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Accurate understanding of passenger flow distribution is crucial for effective station crowd management. However, due to the complexity and randomness of passenger flow and the unclear spatial-temporal correlation between functional areas within the station, predicting the spatiotemporal distribution dynamics of inflow and future short-term distribution trends is challenging. Emerging deep learning models offer valuable insights for accurately predicting passenger flow distribution. Thus, we propose a deep learning architecture, named "ST-Bi-LSTM," which combines a bidirectional long short-term memory network with a spatial-temporal attention mechanism. Initially, we outline the methodologies of Bi-LSTM, the DeepWalk-based spatial attention mechanism, and the temporal attention mechanism. The spatial attention mechanism is employed to extract station spatial network topology information and enhance the representation of passenger flow characteristics in highly correlated areas during the forecasting process. Simultaneously, the temporal attention Bi-LSTM is utilized for capturing temporal correlations. The architecture comprises four branches dedicated to station real-time video monitoring data, spatial network topology, function area attributes, and train timetables. Subsequently, leveraging in-station CCTV data, passenger travel behavior data, and train timetables, we apply the architecture to the Tianjin West High-Speed Railway Station. We conduct a comparative analysis of the prediction performance and time complexity of the proposed architecture against existing baseline models, demonstrating superior performance and robustness exhibited by the ST-Bi-LSTM model (achieving a reduction in RMSE of over 10%). This study facilitates the transition of station management from passive response to active prediction of station passenger flow dynamics. [ABSTRACT FROM AUTHOR]

Trichogramma chilonis Ishii (Hymenoptera: Trichogrammatidae) is the most important parasitoid of sugarcane borers in China. T. chilonis are produced from either rearing on Corcyra cephalonica (Stainton) (Hymenoptera: Galleriidae) eggs in the laboratory or at factory level and released into the field to control sugarcane borers in China. Limited reports on the efficacy of different populations of T. chilonis in the sugarcane field against C. cephalonica are available. This study reports the parasitism efficacy and functional response of different T. chilonis populations against C. cephalonica under laboratory conditions. The number of eggs parasitized by the laboratory population of T. chilonis (total 57.05 eggs) on C. cephalonica was the highest among the different populations investigated; parasitism by the Baise population was the lowest (total 21.30 eggs). The parasitoid emergence rate of T. chilonis Baise population on C. cephalonica (102.16%) was the highest among the different populations, whereas that from the C. cephalonica laboratory population was the lowest (84.73%). The sex ratio (female/male) of T. chilonis laboratory population (3.51) was significantly higher than the other three sugarcane field populations. The functional responses of T. chilonis on C. cephalonica were determined as Holling type II. The search rate of T. chilonis laboratory population (1.0392) parasitizing on the eggs of C. cephalonica was higher than other Trichogramma populations, whereas the handling time (0.00809) was shorter than that of the other populations. The results from this study indicate that the origin of the population of T. chilonis can affect its parasitic ability upon C. cephalonica; therefore, further evaluation for control efficacy of different populations of T. chilonis against sugarcane borers, especially in the field, is warranted. [ABSTRACT FROM AUTHOR]

A robust tube‐based distributed model predictive control method is proposed for spatially interconnected systems with constraints and disturbances. The system contains multiple discrete‐time piecewise affine subsystems, which are coupled to each other through states. The predictive states of each subsystem are dependent on its states, inputs, and neighbouring states, and shrinking constraints are constructed to deal with the disturbances and the model mismatches. The differences between the predicted states at the current time instant and the optimal states at the previous time instant are constrained, which improves the accuracy of the predicted model and enhances the control performance. The conditions which ensure the recursive feasibility of optimization problems and the asymptotic stability of closed‐loop system are obtained. The effectiveness of the proposed method is verified by the simulation results. [ABSTRACT FROM AUTHOR]

Infection with the cestode Echinococcus multilocularis (E. multilocularis) causes alveolar echinococcosis (AE), a tumor-like disease predominantly affecting the liver but able to spread to any organ. T cells develop functional defects during chronic E. multilocularis infection, mostly due to upregulation of inhibitory receptors such as T-cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domains (TIGIT) and programmed death-1 (PD-1). However, the role of lymphocyte activation gene-3 (LAG3), an inhibitory receptor, in AE infection remains to be determined. Here, we discovered that high expression of LAG3 was mainly found in CD4+ T cells and induced regulatory T cells (iTregs) in close liver tissue (CLT) from AE patients. In a mouse model of E. multilocularis infection, LAG3 expression was predominantly found in T helper 2 (Th2) and Treg subsets, which secreted significantly more IL-4 and IL-10, resulting in host immune tolerance and disease progression at a late stage. Furthermore, LAG3 deficiency was found to drive the development of effector memory CD4+ T cells and enhance the type 1 CD4+ T-cell immune response, thus inhibiting metacestode growth in vivo. In addition, CD4+ T cells from LAG3-deficient mice produced more IFN-γ and less IL-4 when stimulated by E. multilocularis protoscoleces (EmP) antigen in vitro. Finally, adoptive transfer experiments showed that LAG3-knockout (KO) CD4+ T cells were more likely to develop into Th1 cells and less likely to develop into Tregs in recipient mice. Our work reveals that high expression of LAG3 accelerates AE disease progression by modulating the immune imbalance of CD4+ T-cell subsets. These findings may provide a novel immunotherapeutic strategy against E. multilocularis infection. Author summary: Alveolar echinococcosis (AE) is a parasitic disease with high mortality caused by E. multilocularis infection. Our previous studies confirmed that T-cell-mediated cellular immunity, especially that mediated by CD4+ T cells, plays a key role in limiting metacestode growth. Here, we report that high LAG3 expression in liver-infiltrating CD4+ T cells is associated with their immune imbalance in AE patients and a mouse model of later-stage disease. High expression of LAG3 promotes the formation of Th2 cells and Tregs, and LAG3 deficiency induces a shift in the immune response to the Th1 type to delay the growth of metacestodes in chronic E. multilocularis infection. These findings suggest that LAG3 blockade may be an effective immune strategy for the treatment of AE patients. [ABSTRACT FROM AUTHOR]

Abstract The red palm weevil, Rhynchophorus ferrugineus is one of the alien species of high risk in China and a key pest of palm trees. This paper presents the first status report of the damage caused to sugarcane by R. ferrugineus in China. The first observation of damage was recorded in Changling farm in Shangsi County, Guangxi Province, on a small plot in October 2017 and the recorded damage level, exceeded 80% with cane stalks completely destroyed. In September 2021, R. ferrugineus was found in sugarcane fields (11.85 hm2) in Liucheng County, in Liuzhou City, but the percentage of plant damaged was sporadic and didn’t exceed 1%. The mean percent of damaged stalks and the length of damaged tunnels in plants recorded were 0.76%, and 19.54 cm, respectively when the canes were harvested. This discovery allowed us to prevent potential establishment and spread of the red palm weevil in the sugarcane regions in China, as well as serve a warning notice for other sugarcane regions worldwide. [ABSTRACT FROM AUTHOR]

