The Hard-Constraint PINNs for Interface Optimal Control Problems

We show that the physics-informed neural networks (PINNs), in combination with some recently developed discontinuity capturing neural networks, can be applied to solve optimal control problems subject to partial differential equations (PDEs) with interfaces and some control constraints. The resulting algorithm is mesh-free and scalable to different PDEs, and it ensures the control constraints rigorously. Since the boundary and interface conditions, as well as the PDEs, are all treated as soft constraints by lumping them into a weighted loss function, it is necessary to learn them simultaneously and there is no guarantee that the boundary and interface conditions can be satisfied exactly. This immediately causes difficulties in tuning the weights in the corresponding loss function and training the neural networks. To tackle these difficulties and guarantee the numerical accuracy, we propose to impose the boundary and interface conditions as hard constraints in PINNs by developing a novel neural network architecture. The resulting hard-constraint PINNs approach guarantees that both the boundary and interface conditions can be satisfied exactly and they are decoupled from the learning of the PDEs. Its efficiency is promisingly validated by some elliptic and parabolic interface optimal control problems

Fleet rebalancing for expanding shared e-mobility systems : a multi-agent deep reinforcement learning approach

The electrification of shared mobility has become popular across the globe. Many cities have their new shared e-mobility systems deployed, with continuously expanding coverage from central areas to the city edges. A key challenge in the operation of these systems is fleet rebalancing, i.e., how EVs should be repositioned to better satisfy future demand. This is particularly challenging in the context of expanding systems, because i) the range of the EVs is limited while charging time is typically long, which constrain the viable rebalancing operations; and ii) the EV stations in the system are dynamically changing, i.e., the legitimate targets for rebalancing operations can vary over time. We tackle these challenges by first investigating rich sets of data collected from a real-world shared e-mobility system for one year, analyzing the operation model, usage patterns and expansion dynamics of this new mobility mode. With the learned knowledge we design a high-fidelity simulator, which is able to abstract key operation details of EV sharing at fine granularity. Then we model the rebalancing task for shared e-mobility systems under continuous expansion as a Multi-Agent Reinforcement Learning (MARL) problem, which directly takes the range and charging properties of the EVs into account. We further propose a novel policy optimization approach with action cascading, which is able to cope with the expansion dynamics and solve the formulated MARL. We evaluate the proposed approach extensively, and experimental results show that our approach outperforms the state-of-the-art, offering significant performance gain in both satisfied demand and net revenue

Systematic Analysis of the Expression and Prognostic Significance of P4HA1 in Pancreatic Cancer and Construction of a lncRNA-miRNA-P4HA1 Regulatory Axis

Objectives. Prolyl 4-hydroxylase subunit alpha 1 (P4HA1) plays a crucial role in modulating extracellular matrix component and promoting tumor progression by changing tumor adhesion, migration, and other biological behaviors in some cancers. However, its expression pattern, biological function, and underlying mechanism in pancreatic cancer remain largely unclear. Materials and Methods. In this study, a set of bioinformatics tools were used to analyze the expression of P4HA1 and its prognostic value in pancreatic cancer. In addition, the mechanism through which P4HA1 promotes the progression of pancreatic cancer was explored by constructing a competing endogenous RNA (ceRNA) regulatory axis. Results. It was found that the mRNA and protein expression of P4HA1 was significantly higher in pancreatic cancer tissues than in normal tissues. Its high P4HA1 expression correlated with poor clinicopathological features (T stage: P=0.0078; N stage: P=0.0124; TNM stage: P=0.0013; pathological grade: P=0.0108) and poor prognosis [OS: HR=1, 95% CI (1-1.01), P=0.00028; DSS: HR=1, 95% CI (1-1.01), P=0.00049; PFI: HR=1.01, 95% CI (1.01-1.02), P=0.0057; and DFI: HR=1, 95% CI (1-1.01), P=0.0034]. The LINC01503/miR-335-5p/P4HA1 axis might mediate the effects of P4HA1 in promoting the progression on pancreatic cancer. Conclusions. Collectively, our findings suggest that high expression of P4HA1 may be used as a promising prognostic biomarker and could be considered for the development of a novel therapeutic strategy for pancreatic cancer in the future

Optimization of Cold Pressing Process Parameters of Chopped Corn Straws for Fuel

Pressed condensation is a key process before the reclamation of loose corn straws. In this study, the effects of stabilization time on the relaxation density and dimensional stability of corn straws were studied firstly, and then the stabilization time was determined to be 60 s by comprehensively considering the compression effect, energy consumption, efficiency and significance. On this basis, the effects of the water content (12%, 15%, 18%), ratio of pressure maintenance time to stabilization time (0, 0.5, 1), maximum compression stress (60.4, 120.8, 181.2 kPa) and feeding mass (2.5, 3, 3.5 kg) on the relaxation density, dimensional stability coefficient, and specific energy consumption of post-compression straw blocks were investigated by the Box–Behnken design. It was found that the water content, ratio of pressure maintenance time to stabilization time, maximum compression stress, and feeding mass all very significantly affected the relaxation density, dimensional stability coefficient and specific energy consumption. The interaction between water content and maximum compression stress significantly affected both relaxation density and specific energy consumption. The interaction between the ratio of pressure maintenance time to stabilization time and feeding mass significantly affected the dimensional stability coefficient. The factors and the indices were regressed by quadratic equations, with the coefficients of determination larger than 0.97 in all equations. The optimized process parameters were water content of 13.63%, pressure maintenance time of 22.8 s, strain maintenance time of 37.2 s, maximum compression stress of 109.58 kPa, and raw material feeding mass of 3.5 kg. Under these conditions, the relaxation density of cold-pressed straw blocks was 145.63 kg/m3, the dimensional stability coefficient was 86.89%, and specific energy consumption was 245.78 J/kg. The errors between test results and predicted results were less than 2%. The low calorific value of cold-pressed chopped corn straw blocks was 12.8 MJ/kg. Through the situational analysis method based on the internal and external competition environments and competition conditions (SWOT analysis method), the cold-pressed chopped corn straw blocks consumed the lowest forming energy consumption than other forming methods and, thus, are feasible for heating by farmers. Our findings may provide a reference for corn straw bundling, cold-press forming processes and straw bale re-compressing

Guest Editorial [Special Issue: Data-driven Control, and Data-Based System Modelling, Monitoring, and Control]

In modern industrial processes, aerospace systems, vehicle systems, and elsewhere there are increased demands for fuel efficiency, conservation of resources, cost and energy savings, and other optimal performance requirements. However, there is generally no dynamical model available for the process, or the process model is too complex to be tractable for controller design. Modelling and system identification are expensive and time-consuming, and models may be time-varying, or non-linear, or contain delays. The term ‘Data-driven Control’ (DDC) originated in the 1990s in Computer Science and it shares the same context as ‘big data’, ‘data mining’, and ‘data fusion’. On the other hand, Data-Based System Modelling, Monitoring, and Control are a set of topics used in the Control Systems community. The development of all these topics was driven by the huge amounts of data measured in complex process control systems, both stored historical data from prior measurements and on-line data available in real time during process runs. In these fields, the intent is to efficiently use the information in huge amounts of process input/output data to design predictors, controllers, and monitoring systems that provide guaranteed performance of the process. This Special Issue presents the latest developments on datadriven modelling and control, iterative learning control and reinforcement learning, and their applications in process industries. It contains eighteen papers