bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative analysis of two types of mechanical grippers for gripping flexible packaging materials
Authors: Zhang Zhiyuan , Zhu Chufeng , Ren Hao , Yuan Kanghui 
Published: July 2024

Abstract
To study the different structure types of flexible packaging material gripping mechanical grippers and their application to the working environment, two types of cylinder-type soft packaging material gripping mechanical grippers are designed in this paper. According to the structural characteristics of the mechanical gripper, mathematical analysis is carried out to determine the coordinate relationship of each joint of the mechanical gripper. The plot function in MATLAB is used to plan and simulate the path curves of the two types of mechanical grippers and to determine the working environment of the two types of mechanical grippers applied respectively; the movement of the mechanical gripper is analyzed by using the fifth-degree polynomial, and MATLAB Robotics toolbox is used for simulation, which results in the displacement, velocity, and speed of the end of the two mechanical grippers. The fifth-degree polynomial is used to analyze the motion of the mechanical gripper, and the MATLAB Robotics toolbox is adopted for simulation, as well as the displacement, velocity, and acceleration curves of the end of the two mechanical grippers. The aim is to understand the motion characteristics of the two kinds of mechanical grippers, master the two kinds of mechanical grippers’ working state, and provide a reference for subsequent research on flexible packaging material gripping mechanical grippers.
Preparation of printable copper submicron particle paste
Authors: Wu Zijian , Xie Xiaosheng , Pan Daocheng , Shi Xinan 
Published: July 2024

Abstract
Thanks to the rise of flexible printed electronics, printable conductive pastes have received more and more attention, especially copper paste that can be prepared on a large scale at low cost. We first prepared submicron copper particles by reducing copper sulfate solution with glucose and hydrazine as the reducing agents and then modified the surface of copper submicron particles with formate and ethylene glycol for antioxidant treatment. The surface-modified copper particles exhibit better corrosion and oxidation resistance. Next, the copper submicron particles were dispersed in propylene glycol to make copper slurry. The copper conductive pattern with a low resistivity of 6.09×10−4 Ω·cm can be fabricated by printing copper paste with a dispensing machine, followed by a sintering process in an argon atmosphere at 200°C for 10 min. As prepared copper conductive patterns have excellent mechanical flexibility on polyimide (PI) substrate, which will be conducive to the large-scale application of copper paste on flexible substrates.
Slope estimation based on multi-source information fusion
Authors: Zhu Yifei , Chen Luran , Yin Dejun 
Published: July 2024

Abstract
This study presents a novel methodology for slope estimation, addressing limitations in current unidirectional estimation research. A comprehensive vehicle kinematics and dynamics model is developed using Inertial Measurement Unit (IMU) data, enabling a thorough assessment of vehicle slope. The proposal advocates employing the Interactive Multiple Model Unscented Kalman Filter algorithm to enhance algorithmic stability and prevent divergence from singular model errors. This approach ensures precise computation of dynamic slopes. The algorithm’s efficacy is demonstrated through collaborative simulations on the CarSim/Simulink platform. Introducing innovative concepts and methods for slope estimation, this research has significant practical and theoretical implications.
Influence of Pre-strain on the Microstructure and Mechanical Properties of GH4169 Superalloy
Authors: Liu Wentao , Shi Songyi , Xie Mingzhao , Liu Yaojie , Qin Hailong , Lv Yuting 
Published: July 2024

Abstract
In this study, GH4169 superalloy was subjected to pre-straining after aging, with pre-strain deformations ranging from 0.2% to 10%. Subsequently, the tensile performance of the alloy was tested at various deformation levels. The microstructure of several pre-tensile deformed alloys was also examined. The results show that when the pre-strain deformation increases, the strength of the alloy is improved, the elongation is greatly reduced, and the orientation of equiaxed crystals increases. The pre-strain of GH4169 superalloy increases LAGB and dislocation density while decreasing HAGB and annealing twins.
Effect of hot isostatic pressing on microstructure and mechanical properties of TZM alloy formed by powder metallurgy
Authors: Su Pengsheng , Zhou Leyu , Ren Dongchao , Yang Hao , Zhang Linping , Zhai Yuewen 
Published: July 2024

Abstract
TZM alloy billets were fabricated using powder metallurgy (PM) and powder metallurgy + hot isostatic pressing (PM+HIP) techniques, and the influence of these two processes on the microstructure and mechanical properties of TZM alloy billets was compared. The experimental findings indicate that an enhanced hot isostatic pressing process effectively reduces the internal porosity of TZM alloy, leading to increased tensile strength and elongation at room temperature. However, there is no significant improvement in hardness and tensile strength at 600℃.
Study on Preparation Technology of Novel Fluorosilicone Rubber Composite Insulator
Authors: Xu Mingshen , Yu Shumin , Xu Ruixin , Zhang Jingzhong , Wang Shijie , Wang Wentao , Zheng Suhang , Geng Jianghai 
Published: July 2024