In many cities and regions, decision makers independently develop Train Operation Plan (TOP) for each line in the rail transit network, resulting in a lack of TOP Synchronization (TOPS). Considering the entire network as a whole, researchers have realized that synchronous optimization is of great significance. In this paper, we formulate two Mixed-Integer Linear Programming (MILP) models to optimize demand-driven TOP in the network. The former is an Asynchronous TOP Optimization (ATOPO) model, while the latter is a Synchronous TOP Optimization (STOPO) model. The bi-objective models simultaneously determine train frequency, train timetable, and rolling stock circulation under small-granularity passenger demand to minimize trains' total cost and passengers' total time. Then, we propose the Nondominated Sorting Coevolutionary Memetic Algorithm (NSCMA) to solve the combinatorial optimization problems. The hybrid heuristic algorithm incorporates Coevolutionary Memetic Algorithm (CMA) into Advanced and Adaptive Nondominated Sorting Genetic Algorithm II (AANSGA-II) to ameliorate the evolution process for elite individuals. On this basis, we study the case of Shenyang Metro to verify the models and the algorithm. The results demonstrate that the STOPO model is better than the ATOPO model in reducing trains' total cost and passengers' total time. In addition, NSCMA is better than AANSGA-II in obtaining elite individuals. [ABSTRACT FROM AUTHOR]

Semantic segmentation is the underlying technology for many intelligent applications in underground mines. Unlike the ordinary scenario, the lighting of underground mines varies drastically, and there is heavy dust and mist, which results in poor image quality and greatly hinders the application of image semantic segmentation in underground mines. Because there is currently no datasets for underground mine tunnels available, a dataset named Underground Mine Tunnel Semantic Segmentation Dataset (UMTSSD) have to be constructed to support our research. UMTSSD consists of 3461 meticulously annotated images and 17 annotated categories. A real-time semantic segmentation algorithm named Fast Adaptive Deep Dual-resolution Network (FA-DDRNet) which uses Deep Dual-resolution Network (DDRNet) as the backbone is proposed for underground mines. To enhance the semantic segmentation accuracy in underground environment, FA-DDRNet introduces two modules: Fast Adaptive Input Normalization Module (FAINM) and Scale-wise Residual Cascade Module (SRCM). FAINM can autonomously and quickly adjust normal lighting images, weak lighting images, and overexposed images to improve the robustness of semantic segmentation algorithms. SRCM is integrated into the backbone to swiftly fuse multi-scale features in a cascade fashion, resulting in enhanced detection of objects with diverse shapes in underground environment. Finally, our method achieves exceptional performance with a superior inference speed compared to other semantic segmentation algorithms in UMTSSD. The method can realize running in real-time on low computational power embedded devices, which is well adapted to underground environment. • A real-time semantic segmentation algorithm for underground mines is proposed. • A semantic segmentation dataset of underground mines is constructed and utilized. • FAINM aims to enhance the robustness of network in varying lighting conditions. • SRCM is proposed to enhance the performance of network in underground mines. • Extensive comparative experiments have demonstrated the efficacy of FA-DDRNet. [ABSTRACT FROM AUTHOR]

• Experimental method of pedestrian wayfinding based on virtual reality. • Wayfinding model considering both spatial knowledge and visual information. • Analysis based on mathematical statistics analysis and deep learning framework. • Comparing the impact of dimensional differences on pedestrian wayfinding behavior. • Incorporating spatial knowledge at multiple scales. Understanding pedestrian wayfinding behavior is crucial for traffic management and building design. The use of virtual reality technology presents an efficient approach for investigating pedestrian wayfinding behavior in large public spaces, offering numerous advantages for data collection. However, the impact of different scenario dimensions on pedestrian wayfinding behavior in large public spaces remains unclear. Additionally, the selection of virtual experiment scenario dimensions currently relies primarily on researchers' experience and practical conditions, lacking sufficient evidence to support their rational. Another challenge is the limited focus on spatial knowledge's effect on wayfinding behavior, with insufficient analysis of the utility of pedestrian visual information and a lack of precise methods to quantify visual field information accurately. This study addresses these gaps by incorporating spatial knowledge at multiple scales and pedestrian visual field information as influencing factors in the analysis of wayfinding behavior. Furthermore, it distinguishes between three-dimensional and two-dimensional scenarios to compare the impact of dimensional differences on pedestrian wayfinding behavior. By analyzing behavior data from non-immersive wayfinding experiments, this research employs statistical analysis methods and a deep learning framework to derive results regarding the factors influencing wayfinding behavior. The findings demonstrate that considering both spatial and visual field information effectively enhances the predictive ability of the wayfinding model. Additionally, dimensional differences significantly influence the pedestrian wayfinding process. These results offer empirical evidence to guide researchers in selecting experimental scenarios of pedestrian behavior and provide insights for public space layout, signage design, and improving pedestrian efficiency. [ABSTRACT FROM AUTHOR]

Background: Cough caused by endotracheal tube (ETT) placement is ubiquitous and correlates with adverse outcomes. Remifentanil administration via target-controlled infusion (TCI) is one of the cough prevention measures used during recovery. In a pilot study, lidocaine administered via the perforated outer cuff of a dual-cuff endotracheal tube was also found to prevent cough due to ETT placement. We therefore compared these two cough prevention approaches during recovery after thyroidectomy in a single-centre, double-blind, randomised study conducted in China during the period from 09/10/2020 to 30/04/2021. Methods: Ninety-eight female patients aged 18–65 years with American Society of Anaesthesiologists Physical Status scores of I and II were scheduled to undergo thyroidectomy. The ETT contained an internal cuff covered by a perforated outer cuff to allow for lidocaine delivery. Patients were randomised to receive either 4 ml of saline solution (Group R, n = 49) or 4 ml of 2% lidocaine in the outer cuff (Group L, n = 49) at the beginning of skin suturing. Remifentanil (2 ng/ml) was maintained in Group R until extubation, while remifentanil was maintained in Group L until the end of skin suturing. The primary outcome was cough during patient transfer, at 1 min before extubation, and at extubation. The secondary outcomes were haemodynamics and other recovery parameters. Results: Primary outcomes were compared between remifentanil vs. lidocaine application, namely, the incidence of cough during patient transfer (0% in Group R vs. 0% in Group L), at 1 min before extubation (22.45% in Group R vs. 4.08% in Group L; P = 0.015), and at extubation (61.22% in Group R vs. 20.41% in Group L; P < 0.001). Compared with remifentanil, lidocaine more effectively decreased heart rate elevation and hypoxemia at 5 min after extubation, the spontaneous respiration recovery time, the extubation time, the duration of post-anaesthesia care unit (PACU) stay, Richmond Agitation-Sedation Scale scores in the agitated range and Critical-Care Pain Observation Tool scores. Conclusion: Lidocaine administered via the perforated outer cuff of the ETT significantly improved recovery from general anaesthesia compared to remifentanil in female patients after thyroidectomy. Trial registration: Chinese Clinical Trial Registry (No. ChiCTR2000038653), registered on 27/09/2020. [ABSTRACT FROM AUTHOR]

