Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)最新文献_第4页

SuperResNET: Model-Free Single-Molecule Network Analysis Software Achieves Molecular Resolution of Nup96

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-25 DOI: 10.1002/aisy.202400521

Yahongyang Lydia Li, Ismail M. Khater, Christian Hallgrimson, Ben Cardoen, Timothy H. Wong, Ghassan Hamarneh, Ivan R. Nabi

{"title":"SuperResNET: Model-Free Single-Molecule Network Analysis Software Achieves Molecular Resolution of Nup96","authors":"Yahongyang Lydia Li, Ismail M. Khater, Christian Hallgrimson, Ben Cardoen, Timothy H. Wong, Ghassan Hamarneh, Ivan R. Nabi","doi":"10.1002/aisy.202400521","DOIUrl":"https://doi.org/10.1002/aisy.202400521","url":null,"abstract":"SuperResNET is an integrated machine learning-based analysis software for visualizing and quantifying 3D point cloud data acquired by single-molecule localization microscopy (SMLM). SuperResNET computational modules include correction for multiple blinking of single fluorophores, denoising, segmentation (clustering), feature extraction used for cluster group identification, modularity analysis, blob retrieval, and visualization in 2D and 3D. Here, a graphical user interface version of SuperResNET was applied to publicly available direct stochastic optical reconstruction microscopy (dSTORM) data of nucleoporin Nup96 and Nup107 labeled nuclear pores that present a highly organized octagon structure of eight corners. SuperResNET effectively segments nuclear pores and Nup96 corners based on differential proximity threshold analysis from 2D and 3D SMLM datasets. SuperResNET quantitatively analyzes features from segmented nuclear pores, including complete structures with eightfold symmetry, and from segmented corners. SuperResNET modularity analysis of segmented corners from 2D SMLM distinguishes two modules at 10.7 ± 0.1 nm distance, corresponding to two individual Nup96 molecules. SuperResNET is therefore a model-free tool that can reconstruct network architecture and molecular distribution of subcellular structures without the bias of a specified prior model, attaining molecular resolution from dSTORM data. SuperResNET provides flexibility to report on structural diversity in situ within the cell, providing opportunities for biological discovery.","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"7 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202400521","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143633099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigation of Analog Memristor Characteristics for Hardware Synaptic Weight in Multilayer Neural Network

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-25 DOI: 10.1002/aisy.202400710

Jingon Jang, Yoonseok Song, Sungjun Park

{"title":"Investigation of Analog Memristor Characteristics for Hardware Synaptic Weight in Multilayer Neural Network","authors":"Jingon Jang, Yoonseok Song, Sungjun Park","doi":"10.1002/aisy.202400710","DOIUrl":"https://doi.org/10.1002/aisy.202400710","url":null,"abstract":"Analog conductance switching characteristics of memristor devices have been studied to be utilized for constituent elements of synaptic weight matrix in neural networks, related to system design of hardware-level parallel neuromorphic computing architecture for the artificial intelligence application. In this manner, it is important to systematically investigate the specific requirements of memristor characteristics associated with the capability to emulate plenty of synaptic weight elements linked between constituent layers in neural networks. Here, the learning capabilities of analog conductance state of memristor device for the perceptron of unstructured complex dataset in multilayer neural network are analyzed in terms of the number of analog state, nonlinearity, and conductance error. It is found that the requirable number of analog state is analyzed in about ≈50 states and conductance deviation of each analog state is until ≈5% of original value with nonlinearity of ≈0.142 according to constant programming pulse scheme. With the memristor characteristics enough to mimic synaptic weight to be learnt and infer the Fashion-mnist dataset, the classification accuracy is satisfied as ≈84.36% with the loss of ≈16.8% to original level. Owing to this investigation, applicability of novel memristor device could be conveniently examined for the utilization as synaptic weight in multilayer neural networks.","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"7 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202400710","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143633123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Printed Wearable Sweat Rate Sensor for Continuous In Situ Perspiration Measurement

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-25 DOI: 10.1002/aisy.202400927

Mohammad Shafiqul Islam, Sangwon Cha, Md Farhad Hassan, Wenxin Cai, Tahsin Sejat Saniat, Cedar Rose Leach, Yasser Khan