Abstract
Currently, commonly used external insulation materials for power products lack operational stability compared to high-temperature vulcanized silicone rubber. The excellent hydrophobicity of PTFE provides a direction for further enhancing high-temperature vulcanized silicone rubber. By utilizing fluorinated occupation directional design technology and improving the dispersion degree of the dispersed phase, we have successfully prepared high-temperature vulcanized fluorine silicone rubber. After testing its flammability and other aspects, the prepared material achieved HC1-level hydrophobicity, surpassing conventional silicone rubber insulators in terms of self-cleaning performance.
Effect of Compound Curing System on Properties of NBR/CM Blends
Authors: Han Xiao , Zhang Zhaoyang , Zhang Ling , Cheng Wei 
Published: July 2024

Abstract
The effects of conventional vulcanization (CV), semi-efficient vulcanization (SEV), efficient vulcanization (EV), and equilibrium cure (EC) on the properties of nitrile rubber (NBR)/ chlorinated polyethylene (CM) blends were studied. The results showed that the SEV system had a faster vulcanization rate, larger torque, and larger vulcanization temperature coefficient, and the EC system had reversion resistance and a longer vulcanizing period. The modulus of the SEV system, the tensile strength of the CV system, and the elongation at the break of the EC system were high. The SEV system had good seawater and oil resistance; SEV > EV > CV >EC appeared in G’ and G”. The tanδ of the SEV system was larger than that of the EC system, but the tanδ of CV and EV system was close.
Research on the key technologies of the whole process of digital intelligent detection system of silicate binding material
Authors: Xu Tongxin , Li Pengju , Liu Lu , Hu Penghui 
Published: July 2024

Abstract
In construction engineering, silicate binding material detection is the key work to ensure the safety of building structures. Although the current detection instruments have made great progress compared with the traditional detection methods, there are still problems such as low efficiency, heavy workload, and error-prone data due to manual operation. Based on the practical problems faced by silicate binding material quality detection, this paper studies the key technologies of the whole process digital intelligent detection system of silicate binding material, including multi-machine cooperative automatic dumping and scraping technology, multi-strategy robot scheduling algorithm and full-link traceability technology, and shows the application effect through the LIMS system function platform. The research effectively improves the working efficiency of silicate binding material detection, realizes real-time monitoring of data and unique coding of the whole process, and ensures the timeliness, authenticity, accuracy, and traceability of detection data.
Prediction of Longitudinal Resonance Elastic Modulus of Wood-plastic Composites Based on GM (1, N) Neural Network
Authors: Qin Nan , Bing Shi 
Published: July 2024

Abstract
Based on the grey neural network combined prediction model, the prediction model of the mechanical properties of WPC composites was established by taking the orthogonal design test results of the main processing factors of WPC composites as data samples. Taking the main processing factors of WPC as input, grey GM (1, N) prediction of these variables was carried out, and then BP neural network prediction was carried out to predict the mechanical process index of WPC. The results show that the established prediction model can predict the mechanical properties and technological indexes of WPC composites well.
Implementation of energy reduced 90Sr/90Y radiation fields, or: Propagation of beta radiation, a case study
Authors: Behrens R. 
Published: July 2024

Abstract
Reference beta-particle radiation fields are well described
in the standard series ISO 6980 issued by the International
Organization for Standardization (ISO). In its 2022/2023 edition,
two new radiation fields are defined consisting of a radioactive
90Sr/90Y source and a 3 mm or 4 mm plastic absorber
located 4 cm in front of the source — with the reference plane of
the radiation field being located 20 cm from the source. In this
work, the detailed method of how to implement and use these new
radiation fields using a Beta Secondary Standard, BSS 2, is
described. Furthermore, the influence of the position and thickness
of the plastic absorber on the radiation field, i.e., on its
spectral and angular distribution as well as on the dose rate, was
investigated. It turned out that a change of position by one
centimeter or a thickness change of a tenth of a millimeter result
in significant changes of the dose rate (∼ 7 % to
∼ 10 %). Finally, the dependence of the angular and spectral
distribution on the position and thickness of the absorber was
investigated in detail.
The forest loss within small reservoirs surpasses that of large reservoirs across the tropics
Authors: Yang Feng , Qin Yingzuo , Xu Rongrong , Deng Lei , Zeng Zhenzhong 
Published: July 2024