Data‐driven model predictive control (MPC) is an effective control method in controlling unknown constrained systems. The existing data‐driven MPC methods either estimate the system online (adaptive) with extra computation efforts, or use the initially measured trajectory from offline trials to design controller. The offline trials are economically expensive for many practical systems. To overcome these limitations, we propose a multistep input‐mapping data‐driven scheme. It maps the current and future inputs to the past online measured input‐state trajectories and expresses the future state as a linear combination of the past states. A moving data window is used to update the data online. Based on this scheme, two computationally efficient multistep input‐mapping data‐driven robust model predictive controllers with guarantee of feasibility and stability are proposed. The simulation results verify the improved performance and better computation efficiency of the resulting methods. [ABSTRACT FROM AUTHOR]

Designing railway operational planning is a complex and challenging task to cope with fluctuating passenger demand and satisfy actual operational requirements. Focusing on high-speed railway (HSR) lines, this paper proposes an integrated modeling framework for phase-oriented railway operational planning. The framework simultaneously optimizes the stop planning, train timetable problem (TTP) and rolling stock planning (RSP). A multi-objective optimization model, with macro passenger demand, two train speed levels, and overtaking operations taken into account, is formulated to minimize the total train running distance, train dwell time, and rolling stock resources. The constraints involve the train operation zones, train operation process, passenger demand, rolling stock circulation conditions, and actual operational requirements. The proposed model is addressed using a framework based on the ε -constraint method, from which an approximate Pareto-optimal solution set is obtained. A case study on the Beijing-Shanghai HSR corridor demonstrates the effectiveness of the proposed approach and reveals some trade-offs between the proposed objectives. [ABSTRACT FROM AUTHOR]

Background: Macular corneal dystrophy (MCD) is a rare corneal stromal dystrophy with bilateral progressive vision loss. The pathogenic gene of MCD is carbohydrate sulfotransferase 6 (CHST6). Herein, we report a novel missense mutation and a rare exon deletion mutation in the CHST6 gene in a Chinese family with MCD. Methods: Genomic DNA was extracted from the peripheral blood, and next generation sequencing was used to analyse the gene sequence. The pathogenic mutations were identified in all affected family members. The proband successively received binocular penetrating keratoplasty (PKP), and the corneas were examined by histopathology and colloidal iron staining to prove the diagnosis. A long-term follow-up was made to observe the changes after PKP. Results: Genetic analysis demonstrated hemizygous mutations in the proband, including a novel c.520A>C (p.K174Q) missense mutation and a rarely reported exon 3 deletion mutation, which were co-segregated with the MCD phenotypes in the pedigree. The positive colloidal iron staining confirmed the diagnosis of MCD in the proband. However, the clinical phenotype and pathological manifestation of both eyes were different from each other because of complicated keratitis in the left eye. During the nine years of follow-up, visual acuity was improved significantly, and the cornea was transparent without rejection and postoperative recurrence in both eyes. Conclusions: The novel hemizygous mutations were thought to contribute to the loss of CHST6 function, which induced typical clinical and pathological features of MCD. PKP was an effective treatment for MCD. [ABSTRACT FROM AUTHOR]

For the reverse flotation of phosphate ore, a high amount of sulfuric acid is required. In this study, we used NaF as a flotation activator to separate dolomite and fluorapatite to reduce acid consumption in the flotation process. Both single and actual mineral flotations were conducted. The NaF addition significantly improved the floatability of dolomite and reduced the flotation system's acid consumption. We employed X‐ray photoelectron spectroscopy and Material Studio simulation to examine the effect of dolomite activator (NaF) on the surface of dolomite and fluoroapatite. The addition of NaF showed much less effect on fluoroapatite than dolomite, thereby increasing the difference in floatability between dolomite and fluoroapatite, which is beneficial to flotation. [ABSTRACT FROM AUTHOR]

When passengers are oversaturated in the urban rail transit system and a further increase of train frequency is impossible, passenger flow control strategy is an indispensable approach to avoid congestion and ensure safety. To make the best use of train capacity and reduce the passenger waiting time, coordinative flow control is necessary at each station on a line. In most published studies, the equilibrium of passenger distributions among different stations and periods is not considered. As a result, two issues occur making it hard to implement in practical. First, a large number of passengers are held up outside a small number of stations for very long time. Second, there is a large variation of controlled flows for successive time intervals. To alleviate this problem, a single-line equilibrium passenger flow control model is constructed, which minimizes the total passenger delay. By applying different forms of the delay penalty function (constant and linear), flow control strategies such as independent flow control and equilibrium flow control can be reproduced. An improved simulated annealing algorithm is proposed to solve the model. A numerical case is studied to analyze the sensitivity of the functions, and the best parameter relationship in different functions could be confirmed. A real-world case from Batong Line corridor in Beijing subway is used to test the applicability of the model and algorithm, and the result shows that the solution with linear delay penalty functions can not only reduce the total passenger delay but also equilibrate the number of flow control passengers on spatial and temporal. [ABSTRACT FROM AUTHOR]

In this paper, we present a feature fusion method designed for the Gaussian process model's kernel functions for the probabilistic long-term load forecasting. To enrich the amount of information sent to the Gaussian kernel function, we propose a data conversion method that can convert one dimension time series data into multidimensional feature space. Besides, to improve computing efficiency in this data conversion method, we design a sparse approximation method according to the variational free energy principle with the Determinantal Point Processes (DPPs) as the pseudo inputs selection method. Also, the probabilistic distribution of our prediction is specially modified to bridge further the gap between the forecast and the original data set. Our approach is tested and presents a notable performance in the Global Energy Forecasting Competition data set. [ABSTRACT FROM AUTHOR]

