Article
Astronomy & Astrophysics
Motoi Endo, Satoshi Mishima
Summary: Recently, the CDF Collaboration reported an updated result on the measurement of the W-boson mass, which showed a deviation of 7 sigma from the standard model prediction. This discrepancy may indicate the presence of new contributions to the Fermi coupling constant. Simple extensions of the standard model were studied, and it was found that the tension implies the existence of new physics at multi-TeV scales if the new coupling to the electron and/or muon is of order unity.
Article
Food Science & Technology
Xueting Liu, Xueli Wang, Yanwei Cheng, Yuangen Wu, Yan Yan, Zhen Li
Summary: In this study, changes in volatile organic compounds (VOCs) in Zhenyuan Daocai at different storage durations were analyzed. Various methods including electronic tongue (E-tongue), electronic nose (E-nose), and gas chromatography-ion mobility spectrometry (GC-IMS) were used. The results showed that the VOCs in Zhenyuan Daocai originated from raw materials and processing, and different compounds were responsible for different flavors. Furthermore, the characteristic VOCs changed after different durations of storage, contributing to floral, fruity, almond, roasted meat, fried coffee, and apple flavors.
LWT-FOOD SCIENCE AND TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Gongyang Li, Yike Wang, Zhi Liu, Xinpeng Zhang, Dan Zeng
Summary: This paper proposes a novel network model named LASNet for RGB-T semantic segmentation, which fully considers the characteristics of cross-modal features at different levels and introduces three specific modules for better segmentation. Experimental results demonstrate the superiority of LASNet over other methods on two public datasets.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Astronomy & Astrophysics
Junichiro Kawamura, Stuart Raby
Summary: This study investigates the effects of vectorlike leptons with a U(1)' gauge symmetry on the W-boson mass, and provides explanations for recent anomalies in the muon anomalous magnetic moment and the semileptonic decays of B mesons. The findings suggest that the precise measurement of the W-boson mass at CDF can be explained if charged or neutral vectorlike leptons have a mass smaller than a specific value. These light vectorlike leptons may not be excluded by collider experiments if they decay into a physical mode of the U(1)'-breaking scalar field.
Article
Astronomy & Astrophysics
Priya Mishra, Mitesh Kumar Behera, Rukmani Mohanta
Summary: In this study, a model incorporating two right-handed fermion triplet superfields is introduced to illustrate neutrino phenomenology. By utilizing a double cover of the A4 modular symmetry, the model extends the modular invariance approach to odd weight modular forms. The model correctly explains neutrino phenomenology and establishes constraints on the mass of the right-handed fermion.
Article
Engineering, Electrical & Electronic
Junyi Guan, Sheng Li, Xiongxiong He, Jiajia Chen
Summary: The traditional Fuzzy c-means algorithm lacks local spatial information preservation in image segmentation, while solutions like superpixel technologies and Density peak clustering algorithm have limitations. A fast density peak clustering method based on kNN distance matrix is proposed for more robust spatial information reconstruction and high-consistent image segmentation. Experiments show its applicability for image segmentation.
IEEE SIGNAL PROCESSING LETTERS
(2021)
Article
Astronomy & Astrophysics
Amir Subba, Ritesh K. Singh
Summary: We study anomalous W-W+Z = gamma couplings in the production process e-e+ -> W-W+ followed by semileptonic decay using polarizations and spin-spin correlations of W bosons. We develop an artificial neural network and a boosted decision tree to distinguish down-type jets from up-type jets and use them to put constraints on anomalous couplings at the International Linear Collider (ILC). Our findings show that the use of polarization and spin-correlation observables significantly improves the limits on anomalous coupling compared to earlier studies.
Article
Astronomy & Astrophysics
Giacomo Cacciapaglia, Antimo Cagnotta, Roberta Calabrese, Francesco Carnevali, Agostino De Iorio, Alberto Orso Maria Iorio, Stefano Morisi, Francesco Sannino
Summary: The Standard Model of particle physics is being challenged by recent precision measurements performed using different accelerator machines. Anomalies have been observed in the measurement of the muon magnetic momentum and the mass of the W boson. This paper introduces a radiative extension of the Standard Model that can reconcile these experimental results and predict the existence of new bosons and fermions within the energy range of the proton-proton collisions at the LHC experiments.
