| 1. |
- Beal, Jacob, et al.
(författare)
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Robust estimation of bacterial cell count from optical density
- 2020
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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| 2. |
- Feng, Hailin, et al.
(författare)
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Innovative soft computing-enabled cloud optimization for next-generation IoT in digital twins
- 2023
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Ingår i: Applied Soft Computing. - : Elsevier. - 1568-4946 .- 1872-9681. ; 136
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Tidskriftsartikel (refereegranskat)abstract
- The research aims to reduce the network resource pressure on cloud centers (CC) and edge nodes, to improve the service quality and to optimize the network performance. In addition, it studies and designs a kind of edge–cloud collaboration framework based on the Internet of Things (IoT). First, raspberry pi (RP) card working machines are utilized as the working nodes, and a kind of edge–cloud collaboration framework is designed for edge computing. The framework consists mainly of three layers, including edge RP (ERP), monitoring & scheduling RP (MSRP), and CC. Among the three layers, collaborative communication can be realized between RPs and between RPs and CCs. Second, a kind of edge–cloud matching algorithm is proposed in the time delay constraint scenario. The research results obtained by actual task assignments demonstrate that the task time delay in face recognition on edge–cloud collaboration mode is the least among the three working modes, including edge only, CC only, and edge–CC collaboration modes, reaching only 12 s. Compared with that of CC running alone, the identification results of the framework rates on edge–cloud collaboration and CC modes are both more fluent than those on edge mode only, and real-time object detection can be realized. The total energy consumption of the unloading execution by system users continuously decreases with the increase in the number of users. It is assumed that the number of pieces of equipment in systems is 150, and the energy-saving rate of systems is affected by the frequency of task generation. The frequency of task generation increases with the corresponding reduction in the energy-saving rate of systems. Based on object detection as an example, the system energy consumption is decreased from 18 W to 16 W after the assignment of algorithms. The included framework improves the resource utility rate and reduces system energy consumption. In addition, it provides theoretical and practical references for the implementation of the edge–cloud collaboration framework.
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| 3. |
- He, Tongyue, et al.
(författare)
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Toward Wearable Sensors : Advances, Trends, and Challenges
- 2023
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Ingår i: ACM Computing Surveys. - : Association for Computing Machinery (ACM). - 0360-0300 .- 1557-7341. ; 55:14S
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Forskningsöversikt (refereegranskat)abstract
- Sensors suitable for wearable devices have many special characteristics compared to other sensors, such as stability, sensitivity, sensor volume, biocompatibility, and so on. With the development of wearable technology, amazing wearable sensors have attracted a lot of attention, and some researchers have done a large number of technology explorations and reviews. However, previous surveys generally were concerned with a specified application and comprehensively reviewed the computing techniques for the signals required by this application, as well as how computing can promote data processing. There is a gap in the opposite direction, i.e., the fundamental data source actively stimulates application rather than from the application to the data, and computing promotes the acquisition of data rather than data processing. To fill this gap, starting with different parts of the body as the source of signal, the fundamental data sources that can be obtained and detected are explored by combining the three sensing principles, as well as discussing and analyzing the existing and potential applications of machine learning in simplifying sensor designs and the fabrication of sensors.