The research on sliding mode control strategy typically utilizes a robust approach, which may come at the cost of sacrificing some performance because large parameter space is considered. Recently, the data-driven sliding mode control method has attracted much attention and shows excellent benefits in the fact that data is introduced to compensate the controller. Nevertheless, most of the research on data-driven sliding mode control relied on identification techniques, which limits its online applications due to the special requirements of the data. In this paper, an input-mapping technique is inserted into the design framework of sliding mode control to compensate for the influence generated by the unknown dynamic of the system. The principal novelty of the proposed input-mapping sliding mode control strategy lies in that the sliding mode surface and the sliding mode controller are co-designed through online learning from historical input–output data to minimize an objective function. Then, the convergence rate of the system is improved significantly based on the method designed in this work. Finally, some simulations are provided to show the effectiveness and superiority of the proposed methods. [ABSTRACT FROM AUTHOR]

Rare earth oxide promoted transition metal composite catalyst Eu 2 O 3 –Cu/NC with outstanding oxygen reduction reaction (ORR) performance, is constructed by hydrothermal and subsequent high-temperature calcination, considering replacing Pt/C. This synthesis method yields Eu 2 O 3 –Cu nanoparticles with uniform distribution, improved oxygen vacancies and increased content of N-doping. And the strong synergistic effect was created between promoter Eu 2 O 3 and chief Cu. In addition, the accommodate adsorption and transfer of O species endow Eu 2 O 3 –Cu/NC the improved ORR activity than Eu 2 O 3 /NC and Cu/NC. Meanwhile, the stability of Eu 2 O 3 –Cu/NC is also strengthened compared to Cu/NC on account of the interaction of active sites, and the H 2 O 2 yield of Eu 2 O 3 –Cu/NC is very low. For practical application, a rechargeable Zn-air battery with an air cathode of Eu 2 O 3 –Cu/NC displays a larger power density, excellent charge-discharge cycle stability and good rate capability. The designed composite shows potential application prospects in the fields of energy conversion. Image 1 • A novel rare earth improved composite Eu 2 O 3 -Cu/NC ORR catalyst was prepared. • The strong interaction between Eu 2 O 3 and Cu boosts the ORR activity. • The introduction of Eu 2 O 3 brings huge oxygen vacancies. • Zn-air battery performance of Eu 2 O 3 -Cu/NC is comparable to Pt/C based battery. [ABSTRACT FROM AUTHOR]

The authors examine the distributed multi‐agent optimisation problem allowing each agent to explicitly utilise the information of its first‐order and second‐order nearest neighbours for cooperative decision‐making. Specifically, at each time instance, each agent in the network employs the states of its second‐order nearest neighbours at the last time instance, which can be transmitted from its first‐order nearest neighbours in practice. Under the proposed framework, they propose a distributed subgradient algorithm asymptotically leading all agents to an agreement on the optimal solution of the multi‐agent optimisation problem under an appropriate selection of stepsize. Furthermore, they show that the proposed algorithm outperforms the existing algorithms built on the assumption that only the states of first‐order neighbours are available in terms of the convergence rate. [ABSTRACT FROM AUTHOR]

Mass production has been a trend for modern manufacturing. Information from peer processes may be used to improve the individual process control performance. The idea of 'incremental step mimicking' has been presented in our previous work [1] which resulted in 'incremental inter-agent learning' (IIAL) adaptive control. However, that work is based on a primitive adaptive control in which the control input is directly calculated from the online-identified model. This paper explores the inter-agent learning adaptive control based on a more complicated adaptive control, and proposes a more general Full-Scale IIAL (FS-IIAL) of which the previous IIAL [1] can be viewed as a special case. With the ensured robust stability, the proposed work is applied on the case when LIP formulae is a single layer RBF neural network, and simulation result validates the superior control performance of each process over the original adaptive control and the previous IIAL. [ABSTRACT FROM AUTHOR]

The iterative learning control (ILC) combining with model predictive control (ILC–MPC) is an effective control method for constrained batch processes. However, in real applications, model uncertainty usually makes it slow for the controlled process to converge to the reference trajectory. To eliminate the restrictions in previous works, a data-driven approach is proposed, which directly describes the relationship between inputs and outputs according to the past data. Based on this method, a novel data-driven ILC–MPC controller is proposed, where the two-mode framework and the invariant updating strategy are employed to guarantee the convergence. Since the outputs caused by model uncertainty are partly known from the past data, better performance can be achieved by the proposed design which is verified by experimental studies on a manipulator. [ABSTRACT FROM AUTHOR]

A Plectosphaerella sp. was isolated from an undescribed root rot of the herbaceous ornamental Heuchera sanguine (Coral Bells) in Massachusetts. Morphological examination and phylogenetic analysis using DNA sequences derived from internal transcribed spacer (ITS) of rDNA, large subunit (LSU) of rDNA, calmodulin (CaM), β‐tubulin 2 (Tub) and transcription elongation factor 1 (Ef‐1α) loci identified the fungus as a member of Plectosphaerella cucumerina. A test of Koch's postulates on an isolate of P. cucumerina indicated that it was a pathogen causing root rot in Heuchera sp. The pathogen caused greatest damage to small transplants (1–4 g) as opposed the larger transplants (9–13 g) and in separate studies was found to infect seedlings of cucumbers, melons and pumpkins. Plectosphaerella cucumerina is reported for the first time in Heuchera sp. Given the importance of Heuchera to the ornamental industry, and the rapid dissemination that can occur with propagated material in the horticultural trade, caution should be exercised to prevent the spread of P. cucumerina. [ABSTRACT FROM AUTHOR]

When minimizing the total waiting time of all passengers during timetabling, a small number of passengers would have to endure a very long waiting time. This suggests that fairness issue exists in timetabling. This study analyzes this problem by taking an urban railway timetable as a case. Two fairness criteria are analyzed, namely min–max fairness and α-fairness. A mixed-integer programming model is developed to optimize the timetable by considering the trade-off between efficiency and fairness. A simulated annealing-based adaptive large neighborhood search metaheuristic algorithm is applied to solve the problem. A real-world experiment is conducted to demonstrate the effectiveness of the proposed model. It finds that an 'efficient' timetable does not always perform well from the perspective of fairness. Moreover, there is a trade-off between the fairness and efficiency. The proposed model can effectively optimize the rail timetable by keeping its efficiency while simultaneously improving fairness. [ABSTRACT FROM AUTHOR]

In this paper, we propose a game-based policy for the signal control of an isolated intersection together with the rerouting of the vehicles in this intersection using V2I technology. The game starts at the beginning of each signal cycle on the condition that there are vehicles delayed in the intersection at the end of current cycle. Then the delayed vehicles negotiate with the signal controller to decide the signal setting for next cycle while rerouting their next movements. During the game, each delayed vehicle selects one route from their own acceptable route space to reduce the intersection delay time, while the signal controller adjusts the signal setting to meet the traffic demand of the delayed vehicles as well as maximize the throughput. The game can be terminated with end conditions in finite rounds. The vehicles coming after the end of game reroute in a similar way with the delayed vehicles but based on the game results. Numerical experiments on a calibrated network of Caohejing District in Shanghai indicate that our proposed method can effectively mitigate road congestions and improve the network capacity, especially at high traffic loads. [ABSTRACT FROM AUTHOR]

