bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Thermal Error Modeling Method of Machine Tool Spindle Based on Optimized BP Neural Network
Authors: Zhou Mengjie , Yin Ling 
Published: February 2024

Abstract
Addressing the limitations of the single-temperature measurement point monitoring for detecting the temperature changes in the CNC machine tool spindle, and the shortcomings of the thermal error model based on back propagation neural network (BP) in accuracy, convergence and robustness. This paper studies the thermal error identification model and method of spindle based on multiple temperature sensors. An Adaptive particle swarm algorithm-back-propagation neural network (IAPSO-BP) model for thermal error identification of principal axes is proposed. To enhance modeling accuracy and comprehensively monitor the temperature information of the machine tool spindle, the input of this model is generated by processing the data collected through five temperature sensors. The IAPSO algorithm is employed for the automatic identification of BP parameters, reduce manual intervention, and enhancing the model’s capacity for generalization.
Analysis of the Drive Torque for the Spiral Pipe Section of an Earth Pressure Balanced Shield Tunnel Screw Conveyor
Authors: Chun Li Xing , Fu Yue , Li Ziliang 
Published: February 2024

Abstract
The driving torque of the shield machine is intricately linked to the soil silo muck load and the mechanical properties of both the muck and the spiral structure. Considering the shield screw conveyor’s mechanical structure and operational conditions, based on the momentum moment balance conditions, the separate calculation method was used to derive the spiral driving torque in the helical casing, the friction torque in the bearing and the friction torque of the sealing ring through mechanical analysis, and finally a torque calculation expression for the spiral pipe section was established. The research results provide technical guidance for the design and manufacture of shield screw conveyors and safe tunneling during construction.
Study on Cumulative Damage Law of Surrounding Rock Induced by Blasting during Tunnel Construction
Authors: Liu Erping 
Published: February 2024

AbstractThe tunnel drilling and blasting method inevitably causes damage to the rock mass during the blasting process. This article takes a certain tunnel as the research object, proposes to simulate the process of continuous blasting and repeated blasting in each section of the tunnel, and quantifies the cumulative damage range of surrounding rock under each working condition. The results indicate that: (1) it is advantageous to use equivalent loads to simulate blasting loads when calculating large-scale blasting damage simulations; (2) The damage to surrounding rock caused by single cycle excavation of tunnels is caused by the superposition of multiple stages of delayed blasting, mainly influenced by adjacent auxiliary equipment.
Research on Early Warning Technology for High-Grade Highway Pavement Damage
Authors: Liu Cheng , Wang Chao 
Published: February 2024

Abstract
Road surface damage on high-grade highways has a serious impact on driving safety. Conventional road surface damage detection mainly adopts manual detection technology. The detection efficiency is low, and the detection results are easily disturbed by human factors, which is not conducive to objective and accurate evaluation of road damage. To solve the problems of accurate collection, efficient identification, and correct positioning of high-grade road surface damage data, the research group has studied the early warning technology of road surface damage. The proposed early warning technology scheme enables drivers to take preventive driving action in a timely manner, avoids the occurrence of traffic accidents, and provides drivers with reasonable avoidance schemes to effectively prevent the occurrence of traffic accidents.
Development Research of an ADT-24 Multichannel Real-time Fluorescence PCR Detection System
Authors: Tang Qingtai , Li Xinqi 
Published: February 2024

Abstract
This study describes a fluorescence detection system for purified ADT-24 nucleic acids and a PCR detection-integrated machine. The application background and structure of the fluorescence scanning system are described. We designed a confocal 4-channel fluorescence detection scanning head with monochromatic LEDs as the excitation light source, a multipixel photon counter (MPPC) as the optical path detection terminal, and a narrow-band filter and dichroic mirror as the main optical components. This paper discusses in detail the principle of the working system, including optical path selection and standards, optical component selection, channel spectral characteristic analysis, detector selection, performance evaluation, optical path simulation, and sensitivity verification. The design and use of this optical system are also summarized. Finally, a 4-fold dilution of mix reagent was used to test the detection effect of the fluorescence scanning head, and a scheme to control “temperature drift” and optimize performance was proposed. According to the verification results and subsequent live debugging, the debugged results achieved the expected design objectives.
KNN-SVM Classifiers in Complex Diagnosis
Authors: Cheng Hao 
Published: February 2024