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| 4. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Cognitive Computing for Brain-Computer Interface-Based Computational Social Digital Twins Systems
- 2022
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Ingår i: IEEE Transactions on Computational Social Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2329-924X. ; 9:6, s. 1635-1643
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Tidskriftsartikel (refereegranskat)abstract
- To accurately and effectively analyze electroencephalogram (EEG) with high complexity, large amount of data, and strong uncertainty, brain-computer interface (BCI) cognitive computing and its signal analysis algorithms are studied based on the digital twins (DTs) cognitive computing platform. To avoid the influence of noise on EEG analysis results, it is necessary to use filtering and defalsification methods to process EEG. Four methods, including Butterworth filter, finite impulse response (FIR) filter, elliptic filter, and wavelet decomposition, are summarized. Based on the Riemann manifold theory, a feature extraction algorithm under transfer learning based on tangent space selection (TL-TSS) is proposed. In the process of decoding EEG, an EEG decoding method combining entropy measure and singular spectrum analysis (SSA) is proposed. An algorithm performance is tested on the motor imagery dataset of the two International BCI Competitions. It is found that when the training sample size accounts for 5%, the TL-TSS algorithm proposed in this work is superior to other algorithms in classification accuracy. In particular, compared with common spatial pattern (CSP) algorithm, it has great advantages. The classification accuracy of A2, A4, A8, and A9 users is the best, and especially for A8 users, the classification accuracy reaches 97.88%. In summary, in the EEG interface technology of DT cognitive computing platform, the combination of cognitive computing and deep learning can improve the recognition and analysis effect of EEG, which is of great value for further optimization of DT cognitive computing system.
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| 5. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Energy-Efficient Resource Allocation of Wireless Energy Transfer for the Internet of Everything in Digital Twins
- 2022
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Ingår i: IEEE Communications Magazine. - : Institute of Electrical and Electronics Engineers (IEEE). - 0163-6804 .- 1558-1896. ; 60:8, s. 68-73
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Tidskriftsartikel (refereegranskat)abstract
- The work aims to improve the stability of wireless energy transfer (WET) in the Internet of Things (IoT), prolong the service life of wireless devices, and promote green communication. Based on a digital twins (DTs) IoT environment, we depict how to optimize the energy efficiency of large-scale multiple-input multiple-output (MIMO) systems under WET technology. The large-scale distributed antenna array is applied to the wireless sensor network. MIMO can produce extremely narrow beams so that the system reduces interference to other users. Our MIMO system's energy efficiency optimization uses fractional planning and the block coordinate descent algorithm. The simulation results show that the algorithm has the best throughput performance when the maximum transmission power reaches 19 dBm. The total energy consumption of the proposed resource allocation algorithm is only about 9 percent higher than that of the power minimization algorithm. In the case of different maximum transfer powers, the number of iterations in which the proposed algorithm is required to converge is within four. Changes in the number of users cannot affect the convergence performance of the proposed algorithm. After the antenna selection mechanism is introduced, the average power of the energy received by the user is improved notably compared to the case of simply using the large-scale distributed antenna array. The research results can reference large-scale MIMO systems' energy efficiency optimization problems under WET conditions in the DTs IoT environment.
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| 6. |
- Tu, Zhen, et al.
(författare)
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Digital Twins-Based Automated Pilot for Energy-Efficiency Assessment of Intelligent Transportation Infrastructure
- 2022
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; 23:11, s. 22320-22330
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Tidskriftsartikel (refereegranskat)abstract
- To realize the great potential of the intelligent transportation infrastructure, the investment in the transportation infrastructure in the intelligent transportation system should be rationally planned. Firstly, the application status of cutting-edge Data Envelopment Analysis (DEA) model in transportation infrastructure efficiency evaluation is analyzed, and based on this, a DEA model of transportation infrastructure efficiency evaluation under Digital Twins technology is established. Secondly, with the transportation infrastructure of 12 prefecture-level cities in Jiangsu Province from 2005 to 2020 as the research object, the Digital Twins DEA model and the traditional Stochastic Frontier Approach (SFA) model are used to estimate the efficiency of transportation infrastructure in 12 cities. Finally, the traffic flow data of a certain road section in Zhenjiang City (J11 City) is simulated and predicted by using the Long Short-term Memory (LSTM) traffic flow prediction model. The results show that the average efficiency of the 12 cities estimated by the DEA model based on the Digital Twins is 0.7083, the average efficiency of the 12 cities estimated by the SFA model is 0.6445, and there are significant differences in the efficiency rankings of the cities. Compared with the actual efficiency, the established Digital Twins DEA model is more reasonable for the calculation of transportation infrastructure efficiency. The results of the LSTM traffic flow prediction model show that the Mean Absolute Error (MAE) of the LSTM model is 24.29, the Root Mean Square Error (RSME) is 0.1186, and the Mean Absolute Perce (MAPE) is 17.78, which are all lower than other models. Compared with other models, the proposed LSTM-based traffic flow prediction model is more accurate in traffic flow prediction. Hence, the research content provides a reference for the investment planning of intelligent transportation system infrastructure.
