bluuebunny/crossref_metadata_2025_split huggingface dataset:
Three-dimensional turning force sensor based on a decagonal ring structure: crosstalk mitigation design and performance optimization
Authors: Wang Peng , Han Jike , Qiangdai Yu , Zhang Changming , Hu Shanzhou , Yang Fan , Yan Zhipeng , Shen Bo , Zhang Xian , Wang kun 
Published: October 2024

Abstract
Real-time data acquisition using high-precision three-dimensional turning force sensors is crucial for intelligent manufacturing. However, existing sensors face challenges such as low integration levels and poor crosstalk resistance, rendering them inadequate for high-precision measurements. Theoretical analysis has demonstrated that a fully centered decagonal ring structure can effectively mitigate the effects of eccentric loading. This study focuses on designing a fully centered turning force sensor with minimal crosstalk. To achieve a balance between sensitivity and stiffness, multi-objective optimization was conducted using GWO-BP and TOP algorithms, resulting in a twofold increase in the sensitivity of the decagonal ring. The sensor design incorporates metallic strain gauges, instrumentation amplifiers, and peripheral circuits into the ring arms of the decagonal structure. Experimental results show that the sensor’s amplified sensitivities in the Fc, Ff, and Fp directions are 11.78 mV N−1, 10.31 mV N−1, and 1.78 mV N−1, respectively. The first three natural frequencies in an unconstrained state are 2518.5 Hz, 2537.5 Hz, and 4256.4 Hz. The sensor’s Fc-Fy crosstalk ranges from 0.18% to 0.88%, with noise limits for the Fc, Ff, and Fp directions of 0.035 N, 0.03 N, and 0.23 N, respectively. Cutting experiments have demonstrated that under varying spindle speeds and cutting depths, the sensor effectively detects surges in cutting force caused by rapid tool retraction, as well as other common faults.
Road boundary extraction method from mobile laser scanning point clouds
Authors: Xin Gongfeng , Cong Bori , Liu Rufei , Zhang Zhenhu , Liu Mengya 
Published: October 2024

Abstract
With the rapid development of mobile laser scanning (MLS) technology, high-precision three-dimensional (3D) point cloud data has shown great potential in different fields such as topographic mapping, road asset management and smart city construction. 3D point cloud data contains not only position information, but also the shape and attributes of the target, which is very convenient for obtaining road information. Accurate extraction of the road boundary is a basic task for obtaining road infrastructural data, which can support the generation of high-precision maps, vehicle navigation and autonomous driving. However, road boundary extraction in urban environments is easily occluded such as vehicles and pedestrians on the road, which leads to problems such as difficulty and incomplete extraction of MLS point cloud road boundaries. To address this problem, this study proposes a road boundary extraction method that integrates pavement edge information, accurately considers the position of the road boundary from two dimensions, eliminates false boundaries, and completes the missing boundary through the extracted boundary spatial relationship. First, grid elevation filtering is used to remove high-level non-ground points. Then the pavement edges and curb stone points are extracted from the preprocessed point cloud, and they are superimposed to remove false boundary points to obtain accurate road boundaries. Finally, based on the spatial relationship of road boundaries, missing parts are detected and repaired to obtain complete road boundaries. Experimental results show that the accuracy on real road scenes exceeds 98%, the completeness rate is above 91%, and the extraction quality is above 90%, which verifies the effectiveness and accuracy of this method.
Computational design of 4D printed shape morphing lattices undergoing large deformation
Authors: Walker Andreas , Shea Kristina 
Published: October 2024

