bluuebunny/crossref_metadata_2025_split huggingface dataset:
Segmentation of tooth enamel microstructure images using classical image processing and U-Net approaches
Authors: Fogalli Giovani Bressan , Line Sérgio Roberto Peres , Baum Daniel 
Published: March 2023

Abstract
Brackground: Tooth enamel is the hardest tissue in human organism, formed by prism layers in regularly alternating directions. These prisms form the Hunter-Schreger Bands (HSB) pattern when under side illumination, which is composed of light and dark stripes resembling fingerprints. We have shown in previous works that HSB pattern is highly variable, seems to be unique for each tooth and can be used as a biometric method for human identification. Since this pattern cannot be acquired with sensors, the HSB region in the digital photograph must be identified and correctly segmented from the rest of the tooth and background. Although these areas can be manually removed, this process is not reliable as excluded areas can vary according to the individual`s subjective impression. Therefore, the aim of this work was to develop an algorithm that automatically selects the region of interest (ROI), thus, making the entire biometric process straightforward.
Results: We used two different approaches: a classical image processing method which we called anisotropy-based segmentation (ABS) and a machine learning method known as U-Net, a convolutional neural network. Both approaches were applied to a set of extracted tooth images. U-Net with some post processing outperformed ABS in the segmentation task with an Intersection Over Union (IOU) of 0.837 against 0.766.
Conclusions: Even with a small dataset, U-Net proved to be a potential candidate for fully automated in-mouth application. However, the ABS technique has several parameters which allow a more flexible segmentation with interactive adjustments specific to image properties.
Mechanical properties of coarse-fine particle mixtures: a laboratory investigation of the effect of fine content
Authors: Chang Liucheng , Wang Ya , Wang Hongyu 
Published: March 2023

Abstract
The impacts of fine content on the microstructure and mechanical behavior of five types of coarse-fine particle mixtures with different fine contents are examined based on laboratory tests. The samples used in these tests all had a void ratio of 0.6. As the fine content decreases, the pore-size distribution of the soil changes from unimodal to bimodal, and the variation rate of the initial elastic modulus with confining pressure becomes great. If the fine content is greater than 71.43%, the pore volume of the mixtures reduces progressively progressively with decreasing the fine content, resulting in the growth of peak deviator stress, cohesiveness, and internal friction angle. If the concentration of the fine grains is less than 71.43%, the pore volume of the mixtures shows an increasing trend, which causes a decrease in peak deviator stress,cohesiveness, and internal friction angle.
Holo-omics analysis reveals the influence of gut microbiota on obesity indicators in Jinhua pigs
Authors: Liu Shuang , Lai Xueshuang , Xie Qinqin , Wang Zhen , Pan Yuchun , Zhang Zhe , Wang Qishan 
Published: March 2023

Abstract
Background

The mechanisms behind obesity are complex and multi-faceted, involving the interplay of both host genomics and gut microbiome. In recent years, research has largely focused on these factors separately, but rarely from the viewpoint of holo-omics, which considers the host and microbiome as an integrated entity. To address this gap in knowledge, the present study aimed to investigate the holo-omics basis of obesity in Jinhua pigs, a Chinese indigenous breed known for its high degree of fat deposition and superior meat quality.

Methods

Six pigs with extreme obesity phenotype were selected from a larger cohort of 18 Jinhua pigs, and the contents of the jejunum, cecum, and colon regions were collected after slaughter at 240 days of age. The data obtained was processed, denoised, and annotated using QIIME2, with expression differences being analyzed using edgeR software.

Results

The results showed significant differences in jejunal microbial diversity and composition between the two groups, with gut transcriptomics also indicating that differentially expressed genes in the jejunum were enriched in lipid metabolism pathways. These findings provide further evidence of the influence of the gut microbiome and host gene expression on fat deposition in Jinhua pigs.