Article
Food Science & Technology
Ning Yuan, Xuelu Chi, Qiaoyan Ye, Huimin Liu, Nan Zheng
Summary: We investigated the influence of heat treatment on the volatile organic compounds (VOCs) of milk using various technologies. The results showed that heat treatment at 65 degrees C for 30 min preserved the original taste of milk, while treatment at 135 degrees C significantly altered the overall flavor. Different processing techniques also affected taste presentation, with the sweetness, saltiness, and bitterness of the milk being influenced by the temperature of the heat treatment. Additionally, the composition of VOCs in milk changed with heat treatment, with acid compounds decreasing and ketones, esters, and hydrocarbons increasing. Our study provides new evidence for understanding the impact of processing on milk VOCs and insights into quality control during milk production.
Article
Computer Science, Information Systems
Zhifang Tan, Fei Dong, Xinfang Liu, Chenglong Li, Xiushan Nie
Summary: Video-moment location by query is a hot topic in video understanding. In this study, we propose an efficient video moment location method via hashing. By encoding query sentences and video clips into hash codes, we predict the corresponding timestamp based on the similarity among hash codes, improving location efficiency without real-time input of video clips.
Article
Multidisciplinary Sciences
Shigetaka Nishiguchi, Tadaomi Furuta, Takayuki Uchihashi
Summary: Classical cadherins are important in cell-cell adhesion. High-speed atomic force microscopy (HS-AFM) was used to observe full-length ectodomains of E-cadherin and discovered previously unreported dimeric structures, with about half of the cadherin dimers showing S-shaped conformations and greater dynamics than SS and X-like dimers. Mutational and molecular modeling analyses revealed that the S-shaped dimer had a different binding interface from SS and X-dimers. The formation of SS-dimer from S-shaped and X-like dimers was directly visualized.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Optics
Hong Jiang, Yihan Zhao, Chenyang Wang, Lina Cui
Summary: A DIDE-FPP composite algorithm is proposed to improve the spatial location accuracy of tunnel fires. The algorithm combines the distributed individuals differential evolution (DIDE) algorithm and the four-point positioning (FPP) method. Experimental results show that the proposed algorithm has advantages in peak finding and solving multimodal optimization problems compared to existing methods. The spatial positioning accuracy of a tunnel fire warning system using this method can reach the centimeter level.
Article
Computer Science, Artificial Intelligence
Xiangzhou Zhang, Bingpeng Ma, Hong Chang, Shiguang Shan, Xilin Chen
Summary: Location is crucial in instance segmentation, and LSNet integrates instance-specific location information for distinguishing instances. The proposed model also utilizes the Keypoints Sensitive Combination operation to effectively reduce mis-classified pixels, achieving superior performance compared to its peers, especially in cases of severe occlusion.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Zhonghao Chen, Guoyong Wu, Hongmin Gao, Yao Ding, Danfeng Hong, Bing Zhang
Summary: In this article, a novel superpixel generation strategy is proposed to segment hyperspectral images using both raw and deep abstract spectral features. Furthermore, a local aggregation and global attention block (LAGAB) is introduced to hierarchically explore local and global spatial information. Experimental results show that the proposed method achieves promising classification performance on four highly regarded HS datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Chemistry, Applied
Zebin Weng, Lu Sun, Fang Wang, Xiaonan Sui, Yong Fang, Xiaozhi Tang, Xinchun Shen
Summary: The study utilized E-nose, E-tongue, and HS-SPME-GC-MS technology combined with PCA to evaluate the flavor characteristics of soybean meal during enzymatic hydrolysis. Results showed that moderate enzymatic hydrolysis improved flavor, while excessive enzymatic hydrolysis led to flavor deterioration. E-tongue radar graph and PCA provided distribution of flavor substances during the hydrolysis process, offering theoretical basis for flavor enhancement in soybean meal and its products.