Abstract
In 4D Printing, active materials are embedded in structures such that the application of an external stimulus, usually coming from the environment, results in a structural response. To design structures that achieve a targeted shape change for a defined stimulus, also known as shape morphing, the material distribution and structure needs to be tuned. However, the computational design of such material distributions and structures is a challenging task and remains, despite recent advances, unable to fully leverage the entire design freedom offered by state-of-the-art 4D printing technology. Notable gaps concern the handling of large and complex deformations, the high computational cost, and the exploration of the design space by the generation of alternative solutions. In this article, a method is presented to fill this gap. First, an artificial neural net is trained that represents a deformation map that occurs during actuation. Then, a shape morphing truss is designed that achieves this deformation during actuation. The method is used to solve four shape morphing problems, where superior capabilities are demonstrated in terms of magnitude and complexity of deformations that can be handled, efficient generation of alternative solutions and versatility. Due to these capabilities, the method enables exploration of the full potential of 4D printing technology to create stimuli-responsive, multifunctional structures.
Improved gas sensing properties of copper-doped MoSe2 monolayers: a first-principles study on CO2 and NO2 adsorption
Authors: Jaiswal S N , Pandey Bramha P , Kumar Dharmendra , Mishra Neha , Tomar V K , Kumar Santosh 
Published: October 2024

Abstract
This paper investigates the impact of copper (Cu)-doped MoSe2 monolayer (ML) on gas sensing properties. The adsorption behaviour of CO2 and NO2 gas molecules on Cu-doped MoSe2 ML is reported and various sensing and electronic parameters such as adsorption energy, charge transfer and recovery time, are computed to delineate the adsorption characteristics and gas sensitivity. In our study, the doped Cu-atom in Se vacant MoSe2 ML shows the adsorption energies to be −1.32 eV (O-orient) and −1.72 eV (C-orient), for CO2, while for NO2, they are −0.879 eV (O-orient) and −1.06 eV (N-orient), respectively. The negative formation energy of −6.62 eV shows the electronic stability of Cu-doped MoSe2 ML and thermal stability, demonstrated by the molecular dynamics study through the Nose-Vervlet Thermostat (NVT) algorithm. Also, significant changes are observed in electronic conductivity and work function upon adsorbed Cu-MoSe2 ML. Lastly, these outcomes illustrate that Cu doping amplifies the capture ability of MoSe2 ML towards gas molecules, fostering improved electron interaction between the substrate surface and gas molecules, consequently enhancing its gas sensing ability.
Photon-modulated displaced thermal states: negativity and non-Gaussianity
Authors: Wu Wei-feng , Fang Yong , Fu Peng 
Published: October 2024

Abstract
In this paper, we propose a new non-Gaussian quantum state, termed the photon-modulated displaced thermal state (DTS). Using the operator ordering method, we obtain the normal ordering product of its density operator and then investigate its normalization, the negativity of its Wigner function and its non-Gaussianity.
Tunable mid-infrared absorber based on Dirac metamaterials
Authors: Li Bo , Cheng Guolun , Sun Bo , Yu Yingying , Yang Wenxing 
Published: October 2024

Abstract
In this paper, a tunable metamaterial absorber based on a Dirac semimetal is proposed. It consists of three different structures, from top to bottom, namely a double semicircular Dirac semimetal resonator, a silicon dioxide substrate and a continuous vanadium dioxide (VO2) reflector layer. When the Fermi energy level of the Dirac semimetal is

10

meV
,

the absorber absorbs more than 90% in the 39.06–84.76 THz range. Firstly, taking advantage of the tunability of the conductivity of the Dirac semimetal, dynamic tuning of the absorption frequency can be achieved by changing the Fermi energy level of the Dirac semimetal without the need to optimise the geometry and remanufacture the structure. Secondly, the structure has been improved by the addition of the phase change material VO2, resulting in a much higher absorption performance of the absorber. Since VO2 is a temperature-sensitive metal oxide with an insulating phase below the phase transition temperature (about 68 °C) and a metallic phase above the phase transition temperature, this paper also analyses the effect of VO2 on the absorptive performance at different temperatures, with the aim of further improving absorber performance.
An improved AlexNet deep learning method for limb tumor cancer prediction and detection
Authors: Perumal Arunachalam , Nithiyanantham Janakiraman , Nagaraj Jamuna 
Published: October 2024

