| 1. |
- You, Xiaohu, et al.
(författare)
-
Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts
- 2021
-
Ingår i: Science China Information Sciences. - : Science Press. - 1674-733X .- 1869-1919. ; 64:1
-
Forskningsöversikt (refereegranskat)abstract
- The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.
|
|
| 2. |
- Fan, Qunping, 1989, et al.
(författare)
-
A Non-Conjugated Polymer Acceptor for Efficient and Thermally Stable All-Polymer Solar Cells
- 2020
-
Ingår i: Angewandte Chemie - International Edition. - : Wiley. - 1433-7851 .- 1521-3773. ; 59:45, s. 19835-19840
-
Tidskriftsartikel (refereegranskat)abstract
- A non-conjugated polymer acceptor PF1-TS4 was firstly synthesized by embedding a thioalkyl segment in the mainchain, which shows excellent photophysical properties on par with a fully conjugated polymer, with a low optical band gap of 1.58 eV and a high absorption coefficient >105 cm−1, a high LUMO level of −3.89 eV, and suitable crystallinity. Matched with the polymer donor PM6, the PF1-TS4-based all-PSC achieved a power conversion efficiency (PCE) of 8.63 %, which is ≈45 % higher than that of a device based on the small molecule acceptor counterpart IDIC16. Moreover, the PF1-TS4-based all-PSC has good thermal stability with ≈70 % of its initial PCE retained after being stored at 85 °C for 180 h, while the IDIC16-based device only retained ≈50 % of its initial PCE when stored at 85 °C for only 18 h. Our work provides a new strategy to develop efficient polymer acceptor materials by linkage of conjugated units with non-conjugated thioalkyl segments.
|
|
| 3. |
- Meng, Yuan, et al.
(författare)
-
Influence of land use type and urbanization level on the distribution of pharmaceuticals and personal care products and risk assessment in Beiyun River, China
- 2021
-
Ingår i: Chemosphere. - : Pergamon Press. - 0045-6535 .- 1879-1298. ; 287:Pt 1
-
Tidskriftsartikel (refereegranskat)abstract
- Influence of land use type and urbanization level on the distribution of pharmaceuticals and personal care products (PPCPs) from the developed regions of Beijing-Tianjin-Hebei in the northern China was evaluated. The seasonal and spatial variations of the 22 target PPCPs were analyzed in the 63 sampling sites along the whole Beiyun River Basin. Results showed that the total PPCPs concentration had a wide variation range, from 132 ng L-1 to 25474 ng L-1. Spatial interpolation analysis showed that agricultural land presented higher PPCPs contamination level than build-up land (p < 0.05) and the concentration was negatively correlated with urbanization level. Source apportionment showed the untreated sewage source contributed to 34%-53% of the PPCPs burden in the Beiyun River. Risk assessment indicated that diethyltoluamide, carbamazepine, octocrylene, gemfibrozil and triclocarban had high risks (RQ > 1), and small tributaries had the highest mixed risk (MRQ = 34). Species sensitivity distribution combined with the safety threshold method showed that PPCPs would have potential risk on aquatic organisms even at very low concentrations and triclocarban posed the highest risk in the Beiyun River.
|
|
| 4. |
- Wu, Chuanyan, et al.
(författare)
-
PEPRF : Identification of Essential Proteins by Integrating Topological Features of PPI Network and Sequence-based Features via Random Forest
- 2021
-
Ingår i: Current Bioinformatics. - : Bentham Science Publishers Ltd.. - 1574-8936. ; 16:9, s. 1161-1168
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Essential proteins play an important role in the process of life, which can be identified by experimental methods and computational approaches. Experimental approaches to identify essential proteins are of high accuracy but with the limitation of time and resource-consuming. Objective: Herein, we present a computational model (PEPRF) to identify essential proteins based on machine learning. Methods: Different features of proteins were extracted. Topological features of Protein-Protein Interaction (PPI) network-based are extracted. Based on the protein sequence, graph theory-based features, in-formation-based features, composition and physichemical features, etc., were extracted. Finally, 282 features are constructed. In order to select the features that contributed most to the identification, Re-liefF-based feature selection method was adopted to measure the weights of these features. Results: As a result, 212 features were curated to train random forest classifiers. Finally, PEPRF get the AUC of 0.71 and an accuracy of 0.742. Conclusion: Our results show that PEPRF may be applied as an efficient tool to identify essential pro-teins.
|
|
| 5. |
- Muhammed, Alemu Jorgi, et al.