{"title":"Printed Wearable Sweat Rate Sensor for Continuous In Situ Perspiration Measurement","authors":"Mohammad Shafiqul Islam, Sangwon Cha, Md Farhad Hassan, Wenxin Cai, Tahsin Sejat Saniat, Cedar Rose Leach, Yasser Khan","doi":"10.1002/aisy.202400927","DOIUrl":"https://doi.org/10.1002/aisy.202400927","url":null,"abstract":"Sweat rate measures key physiological states such as hydration levels and heat tolerance. Incorporating wearable technology with sweat rate sensors allows individuals to conveniently monitor their health, optimize workouts, and enhance occupational safety. However, challenges persist in such integration techniques, including intricate manufacturing, nonlinear responses to changes in sweat rates, and errors from the intermediate measurement of the distance sweat travels in the sensor. To address these issues, we present a comprehensive wearable platform that includes a fully printed, flexible sensor patch, readout electronics, and a mobile app for continuous, real-time monitoring of sweat rate. We fabricate a sensor patch with an area of 700 mm2 and a weight of 380 mg by utilizing direct 3D printing and scalable microfluidic fabrication. The microfluidic channels are 850 μm wide and 164 μm thick, with serpentine electrodes measuring sweat rate using capacitance. The custom readout electronics capture these changes in capacitance to accurately measure sweat rate, achieving a sensitivity of 0.01 μL min−1. The sensor's performance is validated against analytical models, simulations, and on-body trials with commercial sensors. This cost-effective, flexible, and fully integrated sweat-sensing solution has significant potential in precision health.","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"7 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202400927","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143633124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting Performance of Hall Effect Ion Source Using Machine Learning 利用机器学习预测霍尔效应离子源的性能

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-25 DOI: 10.1002/aisy.202400555

Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su-Jin Shin, Young-Chul Ghim, Sanghoo Park, Wonho Choe

{"title":"Predicting Performance of Hall Effect Ion Source Using Machine Learning","authors":"Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su-Jin Shin, Young-Chul Ghim, Sanghoo Park, Wonho Choe","doi":"10.1002/aisy.202400555","DOIUrl":"https://doi.org/10.1002/aisy.202400555","url":null,"abstract":"Accurate performance prediction methods are essential for the development of high-efficiency Hall effect ion sources, which are employed in industries ranging from material surface treatment to spacecraft electric propulsion (known as Hall thrusters). Traditional methods rely on simplified scaling laws and computationally intensive numerical simulations. Herein, a robust machine learning model is introduced that uses a neural network ensemble to predict the performance of Hall effect ion sources based on design parameters such as discharge channel dimensions and magnetic field structure. The neural networks are trained using 18 000 data points generated from numerical simulations with input powers ranging from sub-kW- to kW-class. The accuracy of the developed machine learning model is demonstrated using untrained 700 W- and 1 kW-class Hall effect ion sources, producing results with deviations of less than 10% compared to the experimentally measured thrust and discharge current, thus surpassing the accuracy of conventional scaling laws. As a high-fidelity surrogate for numerical simulations, the proposed prediction tool provides high prediction accuracy and calculation speed, offering an excellent complement to conventional scaling laws and enhancing the understanding of Hall effect ion source performance characteristics.","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"7 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202400555","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143633100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Long-Distance Autonomous Navigation of Optical Microrobotic Swarms in Complex Environments

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-23 DOI: 10.1002/aisy.202470056

Zhihan Chen, Siyuan Huang, Yuebing Zheng

引用次数: 0

Classification of Vaginal Cleanliness Grades through Surface-Enhanced Raman Spectral Analysis via The Deep-Learning Variational Autoencoder–Long Short-Term Memory Model

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-23 DOI: 10.1002/aisy.202470059

Jia-Wei Tang, Xin-Ru Wen, Hui-Min Chen, Jie Chen, Kun-Hui Hong, Quan Yuan, Muhammad Usman, Liang Wang

{"title":"Classification of Vaginal Cleanliness Grades through Surface-Enhanced Raman Spectral Analysis via The Deep-Learning Variational Autoencoder–Long Short-Term Memory Model","authors":"Jia-Wei Tang, Xin-Ru Wen, Hui-Min Chen, Jie Chen, Kun-Hui Hong, Quan Yuan, Muhammad Usman, Liang Wang","doi":"10.1002/aisy.202470059","DOIUrl":"https://doi.org/10.1002/aisy.202470059","url":null,"abstract":"Deep-Learning-Guided Surface-Enhanced Raman Spectroscopy\u0000 In article number 2400587, Muhammad Usman, Liang Wang, and co-workers present a novel approach combining deep-learning-guided surface-enhanced Raman spectroscopy (SERS) and a variational autoencoder (VAE) with a long short-term memory (LSTM) neural network to classify vaginal cleanliness levels rapidly and accurately. Enhanced spectral quality and an optimized VAE–LSTM model yielded an 85% accuracy on blind test data. This method, which improves signal-to-noise ratios and diagnostic efficiency, shows strong potential for clinical applications in assessing vaginal cleanliness through SERS analysis of vaginal secretions.\u0000\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture>\u0000 </div>\u0000 </figure>","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"6 12","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202470059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143253111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decoupling Implantation Prediction and Embryo Ranking in Machine Learning: The Impact of Clinical Data and Discarded Embryos

