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Abstract Cathode interlayers (CILs) play a crucial role in improving the photovoltaic efficiency and stability of OSCs. CILs generally consists of two kinds of materials, interfacial dipole‐based CILs and SPS‐based CILs. With good charge transporting ability, excellent compatibility with large‐area processing methods, and highly tunable optoelectronic properties, the SPS‐based CILs exhibit remarkable superiorities to their interfacial dipole‐based counterparts in practical use, making them promising candidate in developing efficient CILs for OSCs. This mini‐review highlights the great potential of SPS‐based CILs in OSC applications and elucidates the working mechanism and material design strategy of SPS materials. Afterward, the SPS‐based CIL materials are summarized and discussed in four sections, including organic small molecules, conjugated polymers, nonconjugated polymers, and TMOs. The structure‐property‐performance relationship of SPS‐based CIL materials is revealed, which may provide readers new insight into the molecular design of SPS‐based CILs. The mechanisms to endow SPS‐based CILs with thickness insensitivity, resistance to environmental erosion, and photo‐electric conversion ability are also elucidated. Finally, after a brief summary, the remaining issues and the prospects of SPS‐based CILs are suggested.

During the operation of automated guided vehicles (AGVs) at automated terminals, the occurrence of conflicts and deadlocks will undoubtedly increase the ineffective waiting time of AGVs, so there is an urgent need for path planning and tracking control schemes for autonomous obstacle avoidance in AGVs. An innovative AGV autonomous obstacle avoidance path planning and trajectory tracking control scheme is proposed, effectively considering static and dynamic obstacles. This involves establishing three potential fields that reflect the influences of obstacles, lane lines, and velocities. These potential fields are incorporated into an optimized model predictive control (MPC) cost function, leveraging artificial potential fields to ensure effective obstacle avoidance. To enhance this system’s capability, a fuzzy logic system is designed to dynamically adjust the weight coefficients of the hybrid artificial potential field model predictive controller, strengthening the autonomous obstacle avoidance capabilities of the AGVs. The tracking control scheme includes a fuzzy linear quadratic regulator based on a fuzzy logic system, a dynamics model as a lateral controller, and a PI controller as a longitudinal tracker to track the pre-set trajectory and speed autonomously. Multi-scenario simulation tests demonstrate the effectiveness and rationality of our autonomous obstacle-avoidance control scheme.

Uncertain handling chain system (HCS) of container ports brings steady-state error to the original control decisions, and even worse, dramatically degrades the system performance. The steady-state error will cause unsatisfied freight requirement to be much higher than the expected value for a long time, resulting in the decrease of system robustness and resilience. In this work, a novel sliding mode control with power integral reaching law (SMC-P) is presented for nonlinear HCS of container ports under uncertainty. Specifically, the integral of system state variable, the exponential reaching law and the power of the switching function are integrated to the traditional reaching law. And it is proven that the eliminated steady-state error, the accelerated approach speed, and the reduced chattering can be effectively obtained by SMC-P. A nonlinear HCS in container ports with uncertain freight requirement and handling ability is considered. SMC-P is compared with traditional method, genetic algorithm, quasi-sliding mode control and integral sliding mode control. Simulation results show that SMC-P does not only balance both steady-state error reduction and chattering avoidance caused by uncertainty, but also optimize the performance, robustness, and resilience of the uncertain nonlinear HCS. This study also brings economic and sustainability contributions for port authorities.

Container throughput in Shanghai port has been continually increasing, with severe congestion impacts. The effectiveness and comprehensiveness of governance policies are increasingly important. This study proposes a conceptual framework and simulation of congestion governance measures and their effectiveness on congestion control in a data-driven case study for Shanghai port. The contributions herein serve as a starting point in the identification of four alternative congestion governance options (Increasing the transport infrastructure, Multimodal transport, Smart strategy, and Interconnection), and explore dynamic interactions among them. The design of this study is to firstly develop a container port congestion evaluation model to measure the governance effect; secondly to undertake the triangle analysis in the dynamic system balance: demand of hinterland transport, container port congestion, and container port congestion governance; thirdly to describe four congestion governance options with a detailed description of feedback loops generated by the system dynamics model; finally to propose a policy framework aiming at the advantages and disadvantages of these four options. This study function as stepping stones towards the revealing of the biggest challenge in effective mitigation of port congestion systems by being considered not just a hardware issue (option 1) but more importantly organizational issues (options 2~4). It should be a process of coordination and optimization among 4 options.

