bluuebunny/crossref_metadata_2025_split huggingface dataset:
Vibration modal analysis of adaptive grinding and polishing tools
Authors: Xie Guosheng , Xu Yang , Sheng Xiaowei , Zhang Yixin , Yuan Qiuwei , Lv Xiao 
Published: October 2024

Abstract
Adaptive grinding and polishing tools have a compact structure, fast response, and stable performance, and they can improve the efficiency of polishing and grinding large welded parts. The vibration frequency of the AGP600 adaptive grinding and polishing tool is abnormal after switching from the blade grinding head to the nylon grinding head, and the vibration is noticeable when the rotational speed is more significant than 4, 000 r/min, which seriously affects the quality of grinding. To analyze the cause of its vibration, this paper adopts HyperWorks to analyze the modal frequency of AGP600, carries out the vibration modal test, and analyses the reasons for the different vibration harmonic frequencies of AGP600. Finally, improvement measures for AGP600 are proposed, through which the vibration of AGP600 grinding tools can be reduced.
Research progress on constructability of 3D printed concrete
Authors: Liu Bo , Wang Liuyan 
Published: October 2024

Abstract
3D printing concrete technology is an innovative building technology for green manufacturing, but in order to achieve better performance requirements, there is still a need for continuous in-depth research on 3D printing parameters, theory and the use of admixtures. In this paper, the effects of three printing parameters, interlayer interval time, nozzle travel speed and height, and nozzle shape on the constructability of 3D printed concrete were discussed, and the effects of bonding properties on the constructability of 3D printed concrete were analyzed. The study found that shortening the interval time between layers, reducing the travel speed and height of the nozzle, and choosing a triangular nozzle can improve the interlayer bond strength of 3D printed concrete, and thus improve the constructability of 3D printed concrete.
Point cloud-based feature extraction and surface reconstruction for parts paring in high-precision assembly
Authors: Li Xuezhen , Chen Zhehan , Lu Xiao , Zhao Ning 
Published: October 2024

Abstract
Assembly plays a crucial role in industrial manufacturing in industrial manufacturing, but the efficiency of conventional manual methods for parts pairing is limited. Previous research has demonstrated the feasibility of deep learning for point cloud feature extraction and 3D reconstruction. An innovative method utilizing deep learning for high-precision feature extraction and surface reconstruction is introduced to optimize parts pairing in this paper. Geometric dimensions and surface topography data are obtained by defining key assembly features and utilizing the Random Sample Consensus method. Deep learning is then used to directly regress the Surface Distance Function from point samples, facilitating comprehensive part surface modeling and supporting assembly simulation in the digital twin. To validate this approach, a case study demonstrates successful matching of 30 shaft parts and 30 hole parts after optimization, with an increase in average uniformity by 0.024. This highlights the proposed method’s superior effectiveness and accuracy in feature extraction and surface reconstruction.
Finite element analysis of hysteretic characteristics of steel member under cyclic loading
Authors: Zhao Jianwen , Fang Jianjun , Li Wang , Jun Peng , Chen Sun 
Published: October 2024

Abstract
In this paper, the square, H-shaped and C-shaped steel members with three cross-section forms are selected. Considering the cross-section form, slenderness ratio and constraints at both ends, the force-displacement curve and single-ring energy consumption of steel members under cyclic loading are simulated and analysed. The buckling performance and hysteresis characteristics of steel members with different properties are compared. The results show that the section form has the greatest influence on the energy dissipation level of the component. The hysteretic performance of H-shaped section member is the best, followed by C-shaped section member and square section member. The energy consumption level of the component is negatively correlated with the slenderness ratio, and the single-cycle energy consumption of the steel component decreases with the increase of the slenderness ratio. The constraint condition has the smallest influence on the energy consumption level of the component, and the energy consumption of the component under the end solid-solid constraint and solid-hinge constraint is similar.
Nonintrusive load disaggregation by fusion of graph signal processing and CNN
Authors: Chen Zhigang , Peng Haotian , Su Yongxin 
Published: October 2024

