keyword
https://read.qxmd.com/read/38477440/the-future-of-material-scientists-in-an-age-of-artificial-intelligence
#21
JOURNAL ARTICLE
Ayman Maqsood, Chen Chen, T Jesper Jacobsson
Material science has historically evolved in tandem with advancements in technologies for characterization, synthesis, and computation. Another type of technology to add to this mix is machine learning (ML) and artificial intelligence (AI). Now increasingly sophisticated AI-models are seen that can solve progressively harder problems across a variety of fields. From a material science perspective, it is indisputable that machine learning and artificial intelligence offer a potent toolkit with the potential to substantially accelerate research efforts in areas such as the development and discovery of new functional materials...
March 13, 2024: Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
https://read.qxmd.com/read/38474909/gy-slam-a-dense-semantic-slam-system-for-plant-factory-transport-robots
#22
JOURNAL ARTICLE
Xiaolin Xie, Yibo Qin, Zhihong Zhang, Zixiang Yan, Hang Jin, Man Xu, Cheng Zhang
Simultaneous Localization and Mapping (SLAM), as one of the core technologies in intelligent robotics, has gained substantial attention in recent years. Addressing the limitations of SLAM systems in dynamic environments, this research proposes a system specifically designed for plant factory transportation environments, named GY-SLAM. GY-SLAM incorporates a lightweight target detection network, GY, based on YOLOv5, which utilizes GhostNet as the backbone network. This integration is further enhanced with CoordConv coordinate convolution, CARAFE up-sampling operators, and the SE attention mechanism, leading to simultaneous improvements in detection accuracy and model complexity reduction...
February 20, 2024: Sensors
https://read.qxmd.com/read/38471350/a-novel-approach-for-intelligent-diagnosis-and-grading-of-diabetic-retinopathy
#23
JOURNAL ARTICLE
Zeru Hai, Beiji Zou, Xiaoxia Xiao, Qinghua Peng, Junfeng Yan, Wensheng Zhang, Kejuan Yue
Diabetic retinopathy (DR) is a severe ocular complication of diabetes that can lead to vision damage and even blindness. Currently, traditional deep convolutional neural networks (CNNs) used for DR grading tasks face two primary challenges: (1) insensitivity to minority classes due to imbalanced data distribution, and (2) neglecting the relationship between the left and right eyes by utilizing the fundus image of only one eye for training without differentiating between them. To tackle these challenges, we proposed the DRGCNN (DR Grading CNN) model...
March 6, 2024: Computers in Biology and Medicine
https://read.qxmd.com/read/38470428/printable-and-flexible-integrated-sensing-systems-for-wireless-healthcare
#24
REVIEW
Kemeng Zhou, Ruochen Ding, Xiaohao Ma, Yuanjing Lin
The rapid development of wearable sensing devices and artificial intelligence has enabled portable and wireless tracking of human health, fulfilling the promise of digitalized healthcare applications. To achieve versatile design and integration of multi-functional modules including sensors and data transmission units onto various flexible platforms, printable technologies emerged as some of the most promising strategies. This review first introduces the commonly utilized printing technologies, followed by discussion of the printable ink formulations and flexible substrates to ensure reliable device fabrication and system integration...
March 12, 2024: Nanoscale
https://read.qxmd.com/read/38466764/enhancing-vault-prediction-and-icl-sizing-through-advanced-machine-learning-models
#25
JOURNAL ARTICLE
Jun Zhu, Fen-Fen Li, Gao-Xiang Li, Shang-Yang Jiang, Dan Cheng, Fang-Jun Bao, Shuang-Qing Wu, Qi Dai, Yu-Feng Ye
PURPOSE: To use artificial intelligence (AI) technology to accurately predict vault and Implantable Collamer Lens (ICL) size. METHODS: The methodology focused on enhancing predictive capabilities through the fusion of machine-learning algorithms. Specifically, AdaBoost, Random Forest, Decision Tree, Support Vector Regression, LightGBM, and XGBoost were integrated into a majority-vote model. The performance of each model was evaluated using appropriate metrics such as accuracy, precision, F1-score, and area under the curve (AUC)...