Conclusions

This study provides valuable insights into the mechanisms of fat deposition in Jinhua pigs from the viewpoint of holo-omics. The integration of host transcriptomics and microbiome data helps shed light on the complex interactions between the host and gut microbiome, and highlights the importance of considering both factors in our understanding of obesity.
Haptic feature selection using Statistical Analysis for Haptic Object Reconstruction
Authors: Dewapura Praveena Wimarshani , Abeykoon A. M. Harsha S. 
Published: March 2023

Abstract
Object reconstruction in the haptic dimension is crucial in achieving realization in the virtual world. Prior studies have employed the conventional spring damper model to interpret the behavior of the object and motion parameters were treated as determinants of force feedback. However, haptic information is nonlinear, and more factors could influence haptic feedback recreation than just motion parameters. In recent years, scientists have paid much attention on employing AI technologies with haptics. AI approach is data-driven, and a thorough understanding of the data is critical to its performance. As a result, a detailed statistical analysisis required to discover patterns and relationships within the features and identify significant features as utilizing all the factors affecting haptic object reconstruction will cost higher computational time and resources. Thus, this study focused on statistically analyzing data extracted through a Disturbance Observer (DOB) and Reaction Force Observer(RFOB) based sensorless technique and selecting the significant features to precisely reconstruct an object in a haptic dimension. Furthermore, a mathematical notation was introduced to define a feature and the feature matrix was derived using the notation. Finally, a comparison of the performance of the same AI algorithm was conducted by varying the feature input to the AI model to understand the effect of the feature matrix.
Divide and conquer： Machine learning accelerated design of lead-free solder alloys with high strength and high ductility
Authors: Zhang Tong-Yi , Wei Qinghua , Cao Bin , Yuan Hao , Chen Youyang , You Kangdong , Yu Shuting , Yang Tixin , Dong Ziqiang 
Published: March 2023

Abstract
The attainment of both high strength and high ductility is always the goal for structure materials, because the two properties generally are mutually competing, called strength-ductility trade-off. Nowadays, the data-driven paradigm combined with expert domain knowledge provides the state-of-the-art methodology to design and discovery for structure materials with high strength and high ductility. To enhance both strength and ductility, a joint feature is proposed here to be the product of strength multiplying ductility. The strategy of “divide and conquer” is developed to solve the contradictory problem, that material experimental data of mechanical behaviors are, in general, small in size and big in noise, while the design space is huge, by a newly developed data preprocessing algorithm, named the Tree-Classifier for Gaussian Process Regression (TCGPR). The TCGPR effectively divides an original dataset in a huge design space into three appropriate sub-domains and then three Machine Learning (ML) models conquer the three sub-domains, achieving significantly improved prediction accuracy and generality. After that the Bayesian sampling is applied to design next experiments by balancing exploitation and exploration. Finally, the experiment results confirm the ML predictions, exhibiting novel lead-free solder alloys with high strength high ductility. Various material characterizations were also conducted to explore the mechanism of high strength and high ductility of the alloys.
Archaeological Bolivian maize genomes suggest diversity is associated with Inca cultural expansion and environmental variation in South America
Authors: Chen Huan , Baetsen-Young Amy , Thompson Addie , Day Brad , Lovis William , Wrobel Gabriel 
Published: March 2023

Abstract
Previous archaeological and anthropological studies have demonstrated the myriad ways that cultural and political systems shape access to and preferences for food. However, few studies have carried out biocultural analysis linking specific genotypic/phenotypic traits as evidence of cultural selection in ancient contexts. Here, we address this issue by comparing genomes of maize samples from Bolivia dating to ~500 BP included as an offering with the mummified remains of a young girl to 13 archaeological maize samples spanning at least 5,000 years of evolution, and 87 modern western hemisphere maize samples. Our phylogenetic analysis showed that the archaeological Bolivian maize (aBM) has the closest genetic distance to archaeological maize from ancient Peru, which in turn was derived from central Mexican domesticated varieties. The comparative genomic analysis identified specific modifications in the aBM genome representing traits that were selected by ancient farmers, including shorter growing season, greater drought28 istant, and sweeter taste. The genome of the aBM appears to reflect gene flow from Chile coinciding with the arrival of the Inca empire in the altiplano region surrounding modern-day La Paz, as well as selection for specific traits related to growth in this marginal environment and taste preference. Our study provides insights into the complex biocultural role that Inca culture had in determining the direction of maize diversity in South America.
Effect of the geometry and severity of left ventricular hypertrophy on cardiovascular mortality in patients on chronic hemodialysis
Authors: Huang Yi-Chen , Wu Chung-Kuan 
Published: March 2023

