Collaborative delivery using electric vehicles and drones represents a promising modern logistics solution for last-mile delivery. Though existing studies on the electric vehicle-drone routing problem have expanded, most still rely on oversimplified energy-consumption assumptions, resulting in theoretically “optimal” solutions that may be less reliable in practical applications. To address these limitations, this study proposes a novel multi-objective electric vehicle-drone routing problem (MO-EVDRP) incorporating multi-factor energy-consumption inputs to support routing optimization under energy-related constraints. To enhance operational flexibility, this study adopts a partial recharging strategy for electric vehicles. A mixed-integer linear programming (MILP) model is further developed to balance the conflicting goals of minimizing energy consumption, shortening delivery time, and reducing operational cost, with these energy-consumption inputs incorporated as input parameters. Additionally, a solution framework based on the non-dominated sorting genetic algorithm II (NSGA-II), denoted as INSGA-II, is applied to solve the MILP model, integrating heuristic initialization with a problem-specific three-layer coding scheme, adaptive crossover-mutation, and variable neighborhood search. Experimental results show that the integration of the proposed components improves the performance of INSGA-II.

As a key enabler of location-based services, next location prediction models user mobility to improve service personalization, benefiting applications such as Point-of-Interest (POI) recommendation and route planning. Although Large Language Model (LLM)-based prediction methods have attracted increasing attention in recent years, their performance remains suboptimal due to the difficulty of compactly and effectively modeling both semantic information and mobility patterns in trajectories, as well as the stepwise-biased training and inference that hinder the capture of long-term behavioral regularities. To address these challenges, a novel framework named MoveAlign is proposed to learn compact location representations and multi-step behavioral correlations in user mobility, thereby enhancing LLMs for next location prediction. First, a new mobility-aware semantic tokenization method is proposed to represent locations as compact mobility-token tuples, preserving semantic structure and mobility patterns as concise and structured inputs. Second, a new multi-step imitation learning strategy is proposed for supervised LLM fine-tuning, enabling the LLM to learn stable mobility patterns with higher-order behavioral correlations across multiple steps, thereby improving the accuracy of next location prediction. Extensive experiments on three real-world urban datasets demonstrate that MoveAlign consistently outperforms the existing baselines across standard evaluation metrics.

The repetitiveness prerequisite of iterative learning control has always been the main obstacle to promoting its practical applications. In this article, a novel adaptive iterative learning reliable control scheme is proposed for the nonrepetitive systems with multiple iteration-varying parametric uncertainties, where actuator faults and state delays are considered simultaneously. During the design of the controller, the class- $k_{\infty } $ function is leveraged to dispose of the unmodeled lumps of systems through neural networks, and the transformation of control signals is established to compensate for the negative impact of the inefficient actuator. The technical features of our approach lie in an innovative parametric estimation mechanism that integrates the hyperbolic tangent function and an auxiliary sequence is presented to accommodate the nonrepetitive uncertainties, thus achieving the zero-error convergence of output. As the main merits, the proposed control scheme is promising to manifest better performance and practicality than the existing methods, owing to the weak assumptions on the system dynamics, the little prior knowledge of parametric uncertainties, and the strong learning ability of the controller.

In this paper, a comprehensive method that integrates the energy optimization with the AC traction power supply modeling is proposed to achieve the energy-saving operation in the single-train and double-train scenarios. Concretely, a novel non-iterative traction power flow (TPF) method is introduced to reduce the computation time for time-varying load flow analysis. Meanwhile, a customized dynamic programming algorithm is designed to generate the optimal speed profile based on the TPF model. In the case of multiple high-speed trains, the optimization process enables the tracking train to maximize the utilization of regenerative braking energy (RBE) generated by the braking train. The energy cost function for the double-train coupled system integrates the substation supply and non-supply conditions to further enhance the energy efficiency. Finally, a real-world case study based on the Wuhan-Guangzhou high-speed railway is presented to demonstrate the effectiveness of proposed method. The results indicate that, compared with the traditional mechanical energy optimization schemes, the proposed method can reduce the total line-wide energy consumption by up to 2%, mitigating the catenary voltage fluctuations, and ensuring the precise power matching during the RBE transmission phases.