Abstract
Synovial sarcoma (SS) is a rare cancer that forms in soft tissues around joints, and early detection is crucial for improving patient survival rates. This study introduces a convolutional neural network (CNN) using an improved AlexNet deep learning classifier to improve SS diagnosis from digital pathological images. Key preprocessing steps, such as dataset augmentation and noise reduction techniques, such as adaptive median filtering (AMF) and histogram equalization were employed to improve image quality. Feature extraction was conducted using the Gray-Level Co-occurrence Matrix (GLCM) and Improved Linear Discriminant Analysis (ILDA), while image segmentation targeted spindle-shaped cells using repetitive phase-level set segmentation (RPLSS). The improved AlexNet architecture features additional convolutional layers and resized input images, leading to superior performance. The model demonstrated significant improvements in accuracy, sensitivity, specificity, and AUC, outperforming existing methods by 3%, 1.70%, 6.08%, and 8.86%, respectively, in predicting SS.
Pioneering diabetes screening tool: machine learning driven optical vascular signal analysis
Authors: M Sameera Fathimal , Kumar JS , Jeya Prabha A , Selvaraj Jothiraj , Kirubha S P Angeline 
Published: October 2024

Abstract
The escalating prevalence of diabetes mellitus underscores the critical need for non-invasive screening tools capable of early disease detection. Present diagnostic techniques depend on invasive procedures, which highlights the need for advancement of non-invasive alternatives for initial disease detection. Machine learning in integration with the optical sensing technology can effectively analyze the signal patterns associated with diabetes. The objective of this research is to develop and evaluate a non-invasive optical-based method combined with machine learning algorithms for the classification of individuals into normal, prediabetic, and diabetic categories. A novel device was engineered to capture real-time optical vascular signals from participants representing the three glycemic states. The signals were then subjected to quality assessment and preprocessing to ensure data reliability. Subsequently, feature extraction was performed using time-domain analysis and wavelet scattering techniques to derive meaningful characteristics from the optical signals. The extracted features were subsequently employed to train and validate a suite of machine learning algorithms. An ensemble bagged trees classifier with wavelet scattering features and random forest classifier with time-domain features demonstrated superior performance, achieving an overall accuracy of 86.6% and 80.0% in differentiating between normal, prediabetic, and diabetic individuals based on the optical vascular signals. The proposed non-invasive optical-based approach, coupled with advanced machine learning techniques, holds promise as a potential screening tool for diabetes mellitus. The classification accuracy achieved in this study warrants further investigation and validation in larger and more diverse populations.
Weighted naïve bayes multi-user classification for adaptive authentication
Authors: Mavhemwa Prudence M , Zennaro Marco , Nsengiyumva Philibert , Nzanywayingoma Frederic 
Published: October 2024

Abstract
Machine learning classification algorithms have been extensively utilized in addressing user authentication challenges. Nonetheless, a majority of solutions categorize users into three classes, whereas adaptive authentication scenarios necessitate classification beyond this threshold. The rationale behind this limitation has not been thoroughly explored. The current study leveraged the Naive Bayes theorem for user authentication endeavors to assess the risk associated with login attempts. The Naive Bayes Machine Learning algorithm, along with its variations such as Gaussian, Categorical, and Bernoulli, was applied on both weighted and unweighted datasets to ascertain risk levels and categorize them into six classes. Additionally, the classification task was executed using alternative algorithms. The outcomes of cross-validation and comparative analyses revealed that the performance was commendable for up to three classes, after which a decrease was observed in certain Naive Bayes and SVM classifiers. Among the Naïve Bayes family, the Bernoulli NB algorithm exhibited superior performance but was surpassed by Decision Trees, SVM, XGB, and Random Forests. Notably, the weighted dataset consistently outperformed the unweighted counterpart, with the allocation of weights significantly influencing algorithmic efficacy. The 80:20 split strategy yielded the most favorable outcomes in contrast to the 70:30 and 60:40 splits, albeit no significant variances were detected during cross-validation. Non-Naïve Bayes algorithms demonstrated superior performance compared to Naïve Bayes algorithms. For Naïve Bayes, optimal performance is achieved with three classes, highlighting its utility in conditional risk calculation, while non-Naïve Bayes multi-classification algorithms are more suitable for classification tasks due to the problem’s inherent compatibility with conditional probabilities. In conclusion, it is imperative to acknowledge that the characteristics of the data, the use of weights, and the data splitting methodology significantly influence the accuracy of machine learning algorithms in multi-class user classification.
The impacts of climate change on violent conflict risk: a review of causal pathways
Authors: Xie Xiaolan , Hao Mengmeng , Ding Fangyu , Scheffran Jürgen , Ide Tobias , Maystadt Jean-François , Qian Yushu , Wang Qian , Chen Shuai , Wu Jiajie , Sun Kai , Ma Tian , Jiang Dong 
Published: October 2024