Abstract
In many applications, classification plays an indispensable role due to its powerful detection and diagnosis function. Especially in real data on disease, the detection of important factors and the diagnosis of the result usually bring huge contributions to patients. Simultaneously, complex problems in real data such as imbalanced data and missing data also lead to more challenges and difficulties. The ignorance of missing data will undermine study efficiency, and sometimes introduce substantial bias. Imbalanced data tends to be overwhelmed by the majority classes and ignoring the minority ones. The paper develops new support vector machine classifiers using k-nearest neighbors’ information (KNN-SVM), to impute missing data by calculating k-nearest neighbors’ statistical characteristic values and to interpolate some new samples between k-nearest minority class examples. As comparisons, the paper uses different kernel functions in KNN-SVM classifiers to show the different performances in disease diagnosis accuracy.
Impedance modelling and single-bunch collective instability simulations for the SuperKEKB main rings
Authors: Ishibashi T. , Migliorati M. , Zhou D. , Shibata K. , Abe T. , Tobiyama M. , Suetsugu Y. , Terui S. 
Published: February 2024

Abstract
The SuperKEKB is an electron-positron collider consisting of
two storage rings: the 4 GeV-positron low-energy ring and the
7 GeV-electron high-energy ring. The impedance of the rings has
been modeled using electromagnetic simulation codes and used to
study the single-bunch collective instabilities. The wake potentials
are exported from the impedance model and included in a particle
tracking simulation code, PyHEADTAIL. In this paper, we review the
impedance modeling and the results of the collective instability
simulations.
FCC circumference studies based on RF synchronization
Authors: Zhang L.H. , Damerau H. , Karpov I. , Vanel A.L. 
Published: February 2024

Abstract
As the study of the Future Circular Collider (FCC) project
progresses, the choice of the exact circumference of the accelerator
tunnel becomes essential. It is defined by RF-related constraints,
mainly the RF frequency of the FCC-hh, as well as the revolution
frequency ratios with the hadron injector. It is shown firstly how
favourable circumference options assuming the present LHC RF
frequency have been identified and ranked from the RF point of
view. Secondly, the entire frequency range of interest has been
systematically scanned, assuming the possibility of operating SPS or
LHC as the potential injector of FCC-hh. Considerations for the RF
synchronization between injector and collider have been taken into
account. Several options are studied in detail, amongst them the
particularly attractive circumferences of 90478.6 m and 90837.7 m,
and compared to the present working hypothesis of 90658.2 m. The
results are analysed and rated from the point of view of flexibility
and longitudinal beam dynamics.
CEPC accelerator status overview
Authors: Li Yuhui , Gao Jie 
Published: February 2024

Abstract
In 2012, the Higgs boson was successfully observed at the
LHC. Due to the relatively low mass of the Higgs, it is possible
to build a Higgs factory using a circular e+e-
collider. Consequently, the Circular Electron-Positron Collider
(CEPC) was proposed shortly after the observation of the
Higgs. After several years of pre-studies, the CEPC study group
completed the Conceptual Design Report (CDR) in 2018. Since then, a
series of key technology R&D efforts have been carried out, and the
accelerator design has been continuously optimized. The accelerator
design can meet the scientific objectives by allowing operation at
different energies for Higgs/W/Z, and ttbar, with high
luminosities. Key technologies required for mass production have
been developed, including the high-performance superconducting
accelerating cavities and modules, high-efficiency RF power sources,
magnets, and vacuum systems, etc,. This paper will present the
selected key technology R&D accomplishments and updated accelerator
key parameters.
Generating arbitrary analytically solvable two-level systems
Authors: Liang Hongbin 
Published: February 2024

Abstract
We present a new approach for generating arbitrary analytically solvable two-level systems. This method offers the ability to completely derive all analytically solvable Hamiltonians for any analytical evolutions of two-level systems. To demonstrate the effectiveness of this approach, we reconstruct the Rosen–Zener model and generate several new exact solutions. Using this approach, we present the exact evolution of the semi-classical Rabi model with new analytical properties. The parameters used to generate Hamiltonians have direct physical interpretations within the Bloch sphere, the quantum speed limit, and the geometric phase. As a result, the physical properties of the generated Hamiltonian are highly controllable, which plays a significant role in the fields of quantum control, quantum computing, and quantum information.
Analysis of traffic flow due to lane changes by heavy vehicles
Authors: Sari Zuniar Ayu Permata , Nahry  , Mauramdha Gari 
Published: February 2024

Abstract
The transportation of goods via heavy vehicles (HV) plays a crucial role in urban economics. However, the operational and physical characteristics of these vehicles negatively impact traffic flow, leading to reduced speeds and increased congestion. This study examines the influence of HV lane changes on average traffic speed by implementing diverse lane change intensities and alternative strategies that restrict HV to specific lanes, narrowing their lane-changing space. Five HV classes based on axle count were analyzed using Vissim traffic simulation software on a 600 m JORR toll road segment. Three strategies were assessed across various scenarios, with differing HV composition percentages. Results show that HV lane changes, simulated using Vissim, significantly affect average traffic speed. Comparing high and low-intensity lane changes revealed an approximately 12% speed increase. Additionally, every 5% increment in HV composition led to an average 3% traffic speed reduction. In high-density conditions for every 5% increase in the composition of HV from high-intensity compared to medium and low-intensity lane changes, the average speed of traffic flow increases by 4% and 8% in low-density conditions. This research highlights the importance of enforcing designated lane usage for HV on toll roads to enhance traffic flow performance.
Investigation on surface metallization of epoxy resin and its electromagnetic interference shielding performance
Authors: Liu Shikai , Zhu Yingfang , Tian Zheyu , Chen Xin , Li Xingyu , Tang Minghua , Yan Shaoan 
Published: February 2024