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-23 DOI: 10.1002/aisy.202470058

Itay Erlich, Sotirios H. Saravelos, Cristina Hickman, Assaf Ben-Meir, Iris Har-Vardi, James A. Grifo, Semra Kahraman, Assaf Zaritsky

引用次数: 0

AS-XAI: Self-Supervised Automatic Semantic Interpretation for CNN

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-23 DOI: 10.1002/aisy.202470055

Changqi Sun, Hao Xu, Yuntian Chen, Dongxiao Zhang

引用次数: 0

A Novel Parameter Estimation Method for Pneumatic Soft Hand Control Applying Logarithmic Decrement for Pseudo-Rigid Body Modeling

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-15 DOI: 10.1002/aisy.202400637

Haiyun Zhang, Kelvin HoLam Heung, Gabrielle J. Naquila, Ashwin Hingwe, Ashish D. Deshpande

{"title":"A Novel Parameter Estimation Method for Pneumatic Soft Hand Control Applying Logarithmic Decrement for Pseudo-Rigid Body Modeling","authors":"Haiyun Zhang, Kelvin HoLam Heung, Gabrielle J. Naquila, Ashwin Hingwe, Ashish D. Deshpande","doi":"10.1002/aisy.202400637","DOIUrl":"https://doi.org/10.1002/aisy.202400637","url":null,"abstract":"Controlling soft robots, especially soft hand grasping, is complex due to their ubiquitous deformation, prompting the use of reduced model-based controllers to provide sufficient state information for high dynamic response control performance. However, most modeling techniques face computational efficiency and complexity of parameter identification issues. To alleviate this, a paradigm coupling an analytical modeling approach based on pseudo-rigid body modeling and the logarithmic decrement method (PRBM + LDM) for parameter estimation is proposed. Using a soft robot hand test bed, the PRBM + LDM model for a closed-loop position controller is applied and is compared with a simple proportional–integral–derivative controller (PID controller) static shape control of soft continuum robots using deep visual inverse kinematic models. Furthermore, the PRBM + LDM model-based force controller is compared with simple constant pressure grasping control by pinching tasks on low-weight, small objects—a screwdriver, a potato chip, and a brass coin. The PRBM + LDM-based position controller outperforms the simple PID position controller, and the PRBM + LDM-based force controller achieves a higher success rate than the constant pressure grasping control in the pinching tasks. In conclusion, the PRBM + LDM modeling technique proves to be a convenient and efficient way to model the dynamic behavior of soft actuators closely and can be applied to build high-precision position and force controllers.","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"7 3","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202400637","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Risks and Rewards of Embodying Artificial Intelligence with Cloud-Based Laboratories

IF 6.8

Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-12-15 DOI: 10.1002/aisy.202400193

Nicolas Rouleau, Nirosha J. Murugan

{"title":"The Risks and Rewards of Embodying Artificial Intelligence with Cloud-Based Laboratories","authors":"Nicolas Rouleau, Nirosha J. Murugan","doi":"10.1002/aisy.202400193","DOIUrl":"https://doi.org/10.1002/aisy.202400193","url":null,"abstract":"Autonomous, cloud-based laboratories (CBLs) are transforming scientific research by democratizing access to advanced instruments that accelerate high-throughput discovery. As artificial intelligences (AIs) become integrated or “embodied” with CBLs and gain independence from human oversight, efforts to identify novel pharmaceuticals, renewable energies, and agricultural biotechnologies will accelerate. AI-driven CBLs can perform tasks more efficiently and accurately than human scientists at lower costs, achieving results in weeks rather than years. However, as AI systems approach or exceed human intelligence, their decision-making abilities could outpace the need for human input, raising ethical, economic, and safety concerns. Aligning AI goals with human values is critical, as unregulated systems could pose existential risks, including global health hazards or the distortion of knowledge-generating systems. AI-driven misinformation in research highlights the need for transparency and data integrity, which may be achieved by aligning incentivizes and engineered fail-safes to promote long-term human flourishing. To mitigate risks, strict compartmentalization of AI systems and CBLs with third-party supervision at fine temporal resolutions will be necessary. While current CBLs are piloted by humans, future AI systems may relegate humans to the role of co-pilot. Anticipating increased AI-CBL integration, policies must balance innovation with caution to maximize benefits and avoid unintended harm.","PeriodicalId":93858,"journal":{"name":"Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)","volume":"7 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aisy.202400193","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0