There are many inevitable disruptive events, such as the COVID-19 pandemic, natural disasters and geopolitical conflicts, during the operation of the container port supply chain (CPSC). These events bring ship delays, port congestion and turnover inefficiency. In order to enhance the resilience of the CPSC, a modified two-stage CPSC system containing a container pretreatment system (CPS) and a container handling system (CHS) is built. A two-dimensional resilience index is designed to measure its affordability and recovery. An adaptive fuzzy double-feedback adjustment (AFDA) strategy is proposed to mitigate the disruptive effects and regulate its dynamicity. The AFDA strategy consists of the first-level fuzzy logic control system and the second-level adaptive fuzzy adjustment system. Simulations show the AFDA strategy outperforms the original system, PID, and two pipelines for improved dynamic response and augmented resilience. This study effectively supports the operations manager in determining the proper control policies and resilience management with respect to indeterminate container waiting delay and allocation delay due to disruptive effects.

A new layout of the U-shaped automated terminal is more convenient to connect between the terminal and the railway. In this study, a sort of cluster scheduling method for multiple equipment between a U-shaped automated terminal yard and a railway yard is proposed. The innovation points are as follows: (1) Considering that the rail gantry crane (RGC), intelligent guided vehicle (IGV), and double cantilever rail crane (DCRC) usually work in groups, they are grouped and cluster-scheduled. (2) A hybrid integer programming model is established to minimize container transit times, and non-crossing constraints and safe distance constraints are included to reflect complex interactions among terminal equipment. (3) An ADMM (Alternating Direction Method of Multipliers)-based framework is proposed to dualize the hard-edge constraints and break the cluster scheduling problem down into a specific subproblem set of RGCs, IGVs, and DCRCs, and their time cost is iteratively adjusted to improve the solution quality. The experiment results show that the solution of the proposed method, which can effectively avoid IGV conflict, is better than that of the standard Lagrange relaxation (LR) when the number of equipments participating in scheduling increases. This study on multi-equipment cluster scheduling is conducive to improving the collaborative handling and continuous work of terminal equipment, and improving the efficiency of the automated terminal and the sea–rail intermodal transport.

Halogenation has proved an effective strategy to improve the power conversion efficiencies of organic solar cells but it usually leads to lower open-circuit voltages. Here, Cui et al. unexpectedly obtain higher open-circuit voltages and achieve a record high PCE of 16.5% by chlorination.

Automated guided vehicles (AGVs) in the U-shaped automated container terminal travel longer and more complex paths. The conflicts among AGVs are trickier. The scheduling strategy of the traditional automated container terminal is difficult to be applied to the U-shaped automated container terminal. In order to minimize the handling time of all tasks and avoid AGV conflicts simultaneously in the U-shaped automated container terminal, this paper establishes a hybrid programming model for conflict-free integrated scheduling of quay cranes, AGVs, and double-cantilever rail cranes in the unloading process. It consists of a discrete event dynamic model and a continuous time dynamic model. An improved genetic seagull optimization algorithm (GSOA) is designed. A series of numerical experiments are conducted to verify the effectiveness and the efficiency of the model and the algorithm. The results show that the proposed method can simultaneously realize the AGVs collision avoidance and multi-equipment integrated scheduling optimization in the U-shaped automated container terminal.

Due to its advantages of large transportation volume and low transportation cost, container shipping has become an important transportation mode in current international trade. The recovery of the shipping industry in 2021 and the signing of RCEP make the reconfiguration and optimization of the container shipping network a very important task at present. The network service capability is an important factor affecting the container shipping network. Based on the complex network theory, the coefficients of port location, the importance of distance and route among ports are taken as the service capacity reconfiguration coefficients of the global container shipping network. A max-min mixed integer model is established for global container shipping network reconfiguration. A sort of communication-reducing conjugate gradient method based on Krylov Subspace (CR-CG-KS) is proposed to reduce the reconfiguration computation. The results show that the global container shipping network does not need large-scale reconfiguration but requires small-scale changes to optimize the network feature vector centrality and make the network more balanced and stable. This study is beneficial for business managers to proactively respond to the future development of the shipping network and improve the operational efficiency of the global container shipping network.