Bias flow of molten metal in slab mold caused by unstable flow from submerged entry nozzle can deteriorate the slab quality during continuous casting. In this study, a novel electromagnetic swirling flow in nozzle (EMSFN) technique, which utilizes electromagnetic force to stabilize the flow in the nozzle and subsequently control the flow in the mold, is proposed. The mechanism for controlling unstable flow through EMSFN is explored through numerical simulation and water model experiments. In the results, it is shown that the molten steel is rotated under the effect of magnetic field and flows into the mold along the upper edge of the nozzle outlet, which weakens the impact of molten steel on the bottom of nozzle and stabilizes the outflow from nozzle. The flow field is consistently symmetrical on both sides of the nozzle. The impact depth of molten steel flowing down along the narrow sides of the mold on both sides is greatly reduced. The surface velocity on both sides of the nozzle changes uniformly. The range of surface velocity variation with EMSFN is only 10% of that with a conventional nozzle. The EMSFN technique can help prevent slag entrapment and improve the flotation of inclusions and bubbles. [ABSTRACT FROM AUTHOR]

[Display omitted] • A high-performance gold capture material C-S-180 has been reported. • C-S-180 exhibits excellent gold selectivity in intricate wastewater. • Sulfur modulates the electronic structure of the skeleton to promote gold capture. • C-S optimized energy band gap for a more efficient gold reduction process. The efficient and selective recovery of gold from secondary sources is key to sustainable development. However, the complexity of the recovery environment can significantly complicate the compositions of utilized sorbents. Here, we report a straw-derived mesoporous carbon as an inexpensive support material. This mesoporous carbon is modified by anions (sulfur modulation, C-S-180) to improve its electron-transfer efficiency and tune the electronic structure of its skeleton toward enhanced gold reduction. The high surface area of C-S-180 (989.4 m2/g), as well as the presence of abundant C–S in the porous structure of the adsorbent, resulted in an outstanding Au3+-uptake capacity (3422.75 mg/g), excellent resistance to interference, and favorable Au3+ selectivity. Dissimilar to most existing carbon-based adsorbents, electrochemistry-based studies on the electron-transfer efficiencies of adsorbents reveal that sulfur modulation is crucial to optimizing their adsorption performances. Furthermore, the density functional theory reveals that the optimization mechanism is attributable to the adjustment of the electronic structure of the carbon skeleton by C–S, which optimizes the band-gap energy for enhanced Au3+ reduction. These findings offer a strategy for constructing green and efficient adsorbents, as well as a basis for extending the applications of inexpensive carbon materials in gold recovery from complex environments. [ABSTRACT FROM AUTHOR]

The optimization problem in model predictive control (MPC) algorithm for piecewise affine (PWA) systems often contains substantial logic variables, which requires extensive computing power for online implementation. This paper proposes a one-step distributed MPC algorithm for spatially interconnected PWA systems with both input and state constraints, where the algorithm is based on a series of r -step robustly controllable sets. Algorithms for offline computing terminal sets and a series of r -step robustly controllable sets are developed using the step-by-step backward approach. The states that lie in the robustly controllable sets are steered into the terminal set in finite steps and ultimately converge into a robustly positively invariant set. The proposed algorithm is demonstrated to significantly reduce the online computational burden. Simulation results are provided to showcase the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The Grid-connected inverter control strategies are becoming a more attractive subject as the growing requirements for renewable power. The model predictive control (MPC) strategy is thought as a prospective method for the control of inverters due to its capability of handling constraints and nonlinearity properly. However, the complicated computations at each sampling instant makes MPC hardly applied. For the fast implementing of MPC on grid connected inverter control, in this paper, a decaying amplification aggregation strategy based model predictive control is presented on three-phase LCL-filter Grid Connected Inverter control. Additionally, a cumulative incremental state space model is used to depress the possible error. The proposed algorithms can significantly reduce the online computational complexity of MPC and have an infinite prediction horizon. The stability is also discussed and simulation results of a 20 KW grid-connected inverter demonstrate the effectiveness and fast calculation ability of this strategy. [ABSTRACT FROM AUTHOR]

Highlights • Circular RNA were identified from milk exosomes. • CircRNA host gene enrichment was analyzed. • Milk protein mRNAs had the highest percentage of exosomal RNAs. • The result will be helpful to the development of nutritional and healthy formula. Abstract RNA in milk exosome can be absorbed in the mammalian gastrointestinal tract and plays important regulatory function. Circular RNA is one kind of covalently closed RNA molecule derived by backsplicing that have important physiological functions. In the present study, we examine in detail the landscape of circular RNA and mRNA of the bovine milk exosome in order to provide the molecular basis of functional component of bovine milk. The total RNAs were isolated from exosome in bovine colostrum and mature milk. High throughput RNA-sequencing were used to characterize the profiles of the circular RNAs and mRNAs. 2059 distinct circular RNAs were identified from milk exosomes, most of which were expressed either specific in colostrum or in mature milk exosomes. Circular RNA host genes were enriched for genes involved in cytoplasm, endoplasmic reticulum, transport, and transcription factor. The species of mRNA presented in milk exosome were consistent, where 18,612 mRNAs were common to both kinds of exosome. The circular RNAs profile of colostrum or mature milk exosomes differed greatly. As RNAs in milk exosome can be absorbed and function in receipt cells, these RNAs might act as a novel regulatory nutrient for infant and adult consumers which need further study. [ABSTRACT FROM AUTHOR]