Abstract
The potential impacts of climate change on violent conflict are high on the agenda of scholars and policy makers. This article reviews existing literature to clarify the relationship between climate change and conflict risk, focusing on the roles of temperature and precipitation. While some debate remains, substantial evidence shows that climate change increases conflict risk under specific conditions. We examine four key pathways through which climate affects conflict: (i) economic shocks, (ii), agricultural decline, (iii) natural resources competition, and (iv) migration. Key gaps include limited long-term data, insufficient integrated studies, and the inadequate understanding of causal mechanisms, necessitating transdisciplinary research that addresses social vulnerability and underlying pathways.
Advanced hybrid techniques for predicting discharge coefficients in ogee-crested spillways: integrating physical, numerical, and machine learning models
Authors: Hien Le Thi Thu , Van Chien Nguyen , Xuan-Hien Le 
Published: October 2024

Abstract
The primary objective of this work was to examine the flow characteristics over an ogee spillway using both a numerical model and the Machine Learning (ML) approach. A 3D computational fluid dynamics (CFD) model was employed to simulate the flow over an ogee spillway, utilizing the Reynolds averaged Navier–Stokes equations. The simulation encompassed a wide variety of head ratios, ranging from 0.1 to 6.0, to extend the rating curve of discharge coefficient (C) and head ratio (H

e

/H
0). The formation of the negative pressure zone rapidly occurred, and the maximum velocity area developed from toe to top of the spillway surface as the head ratio increased. Then, four ML models—RF, FNN, ADB, and KNN—were utilized to estimate the discharge coefficient of the spillway. Hyperparameter tuning using the Tree-Structured Parzen Estimator (TPE) and five-fold cross-validation ensured robust model performance. The ML model’s efficacy was assessed by conducting 200 random seed simulations. The RF and ADB models exhibited the highest predictive accuracy and consistency, with mean correlation coefficient (CC) values of 0.979 and 0.975, respectively. FNN and KNN also performed well but showed greater variability in their prediction. The results demonstrated reasonably good agreement between the physical, numerical, and ML models. Both numerical simulation methods and ML models, particularly RF, proved to be cost-efficient and reliable tools for designing and analyzing flow over an ogee spillway. These findings highlight the potential of integrating numerical simulations and advanced ML techniques to enhance the prediction and analysis of hydraulic structures, providing valuable insights for the design and management of spillway systems.
Precise modulation of the debonding behaviours of ultra-thin wafers by laser-induced hot stamping effect and thermoelastic stress wave for advanced packaging of chips
Authors: Zhang Jieyuan , Hu Yanlei , Wang Fangcheng , Liu Qiang , Niu Fangfang , Li Jinhui , Huang Mingqi , Zhang Guoping , Sun Rong 
Published: October 2024