Abstract
Background
Left ventricular hypertrophy (LVH) is a vital risk factor for mortality in patients on dialysis. The association of the geometry and severity of LVH with cardiovascular mortality and mortality in patients on hemodialysis (HD) remains unknown. This study investigated clinical outcomes among patients on HD with different LVH geometric patterns and severity.
Methods
Patients on chronic HD from a single medical center who underwent echocardiography for the assessment of LVH geometric patterns and severity were enrolled. They were divided into concentric and eccentric groups and then subdivided into four groups based on LVH severity: mild-to-moderate eccentric LVH, mild-to-moderate concentric LVH, severe eccentric LVH, and severe concentric LVH. The risks of all-cause mortality and cardiovascular mortality between groups were evaluated using Cox proportional hazard analysis.
Results
Of the 237 enrolled patients on HD with LVH, 131 had concentric LVH, and 106 had eccentric LVH, with 33, 44, 73, and 87 having mild-to-moderate eccentric LVH, mild-to-moderate concentric LVH, severe eccentric LVH, and severe concentric LVH, respectively. Compared with eccentric LVH, the crude HR of mortality and cardiovascular mortality of concentric LVH was 1.60 (0.96–2.66) and 2.03 (1.13–3.65). Severe concentric LVH was a significant risk factor for all-cause mortality and cardiovascular mortality compared with mild-to-moderate eccentric LVH (crude HR: 2.58, 95% CI: 1.00–6.65 and 3.73, 1.13–12.33, respectively). After adjustment for all variables, concentric LVH and severe concentric LVH remained significant risk factors for cardiovascular mortality (adjusted HR: 2.13, 95% CI: 1.13–4.01 and 3.71, 1.07–12.82, respectively).
Conclusions
Concentric LVH, especially severe concentric LVH, was associated with a high risk of cardiovascular mortality among patients on chronic HD.
Soft computing in computer network security protection system with machine learning based on level protection in the cloud environment
Authors: Thomas Merin , Gupta Mohan Vishal , V Gokul Rajan , R Rajalakshmi , Dixit Rinku Sharma , Choudhary Shailee Lohmor 
Published: March 2023

Abstract
Artificial neural networks (ANN), fuzzy inference systems, approximate reasoning, and derivative-free optimization approaches like evolutionary computation, among others, are all components of Soft Computing (SC), an innovative method for building computationally intelligent methods. Due to its widespread use and benefits, cloud computing is currently a major focus for researchers. The distributed nature of cloud computing and its complete reliance on the internet for service provision present security challenges, the most serious of which is insider Distributed Denial of Service (DDoS), which results in complete deactivation of services. Based on machine learning and the cloud, this study proposes a novel method for improving computing network security. Using a trust-based secure cloud environment and the Kernel principal component encoder architecture, the network monitoring is carried out. The soft computing environment-based detection of a cyberattack on the network is the purpose for the security enhancement. Throughput, QoS, latency, and packet delivery ratio for various monitored cyber security datasets are the focus of the experimental analysis.the proposed technique attainednetwork security analysis of 89%, throughput of 98%, QoS of 66%, latencyof 59%, packet delivery ratio of 83%.
A case of influenza A H3N2 complicated with Kawasaki disease and liver function impairment
Authors: Guo Caili , Song Chunlan , Cheng Yibing , Wang Yuanyuan 
Published: March 2023