(författare)
-
Resource Allocation for Energy-Efficient NOMA System in Coordinated Multi-Point Networks
- 2021
-
Ingår i: IEEE Transactions on Vehicular Technology. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9545 .- 1939-9359. ; 70:2, s. 1577-1591
-
Tidskriftsartikel (refereegranskat)abstract
- This paper studies user scheduling and power allocation problem to maximize the energy efficiency (EE) for non-orthogonal multiple access (NOMA) in downlink Coordinated Multi-Point networks. In the proposed framework, a more practical scenario the imperfect channel state information, imperfect successive interference cancellation and data outage are investigated. To address the considered problem, the optimization problem is formulated constrained by the total power and the outage probability requirements. However, the EE objective function is with a non-convex structure. Accordingly, we first convert the optimization problem to make the objective function concave and analytically tractable. Furthermore, we split the joint optimization problem to find a suboptimal solutions to the original problem. As a result, we first propose a suboptimal user-scheduling algorithm to improve the system's EE. Due to the non-convex function of the transmit power, we invoke a sequential successive convex approach to address the non-convex problem by its lower bound concave function. Besides, the fractional objective function is converted to its equivalent subtractive form. Finally, we derive a power control scheme to address the proposed framework. Simulation results endorse the effectiveness of the proposed algorithm and their performance gains in terms of EE compared to both NOMA and OFDMA variants.
|
|
| 6. |
- Qu, Zhiguo, et al.
(författare)
-
Quantum detectable Byzantine agreement for distributed data trust management in blockchain
- 2023
-
Ingår i: Information Sciences. - Philadelphia, PA : Elsevier. - 0020-0255 .- 1872-6291. ; 637
-
Tidskriftsartikel (refereegranskat)abstract
- No system entity within a contemporary distributed cyber system can be entirely trusted. Hence, the classic centralized trust management method cannot be directly applied to it. Blockchain technology is essential to achieving decentralized trust management, its consensus mechanism is useful in addressing large-scale data sharing and data consensus challenges. Herein, an n-party quantum detectable Byzantine agreement (DBA) based on the GHZ state to realize the data consensus in a quantum blockchain is proposed, considering the threat posed by the growth of quantum information technology on the traditional blockchain. Relying on the nonlocality of the GHZ state, the proposed protocol detects the honesty of nodes by allocating the entanglement resources between different nodes. The GHZ state is notably simpler to prepare than other multi-particle entangled states, thus reducing preparation consumption and increasing practicality. When the number of network nodes increases, the proposed protocol provides better scalability and stronger practicability than the current quantum DBA. In addition, the proposed protocol has the optimal fault-tolerant found and does not rely on any other presumptions. A consensus can be reached even when there are n−2 traitors. The performance analysis confirms viability and effectiveness through exemplification. The security analysis also demonstrates that the quantum DBA protocol is unconditionally secure, effectively ensuring the security of data and realizing data consistency in the quantum blockchain. © 2023 The Authors
|
|
| 7. |
- Qu, Zhiguo, et al.
(författare)
-
Quantum Fuzzy Federated Learning for Privacy Protection in Intelligent Information Processing
- 2024
-
Ingår i: IEEE transactions on fuzzy systems. - Piscataway, NJ : IEEE. - 1063-6706 .- 1941-0034.