Abstract
Nonintrusive load disaggregation techniques play a pivotal role in power system planning and decision making for demand response. The sparsity of low frequency total power data features, coupled with the model’s challenge in effectively extracting and utilizing spatial and temporal correlations in diverse load data, presents obstacles to achieving high precision disaggregation in nonintrusive load disaggregation. To address these challenges, this paper proposes a disaggregation framework that integrates graph signal processing (GSP) with a convolutional neural network (CNN). In this framework, the GSP module tackles the issue of sparse load features, while the CNN disaggregation module extracts and exploits various features, such as spatial and temporal correlations between different loads. The GSP module directly provides load features to the CNN disaggregation module, assigning weights to the outputs for selection and thereby enhancing the accuracy of the disaggregation process. Our method is trained and validated using real datasets, demonstrating satisfactory performance when compared to existing methods.
Investigations on collision entropy and its variant for machine condition monitoring
Authors: Wei Lan , Wang Dong , Wang Yu , Yan Ming 
Published: October 2024

Abstract
The efficacy of sparsity measures and complexity measures in machine condition monitoring is well acknowledged. However, a comprehensive exploration of their theoretical underpinnings and interrelations remains incomplete. This study endeavors to address this gap by employing collision entropy as an illustrative case and establishing its relationship with sparsity measures. Through mathematical derivations, it is demonstrated that collision entropy exhibits a phenomenon termed as “bilateral reduction,” a characteristic indicative of complexity measures. Motivated by foundational concepts such as Shannon entropy and relative entropy, this paper introduces the concept of variation in collision entropy, which serves as a sparsity measure. Notably, both Shannon entropy and collision entropy, as specific cases of Rényi entropy, manifest the “bilateral reduction” effect and can be transformed into sparsity measures through certain methodologies. These insights contribute significantly to the exploration of the theoretical properties of Rényi entropy and its conversion into a cluster of sparse measures. This research augments the understanding of sparsity measures and complexity measures, offering theoretical insights crucial for their practical deployment in real-world applications.
Multi-objective optimization of helical gear transmission geometric parameters using MOPSO
Authors: Tang Huadong , Lv Gang , Luo Zongyuan , Zhang Ruiliang , Wang Wei , Wang Xiang , Liu Zhijian , Wang Gang 
Published: October 2024

Abstract
Helical gears are widely used in mechanical devices, but insufficient tooth strength can easily lead to premature failure, which is an urgent problem to be solved. To overcome this challenge, this paper introduces a multi-objective particle swarm optimization algorithm, which comprehensively considers the comprehensive effects of modification coefficient, number of teeth, and modulus on the performance of helical gears. By constructing a mathematical model with the geometric parameters of helical gears as the core design variables, aiming to minimize the difference between the maximum bending stress of helical gear roots and the contact stress on the tooth surface, we have achieved optimization of gear performance. Using MATLAB for algorithm programming, the established model was efficiently optimized and solved. After optimization, the bending strength difference and tooth surface bearing capacity of the gear pair have been significantly improved, ensuring the stability and reliability of the operation of the high-pressure sleeve disassembly transmission device.
A research and application of a digital failure knowledge model for nuclear power plant
Authors: Yu Na , Yang Xiaohu , Qing Chen , Wang Qiang 
Published: October 2024

Abstract
The accumulation and application of knowledge about equipment component failures in nuclear power plant unit systems is an important way for domestic and foreign nuclear power plants to improve unit availability and economic benefits. Based on the existing equipment component knowledge models in China and the experience of digital transformation, this paper proposes a digital fault knowledge model construction process and method for nuclear power plant components, which is of great significance for standardizing and guiding domestic nuclear power plant knowledge models.
Characterization and novel application of power over fiber for electronics in a harsh environment
Authors: Arroyave M.A. , Behera B. , Cavanna F. , Feld A. , Guo F. , Heindel A. , Jung C.K. , Koch K. , Leon Silverio D. , Martinez Caicedo D.A. , McGrew C. , Paudel A. , Pellico W. , Rivera R. , Rodríguez Rondon J. , Sacerdoti S. , Shanahan P. , Shi W. , Torres Muñoz D. , Totani D. , Uy C. , Vermeulen C. , Vieira de Souza H. 
Published: October 2024