March 2024: Journal of Refractive Surgery
https://read.qxmd.com/read/38466605/permutation-equivariant-graph-framelets-for-heterophilous-graph-learning
#26
JOURNAL ARTICLE
Jianfei Li, Ruigang Zheng, Han Feng, Ming Li, Xiaosheng Zhuang
The nature of heterophilous graphs is significantly different from that of homophilous graphs, which causes difficulties in early graph neural network (GNN) models and suggests aggregations beyond the one-hop neighborhood. In this article, we develop a new way to implement multiscale extraction via constructing Haar-type graph framelets with desired properties of permutation equivariance, efficiency, and sparsity, for deep learning tasks on graphs. We further design a graph framelet neural network model permutation equivariant graph framelet augmented network (PEGFAN) based on our constructed graph framelets...
March 11, 2024: IEEE Transactions on Neural Networks and Learning Systems
https://read.qxmd.com/read/38454735/a-two-stage-grasp-detection-method-for-sequential-robotic-grasping-in-stacking-scenarios
#27
JOURNAL ARTICLE
Jing Zhang, Baoqun Yin, Yu Zhong, Qiang Wei, Jia Zhao, Hazrat Bilal
Dexterous grasping is essential for the fine manipulation tasks of intelligent robots; however, its application in stacking scenarios remains a challenge. In this study, we aimed to propose a two-phase approach for grasp detection of sequential robotic grasping, specifically for application in stacking scenarios. In the initial phase, a rotated-YOLOv3 (R-YOLOv3) model was designed to efficiently detect the category and position of the top-layer object, facilitating the detection of stacked objects. Subsequently, a stacked scenario dataset with only the top-level objects annotated was built for training and testing the R-YOLOv3 network...
February 5, 2024: Mathematical Biosciences and Engineering: MBE
https://read.qxmd.com/read/38452431/recent-advances-in-synchrotron-scattering-methods-for-probing-the-structure-and-dynamics-of-colloids
#28
JOURNAL ARTICLE
Theyencheri Narayanan
Recent progress in synchrotron based X-ray scattering methods applied to colloid science is reviewed. An important figure of merit of these techniques is that they enable in situ investigations of colloidal systems under the desired thermophysical and rheological conditions. An ensemble averaged simultaneous structural and dynamical information can be derived albeit in reciprocal space. Significant improvements in X-ray source brilliance and advances in detector technology have overcome some of the limitations in the past...
March 2024: Advances in Colloid and Interface Science
https://read.qxmd.com/read/38451322/cine-cardiac-magnetic-resonance-to-distinguish-between-ischemic-and-non-ischemic-cardiomyopathies-a-machine-learning-approach
#29
JOURNAL ARTICLE
Riccardo Cau, Francesco Pisu, Alessandra Pintus, Vitanio Palmisano, Roberta Montisci, Jasjit S Suri, Rodrigo Salgado, Luca Saba
OBJECTIVE: This work aimed to derive a machine learning (ML) model for the differentiation between ischemic cardiomyopathy (ICM) and non-ischemic cardiomyopathy (NICM) on non-contrast cardiovascular magnetic resonance (CMR). METHODS: This retrospective study evaluated CMR scans of 107 consecutive patients (49 ICM, 58 NICM), including atrial and ventricular strain parameters. We used these data to compare an explainable tree-based gradient boosting additive model with four traditional ML models for the differentiation of ICM and NICM...