-
Tidskriftsartikel (refereegranskat)abstract
- With the advent of the intelligent information processing era, more and more private sensitive data are being collected and analyzed for intelligent decision making tasks. Such information processing also brings many challenges with existing privacy protection algorithms. On the one hand, the algorithms based on data encryption compromise the integrity of the original data or incur high computational and communication costs to some extent. On the other hand, algorithms based on distributed learning require frequent sharing of parameters between different computing nodes, which poses risks of leaking local model information and reducing global learning efficiency. To mitigate the impact of these issues, a Quantum Fuzzy Federated Learning (QFFL) algorithm is proposed. In the QFFL algorithm, a Quantum Fuzzy Neural Network (QFNN) is designed at the local computing nodes, which enhances data generalization while preserving data integrity. In global model, QFFL makes predictions through the Quantum Federated Inference (QFI). QFI leads to a general framework for quantum federated learning on non-IID data with oneshot communication complexity, achieving privacy protection of local data and accelerating the global learning efficiency of the algorithm. The experiments are conducted on the COVID19 and MNIST datasets, and the results indicate that QFFL demonstrates superior performance compared to the baselines, manifesting in faster training efficiency, higher accuracy, and enhanced security. In addition, based on the fidelity experiments and related analysis under four common quantum noise channels, the results demonstrated that it has good robustness against quantum noises, proving its applicability and practicality. Our code is available at https://github.com/LASTsue/QFFL. © IEEE
|
|
| 8. |
- Tiwari, Prayag, 1991-, et al.
(författare)
-
Quantum Fuzzy Neural Network for multimodal sentiment and sarcasm detection
- 2024
-
Ingår i: Information Fusion. - Amsterdam : Elsevier. - 1566-2535 .- 1872-6305. ; 103, s. 1-14
-
Tidskriftsartikel (refereegranskat)abstract
- Sentiment and sarcasm detection in social media contribute to assessing social opinion trends. Over the years, most artificial intelligence (AI) methods have relied on real values to characterize the sentimental and sarcastic features in language. These methods often overlook the complexity and uncertainty of sentimental and sarcastic elements in human language. Therefore, this paper proposes the Quantum Fuzzy Neural Network (QFNN), a multimodal fusion and multitask learning algorithm with a Seq2Seq structure that combines Classical and Quantum Neural Networks (QNN), and fuzzy logic. Complex numbers are used in the Fuzzifier to capture sentiment and sarcasm features, and QNN are used in the Defuzzifier to obtain the prediction. The experiments are conducted on classical computers by constructing quantum circuits in a simulated noisy environment. The results show that QFNN can outperform several recent methods in sarcasm and sentiment detection task on two datasets (Mustard and Memotion). Moreover, by assessing the fidelity of quantum circuits in a noisy environment, QFNN was found to have excellent robustness. The QFNN circuit also possesses expressible and entanglement capabilities, proving effective in various settings. Our code is available at https://github.com/prayagtiwari/QFNN. © 2023 Elsevier B.V.
|
|
| 9. |
- Zhang, Zhiguo, et al.
(författare)
-
A Meta-Graph Deep Learning Framework for Forecasting Air Pollutants in Stockholm
- 2023
-
Ingår i: 2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023. - : Institute of Electrical and Electronics Engineers Inc..
-
Konferensbidrag (refereegranskat)abstract
- Forecasting air pollution is an important activity for developing sustainable and smart cities. Generated by various sources, air pollutants distribute in the atmospheric environment due to the complex dispersion processes. The emerging sensor and data technologies have promoted the development of data-driven approaches to replace conventional physical models in urban air pollution forecasting. Nevertheless, it is still challenging to capture the intricate spatial and temporal patterns of air pollutant concentrations measured by heterogeneous sensors, especially for long-term prediction of the multi-variate time series data. This paper proposes a deep learning framework for longer-term forecast of air pollutants concentrations using air pollution sensing data, based on a conceptual framework of meta-graph deep learning. The key modules in the framework include meta-graph units and fusion layers, which are designed to learn temporal and spatial correlations respectively. A detailed case was formulated for forecasting air pollutants in Stockholm using air quality sensing data, meteorological data and so on. Experiments were conducted to evaluate the performance of the proposed modelling framework. The computational results show that it outperforms the baseline models and conventional deterministic dispersion models, demonstrating the potential of the framework to be deployed for the real air quality information systems in Stockholm.
|
|
| 10. |
- Zhang, Zhiguo, et al.