Due to the complexity and dynamics of container terminal multilevel handlings, the occurrence of uncertain events often leads to the disruption of many operation plans and forms a chain reaction, resulting in economic losses and impacts. In this paper, the uncertain event propagation network model of container terminal multilevel handlings is constructed by using the hypernetwork theory, and the propagation influence of uncertain events between multilevel handlings at container terminals is considered. Through the topological characteristics of the uncertain event propagation network, the risk analysis is carried out for the relevance of nodes and the influence extent of uncertain events, and a simulation analysis is carried out to verify the validity of this model. The simulation results show that the model conforms to power law distribution, and the number of old nodes has an impact on the growth of hyperedge. The research provides a powerful tool for analysing the influence of uncertain events and their derivative events in the multilevel handlings at container terminals and has positive significance for reducing the losses caused by the changes of terminal plans and improving the operation efficiency.

Uncertainties exist and affect the actual port production. For example, at the beginning of 2020, the sudden outbreak of COVID-19 seriously affected terminal production and increased the short-term pressure of handling at container terminals. Consequently, a large number of containers were stacked at terminals, and the problem of terminal congestion became more serious. To solve the congestion problem of container terminals and ensure the priority dispatch of emergency materials, this study uses the optimized arrival patterns of external trucks and a priority dispatch strategy for emergency materials to establish a bilevel optimization model for container terminals and proposes a chaotic genetic algorithm based on logistic mapping as a solution. Through numerical experiments, the algorithm proposed in this study was compared with the genetic algorithm and adaptive genetic algorithm. The experimental results show that the model and algorithm proposed in this study can effectively reduce the total cost of containers in a terminal while ensuring the priority dispatch of emergency materials, reducing the overlapping part of the time window, optimizing the arrival mode of external trucks, and reducing the waiting time of external trucks, effectively alleviating the terminal congestion problem.

Yard handling is an important part of port logistics and affects the overall efficiency of the container port. The yard crane scheduling is affected by various external factors. For example, the dynamic cut-off time makes the release time of yard cranes variable, and the yard crane task arrangement will change frequently, resulting in a lot of computational time. To increase the flexibility of container yard handling, a twin yard cranes scheduling model is established considering the no-crossing constraints and the dynamic cut-off time. A joint scheduling of PSO and local re-scheduling strategy (LRPSO) is put forward to deal with the problem faster and more effectively. Small-scale and large-scale experiments are simulated to verify the performance of the proposed method. Results show that the scheduling method is more efficient.

The uncertainty of the arrival time of trucks has increased the complexity of terminal operations. The truck appointment system (TAS) cannot respond to this problem in time, which can easily cause appointment invalidation and reduce the efficiency of truck operations and terminal operations. This paper comprehensively considers the related constraints of truck re-scheduling costs, gate waiting costs, and idle emission costs. With the goal of minimizing the comprehensive operating costs of truck companies and port companies, a dynamic appointment rescheduling model for external trucks based on mixed integer nonlinear programming is established. This paper designs an adaptive quantum revolving door update mechanism and proposes a double-chain real quantum genetic algorithm. The simulation experiment results show that compared with the traditional scheduling, the truck dynamic appointment rescheduling model can effectively reduce the comprehensive operating costs of the truck company and the port company and alleviate the congestion of the port. The probability that the truck cannot arrive at the port on time, the advance time for the truck to confirm the arrival time, and the length of time that the external truck cannot arrive at the port on time have a significant impact on the cost of the reschedule of the TAS. This paper favorably supports the manager’s operational decision-making.

Aimed to improve the efficiency of port operations, Shanghai Zhenhua Heavy Industries Co., Ltd. (ZPMC) proposed a new U-shape trafficked automated terminal. The new U-shape trafficked automated terminal brings a new hybrid scheduling problem. A hybrid scheduling model for yard crane (YC), AGV and ET in the U-shape trafficked automated terminal yard is established to solve the problem. The AGV and ET yard lanes are assumed to be one-way lane. Take the YC, AGV and ET scheduling results (the container transportation sequences) as variables and the minimization of the maximum completion time as the objective function. A scheduling model architecture with hierarchical abstraction of scheduling objects is proposed to refine the problem. The total completion time is solved based on a static and dynamic mixed scheduling strategy. A chaotic particle swarm optimization algorithm with speed control (CCPSO) is proposed, which include a chaotic particle strategy, a particle iterative speed control strategy, and a particle mapping space for hybrid scheduling. The presented model and algorithm were applied to experiments with different numbers of containers and AGVs. The parameters of simulation part refer to Qinzhou Port. The simulation results show that CCPSO can obtain a near-optimal solution in a shorter time and find a better solution when the solution time is sufficient, comparing with the traditional particle swarm optimization algorithm, the adaptive particle swarm optimization algorithm and the random position particle swarm optimization algorithm.