Abstract
Power-over-Fiber (PoF) technology has been used extensively
in settings where high voltages require isolation from ground. In a
novel application of PoF, power is provided to photon detector
modules located on a surface at ∼ 300 kV with respect to
ground in the planned DUNE experiment. In cryogenic environments,
PoF offers a reliable means of power transmission, leveraging
optical fibers to transfer optical power. PoF technology excels in
maintaining low noise levels when delivering power to sensitive
electronic systems operating in extreme temperatures and high
voltage environments. This paper presents the R&D effort of PoF in
extreme conditions and underscores its capacity to revolutionize
power delivery and management in critical applications, offering a
dependable solution with low noise, optimal efficiency
(∼ 51%), and superior isolation.
Anisotropic odd elasticity with Hamiltonian curl forces
Authors: Zhang (张一恒) Yi-Heng , Yao (姚振威) Zhenwei 
Published: October 2024

Abstract
A host of elastic systems consisting of active components exhibit path-dependent elastic behaviors not found in classical elasticity, which is known as odd elasticity. Odd elasticity is characterized by antisymmetric (odd) elastic modulus tensor. Here, from the perspective of geometry, we construct the Hamiltonian formalism to show the origin of the antisymmetry of the elastic modulus that is intrinsically anisotropic. Furthermore, both non-conservative stress and the associated nonlinear constitutive relation naturally arise. This work also opens the promising possibility of exploring the physics of odd elasticity in dynamical regime by Hamiltonian formalism.
Clean energy challenges and innovation opportunities in Kazakhstan
Authors: Xenarios Stefanos , Sembayeva Aliya , Tsani Stella , Orazagaliyev Serik , Ansaganova Zhanat 
Published: October 2024

Abstract
Kazakhstan has pledged to transition to a low-carbon economy by implementing national policies and strategies that promote clean energy innovation. However, Kazakhstan is still falling short of its expected targets for energy transition, and there is a lack of knowledge regarding the country’s challenges and opportunities for clean energy development. Towards this end, the current study identifies and assesses the enablers and barriers related to clean energy innovation in Kazakhstan. Using the combination of SWOT analysis, survey data from 41 experts and the DEMATEL decision support tool, we evaluated the key factors affecting Kazakhstan’s clean energy innovation and their implications for energy transition. Assessment results show that the immature business environment, underpinned by technological, institutional, and socioeconomic factors, is perceived as a high-impact constraint for clean energy innovation and green finance deployment in Kazakhstan. Skilled labour shortages, high reliance on hydrocarbons and low retail energy prices are significant challenges to Kazakhstan’s clean energy innovation. The low-profit margin and high investment risk in clean energy projects are identified as transition barriers in the power and energy-intensive industries. In contrast, Kazakhstan’s endowments of resources critical for developing clean energy technologies (rare earth metals, uranium, gas) and the potential of low-carbon investments (e.g. carbon storage) are perceived as prominent enablers of clean energy innovation. Results are consistent across expert subgroups (academia, industry, NGOs, etc). Findings call for policy support to modern and attractive business environments, capacity, and human capital development. The findings can provide helpful insights for countries in Central Asia and beyond with similar socioeconomic structures that aim for a timely energy transition.
A classification and recognition model for multiple fruit tree leaf diseases
Authors: Du Bingbing , Li Wei , Qin Xue 
Published: October 2024