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| 7. |
- Wang, Jinxia, et al.
(författare)
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Deep Transfer Learning-Based Multi-Modal Digital Twins for Enhancement and Diagnostic Analysis of Brain MRI Image
- 2023
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Ingår i: IEEE/ACM Transactions on Computational Biology & Bioinformatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1545-5963 .- 1557-9964. ; 20:4, s. 2407-2419
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Tidskriftsartikel (refereegranskat)abstract
- Objective: it aims to adopt deep transfer learning combined with Digital Twins (DTs) in Magnetic Resonance Imaging (MRI) medical image enhancement.Methods: MRI image enhancement method based on metamaterial composite technology is proposed by analyzing the application status of DTs in medical direction and the principle of MRI imaging. On the basis of deep transfer learning, MRI super-resolution deep neural network structure is established. To address the problem that different medical imaging methods have advantages and disadvantages, a multi-mode medical image fusion algorithm based on adaptive decomposition is proposed and verified by experiments.Results: the optimal Peak Signal to Noise Ratio (PSNR) of 34.11dB can be obtained by introducing modified linear element and loss function of deep transfer learning neural network structure. The Structural Similarity Coefficient (SSIM) is 85.24%. It indicates that the MRI truthfulness and sharpness obtained by adding composite metasurface are improved greatly. The proposed medical image fusion algorithm has the highest overall score in the subjective evaluation of the six groups of fusion image results. Group III had the highest score in Magnetic Resonance Imaging- Positron Emission Computed Tomography (MRI-PET) image fusion, with a score of 4.67, close to the full score of 5. As for the objective evaluation in group I of Magnetic Resonance Imaging- Single Photon Emission Computed Tomography (MRI-SPECT) images, the Root Mean Square Error (RMSE), Relative Average Spectral Error (RASE) and Spectral Angle Mapper (SAM) are the highest, which are 39.2075, 116.688, and 0.594, respectively. Mutual Information (MI) is 5.8822.Conclusion: the proposed algorithm has better performance than other algorithms in preserving spatial details of MRI images and color information direction of SPECT images, and the other five groups have achieved similar results.
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| 8. |
- Zhong, Yanglong, et al.
(författare)
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An Improved Cohesive Zone Model for Interface Mixed-Mode Fractures of Railway Slab Tracks
- 2021
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- The interface crack of a slab track is a fracture of mixed-mode that experiences a complex loading–unloading–reloading process. A reasonable simulation of the interaction between the layers of slab tracks is the key to studying the interface crack. However, the existing models of interface disease of slab track have problems, such as the stress oscillation of the crack tip and self-repairing, which do not simulate the mixed mode of interface cracks accurately. Aiming at these shortcomings, we propose an improved cohesive zone model combined with an unloading/reloading relationship based on the original Park–Paulino–Roesler (PPR) model in this paper. It is shown that the improved model guaranteed the consistency of the cohesive constitutive model and described the mixed-mode fracture better. This conclusion is based on the assessment of work-of-separation and the simulation of the mixed-mode bending test. Through the test of loading, unloading, and reloading, we observed that the improved unloading/reloading relationship effectively eliminated the issue of self-repairing and preserved all essential features. The proposed model provides a tool for the study of interface cracking mechanism of ballastless tracks and theoretical guidance for the monitoring, maintenance, and repair of layer defects, such as interfacial cracks and slab arches.
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