The Seawater Two-Dimensional Piston Pump (STPP) is a novel seawater pumping device that integrates the principles of axial piston pumps with two-dimensional piston mechanisms, specifically developed for Seawater Reverse Osmosis (SWRO) applications. In this study, a lumped parameter mathematical model of the STPP is proposed, incorporating the effects of seawater cavitation. An experimental platform is established to evaluate the pump’s flow characteristics and volumetric efficiency, thereby validating the model. Both simulation and experimental results indicate that the STPP achieves a volumetric efficiency exceeding 85% under a 3 MPa load, with efficiency increasing alongside rotational speed. These findings demonstrate the STPP’s potential for high-speed, high-efficiency seawater pumping in SWRO systems.

Byzantine Fault-tolerant State Machine Replication (BFT SMR) is essential for ensuring the security of high-level services such as blockchain, particularly in scenarios where a subset of nodes may exhibit arbitrary faults. As Byzantine Fault-tolerant broadcast in asynchronous networks is a core component of BFT SMR, this paper studies this broadcast primitive. We propose a novel broadcast protocol in which nodes encode large messages M using erasure codes, aggregate the encoded codewords into a vector via a Bloom filter, and employ error correction codes to encode the vector. This approach achieves the known lower bound on the communication complexity of O(|M|n+kappa n(2)), where kappa represents the output size of a collision-resistant hash function and n is the total number of nodes. Performance tests in the Amazon cloud environment demonstrate that when nodes broadcast large messages (|M|>>kappa n(2)), the throughput of the new protocol is twice that of existing solutions. Furthermore, the Bloom filter enables the broadcast protocol to possess customizable features for the first time, allowing developers to reduce communication costs through parameter tuning.

In traditional text-based systems engineering (TSE) for scenario modeling of civil aircrafts, there are problems such as insufficient linkage between requirements and design, as well as difficulties in traceability. To address these problems, this paper proposes a framework architecture modeling method based on Department of Defense Architecture Framework (DoDAF). By means of multi-view integration and hierarchical modeling analysis, the full-process traceability of scenario requirements can be realized. First, in conjunction with the DoDAF Meta Model (DM2), this paper analyzes the constraint relationships from different perspectives and proposes a constraint-based architectural modeling development process. Second, it establishes a mapping mechanism between DoDAF and SysML, bridging the relationship between system-of-systems level capabilities and system-level functions. Finally, these methods are demonstrated using the aircraft ground deceleration scenario as an example, forming a traceable requirements catalog that ensures consistency between requirements and modeling, and supporting collaborative de-sign at the system-of-systems level.

Functional analysis plays a critical role in early-stage aircraft system design by defining system functions that guide downstream architectural development and verification. In practice, many design deficiencies originate not from incorrect physical realization but from incomplete or ambiguous functional definitions established at conceptual stages. This challenge is particularly pronounced in aircraft systems, where interaction- and physical-effect-induced functions tend to remain implicit and weakly justified. To address this issue, in this study, we conduct a case-study-based comparative evaluation of three functional analysis paradigms: design-theory-oriented functional decomposition, systems-engineering-based functional allocation, and scenario-driven functional analysis. Using an aircraft ground deceleration scenario as a controlled context, this comparison examines how different function-derivation mechanisms influence the identification and justification of interaction- and effect-induced functions. Through structured cross-paradigm comparison, three distinct and, in principle, reproducible derivation mechanisms, namely decomposition-driven, responsibility-driven, and physical-effect-driven, are identified. In this study, the physical-effect-driven mechanism is examined through an effect-strengthened implementation of the scenario-driven paradigm. While all paradigms consistently identify mission-oriented functions, the examined scenario-driven implementation enhances transparency in functional justification and improves sensitivity to interaction- and effect-induced functions, thereby reducing the risk of omission during conceptual design. By formalizing these derivation mechanisms and clarifying their complementary roles, this study contributes to a clearer methodological understanding of functional identification in early-stage complex system design, while providing practical guidance for methodological selection and integration in aircraft system design.