Abstract
In this study, aluminum and aluminum/nickel metal layers with varying thicknesses were deposited on the surface of epoxy resin using electron beam evaporation technology. The research aimed to explore and compare the impact of the thickness and material of the metal layer on electromagnetic interference shielding efficiency. The study revealed that the metal layer on the pre-treated surface of the epoxy resin displays a more complete morphology with uniform coverage and a distinctive stratification in the aluminum/nickel (Al/Ni) metal layer. The application of aluminum and aluminum/nickel metal layers onto the surface of epoxy resin demonstrates effective electromagnetic interference shielding. The investigation reveals that electromagnetic interference shielding efficiency escalates with the thickness of the metal layer within the test frequency range of 4 GHz to 15 GHz. Notably, it attains 70 dB when the aluminum/nickel metal layer achieves a thickness of 1 μm. These findings signify that the aluminum/nickel metal layer displays outstanding electromagnetic interference shielding performance, making it suitable for widespread application in the domain of electromagnetic interference shielding for polymer composites.
Acoustic emission-based assessment of weld effects on the health monitoring of the rail section: an experimental study
Authors: Pal Apurba , Kundu Tamal , Datta Aloke Kumar 
Published: February 2024

Abstract
Railways are a vital lifeline for both passengers and freight, with an extensive network connected by robust welded joints. This study explores the potential of Acoustic Emission (AE) techniques in rail health monitoring, particularly in the context of rail sections and their relationship with welds. While AE-based health monitoring has gained attention, a significant gap exists in understanding welds’ influence. This research bridges this gap by examining the interplay between welds and health monitoring. Welds introduce complexities in AE wave propagation, crucial for precise health assessments. Through artificial AE source simulation using the Pencil Lead Break (PLB) method across diverse rail segments, specialized sensors capture AE signals. Rigorous analysis, involving signal processing and statistical methods, reveals how welds impact AE wave behaviour and health assessment accuracy. Importantly, this research underscores how welds alter AE wave propagation, ensuring accurate health evaluations. This study’s insights hold far-reaching implications, not only for enhancing current rail system maintenance but also for advancing our understanding of the critical interrelationship between welds and health monitoring, thus contributing to the longevity and reliability of rail systems.
A capability approach to analyse well-being impacts of wind energy infrastructure
Authors: Velasco-Herrejón Paola , López Toledo Enoc , Romo López Juana Arely , Antonio Escobar Tania Karina , Carrasco Morales Elizabeth , Hernández Ramírez William , Ignacio Hernández Lizbeth Yessenia , Gutiérrez Felipe Gisel Andrea , Jiménez López Lirio Scaanda , Rueda Rasgado Norma Iris , Martinez Sánchez Jade , Martínez Arreguín Jesús 
Published: February 2024

Abstract
This paper operationalises the capability approach to analyse the tensions and trade-offs in assessing well-being outcomes generated by the production of wind energy, and how these reflect social acceptance at the local level. Specifically, the paper addresses the difficulties in understanding the voice of Indigenous people living near wind energy infrastructure in Southern Mexico and how their conceptions of well-being can be used to estimate the impact of wind energy development on three different communities. The methodology involved a three-stage process that integrated semi-structured interviews, focus groups, a survey, and participatory workshops, involving 450 participants. The findings conclude that community acceptability of energy infrastructure such as wind farms will only be achieved through inclusive community engagement that considers valued ways of being and doing of the local population. These include increasing the opportunities for people to live in good health, skilled employment in the industry, engaging and integrating local culture, values, worldviews and needs, and having a collective approach to the distribution of economic benefits that may strengthen social networks. By focusing on the recognition of valuable human capabilities from a participatory mixed-methods perspective, this paper contributes to a more compelling body of theory on social wind energy impacts that focuses on locally defined priorities and perspectives. Furthermore, this study also shows how the inclusion of community members as co-researchers is crucial to validate and locate local knowledge at the forefront while allowing local stakeholders to exercise greater voice and agency in how the research is conducted and designed. We hope that this methodology can offer useful insights for the design and implementation of future renewable energy projects that have environmental and social sustainability in mind.
Mozambican Coffea accessions from Ibo and Quirimba Islands: identification and geographical distribution
Authors: Navarini Luciano , Scaglione Davide , Del Terra Lorenzo , Scalabrin Simone , Mavuque Lopes , Turello Luca , Nguenha Rafael , Luongo Gianluca 
Published: February 2024