(författare)
-
Improving 3-day deterministic air pollution forecasts using machine learning algorithms
- 2024
-
Ingår i: Atmospheric Chemistry And Physics. - : Copernicus GmbH. - 1680-7316 .- 1680-7324. ; 24:2, s. 807-851
-
Tidskriftsartikel (refereegranskat)abstract
- As air pollution is regarded as the single largest environmental health risk in Europe it is important that communication to the public is up to date and accurate and provides means to avoid exposure to high air pollution levels. Long- and short-term exposure to outdoor air pollution is associated with increased risks of mortality and morbidity. Up-to-date information on present and coming days' air quality helps people avoid exposure during episodes with high levels of air pollution. Air quality forecasts can be based on deterministic dispersion modelling, but to be accurate this requires detailed information on future emissions, meteorological conditions and process-oriented dispersion modelling. In this paper, we apply different machine learning (ML) algorithms - random forest (RF), extreme gradient boosting (XGB), and long short-term memory (LSTM) - to improve 1, 2, and 3d deterministic forecasts of PM10, NOx, and O3 at different sites in Greater Stockholm, Sweden. It is shown that the deterministic forecasts can be significantly improved using the ML models but that the degree of improvement of the deterministic forecasts depends more on pollutant and site than on what ML algorithm is applied. Also, four feature importance methods, namely the mean decrease in impurity (MDI) method, permutation method, gradient-based method, and Shapley additive explanations (SHAP) method, are utilized to identify significant features that are common and robust across all models and methods for a pollutant. Deterministic forecasts of PM10 are improved by the ML models through the input of lagged measurements and Julian day partly reflecting seasonal variations not properly parameterized in the deterministic forecasts. A systematic discrepancy by the deterministic forecasts in the diurnal cycle of NOx is removed by the ML models considering lagged measurements and calendar data like hour and weekday, reflecting the influence of local traffic emissions. For O3 at the urban background site, the local photochemistry is not properly accounted for by the relatively coarse Copernicus Atmosphere Monitoring Service ensemble model (CAMS) used here for forecasting O3 but is compensated for using the ML models by taking lagged measurements into account. Through multiple repetitions of the training process, the resulting ML models achieved improvements for all sites and pollutants. For NOx at street canyon sites, mean squared error (MSE) decreased by up to 60%, and seven metrics, such as R2 and mean absolute percentage error (MAPE), exhibited consistent results. The prediction of PM10 is improved significantly at the urban background site, whereas the ML models at street sites have difficulty capturing more information. The prediction accuracy of O3 also modestly increased, with differences between metrics. Further work is needed to reduce deviations between model results and measurements for short periods with relatively high concentrations (peaks) at the street canyon sites. Such peaks can be due to a combination of non-typical emissions and unfavourable meteorological conditions, which are rather difficult to forecast. Furthermore, we show that general models trained using data from selected street sites can improve the deterministic forecasts of NOx at the station not involved in model training. For PM10 this was only possible using more complex LSTM models. An important aspect to consider when choosing ML algorithms is the computational requirements for training the models in the deployment of the system. Tree-based models (RF and XGB) require fewer computational resources and yield comparable performance in comparison to LSTM. Therefore, tree-based models are now implemented operationally in the forecasts of air pollution and health risks in Stockholm. Nevertheless, there is big potential to develop generic models using advanced ML to take into account not only local temporal variation but also spatial variation at different stations.
|
|