For dense railway lines, timetables are commonly designed periodically to provide regular services. However, such timetables may lead to many low‐occupancy departures and make it inefficient to a railway company. We combine the features of periodic timetabling with those of demand‐responsive timetabling design for a bidirectional rail transit line considering rolling stock circulation. A mixed‐integer programming model with the objective of minimizing total passenger waiting time and weighted total train travel time is formulated and a three‐phase heuristic algorithm is developed. Five performance indicators, namely, total train travel time, passenger average waiting time, event time deviation, event frequency deviation, and train connection time are introduced to evaluate the performance of the model. A case study on Jinshan railway corridor in Shanghai demonstrates that the proposed demand responsive periodic timetabling model can reduce passengers' waiting time and keep periodicity in the meantime with short computational time [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the periodic event‐triggered control problem for distributed networked multiagent systems with interconnected nonlinear dynamics subject to asynchronous communication. A method of state trajectory estimation for the interconnected neighboring agents over each prediction horizon with guaranteed error bounds is addressed to handle the asynchronous communication. Based on it, a distributed robust model predictive control (MPC) is proposed with a distributed periodic event‐triggered scheme for each agent. According to this algorithm, each subsystem generates presumed state trajectories for all its upstream neighbors and computes its own control locally. By checking the designed triggering condition periodically, the optimization problem of MPC will be implemented and solved when the local error of the subsystem exceeds a specified threshold. Then, the optimized control input will be determined and applied until the next time instant when the triggering condition is invoked. Moreover, sufficient condition for ensuring feasibility of the designed algorithm is conducted, along with the analysis of asymptotic stabilization of the closed‐loop system. The illustrative example for a set of coupled Van der Pol oscillators is reported to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Route choice is one of the most critical passenger behaviors in public transit research. The utility maximization theory is generally used to model passengers' route choice behavior in a public transit network in previous research. However, researchers have found that passenger behavior is far more complicated than a single utility maximization assumption. Some passengers tend to maximize their utility while others would minimize their regrets. In this paper, a schedule-based transit assignment model based on the hybrid of utility maximization and regret minimization is proposed to study the passenger route choice behavior in an urban rail transit network. Firstly, based on the smart card data, the space-time expanded network in an urban rail transit was constructed. Then, it adapts the utility maximization (RUM) and the regret minimization theory (RRM) to analyze and model the passenger route choice behavior independently. The utility values and the regret values are calculated with the utility and the regret functions. A transit assignment model is established based on a hybrid of the random utility maximization and the random regret minimization (RURM) with two kinds of hybrid rules, namely, attribute level hybrid and decision level hybrid. The models are solved by the method of successive algorithm. Finally, the hybrid assignment models are applied to Beijing urban rail transit network for validation. The result shows that RRM and RUM make no significant difference for OD pairs with only two alternative routes. For those with more than two alternative routes, the performance of RRM and RUM is different. RRM is slightly better than RUM in some of the OD pairs, while for the other OD pairs, the results are opposite. Moreover, it shows that the crowd would only influence the regret value of OD pair with more commuters. We conclude that compared with RUM and RRM, the hybrid model RURM is more general. [ABSTRACT FROM AUTHOR]

Abstract To enhance the individual control performance over the standalone control of each process in mass production, this paper explores information sharing among processes by proposing an incremental inter-agent learning (IIAL) method for the online estimation of the process model in the adaptive control of a class of processes modeled by linear-in-unknown-constant-parameters (LIP) formulae. Each individual process control system makes use of information from its own and other processes incrementally with time and across process. The application of the proposed work to a single layer RBF neural networks adaptive control shows that the speed of tracking error convergence of each process is improved. [ABSTRACT FROM AUTHOR]

A series of pyrazolone derivatives ligands L 1−7 were successfully synthesized and validated by 1 H NMR and MS, corresponding europium complexes [EuL 1−7 (NO 3 ) 2 ]NO 3 ·EtOAc were synthesized. Physico-chemistry properties of title complexes were determined by Elemental analysis, Molar conductance, UV absorption spectra, IR spectra and Thermogravimetric analysis. The title complexes exhibit characteristic red fluorescence of Eu 3+ . The effect of various substituent groups in ligands on the of title Eu 3+ complexes is ordered: Cl > -Br > -OCH 3  > -F > -CH 3  > -H > -NO 2 , and [EuL 6 (NO 3 ) 2 ]NO 3 ·EtOAc containing Cl possesses the strongest fluorescence intensity, so does fluorescence quantum yield. The electrochemical properties indicate that energy gap Eg and LUMO energy level are huge affected by substituent groups, and variation trends of LUMO energy level affected by diverse substituent groups are also different. The prepared title europium complexes have potential application prospects in the fields of photoelectric functional materials and life sciences. [ABSTRACT FROM AUTHOR]

Multi-view learning (MVL) focuses on the problem of learning from the data represented by multiple distinct feature sets. Various successful SVM-based multi-view learning models have been proposed to improve existing learning tasks performance. Since nonparallel support vector machine (NPSVM) is proposed with several incomparable advantages over the state-of-the-art classifiers, it is potentially beneficial to perform the multi-view classification task using NPSVM. In this paper, we build a new multi-view learning model based on nonparallel support vector machine, termed as MVNPSVM. By combining the large margin mechanism and the consensus principle, MVNPSVM not only inherits the advantages of both NPSVM and multi-view learning, but also brings a new insight of extending NPSVM to the multi-view learning field. To solve MVNPSVM efficiently, we adopt the alternating direction method of multipliers (ADMM) as the solution. We theoretically analyze the performance of MVNPSVM from the viewpoints of the consensus analysis and the comparisons with the other two similar methods SVM-2K and multi-view twin support vector machines. Experimental results on 95 binary data sets confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

As two kinds of popular data mining methods, metric learning and SVM have a interesting and valuable internal relationship. The basic idea of metric learning is to learn a data-dependent metric, instead of Euclidean metric, to shrink the distances between similar points and extend the distances between dissimilar points. From a different view, LSSVM can reach a similar goal as metric learning. It finds two parallel hyperplanes to make the distances between points and corresponding hyperplane as small as possible and the distance between two hyperplanes as large as possible. LSSVM can be looked as a slack version of metric learning. Then, it can be improved by modifying the way in measuring between-class distance, lead to the raise of our novel approach ML-LSSVM, which adds constraints of inter-class distance into LSSVM. Alternating direction method of multipliers algorithm was implemented to solve ML-LSSVM effectively, much faster than handling the original quadratic convex programming problem. Experiments were made to validate the efficacy of ML-LSSVM and prove that different measurements of intra-class distance and inter-class distance have significant impact on classification. At last, the relation between LMNN and ML-LSSVM was discussed to illustrate that the local formulation of LMNN is equivalent to ML-LSSVM. [ABSTRACT FROM AUTHOR]

Train timetabling is crucial for passenger railway operation. Demand-oriented train timetable optimization by minimizing travel time plays an important role in both theory and practice. Most of the current researches of demand-oriented timetable models assume an idealized situation in which the service order is fixed and in which zero overtaking exists between trains. In order to extend the literature, this paper discusses the combinatorial effect of service order and overtaking by developing four mixed-integer quadratic programming timetabling models with different service order as well as overtaking conditions. With the objective of minimizing passengers’ waiting time and in-vehicle time, the models take five aspects as constraints, namely dwell time, running time, safety interval, overtaking, and capacity. All four models are solved by ILOG CPLEX; and the results, which are based on Shanghai-Hangzhou intercity high-speed rail data, show that either allowing overtaking or changing service order can effectively optimize the quality of timetable with respect to reducing the total passengers’ travel time. Although optimizing train overtaking and service order simultaneously can optimize the timetable more significantly, compared to overtaking, allowing the change of service order can help passengers save total travel time without extending the train travel time. Moreover, considering the computation effort, satisfying both of the conditions in the meantime, when optimizing timetable has not got a good cost benefit. [ABSTRACT FROM AUTHOR]