The development of multi-mode transportation systems, e.g., bus, metro, taxi, and bike-sharing, presents a fundamental challenge in forecasting demand across heterogeneous, noisy, and complexly interacting data streams. From a feature modeling perspective, this requires a shift from simple data fusion to a more principled approach. This paper introduces a novel end-to-end framework, Multi-mode Spatiotemporal Adaptive Fusion Network (MSTAFN), that systematically addresses this challenge through a two-stage process: 1) unsupervised shared feature selection, and 2) dynamic asymmetric feature interaction modeling. For the first stage, we design an Infomax module that employs an information-theoretic principle to obtain a clean low-dimensional shared latent representation from cross-mode data. This representation captures the underlying semantic drivers of demand, such as latent commuting patterns, while mitigating noise and redundancy. For the second stage, we propose a Multi-Flashback module to explicitly model the complex asymmetric interactions between heterogeneous features, particularly across different temporal granularities. Its Cross-Flashback mechanism is designed to allow low-frequency modes (e.g., metro) to be informed by the latest fine-grained dynamics of high-frequency modes (e.g., bike-sharing). Experiments on a large-scale real-world dataset from New York City demonstrate that our two-stage paradigm outperforms state-of-the-art baselines, especially on the mode with the coarsest time granularity. This validates the superiority of our proposed modeling framework, supporting the improvement of operational efficiency for multi-mode transportation systems.

The periodic operation pattern of high-speed train (HST) grants the immense potential for iterative learning control (ILC) approach regulating the displacement and velocity, but the non-repetitive uncertainties caused by carrying loads, random disturbances, etc., may weaken the capability of controller. Further, the typical operating situations of rail transit, e.g., station entrance/exit, slowdown sections, can compress the safety margin of HST, increasing the difficulty of precise tracking. In this paper, an adaptive ILC scheme is proposed for HST subject to the safety constraints, where the unknown iteration-varying parameters and the modeling inaccuracies are handled deliberately. Our technical route could be divided into two phases. The transformation mechanism of tracking errors, that can convert the control problem of constrained systems into an unconstrained form, is first established to guarantee that HST is always located within the safety zone. On this basis, the iterative learning controller is devised through integrating the hyperbolic tangent function and iteration-related sequence, where the neural network is leveraged to approximate the unmodeled lumps. The main innovative features lie in that, the iteration-dependent terms of control system are evolved into the parametric compensation components of controller and the iterative convergence parts, while the nested structure of control law is built to accommodate the iteration-variation of loads. As a result, the proposed approach can theoretically achieve the zero-error tracking of HST in the presence of the non-repetitive uncertainties and safety constraints, which indicates the better performance and practicability than the existing ones.

This study investigates the adaptive neural network (NN) identification-based operating control problem for high-speed trains (HSTs) in the presence of unknown dynamic disturbance. Firstly, the train states are monitored by a high-order sliding mode (HOSM) observer, thereby relaxing the assumption of complete knowledge about the train states. Simultaneously, two NNs are employed to identify the unknown dynamics and enhance the approximation accuracy. Subsequently, an adaptive output-feedback controller is developed by combining the observed train states with approximated unknown dynamics, wherein a leakage term is introduced to suppress the accumulated parameter estimation errors and improve the train adaptability to uncertainty. Next, stability analysis demonstrates the convergence of the identified parameters and the tracking performance of HSTs towards the reference displacement and velocity trajectories. Finally, the proposed approach is validated by the simulation results obtained from the real-time hardware-in-the-loop (HIL) test platforms.

With rapid advances in cyber-physical technologies, designers are often required to develop technical products that are integrated with Cyber-Physical Systems to achieve advanced functionalities. Due to the lack of effective consideration for such cyber-physical products in existing functional design methods, this paper is devoted to developing a systems engineering-integrated approach for the functional architecture design of cyber-physical products. Some key concepts from both systems engineering theories and product design approaches are analysed to establish a shared conceptual foundation. Thereafter, a basic process model is developed for the functional architecture design of a cyber-physical product, which is primarily composed of four stages, i.e. need clarification, function identification, solution generation and behaviour validation. The process model, also called as the Need-Function-Solution-Behaviour model, can explicitly illustrate how an ambiguous need can be gradually transformed into a complete solution architecture. The functional design of an Unmanned Aerial Vehicle for plant-protection demonstrates that the proposed model can guide designers to develop an explicit and complete functional architecture for a cyber-physical product.