Abstract
Fruit tree leaf diseases affect both fruit survival rate and orchard revenue. Rapid and accurate classification of tree leaf diseases not only prevents the spread of diseases but also ensures healthy tree growth and improves fruit quality. Existing models mainly focus on individual types of tree leaf diseases, with limited applications in identifying multiple tree leaf diseases. To address the insensitivity of current classification models to disease region features and the issue of increased error rates due to the presence of similar diseases, this study proposes a residual network model combining channel attention mechanism (ECA) and meta-Acon adaptive activation function. The model employs ResNet34 as the backbone network and incorporates the ECA channel attention module after each residual block to focus on relevant feature information. Additionally, a new activation function called meta-Acon is introduced to enhance the model’s generalization ability through its dynamic learning capability. Finally, the model’s recognition performance is improved by fusing bottom-level features with features from other layers using the Feature Pyramid Network (FPN). Experimental results on a dataset augmented with Mosaic processing show that the FPEM-ResNet34 model achieves a classification accuracy of 98.46%. Compared to other common models such as VGG-16, Inception-V1, ResNet50, and Yolo-V8 m, the proposed method in this paper demonstrates more effective improvement in the accuracy of fruit tree leaf classification, making it highly valuable for practical applications.
Nonlinear associations and threshold effects between agricultural industrial development and carbon emissions: insights from China
Authors: Yi Chuanjian , Xu Bo , Lin Fumin , Lin Kejun 
Published: October 2024

Abstract
With the inevitability of global climate change, it has become increasingly important to understand the relationship between Agro-industrial Development (AID) and Agricultural Carbon Emissions (ACE) to promote the development of low carbon production in agriculture. Using a panel of datasets, as based on the ‘element-structure-function’ framework of 30 Chinese provinces over the period from 2011–2021, the entropy weight method was used to calculate the level of AID in each province. With this approach, it was possible to assess correlations and mechanisms between AID and ACE. Here, with use of fixed-effect, regulatory and threshold models, we determined some of the critical factors contributing to the effects of AID and ACE. Our findings revealed: (1) AID displays an inverse U-shape relationship with ACE, as verified through endogeneity and robustness assessment, (2) A review of the mechanisms suggests that the crossing of the turning point of the inverted u-curve can be accelerated by the moderating effect of agricultural finance. and (3) As based on the threshold effect analysis, a two-tier effect of the digital economy, rural human capital and farmers’ net income on AID, with facilitating effects on carbon emission reductions was obtained after the threshold crossing. The significance of this effect increases as a function of the post-threshold interval. Taken together, these findings demonstrate the long-standing interplay between AID and ACE. Thus, additional insights and empirical evidence that can inform the ongoing development of sustainable agricultural practices can be realized.
Comparison of energy prediction models for residential buildings: a case study in Himachal Pradesh, India
Authors: Nainwal Rajat , Sharma Aniket 
Published: October 2024

Abstract
Since the beginning of the 21st century, the demand for the consumption of energy has been increasing globally with the tremendous increase in population. Different studies have proved that inadequate energy management and planning may lead to energy crisis which is a result of inadequacies in energy prediction. Accurate prediction of energy demand is important as underestimation may lead to shortage in supply and overestimation may lead to overinvestment in energy generation. Various available literature has been reviewed for determining the various factors responsible for affecting the energy consumption of residential buildings. Based on the factors determined, a survey questionnaire has been formulated and survey was conducted in the 400 residential buildings in one of the northern states of India, i.e. Himachal Pradesh. It was observed by reviewing various studies that the different models developed for energy consumption by the different researchers were based on either of the three approaches, namely, engineering-based, AI-based, and hybrid approaches. Three tools namely, case-based reasoning, artificial neural network, and multilinear regression, based on these approaches were individually used for developing the model in this study, and their prediction results were compared. It was observed that the accuracy in the overall predicted results was highest in the proposed ANN model, followed by CBR model, and MLR model, with an overall accuracy of 99.93%, 96.3%, and 91.7%, respectively. The error obtained in the predicted results using ANN, CBR and MLR ranges from −4.0% to +3.0%, −15.0% to +26.0%, −30.0% to 20.0%, respectively. The overall RMSE of ANN, CBR, and MLR model was 1.44%, 11.7%, and 19.5%, respectively. It is concluded that ANN model is best suitable for predicting the short and long-term energy consumption with very high accuracy, as compared to the CBR and MLR. The results discussed in this study can be advantageously used for enhancing the consumption of operational energy in the existing as well as proposed buildings.
Glass catfish inspired subaquatic abrasion-resistant anti-fouling window fabricated by femtosecond laser electrodeposition
Authors: Zhang Jialiang , Ren Fangzheng , Yang Qing , Ma Qingyun , Liang Jie , Meng Yizhao , Gou Xiaodan , Xia Chongxiao , Chen Feng 
Published: October 2024

