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The study of multivariate time series forecasting (MTSF) problems has high significance in many areas, such as industrial forecasting and traffic flow forecasting. Traditional forecasting models pay more attention to the temporal features of variables and lack depth in extracting spatial and spatiotemporal features between variables. In this paper, a novel model based on the Transformer, convolutional neural network (CNN), and long short-term memory (LSTM) network is proposed to address the issues. The model first extracts the spatial feature vectors through the proposed Multi-kernel CNN. Then it fully extracts the temporal information by the Encoder layer that consists of the Transformer encoder layer and the LSTM network, which can also obtain the potential spatiotemporal correlation. To extract more feature information, we stack multiple Encoder layers. Finally, the output is decoded by the Decoder layer composed of the ReLU activation function and the Linear layer. To further improve the model's robustness, we also integrate an autoregressive model. In model evaluation, the proposed model achieves significant performance improvements over the current benchmark methods for MTSF tasks on four datasets. Further experiments demonstrate that the model can be used for long-horizon forecasting and achieve satisfactory results on the yield forecasting of test items (our private dataset, TIOB). [ABSTRACT FROM AUTHOR]

Compared with traditional hydrodesulfurization, new nonhydrodesulfurization methods have the advantage of a high removal efficiency for thiophene compounds under mild conditions. However, independent nonhydrodesulfurization technologies are faced with their own shortcomings, such as limitations of the desulfurization performance and regeneration of materials. To overcome these limitations, four nanofluids were prepared by dispersing different metal-modified MCM-41 particles in deep eutectic solvent as multifunctional promoters to develop a comprehensive desulfurization method. Based on the excellent adsorbability and high catalytic activity of the dispersed particles and the outstanding extractability of deep eutectic solvent in nanofluids, a high sulfur removal of 99.33% was achieved for model oil under mild conditions in 15 min. The nanofluids also showed excellent reusability due to their high structural stability. In addition, NF@Cu/Al-MCM-41-2.5% exhibited the best desulfurization performance among the prepared nanofluids. This result was obtained because the introduction of Al ions increased the number of acid sites and defect sites to improve the catalytic activity and adsorbability, and the best affinity of Cu/Al-MCM-41 for the deep eutectic solvent favored the reaction mass transfer. This work opens the door to the development of a comprehensive nonhydrodesulfurization method based on the design of nanofluid materials. [ABSTRACT FROM AUTHOR]

In this paper, an approximation-based event-triggered model predictive control (AETMPC) strategy is proposed to implement event-triggered model predictive control for continuous-time constrained nonlinear systems under the digital platform. In the AETMPC strategy, both of the optimal control problem (OCP) and the triggering conditions are defined in a discrete-time manner based on approximate discrete-time models, while the plant under control is continuous time. In doing so, sensing load is alleviated because the triggering condition does not need to be checked continuously, and the computation of the OCP is simpler since which is calculated in the discrete-time framework. Meanwhile, robust constraints are satisfied in a continuous-time sense by taking inter-sampling behavior into consideration, and a novel constraint tightening approach is presented accordingly. Furthermore, the feasibility of the AETMPC strategy is analyzed and the associated stability of the overall system is established. Finally, this strategy is validated by a numerical example. [ABSTRACT FROM AUTHOR]

Daily activity monitoring plays an important role among frail or elderly people and has caught attention. Surface electromyography (sEMG) can extract the feature of activity, but it is not stable because of electrode displacement, postural changes, and individual-dependent features, such as the condition of muscles, subcutaneous fat, and skin surface. To effectively extract the feature of sEMG signal, we proposed a new method of feature extraction based on coherence analysis. The sEMG signals were recorded from gastrocnemius, tibialis anterior, rectus femoris, and semitendinosus. After de-noising, sEMG signals were decomposed into 32-scale by wavelet transformation, and their wavelet coefficients were employed to calculate wavelet coherence coefficients (WCC). We employed T test to find out if the coherence between sEMG signals was statistically different among six activities. The 32nd scale WCC of RF–ST and ST–TA as eigenvector was entered into the support vector machine (SVM). The six activities, namely, standing, walking, running, stair-ascending, stair-descending, and falling, were successfully identified by the WCC feature with the SVM classifier. [ABSTRACT FROM AUTHOR]

The input-to-state stability problem of a class of nonlinear switched networked control systems subject to time-varying transmission intervals, periodical packet dropouts, and communication constraints is investigated. By adopting the extended input-to-state stability(eISS) protocol and constructing a novel Lyapunov function, the input-to-state stability properties of such systems are discussed. Then, by making use of the small-gain theorem, the maximum allowable transmission interval to guarantee system stability is obtained. A batch reactor is finally considered to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The stability of a class of switched stochastic nonlinear retarded systems with asynchronous switching controller is investigated. By constructing a virtual switching signal and using the average dwell time approach incorporated with Razumikhin-type theorem, the sufficient criteria for pth moment exponential stability and global asymptotic stability in probability are given. It is shown that the stability of the asynchronous stochastic systems can be guaranteed provided that the average dwell time is sufficiently large and the mismatched time between the controller and the systems is sufficiently small. This result is then applied to a class of switched stochastic nonlinear delay systems where the controller is designed with both state and switching delays. A numerical example illustrates the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

Data packet disorder is inevitable in discrete-time networked control systems, which however has been ignored in most literature to date. This work investigates the cause and the negative effects of data packet disorder in discrete-time networked control systems, and a compensation scheme is proposed to deal with these negative effects. This analysis shows that most existing works in the related areas have assumed unrealistic characteristics of the communication network and thus further improvements within this new framework are necessary. For this purpose the closed-loop system with the use of the proposed compensation scheme is mathematically modeled as a Markov jump systems, and stochastic stability and stabilization results are then obtained. Numerical examples illustrate the negative effects caused by data packet disorder and also the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The effect of selenium (Se) in reducing the toxicity of cisplatin in cancer patients was studied. Forty-one patients were randomized into group A (20 patients with Se administration in first cycle of chemotherapy as study cases and without Se in second cycle of chemotherapy as control) and group B (21 patients without Se in first cycle of chemotherapy and with Se in second cycle of chemotherapy). The 400 μg per day of Se as Seleno-Kappacarrageenan were administered from 4 before to 4 d after chemotherapy for study cases. The serum Se increased from 70.4±22.86 to 157.04±60.23 ng/mL ( P<0.001) in patients received Se. The cisplatin dosage was iv administration in 60–80 mg/m2 on the first day. The results showed that the peripheral WBC counts on day 14 after initiation of chemotherapy in study cases was significantly higher than the controls (3.35±2.01 vs 2.31±1.38 [×109L])/L, p<0.05). On the other hand, the consumption of GCSF for the cases was significantly less than the controls (110.1±82.2 vs 723.6±192.6 IU, p<0.05). The volumes of blood transfusion for the study group were also significantly less than the controls (0 vs 62±38mL, p<0.05). The nephrotoxicity of cisplatin was measured by urine enzymes (NAG, GGT, AAP, LAP, and ALP) were determined prior to and at 2, 24, 48, and 72h after initiation of chemotherapy. The urine enzymes NAG, GGT, AAP, and ALP after chemotherapy for cases were significantly lower than the controls. No toxicity of Seleno-Kappacarrageenan was noted. The above results suggest that the Se can be used as an agent for reducing the nephrotoxicity and bone marrow suppression induced by cisplatin. [ABSTRACT FROM AUTHOR]