Abstract
Laser debonding technology has been widely used in advanced chip packaging, such as fan-out integration, 2.5D/3D ICs, and MEMS devices. Typically, laser debonding of bonded pairs (R/R separation) is typically achieved by completely removing the material from the ablation region within the release material layer at high energy densities. However, this R/R separation method often results in a significant amount of release material and carbonized debris remaining on the surface of the device wafer, severely reducing product yields and cleaning efficiency for ultra-thin device wafers. Here, we proposed an interfacial separation strategy based on laser-induced hot stamping effect and thermoelastic stress wave, which enables stress-free separation of wafer bonding pairs at the interface of the release layer and the adhesive layer (R/A separation). By comprehensively analyzing the micro-morphology and material composition of the release material, we elucidated the laser debonding behavior of bonded pairs under different separation modes. Additionally, we calculated the ablation threshold of the release material in the case of wafer bonding and established the processing window for different separation methods. This work offers a fresh perspective on the development and application of laser debonding technology. The proposed R/A interface separation method is versatile, controllable, and highly reliable, and does not leave release materials and carbonized debris on device wafers, demonstrating strong industrial adaptability, which greatly facilitates the application and development of advanced packaging for ultra-thin chips.
Experimental study on optimization of abrasive water jet process parameters
Authors: Pan Yue , Yuan Hao , Pei Kangchao , Zhai Shengyu , Geng Zhongcheng 
Published: October 2024

Abstract
To improve the cutting efficiency of abrasive water jet technology, a high-pressure water jet cutting system was developed to examine the cutting effects on granite and red sandstone under various process parameters. The results reveal that the average slit depth increases linearly with rising jet pressure, while the slit width shows a decreasing trend. In contrast, as the transverse speed increases, both the depth and width of the slit decline. The slit depth initially rises and then falls with an increase in jet target distance, peaking at a target distance of 5 mm, whereas the slit width consistently decreases. Furthermore, as the jet angle increases, the slot depth exhibits an oscillatory pattern of increase and decrease, with the maximum slot depth occurring at a jet angle of 10°. Conversely, the slit width first decreases and then increases with the jet angle, reaching its maximum at a jet angle of 20°. To clarify the relative importance of these parameters, an L16(4^4) orthogonal experimental design was utilized to conduct extensive experiments on granite and red sandstone. Through range analysis and variance analysis, the primary and secondary relationships among the factors affecting abrasive water jet cutting were established in the following order: transverse velocity > jet pressure > jet inclination > target distance. The optimal jet parameters identified were a jet pressure of 40 MPa, a target distance of 5 mm, a transverse velocity of 2 mm s−1, and a jet inclination of 10°.
International trade makes European food markets less vulnerable to concurrent and recurrent climate extremes
Authors: Pieralli Simone , Pérez-Domínguez Ignacio 
Published: October 2024

Abstract
Extreme weather events are occurrences of unusually severe weather or climate conditions that can devastate local communities, agriculture and natural ecosystems. Due to climate change, these extreme events are becoming increasingly more likely, lasting longer, and more severe in impact. In this paper, we illustrate the potential effects of concurrent and recurrent extreme weather events on EU agricultural commodity markets and, in particular, on production and trade. A combination of concurrent and recurrent events is expected to have a negative and compounding effect on domestic food supply and trade, making EU agricultural markets less resilient. A set of scenarios based on record low yields show how EU production could be drastically affected compared to a business-as-usual situation. Market disruptions would be potentially larger for commodities in which the EU has a strong net exporter position, such as wheat. In the absence of extremes in non-EU regions, trade reveals as a key element in buffering the adverse impacts of extreme events in the EU.
Reframing the Debate on Frontline Worker Motivation
Authors: Aiyar Yamini 
Published: October 2024

Abstract
This chapter pulls together the empirical descriptive account presented in the previous two chapters to locate the perspectives, self-image and professional identities of frontline workers—district and block education officers and teachers—within a broader theoretical understanding of state capacity. The effort is to draw on theoretical debates on worker motivation, management practice, and accountability to push boundaries of existing explanations on frontline worker behaviour. It seeks to. Move the debate away from a focus on questions of compliance and monitoring, toward a broader engagement with the organizational sociology of the frontline state. It makes the case for the need to build the debate on state capacity in India around the frameworks of alternative management practices that privilege autonomy, deliberation and align worker incentives to the organizational mission.