Abstract
The vast potential of hydropower remains crucial in addressing the escalating need for clean energy, particularly in Tropical Moist Forests (TMFs) regions. Yet, the widespread construction of reservoirs within TMFs has resulted in the inundation of forested areas, exacerbating the fragmentation of forest landscapes and contributing additional loss of carbon stored in these ecosystems. Despite this, the scale and degree of forest loss within reservoirs due to inundation from reservoir construction remain poorly quantified and mapped across tropical regions. Here, we leverage long-term TMFs data spanning from 1990 to 2021 to investigate forest loss within reservoirs across tropical regions. We reveal that forest losses within reservoirs total 3521 km2, constituting a relatively small fraction of total forest loss. Nonetheless, the spatial distribution of forest loss within reservoirs varies significantly across the tropics, with patchy distributions observed in the American and African TMFs, and striped patterns evident in the Asian TMFs. Contrary to common assumptions, we show that small reservoirs exhibit higher levels of forest loss compared to large reservoirs, particularly pronounced in the African TMFs region. Furthermore, our projections indicate that the exclusive construction of small reservoirs contributes to approximately 27% of Africa’s total forest area lost. We underscore the importance of policymakers carefully evaluating the trade-offs associated with the construction of large versus small reservoirs in TMF regions, to minimize the adverse impacts of hydropower development on forest ecosystems.
Asymptotic matching the self-consistent expansion to approximate the modified Bessel functions of the second kind
Authors: Steinbock Chanania , Katzav Eytan 
Published: July 2024

Abstract
The self-consistent expansion (SCE) is a powerful technique for obtaining perturbative solutions to problems in statistical physics but it suffers from a subtle problem—too much freedom! The SCE can be used to generate an enormous number of approximations but distinguishing the superb approximations from the deficient ones can only be achieved after the fact by comparison to experimental or numerical results. Here, we propose a method of using the SCE to a priori obtain uniform approximations, namely asymptotic matching. If the asymptotic behaviour of a problem can be identified, then the approximations generated by the SCE can be tuned to asymptotically match the desired behaviour and this can be used to obtain uniform approximations over the entire domain of consideration, without needing to resort to empirical comparisons. We demonstrate this method by applying it to the task of obtaining uniform approximations of the modified Bessel functions of the second kind,

K

α

(
x
)

.
Hot deformation behavior and processing workability of a powder forged Fe-2.1Cu-0.5C alloy
Authors: Wang Sui , Chen Zhijun , Yang Xinke , Zhao Tian , Du Quanqing , Fu Anqing 
Published: July 2024

Abstract
Hot deformation behavior of as-sintered Fe-2.1Cu-0.5C alloy was evaluated through the construction of constitutive equations and a hot processing map. The hot deformation behavior was investigated at different temperatures and strain rates and microstructure observations were subsequently conducted. Flow stress for the initiation of dynamic recrystallization mechanism was determined. Tensile and fatigue strength tests were conducted at room temperature. It was observed that the microstructure of the alloy forged at lower temperature exhibited smaller grain size and its grain size increased with higher forging temperatures. Mechanical testing results revealed that an increase in the forging temperature led to a lower tensile strength and a higher fatigue strength. This change can be attributed to the larger grain sizes and the reduction of microcracks. A forging temperature at 1050 °C was recommended to achieve optimal mechanical properties for powder forged Fe-2.1Cu-0.5C products. These results might give some hints for optimizing process conditions and improving mechanical properties of powder metallurgy alloys by the use of constitutive equations and thermal processing maps.
Effects of bubble-induced strain on the magnetic properties of van der Waals ferromagnet CrBr3
Authors: Grebenchuk Sergey , Grzeszczyk Magdalena , Chen Zhaolong , Novoselov Kostya S , Koperski Maciej 
Published: July 2024

Abstract
Two-dimensional materials provide with ability to control their properties with a number of methods. One of such methods is using strain and compression. In this work, we investigated the influence of locally induced strain through bubbles in thin ferromagnetic CrBr3 using low-temperature magnetic force microscopy. As a result, domain pinning and higher coercive and saturation fields were observed in the bubble. In addition, nontrivial spin arrangements are allowed to take place in a non-homogeneously strained area, leading to different responses to the external magnetic field in comparison to a non-strained region. Finally, Raman spectroscopy and magneto-photoluminescence spectroscopy were performed to show alternation of the magnetic properties of the sample under mechanical deformation.
Interpretable and efficient RUL prediction of turbofan engines using EM-enhanced Bi-LSTM with TCN and attention mechanism
Authors: Wang Jianxing , Wang Yue , Li Jian 
Published: July 2024