Article
Computer Science, Artificial Intelligence
Shuping Sun
KNOWLEDGE-BASED SYSTEMS
(2015)
Article
Engineering, Biomedical
Shuping Sun, Haibin Wang
AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE
(2018)
Article
Computer Science, Artificial Intelligence
Shuping Sun, Haibin Wang, Zhongwei Jiang, Yu Fang, Ting Tao
EXPERT SYSTEMS WITH APPLICATIONS
(2014)
Article
Engineering, Electrical & Electronic
Shuping Sun, Haibin Wang, Zhenhui Chang, Baosong Mao, Yefen Liu
IEEE SENSORS JOURNAL
(2019)
Article
Engineering, Electrical & Electronic
Shuping Sun
Summary: A study proposes a heart disease diagnostic system based on heart sound features, incorporating adaptive feature extraction and Mahalanobis distance classification criterion. The system's key features include automatic segmentation and feature extraction of heart sounds, classification using a GMM model, and performance evaluation showing higher accuracy compared to traditional methods.
IEEE SENSORS JOURNAL
(2021)
Article
Multidisciplinary Sciences
Shuping Sun, Tingting Huang, Biqiang Zhang, Peiguang He, Long Yan, Dongdong Fan, Jiale Zhang, Jinbo Chen
Summary: A novel intelligent diagnostic system is proposed for diagnosing heart sounds by automatically segmenting and extracting the features of the heart sounds, and adjusting them based on confidence levels.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Interdisciplinary Applications
Shuping Sun, Yaonan Tong, Biqiang Zhang, Bowen Yang, Peiguang He, Wei Song, Wenbo Yang, Yilin Wu, Guangyu Liu
Summary: This study introduces a novel method for adaptively determining the optimal number of components (M) in a Gaussian mixture model when fitting a dataset, avoiding underfitting and overfitting.
JOURNAL OF COMPUTATIONAL SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Shuping Sun, Yaonan Tong, Biqiang Zhang, Bowen Yang, Long Yan, Peiguang He, Hong Xu
Summary: In this study, a modified incremental Gaussian mixture model (MIGMM) algorithm is proposed as an improvement of FIGMM, along with an adaptive methodology for removing spurious components in MIGMM. The contributions include a more simple and efficient prediction matrix update compared to FIGMM, and the use of an effective exponential model and logical matrix to remove spurious components. Experimental results demonstrate the robust performance of the proposed framework in comparison to other methods.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Alireza Karimi, Reza Razaghi, Siddharth Daniel D'costa, Saeed Torbati, Sina Ebrahimi, Seyed Mohammadali Rahmati, Mary J. Kelley, Ted S. Acott, Haiyan Gong
Summary: This study investigated the biomechanical properties of the conventional aqueous outflow pathway using fluid-structure interaction. The results showed that the distribution of aqueous humor wall shear stress within this pathway is not uniform, which may contribute to our understanding of the underlying selective mechanisms.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Robert V. Bergen, Jean-Francois Rajotte, Fereshteh Yousefirizi, Arman Rahmim, Raymond T. Ng
Summary: This article introduces a 3D generative model called TrGAN, which can generate medical images with important features and statistical properties while protecting privacy. By evaluating through a membership inference attack, the fidelity, utility, and privacy trade-offs of the model were studied.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Hoda Mashayekhi, Mostafa Nazari, Fatemeh Jafarinejad, Nader Meskin
Summary: In this study, a novel model-free adaptive control method based on deep reinforcement learning (DRL) is proposed for cancer chemotherapy drug dosing. The method models the state variables and control action in their original infinite spaces, providing a more realistic solution. Numerical analysis shows the superior performance of the proposed method compared to the state-of-the-art RL-based approach.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Hao Sun, Bao Li, Liyuan Zhang, Yanping Zhang, Jincheng Liu, Suqin Huang, Xiaolu Xi, Youjun Liu
Summary: In cases of moderate stenosis in the internal carotid artery, the A1 segment of the anterior cerebral artery or the posterior communicating artery within the Circle of Willis may show a hemodynamic environment with high OSI and low TAWSS, increasing the risk of atherosclerosis development and stenosis in the CoW.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ilaria Toniolo, Paola Pirini, Silvana Perretta, Emanuele Luigi Carniel, Alice Berardo
Summary: This study compared the outcomes of endoscopic sleeve gastroplasty (ESG) and laparoscopic sleeve gastrectomy (LSG) in weight loss surgery using computational models of specific patients. The results showed significant differences between the two procedures in terms of stomach volume reduction and mechanical stimulation. A predictive model was proposed to support surgical planning and estimation of volume reduction after ESG.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Chun-You Chen, Ya-Lin Chen, Jeremiah Scholl, Hsuan-Chia Yang, Yu-Chuan (Jack) Li