Abstract
Mozambique does not have a tradition of farming Coffea arabica or Coffea canephora, the two species that dominate the worldwide coffee market. However, native coffee plants have been growing spontaneously and in some cases cultivated in the Ibo and Quirimba islands in the north of the country and Inhambane province in the south. Historically there has been confusion over the precise taxonomic classification of these indigenous coffee plants, with different botanists identifying the species as C. racemosa, C. zanguebariae or various synonyms of both. The present research aims to clarify the subject and provide new information on these little-described coffee species which may prove valuable as new breeding material for future cultivars, something that is sorely needed to face the present and future challenges of coffee production. Leaf samples were collected from 40 accessions from Ibo Island, Quirimba Island and Inhambane province. The samples were sequenced by whole-genome technology and WGS reads were filtered to identify relevant SNP variants. Diversity among the samples was assessed by PCA, and a phylogenetic tree including several Coffea species was built using additional data available in public databases. Experimental data confirm the presence of C. zanguebariae as the only coffee species present in both Ibo and Quirimba Islands, while it appears that C. racemosa is exclusive to the southern Inhambane province. The present research provides the most detailed analysis so far on the genetic identity of the traditional Mozambican coffee crops. This is the prerequisite for undertaking further scientific studies on these almost unknown coffee species and for starting agronomic development programs for the economic revival of Ibo and Quirimba islands based on coffee cultivation. Furthermore, these species could provide much-needed genetic material for the breeding of new hybrids with the two main commercial coffee species.
CTISL: a dynamic stacking multi-class classification approach for identifying cell types from single-cell RNA-seq data
Authors: Wang Xiao , Chai Ziyi , Li Shaohua , Liu Yan , Li Chen , Jiang Yu , Liu Quanzhong 
Published: February 2024

Abstract

Motivation
Effective identification of cell types is of critical importance in single-cell RNA-sequencing (scRNA-seq) data analysis. To date, many supervised machine learning-based predictors have been implemented to identify cell types from scRNA-seq datasets. Despite the technical advances of these state-of-the-art tools, most existing predictors were single classifiers, of which the performances can still be significantly improved. It is therefore highly desirable to employ the ensemble learning strategy to develop more accurate computational models for robust and comprehensive identification of cell types on scRNA-seq datasets.

Results
We propose a two-layer stacking model, termed CTISL (Cell Type Identification by Stacking ensemble Learning), which integrates multiple classifiers to identify cell types. In the first layer, given a reference scRNA-seq dataset with known cell types, CTISL dynamically combines multiple cell-type-specific classifiers (i.e. support-vector machine and logistic regression) as the base learners to deliver the outcomes for the input of a meta-classifier in the second layer. We conducted a total of 24 benchmarking experiments on 17 human and mouse scRNA-seq datasets to evaluate and compare the prediction performance of CTISL and other state-of-the-art predictors. The experiment results demonstrate that CTISL achieves superior or competitive performance compared to these state-of-the-art approaches. We anticipate that CTISL can serve as a useful and reliable tool for cost-effective identification of cell types from scRNA-seq datasets.

Availability and implementation
The webserver and source code are freely available at http://bigdata.biocie.cn/CTISLweb/home and https://zenodo.org/records/10568906, respectively.
DeepLocRNA: an interpretable deep learning model for predicting RNA subcellular localization with domain-specific transfer-learning
Authors: Wang Jun , Horlacher Marc , Cheng Lixin , Winther Ole 
Published: February 2024

Abstract

Motivation
Accurate prediction of RNA subcellular localization plays an important role in understanding cellular processes and functions. Although post-transcriptional processes are governed by trans-acting RNA binding proteins (RBPs) through interaction with cis-regulatory RNA motifs, current methods do not incorporate RBP-binding information.

Results
In this article, we propose DeepLocRNA, an interpretable deep-learning model that leverages a pre-trained multi-task RBP-binding prediction model to predict the subcellular localization of RNA molecules via fine-tuning. We constructed DeepLocRNA using a comprehensive dataset with variant RNA types and evaluated it on the held-out dataset. Our model achieved state-of-the-art performance in predicting RNA subcellular localization in mRNA and miRNA. It has also demonstrated great generalization capabilities, performing well on both human and mouse RNA. Additionally, a motif analysis was performed to enhance the interpretability of the model, highlighting signal factors that contributed to the predictions. The proposed model provides general and powerful prediction abilities for different RNA types and species, offering valuable insights into the localization patterns of RNA molecules and contributing to our understanding of cellular processes at the molecular level. A user-friendly web server is available at: https://biolib.com/KU/DeepLocRNA/.