Abstract
Remaining useful life (RUL) prediction for turbofan engines is important in prognostics and health management (PHM) for the maintenance and operation of critical equipment. With continuous innovations in deep learning techniques, the complexity of models continues to increase, but the interpretability and comprehensibility of the prediction results become particularly important in industrial applications. Therefore, in this study, an improved bidirectional long and short-term memory network (Bi-LSTM) based interpretable hybrid deep learning model for RUL prediction of turbofan engines is proposed, which ingeniously integrates time series convolutional networks (TCNs), expectation maximization (EM), Bi-LSTMs, and attention mechanisms. By capturing time-series features at different levels, the model adapts to the complex dynamics of turbofan engine performance evolution in an efficient and cost-effective manner. Experimental validation on the C-MAPSS dataset demonstrated that the model significantly outperforms other methods in terms of RUL prediction performance, especially in improving prediction accuracy and coping with the degradation of complex system dynamics. The largest contribution of key metrics to the model is validated through consistent results from multiple interpretable tools, providing comprehensive and consistent support for understanding and trusting prediction results in industrial applications. This study further enhances the robustness of the model and the reliability of the interpretable results by delving into the dynamic relationships between the properties of the different life stages, which not only reveal the importance of these characteristics in engine life prediction but also provide more comprehensive information about the engine performance variations by observing the dynamic relationships.
Failure evaluation on tailor made aerospace aluminum alloys via underwater friction stir welding employing predictive machine learning technologies
Authors: S Arun Prakash , K Gokul Kumar 
Published: July 2024

Abstract
Employing tailor-made alloys with uneven thickness achieves light weighting, a critical issue for reducing emissions, leading to lower aircraft pollutants and fuel costs. The research utilizes advanced machine learning techniques such as Gaussian process regression (GPR), artificial neural networks (ANN) linear regression (LR), and support vector machines (SVM) to predict the ultimate tensile strength of underwater friction stir welding of AA6082-T6 and A2219-T83 tailor-made joints. The models have been evaluated with an assortment of kernel functions, including the polynomial kernel (PK), the radial basis function (RBF), and the Pearson VII universal kernel (PUK). To acquire experimental data, we used a Central Composite Design (CCD) technique, incorporating various factors in the process encompassing tool tilt angle (TA), rotating speed (RS), and welding speed (WS). The SVM radial basis function model (SRBP) had a maximum correlation coefficient of 0.9995 and a minimum root mean square error value (RMSE) of 0.5433 in the training set and 0.6271 in the test set. The ANN model predicted the UTS with an error margin of 0.21%, while the SRBP model showed a 0.52% error, and the LR model exhibited a significantly higher error of 7.73%. A peak tensile strength of 252.98 MPa was recorded in the S20 specimen, accounting for 85.61% of the base metal’s (AA6082 T6) strength. A reduced acute tearing ridge indicates petite, shallow dimples due to the inherent cooling. Through the analysis of metrics and residuals, high accuracy rates were observed when employing the ANN and SRBP models to predict mechanical traits.
A novel algorithm MPPT controller based on the herd horse optimization for photovoltaic systems under partial shadow conditions
Authors: Agdam Mohammed , Assalaou Khalid , Aitiaz Elmostafa , Hmamou Dris Ben , El aidi Idrissi Yassine , Lidaighbi Souad , Driss Saadaoui , Elyaqouti Mustapha 
Published: July 2024

Abstract
In recent years, a significant scientific issue has been the creation of maximum power point tracking (MPPT) methods to increase the energy production of PV plants. Moreover, to try to cope with the unparalleled operating conditions of PV plants, many bio-inspired meta-heuristic algorithms have already been suggested in the literature, but their implementation is often complex and difficult. In this sense, we propose a novel algorithm for monitoring the (MPPT), using the newly meta-heuristic approach of herd horse optimization (HHO). A DC/DC boost converter is utilised in the suggested controllers to extract the most power possible from the PV resource. The system is programmed and modelled using the MATLAB/SIMULINK software, which also studies four shadow models and a 3S1P topography of single-junction solar arrays. Considering partial shading conditions (PSC), the success of the power values in the global maximum power point (GMPP) of the proposed method is between 99.64% and 99.07%. Besides the time to capture the GMPP by the proposed algorithm is between 0.396 s and 1.666 s, shorter than that of the CSA and FPA algorithms. Comparison with the CSA and FPA optimizers confirmed the quality of the MPPT-based HHO algorithm for GMPP extraction in different (PSC).
Ultrafast jet classification at the HL-LHC
Authors: Odagiu Patrick , Que Zhiqiang , Duarte Javier , Haller Johannes , Kasieczka Gregor , Lobanov Artur , Loncar Vladimir , Luk Wayne , Ngadiuba Jennifer , Pierini Maurizio , Rincke Philipp , Seksaria Arpita , Summers Sioni , Sznajder Andre , Tapper Alexander , Årrestad Thea K 
Published: July 2024

Abstract
Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale with the input size and choice of algorithm. Moreover, the models proposed here are designed to work on the type of data and under the foreseen conditions at the CERN large hadron collider during its high-luminosity phase. Through quantization-aware training and efficient synthetization for a specific field programmable gate array, we show that

O

(
100
)

ns inference of complex architectures such as Deep Sets and Interaction Networks is feasible at a relatively low computational resource cost.