efficiency for the corresponding non-Newtonian fluid is also determined.
Improving the power quality and hydrogen production from renewable energy sources based microgrid
Authors: Balu Vankudothu , Krishnaveni K , Malla Priyanka , Malla Siva Ganesh 
Published: August 2023

Abstract
The world is looking for utilization of renewable energy sources to reduce global warming as well as the consumption of fossil fuels. In this scenario, solar and wind energy are widely used at many places worldwide. However, both solar irradiance and wind speed are depending on nature. Hence, an energy storage device must be required to operate at their best utilization level by converting them to electricity. One of the best energy storage devices for medium power range is the battery. However, batteries require high maintenance and suffering from self-discharge as well as storage capacity will be decreased day by day. Hence, storing hydrogen can be an economically feasible solution instead of using batteries for high power range. Usually an aqua electrolyzer can easily convert water to hydrogen and oxygen through electricity. However, due to slow dynamics of heat transfer from electricity, the generation of hydrogen cannot meet the fast response like electrical devices. Therefore, a novel controlling technique is required to increase the production quality of hydrogen during random changes in both solar irradiance and wind speed. In order to achieve the best utilization, both photovoltaic panels and wind turbines are operated at their maximum power point levels. In this research boost converters are used to operate as maximum power point tracking devices. The whale optimization technique is integrated to respective controllers of all the converters to achieve stable production of hydrogen during rapid changes in irradiance and wind speed. The Whale Optimization Algorithm (WOA) technique is compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to show the benefits of tracking response of the system on improving production of hydrogen from hybrid renewable energy sources based Microgrid. Hardware—in the—Loop (HIL) is developed to analyze the results with the help of OPAL-RT modules. Along with HIL results, a Real Time Digital Simulator (RTDS) based results are also presented to evaluate the performance of the proposed method.
A stroll around the critical Potts model
Authors: Hairer Martin 
Published: August 2023

Over the past decade or so, a broad research programme spearheaded by H. Duminil-Copin and his collaborators has vastly increased our understanding of a number of critical or near-critical statistical mechanics models. Most prominently, these include the

q
q

-state Potts models and, essentially equivalently, the FK cluster models. In this short review, we present a small selection of recent results from this research area.
Stability and weak KAM solutions of contact Hamilton-Jacobi equation
Authors: Xu Yang , Yan Jun , Zhao Kai 
Published: August 2023

We are concerned with the stability of viscosity solutions to contact Hamilton-Jacobi equation

H
(
x
,

∂
x

u
(
x
)
,
u
(
x
)
)
=
0
,

x
∈
M
,

\begin{align*} H(x,\partial _x u(x),u(x))=0, \quad x\in M, \end{align*}

where

H
=
H
(
x
,
p
,
u
)

H=H(x,p,u)

satisfies Tonelli conditions. We study the relationship between Lyapunov stability of viscosity solutions and the structure of the set of weak KAM solutions to the contact Hamilton-Jacobi equation.