Abstract
Background:Studies have found that multiple viral infections are related to the occurrence of Kawasaki disease. It has not been reported in the world which H3N2 influenza A and Kawasaki disease occurred simultaneously. Children with influenza virus infection and symptoms associated with Kawasaki disease should be highly alert.
Case presentation:Here we present a 2 -year -old Chinese girl who was treated with oseltamivir and cefoperazone sulbactam for H3N2influenza infection and sepsis. The girl's body temperature remained high and liver enzymes increased significantly. The child was diagnosed with Kawasaki disease by a pediatric cardiologist and The patient recovered completely after being treated with gamma globulin.
Conclusion: We hope that our case report will guide clinicians to be vigilant about Kawasaki disease when high fever persists in children with H3N2 influenza.
Spatial Attention and Quantization based Contrastive Learning Framework for mmWave Massive MIMO Beam Training
Authors: Jia Haohui , Urakami Taisei , Chen Na , Gao Hui , Okada Minoru 
Published: March 2023

Abstract
Identifying the optimal beam is a major challenge in millimeter wave (mmWave) wireless communication systems, considering the impacts of noise and training overhead for processing massive multiple-input multiple-output (MIMO) channel streams. To address these challenges, the conventional schemes are to extract the feature of received signals and directly predict the optimal beam based on deep learning (DL) during the beam training. However, this process highly relies on the quality and prior knowledge of massive MIMO channels. In this paper, we propose a novel spatial attention and quantization based beam contrastive learning framework to draw the inherent transmitting angular relation between the beam signature and sub array channels from the contrastive environmental prediction. Specifically, we propose two DL-associated beam training solutions to improve transmission reliability and reduce the training overhead. The first solution, named quantified phase-based transformer architecture (QPTNet), applies a high spatial resolution codebook to quantify the received array signals along the subcarriers into finite categorical beamformer codeword and predict the optimal beam based on the attention mechanism. The second solution, named self enhanced QPTNet (SE-QPTNet), enhances the capability of identifying the beam signature by competitively predicting the partial channel arrays with the limited training data. Simulation results show that our proposed schemes obtain significantly higher beamforming gain with lower prior channel requirements compared to the existing DL-based schemes, and achieve highly reliable performance for mmWave massive MIMO systems.
Design and Implementation of Virtual Maintenance Training System for Heavy Duty Gearbox
Authors: Zhu Zi-cheng , Chu Wei-jun , Diao Yan , Li Xiang-hui 
Published: March 2023

Abstract
By analyzing the structural performance characteristics of a heavy-duty gearbox, based on the modular design thinking and the requirements of the actual maintenance training application, the virtual prototype of a heavy-duty gearbox, such as the construction of three-dimensional model, collision detection, 3d observation, disassembly sequence planning and evaluation of training in the virtual maintenance training simulation platform and maintenance simulation implementation process. A desktop virtual maintenance training system based on EON simulation platform is constructed. The training results show that the system can accurately guide the trainees to complete the gearbox maintenance training tasks correctly, with good operability and interaction.
Aggregation algorithm based on consensus verification
Authors: Li Shichao , Qin Jiwei 
Published: March 2023

Abstract
Distributed learning, as the most popular solution for training large-scale data for deep learning, consists of multiple participants collaborating on data training tasks. However, the malicious behavior of some during the training process, like Byzantine participants who would interrupt or control the learning process, will trigger the crisis of data security. Although recent existing defense mechanisms use the variability of Byzantine node gradients to clear Byzantine values, it is still unable to identify and then clear the delicate disturbance/attack. To address this critical issue, we propose an algorithm named consensus aggregation in this paper. This algorithm allows computational nodes to use the information of verification nodes to verify the effectiveness of the gradient in the perturbation attack, reaching a consensus based on the effective verification of the gradient. Then the server node uses the gradient as the valid gradient for gradient aggregation calculation through the consensus reached by other computing nodes. The proposed algorithm outperforms existing common aggregation algorithms (Krum, Trimmed Mean, and Bulyan) in the face of delicate disturbances and attacks on the MNIST and CIFAR10 datasets. In our experiments, we show that the proposed consensus aggregation algorithm successfully defends against a variety of Byzantine problems and effectively solves delicate disturbance/attack in Byzantine problems in distributed learning.
Plant managements but not fertilization mediate soil carbon emission and microbial community composition in subtropical Eucalyptus plantations
Authors: Wu Bin , Liu Wenfei , Wu Ying , Thompson Jill , Wu Jianping 
Published: March 2023