Cheap, efficient, and industrial-grade model predictive controllers are required for extending the use of model predictive control (MPC) to resource-constrained embedded computing platforms and practical industrial fields. In this paper, we implement the matrix inversion on the hardware to improve the computational efficiency of our previously designed MPC controller. For the specific matrices in each iteration of the active-set method, a simple positive definite symmetric matrix inversion algorithm is proposed, which can transform the matrix inversion into iterated matrix multiplication and add operations along with fewer division operations. Its similar matrix calculating formulas in each iteration are block design oriented and able to be speed up by the parallelism and pipeline in the hardware. To avoid extra data transmission, the matrix inversion algorithm and active-set method are combined through the time sequence and architectural design to form an improved predictive controller on a field-programmable gate array (FPGA). The improved predictive controller is implemented on an FPGA-based board especially made for the industrial sites and applied to a holding process of the injection molding, which achieves satisfactory control performance. [ABSTRACT FROM PUBLISHER]

Distance metric learning has been a hot research spot recently due to its high effectiveness and efficiency in improving the performance of distance related methods, such as k nearest neighbors ( k NN). Metric learning aims to learn a data-dependent metric to make intra-class distance smaller and inter-class larger. A large number of methods have been proposed for various applications and a survey to evaluate and compare these methods is imperative. The existing surveys just analyze the algorithms theoretically or compare them experimentally with a narrow time scope. Therefore, the paper reviews classical and influential methods that were proposed between 2003 and 2017 and presents a taxonomy based on the most distinct character of each method. All the methods are categorized into five classes, including pairwise cost, probabilistic framework, boost-like approaches, advantageous variants and specific applications. A comprehensive experimental study is made to compare all the selected methods, exploring the ability in improving accuracy, the relation between distance change and accuracy, the relation between accuracy and k NN neighbor size. [ABSTRACT FROM AUTHOR]

MicroRNA‑205 (miR‑205) has been reported to be downregulated, and serves critical roles in the pathogenesis and progression of several types of cancer, including breast, prostate and lung cancer. However, the underlying mechanism of miR‑205 in thyroid cancer remains unclear. In the present study, it was demonstrated that the expression of miR‑205 was reduced in thyroid cancer tissues compared with non‑cancer tissues. In addition, miR‑205‑knockdown models in the BHT‑101 cell line and ectopic expression models in the 8505‑C cell line were used to measure the biological functions of miR‑205. The results indicated that miR‑205 inhibited certain aspects of thyroid cancer, including cell proliferation, migration and invasion. Furthermore, Yes‑associated protein 1 (YAP1) was identified as a target gene of miR‑205 and its expression was negatively correlated with that of miR‑205 in thyroid cancer tissues. Depletion of YAP1 partially reduced the anti‑miR‑205‑induced cell growth and invasion. The results of the present study suggested that the tumor suppressive functions of miR‑205 via targeting YAP1 could be a novel target for the treatment of thyroid cancer. [ABSTRACT FROM AUTHOR]

[Display omitted] • POP-BPy exhibits excellent stability in extreme water environments. • Selective capture of palladium from aqua regia was achieved by POP-BPy. • Protonation-armed pyridine nitrogen served as active site for Pd capture. • Almost all of Pd in leachate of spent catalysts was recovered by POP-BPy. Due to the hostile environment in which it is recovered, selective separation of palladium from the leachate of spent catalysts containing palladium is of considerable utility but faces substantial obstacles. This work designed and synthesized a bipyridine-based porous organic polymer (POP-BPy) via free radical polymerization. Meanwhile, its adsorption characteristics and the mechanism of adsorption in extreme environments were examined. In hostile conditions, the resultant POP-BPy display exceptional mechanical stability and environmental resistance. Impressively, POP-BPy has an excellent adsorption capacity of Pd(II) (248 mg/g) in harsh environments (aqua regia: AR; half-diluted aqua regia: AR"1 + 1"), and also had superior selectivity and rapid kinetics. The selectivity of POP-BPy for Pd(II) was verified in combination with relevant experiments (β Pd(II)/Pt(II) = 36.19, β Pd(II)/Pt(II) = 23.90). Furthermore, combining density functional theory (DFT) and molecular dynamics (MD) simulations, the mechanism of selectivity of POP-BPy for Pd(II) was revealed from static adsorption sites to dynamic adsorption processes in a holistic manner, which attributed to the strong attraction of protonated pyridine nitrogen to PdCl 4 2- and site suitability. Furthermore, the application of POP-BPy to spent catalyst recovery resulted in up to 100% removal of Pd(II) from the actual leaching solution, and fixed bed column experiments can effectively treat 2688 bed volumes (L) of feed streams (10 mg/L) before reaching the breakthrough point (<1 mg/L). Overall, this work has opened up new avenues for the selective recycling of valuable metals in harsh environments. [ABSTRACT FROM AUTHOR]

Precipitable water vapour (PWV) is a primary factor that affects climate and weather. The accurate retrieval of PWV is crucial for weather prediction and meteorological research. Interferometric Synthetic Aperture Radar (InSAR) has been rapidly developed in recent years and proven effective in PWV retrieval in the regions of good coherence. However, there are large areas of natural terrain where signals decoherence that limit the precision of PWV retrieval. Besides, due to the differential interference processing of InSAR data, the results of its retrieval are differential PWV (ΔPWV). In this study, the StaMPS-InSAR was used to overcome the problem of weak detection ability in low coherence regions. A series of 11 Sentinel-1A images were selected to retrieve space-continuous ΔPWV. Beidou Satellite Navigation System (BDS) can retrieve high-precision PWV. Hence, the BDS data was utilized to convert the InSAR-derived ΔPWV to PWV. Compared with the BDS-derived PWV, the root mean square error (RMSE) of the experimental results is 1.3 mm with a spatial resolution as fine as 20 m. The correlation coefficient between the deviations and Liquid Cloud Water is 0.65, indicating a positive correlation. We demonstrate the advantage of the proposed method for retrieving PWV in computational efficiency by comparing it with the SBAS-InSAR. With the increasing stack size of the SAR images, our method can reduce the taking time for processing. The proportion can reach 12.6% when the stack size of SAR images is 20. By the spatio-temporal analysis of the results, we found some characteristics of PWV in mountain areas especially. The results have certain reference value for the space-continuous PWV retrieval and the study of PWV spatio-temporal characteristics. [ABSTRACT FROM AUTHOR]