Summary: This study evaluated the overall performance of a machine learning-based CDSS (MedGuard) in triggering clinically relevant alerts and intercepting inappropriate drug errors and LASA drug errors. The results showed that MedGuard has the ability to improve patients' safety by triggering clinically valid alerts.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Lingzhi Tang, Xueqi Wang, Jinzhu Yang, Yonghuai Wang, Mingjun Qu, HongHe Li
Summary: In this paper, a dynamical local feature fusion net for automatically recognizing aortic valve calcification (AVC) from echocardiographic images is proposed. The network segments high-echo areas and adjusts the selection of local features to better integrate global and local semantic representations. Experimental results demonstrate the effectiveness of the proposed approach.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
You-Lei Fu, Wu Song, Wanni Xu, Jie Lin, Xuchao Nian
Summary: This study investigates the combination of surface electromyographic signals (sEMG) and deep learning-based CNN networks to study the interaction between humans and products and the impact on body comfort. It compares the advantages and disadvantages of different CNN networks and finds that DenseNet has unique advantages over other algorithms in terms of accuracy and ease of training, while mitigating issues of gradient disappearance and model degradation.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Moritz Rempe, Florian Mentzel, Kelsey L. Pomykala, Johannes Haubold, Felix Nensa, Kevin Kroeninger, Jan Egger, Jens Kleesiek
Summary: In this study, a deep learning-based skull stripping algorithm for MRI was proposed, which works directly in the complex valued k-space and preserves the phase information. The results showed that the algorithm achieved similar results to the ground truth, with higher accuracy in the slices above the eye region. This approach not only preserves valuable information for further diagnostics, but also enables immediate anonymization of patient data before being transformed into the image domain.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ziyang Chen, Laura Cruciani, Elena Lievore, Matteo Fontana, Ottavio De Cobelli, Gennaro Musi, Giancarlo Ferrigno, Elena De Momi
Summary: In this paper, a deep learning-based approach is proposed to recover 3D information of intra-operative scenes, which can enhance the safety of robot-assisted surgery by implementing depth estimation using stereo images.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ao Leng, Bolun Zeng, Yizhou Chen, Puxun Tu, Baoxin Tao, Xiaojun Chen
Summary: This study presents a novel training system for zygomatic implant surgery, which offers a more realistic simulation and training solution. By integrating visual, haptic, and auditory feedback, the system achieves global rigid-body collisions and soft tissue simulation, effectively improving surgeons' proficiency.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Yingjie Wang, Xueqing Yin
Summary: This study developed an integrated computational model combining coronary flow and myocardial perfusion models to achieve physiologically accurate simulations. The model has the potential for clinical application in diagnosing insufficient myocardial perfusion.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Nitzan Avidan, Moti Freiman
Summary: This study aims to enhance the generalization capabilities of DNN-based MRI reconstruction methods for undersampled k-space data. By introducing a mask-aware DNN architecture and training method, the under-sampled data and mask are encoded within the model structure, leading to improved performance. Rigorous testing on the widely accessible fastMRI dataset reveals that this approach demonstrates better generalization capabilities and robustness compared to traditional DNN methods.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Enhao Zhang, Saeed Miramini, Lihai Zhang
Summary: This study investigates the combined effects of osteoporosis and diabetes on fracture healing process by developing numerical models. The results show that osteoporotic fractures have higher instability and disruption in mesenchymal stem cells' proliferation and differentiation compared to non-osteoporotic fractures. Moreover, when osteoporosis coexists with diabetes, the healing process of fractures can be severely impaired.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Yunhao Bai, Wenqi Li, Jianpeng An, Lili Xia, Huazhen Chen, Gang Zhao, Zhongke Gao
Summary: This study proposes an effective MIL method for classifying WSI of esophageal cancer. The use of self-supervised learning for feature extractor pretraining enhances feature extraction from esophageal WSI, leading to more robust and accurate performance. The proposed framework outperforms existing methods, achieving an accuracy of 93.07% and AUC of 95.31% on a comprehensive dataset of esophageal slide images.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