Abstract
Transparent materials utilized as underwater optical windows are highly vulnerable to various forms of pollution or abrasion due to their intrinsic hydrophilic properties. This susceptibility is particularly pronounced in underwater environments where pollutants can impede the operation of these optical devices, significantly degrading or even compromising their optical properties. The glass catfish, known for its remarkable transparency in water, maintains surface cleanliness and clarity despite exposure to contaminants, impurities abrasion, and hydraulic pressure. Inspired by the glass catfish’s natural attributes, this study introduces a new solution named subaquatic abrasion-resistant and anti-fouling window (SAAW). Utilizing femtosecond laser ablation and electrodeposition, the SAAW is engineered by embedding fine metal bone structures into a transparent substrate and anti-fouling sliding layer, akin to the sturdy bones among catfish’s body. This approach significantly bolsters the window’s abrasion resistance and anti-fouling performance while maintaining high light transmittance. The sliding layer on the SAAW’s surface remarkably reduces the friction of various liquids, which is the reason that SAAW owns the great anti-fouling property. The SAAW demonstrates outstanding optical clarity even after enduring hundreds of sandpaper abrasions, attributing to the fine metal bone structures bearing all external forces and protecting the sliding layer of SAAW. Furthermore, it exhibits exceptional resistance to biological adhesion and underwater pressure. In a green algae environment, the window remains clean with minimal change in transmittance over one month. Moreover, it retains its wettability and anti-fouling properties when subjected to a depth of 30 m of underwater pressure for 30 d. Hence, the SAAW prepared by femtosecond laser ablation and electrodeposition presents a promising strategy for developing stable optical windows in liquid environments.
Teaching Music to Students with Differences and Disabilities
Authors: Hammel Alice M , Yee Roberta L 
Published: October 2024

Abstract
Accompanying the third edition of Teaching Music to Students with Differences and Disabilities, this Practical Resource brings together theory, policy, and planning for instruction in K–12 music classrooms. Equipping music educators with an understanding necessary to implement teaching ideas into the domains of cognition, communication, behavioral, emotional, and physical needs, this book provides classroom-tested lesson plans that include procedure outlines and assessments, as well as guides for adaptation, accommodations, and modification needed for successful implementation in K–12 classrooms. As such, this eminently useful guide provides teachers with practical ideas to allow them to begin to create and adapt their own lesson plans for use with students of differing needs and abilities.
The Cognitive Domain
Authors: Hammel Alice M , Yee Roberta L 
Published: October 2024

Abstract
Cognition is essential for all learning. The way in which the brain processes information is unique to each person. Each person has their own set of pathways in the brain that find, make sense of, and store information. Some people process at a very fast rate. Those persons are often given a label of intellectual giftedness. Some people process at a very slow rate. There are several labels that can be applied to students who process slowly. In the end, the cognition one has is what one works with all one’s life.
The Behavioral Domain
Authors: Hammel Alice M , Yee Roberta L 
Published: October 2024

Abstract
If you were casually thumbing through this resource and found yourself choosing to read this chapter first, you are not alone. The behaviors of students can be frustrating and perplexing and can quickly detail a lesson plan. Behavior is also frequently cited as a reason some teachers choose to leave the profession. While many can understand the needs of a student who struggles with communication or cognition, behavioral differences are sometimes misunderstood and can lead to teachers labeling a child as a behavior issue, as a troublemaker, or as a “bad student.” In the experience of the authors, there are no bad students. There are, however, students who have been told they were “bad” many times and who believe that is who they are. We are charged with reaching every student we possibly can. Sometimes, we consider the needs of one student at a time so that the process seems less daunting. None of us can ever be satiated on specific, positive praise.