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Aiming at the problem that the existing graph neural network session recommendation algorithm ignores all kinds of auxiliary information, which leads to the inability to accurately model the session sequence, a session recommendation algorithm combining the item transition relations and time-order information (RTSR) is proposed. Firstly, the shortest path sequence between any two nodes is obtained by using the graph network structure, which is encoded as the item transition relations between corresponding items through the gated recurrent unit (GRU), and then the global dependency information of the session is captured from the perspective of the graph by combining the self-attention mechanism. At the same time, a lossless graph coding scheme is designed to alleviate the problem of information loss in the process of session graph coding. The scheme quantifies the time-order information in the session sequence reasonably, and takes it as the weight of the edges in the session graph, and then combines the gated graph sequence neural network to obtain the local dependency information of the session. Finally, with linear combination of global dependency information and local dependency information, and in combination with reverse position information, the user??s preference for item is finally generated, and the recommendation list is given. The performance comparison experiment with mainstream models such as SR-GNN, GC-SAN and GCE-GNN on the public benchmark datasets Gowalla and Diginetica shows that RTSR improves at least 6.13% and 1.58% in average reciprocal ranking respectively, and the recommendation accuracy is also improved accordingly.

Aiming at the problem that the sequence recommendation models based on the self-attention mechanism ignore all kinds of auxiliary information, which makes the model unable to use them to capture multi-level sequence relationship patterns, a recommendation algorithm integrating time context and feature-level information is proposed (ITFR). Firstly, the item representation is connected with each of its attribute representations and input into an attention network. After attention is weighted, an attribute-based item representation is obtained. Then, ITFR applies the self-attention block of the perception time interval and the self-attention block based on the item-attribute to capture the relationship pattern between the item and the interaction sequence time interval and the implicit relationship between the item and the attribute, respectively. Finally, the output representations of the two self-attention blocks are connected, and the joint output representation is input to the fully connected layer for the recommendation of the next item. Experiments are conducted on two public datasets, and two performance indicators of hit rate (HR) and normalized discounted cumulative gain (NDCG) are used for evaluation. In the Beauty dataset, compared with the optimal baseline method, HR@10 and NDCG@10 are increased by 4.6% and 5.1%, respectively. In the Movie-1M dataset, HR@10 and NDCG@10 are increased by 1.7% and 1.5%, respectively. Experimental results show that the method of incorporating auxiliary information to enhance sequence representation can improve recommendation performance.

Considering the problem of users high processing delay and energy consumption in mobile edge computing (MEC), a joint optimization scheme of resource allocation and offloading decision based on the “cloud-edge-end” three-tier MEC computation offloading structure is proposed. Firstly, considering the system delay and energy con-sumption, the optimization problem is studied in order to maximize the users task offloading gain, which is measured by a weighted sum of reductions in tasks relative processing delay and energy consumption. Secondly, the priority is set for users tasks and the offloading decision is initialized according to the data size of tasks. Then, the channel allocation algorithm that balances transmission performance is proposed to allocate channel resources for offloading tasks. For the tasks that are offloaded to the same edge server, the optimal allocation of computing resources can be achieved by computing for resources with the goal of maximizing resources profit. Finally, the optimization problem is proven to be a potential function about the offloading decision based on game theory, that is, there exists a Nash equilibrium, and the iterative method by comparing the gain value is used to achieve the offloading decision under Nash equilibrium. The simulation results show that the proposed joint optimization scheme achieves the maximum total system gain under meeting the processing delay requirements of users, and effectively improves the performance of computation offloading.

To solve the security problem of industrial cyber‐physical systems (ICPS) suffering from the Denial of Service (DoS) attack, this paper proposes an optimal control strategy with functions of tracking and compensation control. The system model, being depicted as a discrete linear time‐invariant system combined with an improved Bernoulli model, is established to describe the system under DoS attacks by using the feedback control theory. The optimal control strategy is completed by designing linear quadratic Gaussian (LQG) controller consisting of a tracking regulator and a Kalman filter to achieve tracking control of ICPS whose equilibrium state is neither zero state nor zero output, where a periodic event‐triggered mechanism is adopted for ensuring the system's stability. The compensation control strategy is completed by designing a compensator to compensate the system to a certain extent through the multistep state prediction compensation strategy after a DoS attack being detected, where the χ2$$ {\chi}^2 $$ detector is adopted to detect the DoS attack based on the measurement residual obtained by the Kalman filter. The simulation test is conducted by using Simulink/True Time tool with a ball‐beam system as the physical object, whose results show that the tracking regulator can achieve the tracking control of ICPS; compensation strategy can reduce the impact of the attack on the system to a certain extent. [ABSTRACT FROM AUTHOR]

This paper considers identification problems for Wiener systems with saturation and dead-zone nonlinearities. The basic idea is to obtain the identification model of such a nonlinear system using a switching function, and to propose a gradient-based iterative identification algorithm using the iterative technique. An example is provided to show the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

The above paper proposes a method to design optimal control places with self-loops for flexible manufacturing systems by solving an integer linear programming problem (ILPP) at each iteration. However, some constraints in the ILPP are redundant. This technical correspondence shows that they can be removed without changing the feasible region of the ILPP. [ABSTRACT FROM PUBLISHER]

Non-small cell lung cancer (NSCLC), with its high mortality rate, lack of early diagnostic markers and prevention of distant metastases are the main challenges in treatment. To identify potential miRNAs and key genes in NSCLC to find new biomarkers and target gene therapies. The GSE102286, GSE56036, GSE25508, GSE53882, GSE29248 and GSE101929 datasets were obtained from the Gene Expression Omnibus (GEO) database and screened for differentially co-expressed miRNAs (DE-miRNAs) and lncRNAs (DElncRs) by GEO2R and R software package. Pathway enrichment analysis of DE-miRNAs-target genes was performed by String and Funrich database to construct protein-protein interaction (PPI) and competing endogenous RNA (ceRNA) network and visualized with Cytoscape software. Nineteen co-expressed DE-miRNAs were screened from five datasets. The 7683 predicted up- and down-regulated DE-miRNAs-target genes were significantly concentrated in cancer-related pathways. The top 10 hub nodes in the PPI were identified as hub genes, such as MYC, EGFR, HSP90AA1 and TP53, MYC, and ACTB. By constructing miRNA-hub gene networks, hsa-miR-21, hsa-miR-141, hsa-miR-200b and hsa-miR-30a, hsa-miR-30d, hsa-miR-145 may regulate most hub genes and hsa-miR-141, hsa-miR-200, hsa-miR-145 had higher levels in the miRNA and ceRNA regulatory networks, respectively. In conclusion, the identification of hsa-miR-21, hsa-miR-141, hsa-miR-200b hsa-miR-30a, hsa-miR-30d and hsa-miR-145 provides a new theoretical basis for understanding the development of NSCLC.