Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory

Traffic prediction plays an important role in evaluating the performance of telecommunication networks and attracts intense research interests. A significant number of algorithms and models have been put forward to analyse traffic data and make prediction. In the recent big data era, deep learning has been exploited to mine the profound information hidden in the data. In particular, Long Short-Term Memory (LSTM), one kind of Recurrent Neural Network (RNN) schemes, has attracted a lot of attentions due to its capability of processing the long-range dependency embedded in the sequential traffic data. However, LSTM has considerable computational cost, which can not be tolerated in tasks with stringent latency requirement. In this paper, we propose a deep learning model based on LSTM, called Random Connectivity LSTM (RCLSTM). Compared to the conventional LSTM, RCLSTM makes a notable breakthrough in the formation of neural network, which is that the neurons are connected in a stochastic manner rather than full connected. So, the RCLSTM, with certain intrinsic sparsity, have many neural connections absent (distinguished from the full connectivity) and which leads to the reduction of the parameters to be trained and the computational cost. We apply the RCLSTM to predict traffic and validate that the RCLSTM with even 35% neural connectivity still shows a satisfactory performance. When we gradually add training samples, the performance of RCLSTM becomes increasingly closer to the baseline LSTM. Moreover, for the input traffic sequences of enough length, the RCLSTM exhibits even superior prediction accuracy than the baseline LSTM.Comment: 6 pages, 9 figure

Deep Learning with Long Short-Term Memory for Time Series Prediction

Time series prediction can be generalized as a process that extracts useful information from historical records and then determines future values. Learning long-range dependencies that are embedded in time series is often an obstacle for most algorithms, whereas Long Short-Term Memory (LSTM) solutions, as a specific kind of scheme in deep learning, promise to effectively overcome the problem. In this article, we first give a brief introduction to the structure and forward propagation mechanism of the LSTM model. Then, aiming at reducing the considerable computing cost of LSTM, we put forward the Random Connectivity LSTM (RCLSTM) model and test it by predicting traffic and user mobility in telecommunication networks. Compared to LSTM, RCLSTM is formed via stochastic connectivity between neurons, which achieves a significant breakthrough in the architecture formation of neural networks. In this way, the RCLSTM model exhibits a certain level of sparsity, which leads to an appealing decrease in the computational complexity and makes the RCLSTM model become more applicable in latency-stringent application scenarios. In the field of telecommunication networks, the prediction of traffic series and mobility traces could directly benefit from this improvement as we further demonstrate that the prediction accuracy of RCLSTM is comparable to that of the conventional LSTM no matter how we change the number of training samples or the length of input sequences.Comment: 9 pages, 5 figures, 14 reference

Select2Col: Leveraging Spatial-Temporal Importance of Semantic Information for Efficient Collaborative Perception

Collaboration by leveraging the shared semantic information plays a crucial role in overcoming the perception capability limitations of isolated agents. However, existing collaborative perception methods tend to focus solely on the spatial features of semantic information, while neglecting the importance of the temporal dimension. Consequently, the potential benefits of collaboration remain underutilized. In this article, we propose Select2Col, a novel collaborative perception framework that takes into account the {s}patial-t{e}mpora{l} importanc{e} of semanti{c} informa{t}ion. Within the Select2Col, we develop a collaborator selection method that utilizes a lightweight graph neural network (GNN) to estimate the importance of semantic information (IoSI) in enhancing perception performance, thereby identifying contributive collaborators while excluding those that bring negative impact. Moreover, we present a semantic information fusion algorithm called HPHA (historical prior hybrid attention), which integrates multi-scale attention and short-term attention modules to capture the IoSI in feature representation from the spatial and temporal dimensions respectively, and assigns IoSI-consistent weights for efficient fusion of information from selected collaborators. Extensive experiments on two open datasets demonstrate that our proposed Select2Col significantly improves the perception performance compared to state-of-the-art approaches. The code associated with this research is publicly available at https://github.com/huangqzj/Select2Col/

Untargeted LC–MS/MS-Based Metabolomic Profiling for the Edible and Medicinal Plant Salvia miltiorrhiza Under Different Levels of Cadmium Stress

Salvia miltiorrhiza, a medicinal and edible plant, has been extensively applied to treat cardiovascular diseases and chronic hepatitis. Cadmium (Cd) affects the quality of S. miltiorrhiza, posing serious threats to human health. To reveal the metabolic mechanisms of S. miltiorrhiza's resistance to Cd stress, metabolite changes in S. miltiorrhiza roots treated with 0 (CK), 25 (T1), 50 (T2) and 100 (T3) mg kg−1 Cd by liquid chromatography coupled to mass spectrometry (LC–MS/MS) were investigated. A total of 305 metabolites were identified, and most of them were amino acids, organic acids and fatty acids, which contributed to the discrimination of CK from the Cd-treated groups. Among them, S. miltiorrhiza mainly upregulated o-tyrosine, chorismate and eudesmic acid in resistance to 25 mg kg−1 Cd; DL-tryptophan, L-aspartic acid, L-proline and chorismite in resistance to 50 mg kg−1 Cd; and L-proline, L-serine, L-histidine, eudesmic acid, and rosmarinic acid in resistance to 100 mg kg−1 Cd. It mainly downregulated unsaturated fatty acids (e.g., oleic acid, linoleic acid) in resistance to 25, 50, and 100 mg kg−1 Cd and upregulated saturated fatty acids (especially stearic acid) in resistance to 100 mg kg−1 Cd. Biosynthesis of unsaturated fatty acids, isoquinoline alkaloid, betalain, aminoacyl-tRNA, and tyrosine metabolism were the significantly enriched metabolic pathways and the most important pathways involved in the Cd resistance of S. miltiorrhiza. These data elucidated the crucial metabolic mechanisms involved in S. miltiorrhiza Cd resistance and the crucial metabolites that could be used to improve resistance to Cd stress in medicinal plant breeding