March 7, 2024: European Radiology
https://read.qxmd.com/read/38447303/graph-neural-network-contextual-embedding-for-deep-learning-on-tabular-data
#30
JOURNAL ARTICLE
Mario Villaizán-Vallelado, Matteo Salvatori, Belén Carro, Antonio Javier Sanchez-Esguevillas
All industries are trying to leverage Artificial Intelligence (AI) based on their existing big data which is available in so called tabular form, where each record is composed of a number of heterogeneous continuous and categorical columns also known as features. Deep Learning (DL) has constituted a major breakthrough for AI in fields related to human skills like natural language processing, but its applicability to tabular data has been more challenging. More classical Machine Learning (ML) models like tree-based ensemble ones usually perform better...
February 16, 2024: Neural Networks: the Official Journal of the International Neural Network Society
https://read.qxmd.com/read/38442060/coexistence-of-cyclic-sequential-pattern-recognition-and-associative-memory-in-neural-networks-by-attractor-mechanisms
#31
JOURNAL ARTICLE
Jingyang Huo, Jiali Yu, Min Wang, Zhang Yi, Jinsong Leng, Yong Liao
Neural networks are developed to model the behavior of the brain. One crucial question in this field pertains to when and how a neural network can memorize a given set of patterns. There are two mechanisms to store information: associative memory and sequential pattern recognition. In the case of associative memory, the neural network operates with dynamical attractors that are point attractors, each corresponding to one of the patterns to be stored within the network. In contrast, sequential pattern recognition involves the network memorizing a set of patterns and subsequently retrieving them in a specific order over time...
March 5, 2024: IEEE Transactions on Neural Networks and Learning Systems
https://read.qxmd.com/read/38434709/predicting-pharmacodynamic-effects-through-early-drug-discovery-with-artificial-intelligence-physiologically-based-pharmacokinetic-ai-pbpk-modelling
#32
JOURNAL ARTICLE
Keheng Wu, Xue Li, Zhou Zhou, Youni Zhao, Mei Su, Zhuo Cheng, Xinyi Wu, Zhijun Huang, Xiong Jin, Jingxi Li, Mengjun Zhang, Jack Liu, Bo Liu
A mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) model links the concentration-time profile of a drug with its therapeutic effects based on the underlying biological or physiological processes. Clinical endpoints play a pivotal role in drug development. Despite the substantial time and effort invested in screening drugs for favourable pharmacokinetic (PK) properties, they may not consistently yield optimal clinical outcomes. Furthermore, in the virtual compound screening phase, researchers cannot observe clinical outcomes in humans directly...
2024: Frontiers in Pharmacology
https://read.qxmd.com/read/38433089/cardiac-ct-competition-complimentary-or-confounder
#33
JOURNAL ARTICLE
Mehmet Onur Omaygenc, Yoshito Kadoya, Gary Robert Small, Benjamin Joe Wade Chow
Coronary CT angiography (CCTA) has been gradually adopted into clinical practice over the last two decades. CCTA has high diagnostic accuracy, prognostic value, and unique features such as assessment of plaque composition. CCTA-derived functional assessment techniques such as fractional flow reserve and CT perfusion are also available and can increase the diagnostic specificity of the modality. These properties propound CCTA as a competitor of functional testing in diagnosis of obstructive CAD, however, utilizing CCTA in a concomitant fashion to potentiate the performance of the latter can lead to better patient care and may provide more accurate prognostic information...
March 2, 2024: Journal of Medical Imaging and Radiation Sciences
https://read.qxmd.com/read/38408011/cot-contourlet-transformer-for-hierarchical-semantic-segmentation
#34
JOURNAL ARTICLE
Yilin Shao, Long Sun, Licheng Jiao, Xu Liu, Fang Liu, Lingling Li, Shuyuan Yang
The Transformer-convolutional neural network (CNN) hybrid learning approach is gaining traction for balancing deep and shallow image features for hierarchical semantic segmentation. However, they are still confronted with a contradiction between comprehensive semantic understanding and meticulous detail extraction. To solve this problem, this article proposes a novel Transformer-CNN hybrid hierarchical network, dubbed contourlet transformer (CoT). In the CoT framework, the semantic representation process of the Transformer is unavoidably peppered with sparsely distributed points that, while not desired, demand finer detail...