Nonlinearities in a container port handling chain include mainly nonnegative arrive rate of container cargoes, limited container handling completion rate, and nonnegative unsatisfied freight requirement constraints. The nonlinearity influences the operation resources availability and consequently the planned container port handling strategies. Developments presented in this work are devoted to a novel design of sliding mode control with low-pass filter (SMC-LPF) to nonlinear handling chain system (HCS) in container ports. The SMC-LPF can effectively reduce unsatisfied freight requirement of the HCS and make chattering decrease significantly. To illustrate the effectiveness and accuracy of the proposed SMC-LPF, an application to a real container port in China is outlined. The performances of the SMC-LPF for the nonlinear HCS in container ports outperform those of the traditional method, particle swarm optimization algorithm, and slide mode control under simulations with a unit step signal and a sinusoidal signal with offset as the freight requirements. The contributions herein demonstrate the proposed control strategy in weakening chattering, reducing the unsatisfied freight requirements to 0 as close as possible in the HCS, maximizing the operation resilience and robustness of port and shipping supply chain against parametric perturbation, external disturbances, and fluctuant handling abilities.

Container ports are critical components of global port and shipping supply chain (PSSC) systems. Their handling operation resiliencies can affect their performance, along with those of the overall port and shipping supply chain. According to the characteristics of the container handling operations, this study establishes a modeling paradigm for quantifying the resilience of handling chain system (HCS) in container ports. Considering the nonnegative arrive rate and the container handling completion rate, with the upper limit in real container port environments, nonlinear links have been added into the model of the HCS. The resilience of the HCS was analyzed according to the different pole distributions of unsatisfied freight requirement transfer function. Simulation results show that the upper limit of the container handling completion rate has a significant impact on the resilience of the HCS. The contributions herein demonstrate a starting point in the development of a quantitative resilience decision making framework and mitigating the negative impacts for port authorities and other players in the PSSC.

To realize higher coverage rate, lower reading interference, and cost efficiency of radio-frequency identification network in logistics under uncertainties, a novel robust radio-frequency identification network planning model is built and a robust particle swarm optimization is proposed. In radio-frequency identification network planning model, coverage is established by referring the probabilistic sensing model of sensor with uncertain sensing range; reading interference is calculated by concentric map–based Monte Carlo method; cost efficiency is described with the quantity of readers. In robust particle swarm optimization, a sampling method, the sampling size of which varies with iterations, is put forward to improve the robustness of robust particle swarm optimization within limited sampling size. In particular, the exploitation speed in the prophase of robust particle swarm optimization is quickened by smaller expected sampling size; the exploitation precision in the anaphase of robust particle swarm optimization is ensured by larger expected sampling size. Simulation results show that, compared with the other three methods, the planning solution obtained by this work is more conducive to enhance the coverage rate and reduce interference and cost.

Although acceptor-donor-acceptor (A-D-A)-type molecules offer advantages in constructing NIR absorbing photothermal agents (PTAs) due to their strong intramolecular charge transfer and molecular planarity, their applications in photothermal therapy (PTT) of tumors remain insufficiently explored. In particular, the influence of ESP distribution on the optical properties of A-D-A photosensitizers has not been investigated. Herein, we analyze and compare the difference in ESP distribution between A-D-Atype small molecules and polymers to construct NIR absorbing PTAs with a high extinction coefficient (e) and high photothermal conversion efficiency (PCE). The calculation results of density functional theory (DFT) indicate that the large ESP difference makes A-D-A-type small molecules superior to their polymer counterparts in realizing tight molecular packing and strong NIR absorbance. Among the asprepared nanoparticles (NPs), Y6 NPs exhibited an obvious bathochromic shift of absorption peak from 711 nm to 822 nm, with the NIR-II emission extended to 1400 nm. Moreover, a high e value of 5.69 L g-1 cm-1 and a PCE of 66.3% were attained, making Y6 NPs suitable for PTT. With a concentration of 100 mg mL-1, Y6 NPs in aqueous dispersion yielded a death rate of 93.4% for 4T1 cells upon 808 nm laser irradiation (1 W cm-2) for 10 min, which is comparable with the best results of recently reported PTT agents. [ABSTRACT FROM AUTHOR]