Accuracy modeling and analysis for a lock-or-release mechanism of the Chinese Space Station Microgravity Platform

With development of Chinese space science and technology, plenty of microgravity experiments will be conducted in the Chinese Space Station to be built, and therefore demand for high-precision electromechanical equipment increases substantially. In this paper, a comprehensive accuracy analysis of a new type of auto lock-or-release (L/R) mechanism, which is applied in the Space Station Microgravity Platform (SSMP), is implemented. Firstly, two models (vector analysis model and vector differential model) are, therefore, proposed to analyze output errors of the mechanism. Due to transmission errors from the transmission chain of the gear mechanism, influence factors on axial errors of lead screws are analyzed using design of experiment (DOE) for factor sensitivities. It shows that manufacturing tolerances of the lead screw is the dominant factor. Then, verification of the two proposed accuracy models is comparatively implemented through Monte Carlo (MC) simulation and DOE. Using the present accuracy model, location errors of the lead screw throughout the mechanism's working stroke are illustrated, where the non-synchronous error of the mechanism is particularly discussed. A linear relation between the variance of the non-synchronous error and that of the structural error is established, followed by analyzing influence factors on the non-synchronous error

Ten issues of NetGPT

With the rapid development and application of foundation models (FMs), it is foreseeable that FMs will play an important role in future wireless communications. As current Artificial Intelligence (AI) algorithms applied in wireless networks are dedicated models that aim for different neural network architectures and objectives, drawbacks in aspects of generality, performance gain, management, collaboration, etc. need to be conquered. In this paper, we define NetGPT (Network Generative Pre-trained Transformer) -- the foundation models for wireless communications, and summarize ten issues regarding design and application of NetGPT

RESEARCH ON DYNAMIC CHARACTERISTICS OF WIND TURBINE MAIN SHAFT BEARING UNDER DIFFERENT WORKING CONDITIONS

In order to get the dynamic characteristics of wind turbinemain shaft bearing under different working conditions,tooka 1. 5 MW wind turbine manufactured by one turbine factoryas an example,the main shaftwassimplifiedabstractly and the axial and radial loadon main shaft bearingwere given by blade element theory. The 3 D model of main shaft bearingwasdrawn in UG and imported into ADAMS toestablish the multi rigid kinetic model of main shaft bearing,the interactionsbetween the balls of main shaft bearing and inner ring,outer ring and separator wereanalyzed under threedifferent working conditions. The analysis resultsindicate that the interaction forces between the balls of main shaft bearing and inner ring,outer ring and separator aremaximal during theemergency brake period of wind turbine,it is second during thesuddenvariation periodof the wind turbine revolving speedand it is minimalfrom starting until running steadily

HOMA-IR is positively correlated with biological age and advanced aging in the US adult population

Abstract Background Insulin resistance (IR) had been reported to be associated with age; however, few studies have explored the association between IR and biological age (BA). The HOMA-IR value is a useful indicator of the extent of IR. This cross-sectional study is to explore the relationship between HOMA-IR and BA/advanced aging in the US population. Methods This study is a cross-sectional analysis of National Health and Nutrition Examination Survey (NHANES) data. The survey comprised 12,266 people from the NHANES, and their full HOMA-IR data as well as BA data were extracted. Four multiple linear regressions were performed to analyze the association between HOMA-IR and BA, and four multiple logistic regression models were performed to analyze the association between HOMA-IR and advanced aging. In addition, trend tests and stratified analysis were performed and smoothed fitted curves were plotted to test the robustness of the results. Results HOMA-IR was positively correlated with BA [β: 0.51 (0.39, 0.63)], and it was the same to advanced aging [OR: 1.05 (1.02, 1.07)], and both showed a monotonically increasing trend. The trend tests showed that the results were stable (all P for trend < 0.0001). The smoothed fitted curves showed that there were non-linear relationships between HOMA-IR and BA/advanced aging. And the stratified analysis indicated that the relationship between HOMA-IR and BA/advanced aging remained robust in all subgroups. Conclusion The study suggested that HOMA-IR is positively correlated with BA and advanced aging in the US adult population, with a monotonic upward trend. This is a new finding to reveal the relationship between HOMA-IR and age from new standpoint of BA rather than chronological age (CA). And it may contribute to a better understanding of human health aging and may aid future research in this field

Investigation of the dynamic characteristics of a thermal energy storage unit filled with multiple phase change materials

In order to improve the thermal performance of thermal energy storage systems, a packed bed thermal energy storage systems unit using spherical capsules filled with multiple phase change materials (multi-PCM) for use in conventional air-conditioning systems is presented. A 3-D mathematical model was established to investigate the charging characteristics of the thermal energy storage systems unit. The optimum proportion between the multi-PCM was identified. The effects of heat transfer fluid-flow rate and heat transfer fluid inlet temperature on the liquid phase change materials volume fraction, charging time and charging capacity of the thermal energy storage system unit are studied. The results indicate that the charging capacity of multi-PCM units is higher than that of the conventional single-PCM (HY-2). For proportions 0:1:0, 2:3:3, 3:2:3, 3:3:2, 4:1:3, and 4:2:2, the charging capacity decreases by approximately 24.84%, 14.69%, 6.47%, 3.82%, and 1.13%, respectively, compared to the 4:2:2 proportion. Moreover, decreasing the heat transfer fluid inlet temperature can obviously shorten the complete charging time of the thermal energy storage systems unit