Abstract
Background and Aims
The diversity of plant functional groupsin plantations affects soil carbon, but we have limited understanding of the underlying mechanisms for how to plant management affects soil carbon dynamics.
Methods
We conducted a 3-year manipulation experiment of plant functional groups that included understory removal, tree root trenching, and fertilization treatments in 2-year-old and 6-year-old Eucalyptus plantations in the subtropical region, to explore soil carbon emission and microbial community composition.
Results
Soil respiration was significantly suppressed by understory removal (-38%), tree root trenching (-41%), and their interactions (-54%), but that fertilization alone and in interactions had no significant effect. Soil bacterial and fungal communities were also affected by understory removal and tree root trenching. Soil respiration, physicochemical characteristics, microbial diversity, and community composition were significantly affected by plantation age. Reductions in soil carbon emissions were associated with reductions in plant functional groups and soil microbial groups, while increases in soil respiration were associated with soil physicochemical factors, soil temperature, and plantation age.
Conclusion
Our findings highlight that plant managements are of great significance to the soil carbon emission processes in afforested plantations.
Red narrow-linewidth lasing and frequency comb from gain-switched self-injection-locked Fabry-Pérot laser diode
Authors: Shitikov Artem , Galiev Ramzil , Min'kov Kirill , Kondratiev Nikita , Cordette Steevy , Lobanov Valery , Bilenko Igor 
Published: March 2023

Abstract
Narrow-linewidth lasers are in extensive demand for numerous cutting-edge applications. Such lasers operating at the visible range are of particular interest. Self-injection locking of a laser diode frequency to a high-Q whispering gallery mode is an effective and universal way to achieve superior laser performance. We demonstrate ultrastable lasing with less than 10 Hz instantaneous linewidth for 20 μs averaging time at 638 nm using a Fabry-Pérot laser diode locked to a crystalline MgF2 microresonator. The linewidth measured with a β-separation line technique that characterizes 10 ms stability is as low as 850 Hz. Output power exceeds 80 mW. Demonstrated results are among the best for compact visible-range lasers in terms of stability and output power. We additionally report the first demonstration of a gain-switched regime for such stabilized Fabry-Pérot laser diode showing a high-contrast visible frequency comb generation. Tunable linespacing from 10 MHz to 2 GHz is observed. We demonstrated that the beatnote between the lines has sub-Hz linewidth and experiences spectral purification in the self-injection locking regime. This result might be of special importance for spectroscopy in the visible range.
Sampling Based Fuzzy Speech Clustering Systems for Faster Communication with Virtual Robotics Towards Social Applications
Authors: K RAJENDRA PRASAD , Narasimhulu K , KUMAR CH.N SANTHOSH , Reddy N Ramanjaneya 
Published: March 2023

Abstract
For social applications, virtual robotics technologies require the Internet of Things (IoT) and cloud services. IoT based speaker identification is required for virtual social robotics to operate in the most secure manner possible. Moreover, humans communicate using only natural speech to virtual robotics through IoT devices; Virtual robotics are the most emerging and which are required developing the speech-based identification models in the virtual cloud platforms. State-of-the-art of speech models defines the composite technique with the combination of k-means clustering and Gaussian mixture model (GMM) for speaker identification problems. Every speaker has their own annotation, acoustic, and utterance. The Gaussian mixture model (GMM) describes the modelling of features in the speech clustering system for speaker identification applications. These traditional systems are expensive for speech clustering of large datasets when considering the time and space parameters. Sampling based fuzzy-spherical k-means and sampling based fuzzy-mini-batch k-means(mbkm) are proposed to overcome the problem of scalability issue in the implementation of speech based virtual cloud based robotic systems. For demonstrating the effectiveness of the proposed fuzzy based speaker models, extensive experimental research is conducted using a variety of benchmark speech datasets.
On a problem by J. Jarczyk-W. Jarczyk
Authors: Chen Kai , Chen Yong 
Published: March 2023

Abstract
In the note, We use a different method from M. Balcerowskito find all analytic solutions f : C → C of the equation x + f(y + f(x)) = y + f(x + f(y)), which posed by J. Jarczyk-W. Jarczyk in [2].