February 26, 2024: IEEE Transactions on Neural Networks and Learning Systems
https://read.qxmd.com/read/38407496/integrating-social-cognition-into-domain-general-control-interactive-activation-and-competition-for-the-control-of-action-icon
#35
JOURNAL ARTICLE
Robert Ward, Richard Ramsey
Social cognition differs from general cognition in its focus on understanding, perceiving, and interpreting social information. However, we argue that the significance of domain-general processes for controlling cognition has been historically undervalued in social cognition and social neuroscience research. We suggest much of social cognition can be characterized as specialized feature representations supported by domain-general cognitive control systems. To test this proposal, we develop a comprehensive working model, based on an interactive activation and competition architecture and applied to the control of action...
February 2024: Cognitive Science
https://read.qxmd.com/read/38403652/photonic-neuromorphic-architecture-for-tens-of-task-lifelong-learning
#36
JOURNAL ARTICLE
Yuan Cheng, Jianing Zhang, Tiankuang Zhou, Yuyan Wang, Zhihao Xu, Xiaoyun Yuan, Lu Fang
Scalable, high-capacity, and low-power computing architecture is the primary assurance for increasingly manifold and large-scale machine learning tasks. Traditional electronic artificial agents by conventional power-hungry processors have faced the issues of energy and scaling walls, hindering them from the sustainable performance improvement and iterative multi-task learning. Referring to another modality of light, photonic computing has been progressively applied in high-efficient neuromorphic systems. Here, we innovate a reconfigurable lifelong-learning optical neural network (L2 ONN), for highly-integrated tens-of-task machine intelligence with elaborated algorithm-hardware co-design...
February 26, 2024: Light, Science & Applications
https://read.qxmd.com/read/38399392/model-informed-drug-development-in-silico-assessment-of-drug-bioperformance-following-oral-and-percutaneous-administration
#37
REVIEW
Jelena Djuris, Sandra Cvijic, Ljiljana Djekic
The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes...
January 30, 2024: Pharmaceuticals
https://read.qxmd.com/read/38399002/integration-technology-with-thin-films-co-fabricated-in-laminated-composite-structures-for-defect-detection-and-damage-monitoring
#38
JOURNAL ARTICLE
Rogers K Langat, Emmanuel De Luycker, Arthur Cantarel, Micky Rakotondrabe
Despite the well-established nature of non-destructive testing (NDT) technologies, autonomous monitoring systems are still in high demand. The solution lies in harnessing the potential of intelligent structures, particularly in industries like aeronautics. Substantial downtime occurs due to routine maintenance, leading to lost revenue when aircraft are grounded for inspection and repairs. This article explores an innovative approach using intelligent materials to enhance condition-based maintenance, ultimately cutting life-cycle costs...
February 15, 2024: Micromachines
https://read.qxmd.com/read/38398585/recent-progress-of-protein-tertiary-structure-prediction
#39
REVIEW
Qiqige Wuyun, Yihan Chen, Yifeng Shen, Yang Cao, Gang Hu, Wei Cui, Jianzhao Gao, Wei Zheng
The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14)...
February 13, 2024: Molecules: a Journal of Synthetic Chemistry and Natural Product Chemistry
https://read.qxmd.com/read/38392137/the-pine-cone-optimization-algorithm-pcoa
#40
JOURNAL ARTICLE
Mahdi Valikhan Anaraki, Saeed Farzin
The present study introduces a novel nature-inspired optimizer called the Pine Cone Optimization algorithm (PCOA) for solving science and engineering problems. PCOA is designed based on the different mechanisms of pine tree reproduction, including pollination and pine cone dispersal by gravity and animals. It employs new and powerful operators to simulate the mentioned mechanisms. The performance of PCOA is analyzed using classic benchmark functions, CEC017 and CEC2019 as mathematical problems and CEC2006 and CEC2011 as engineering design problems...
February 1, 2024: Biomimetics
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