Observations of precipitable water vapor (PWV) in the atmosphere play a crucial role in weather forecasting and global climate change research. Spaceborne Interferometric Synthetic Aperture Radar (InSAR), as a widely used modern geodetic technique, offers several advantages to the mapping of PWV, including all-weather capability, high accuracy, high resolution, and spatial continuity. In the process of PWV retrieval by using InSAR, accurately extracting the tropospheric wet delay phase and obtaining a high-precision tropospheric water vapor conversion factor are critical steps. Furthermore, the observations of InSAR are spatio-temporal differential results and the conversion of differential PWV (InSAR ΔPWV) into non-difference PWV (InSAR PWV) is a difficulty. In this study, the city of Jinan, Shandong Province, China is selected as the experimental area, and Sentinel-1A data in 2020 is used for mapping InSAR ΔPWV. The method of small baseline subset of interferometry (SBAS) is adopted in the data processing for improving the coherence of the interferograms. We extract the atmosphere phase delay from the interferograms by using SRTM-DEM and POD data. In order to evaluate the calculation of hydrostatic delay by using the ERA5 data, we compared it with the hydrostatic delay calculated by the Saastamoinen model. To obtain a more accurate water vapor conversion factor, the value of the weighted average temperature Tm was calculated by the path integral of the ERA5. In addition, GNSS PWV is used to calibrate InSAR PWV. This study demonstrates a robust consistency between InSAR PWV and GNSS PWV, with a correlation coefficient of 0.96 and a root-mean-square error (RMSE) of 1.62 mm. In conclusion, our method ensures the reliability of mapping PWV by using Sentinel-1A interferograms and GNSS observations. [ABSTRACT FROM AUTHOR]

The comprehensive optimization of the timetables of urban rail transit systems under more realistic conditions is essential for their practical operation. Currently, most time-dependent timetabling models do not adequately consider train capacity and variable operation parameters. To bridge this gap, this study mainly investigates the timetable design problem of the urban rail transit system so as to adapt to time-dependent passenger demand under congested conditions by considering the variable number of trains, train running time, and train dwell time. Two nonlinear non-convex programming models are formulated to design timetables with the objective of minimizing the total passenger travel time (TTT) under the constraints of train operations, and passenger boarding and alighting processes. The difference between the two models is that one is a train-capacity unconstrained model and the other is a train-capacity constrained model. The proposed models are examined through real-world cases solved by the adaptive large neighborhood search algorithm. The results show that the first model can minimize passenger TTT under dynamic passenger demand, whereas the second can comprehensively optimize passenger TTT and meantime keep the train load factor within a reasonable level. Accordingly, it is concluded that the proposed models are more realistic. [ABSTRACT FROM AUTHOR]

The asynchronous communication always exists in networked control systems that consist of interconnected heterogeneous subsystems. To improve the closed‐loop performance, a networked and distributed predictive controller is developed for such a system in this study. At each time instant, a virtual trajectory generator for the interconnected agents with guaranteed error bounds is proposed to implement the receding horizon optimisation. Then, a dual‐mode strategy is designed for the asynchronous distributed model predictive control to make effective use of computational resources. Conditions for ensuring the feasibility of the proposed algorithm and the stabilisation of the closed‐loop networked control systems are developed, respectively. Finally, a simulation example of a set of coupled Van der Pol oscillators is presented to illustrate the results. [ABSTRACT FROM AUTHOR]

Large-scale problems have been a very active topic in machine learning area. In the time of big data, it is a challenge and meaningful work to solve such problems. Standard SVM can make linear classification on large-scale problems effectively, with acceptable training time and excellent prediction accuracy. However, nonparallel SVM (NPSVM) and ramp loss nonparallel SVM (RNPSVM) are proposed with better performance than SVM on benchmark datasets. It is motivated to introduce NPSVMs into the area of large-scale issues. In this paper, we propose large-scale linear NPSVMs, solved by the alternating direction method of multipliers (ADMM), to handle large-scale classification problems. ADMM breaks large problems into smaller pieces, avoiding solving intractable problems and leading to higher training speed. The primal problems of NPSVM are convex and differentiable, and they can be managed directly by ADMM. But the objective functions of RNPSVM, composed of convex ones and concave ones, should first be processed by CCCP algorithm and transformed as a series of convex programs. Then, we apply ADMM to solve these programs in every iteration. Experiments of NPSVMs on large-scale problems verify that the algorithms can classify large-scale tasks effectively. [ABSTRACT FROM AUTHOR]

Train dwell time estimation is a critical issue in both scheduling and rescheduling phases. In a previous paper, the authors proposed a novel dwell time estimation model at short stops which did not require the passenger data. This model shows promising results when applied to Dutch railway stations. This paper focuses on testing and improving the generality of the model by two steps: first, the model is tested by applying more independent datasets from another city and comparing the estimation accuracy with the previous Dutch case; second, the model’s generality is tested by a theoretical approach through the analysis of individual model parameters, variables, model scenarios, and model structure as well as work conditions. The validation results during peak hours show that the MAPE of the model is 11.4%, which is slightly better than the results for the Dutch railway stations. A more generalized predictor called “dwell time at the associated station” is used to replace the square root term in the original model. The improved model can estimate train dwell time in all the investigated stations during both peak and off-peak periods. We conclude that the proposed train dwell time estimation model is generic in the given condition. [ABSTRACT FROM AUTHOR]

Soft sensor techniques have been increasingly applied in chemical processes to predict key variables online. Traditionally, soft sensors modeling needs labeled training samples that contain both subsidiary and key variables. However, labeled samples are limited because key variables are always difficult to be obtained online. Thus, a novel active learning ( AL ) strategy is proposed to reduce the labeling cost, which iteratively select the most informative candidates by jointly evaluating two criteria: (i) representativeness and (ii) uncertainty. The representative samples are defined as those whose features have the best reconstruction of global features extracted by kernel principal component analysis ( KPCA ), while the uncertain samples can be selected by the estimated variance based on Gaussian process regression ( GPR ) model. Then, the optimization scheme is also introduced to solve the optimization problem derived by the two sampling criteria. Three industrial application case studies show that the proposed AL strategy exhibits a good capability to select the most informative samples, which can improve the performance of the soft sensor. [ABSTRACT FROM AUTHOR]

This paper studies the output-feedback model predictive control (MPC) design problem for linear systems with multiplicative and additive random uncertainty. We first present an off-line optimization algorithm to optimize feedback gains of the observer and the dual-mode control policy. After that, by defining a cuboid tube whose center and boundary are both time-varying variables, we develop a set sequence with increased freedom to contain stochastic system trajectories. A quadratic performance function with analytic upper and lower bounds is minimized such that it decreases exponentially to a finite range under the expectation. The resulting MPC algorithms are proved to guarantee practically stochastic input-to-state stability. A numerical example of the wind turbine model illustrates the properties of the MPC algorithms. [ABSTRACT FROM AUTHOR]