Frequency converters serving as power sources for integrated permanent magnet synchronous motors (IPMSMs) introduce significant time-harmonic distortions in current waveforms, leading to harmonic contamination of electromagnetic fields during variable-frequency operation. These harmonics induce parasitic eddy currents and energy dissipation in stator cores, rotor assemblies, and permanent magnets, causing localized overheating that threatens operational reliability through accelerated insulation aging and demagnetization risks. This study addresses transient thermal accumulation in a 12-pole/36-slot high-torque-density IPMSM prototype by developing a multi-physics numerical framework correlating harmonic frequencies, loss distribution, and temperature evolution. Through first-principles analysis, we quantify rated operational losses including volumetric heat generation, hysteresis losses, eddy currents, and ohmic dissipation. The proposed electromagnetic-thermal coupling model, validated with a deviation of less than 5 % from thermometric system, demonstrates thermally-induced electromagnetic performance degradation:a 12.7 % reduction in radial force harmonics and 9.2 % airgap flux density attenuation across 20-120 degrees C, attributed to permanent magnets negatives' temperature coefficient altering the magnetomotive force distribution. These finding provide critical insights for improving lifespan of IPMSM systems.

A novel iterative learning control (ILC) strategy is developed for displacement and velocity tracking control of high-speed trains (HSTs) across all operational phases. In practical operations, HSTs encounter complex nonlinear uncertainties, such as variations in coupler forces and aerodynamic resistance, which are more appropriately characterized by norm-bounded models rather than traditional Lipschitz continuous disturbances. To capture these effects accurately, a multi-particle dynamic model of HSTs with norm-bounded uncertainties is formulated, considering the coupler dynamics, mechanical resistance, and aerodynamic resistance acting upon different carriages. Based on this model, a robust ILC scheme, together with an associated parameter updating law, is designed to ensure precise tracking control despite the presence of nonlinear uncertainties. Furthermore, the classical resetting condition in conventional ILC frameworks is replaced by a practical alignment condition that better reflects the continuous operation characteristics of HSTs. A composite energy function (CEF) is constructed to rigorously prove the convergence of control errors. Real-time hardware-in-the-loop (HIL) simulations are conducted to validate the effectiveness of the proposed method. The proposed strategy achieves effective tracking control and stability across traction, cruising, coasting, and full braking stages.

In practice, the communication bandwidth and physical limitations are the two main categories of threats that the networked control systems may encounter. Therefore, this paper focuses on adaptive iterative learning control (ILC) of nonlinear strict-feedback systems with input quantization and asymmetric output constraint. Through constructing an error transformation mechanism, the original output-constrained control system is converted into the unconstrained form. Subsequently, a novel adaptive ILC algorithm is established by virtue of the command filtered backstepping technique, in which the uncertain terms of Lyapunov function (LF) are decomposed into the parameter compensation components of the controller and the iteration-convergent lumps via the hyperbolic tangent function. Specially, to accommodate the input-related uncertainties brought by quantizer, the devised ILC law adapts a nested structure, thus achieving the estimation of unknown parameters associated with the quantized bias. The convergence of error along the iteration axis is rigorously proven by the composite energy function (CEF). Finally, the proposed approach is applied to two examples, the results of which illustrate the effectiveness of the scheme.

In this study, a predictive bandwidth extended state observer (PB-ESO) with gain optimization is put forward to obtain high dynamic performance and parameters robustness for the model predictive speed control (MPSC) of electrical drives (EDs). The proposed observer preserves the primary feature of standard low bandwidth ESO in terms of strong suppression to measurement noise, while overcoming their main drawbacks, namely the "slow convergence" and "disturbance estimation distortion." First, the conventional ESO-based MPSC scheme is presented, followed by an analysis of the disturbance estimation mechanism of the ESO. Second, the PB, based on the recursive least squares algorithm, is designed to accommodate to the frequency of fast time-varying disturbances in advance. A general mathematical formula for the gain optimization of even-order ESOs is then derived to enhance the estimation accuracy of periodic dynamic interference. In consequence, the gains of PB-ESO are optimized, where stability and fast convergence are ensured rigorously. Finally, a speed control strategy, combining the MPSC scheme and the proposed PB-ESO, is developed in a rational way. Comparative experimental results with existing methods demonstrate the enhanced robustness of the proposed control approach for ED across four representative operating conditions.

In this brief, a secure iterative learning control architecture is proposed for networked systems, aiming to track the desired output and protect the privacy of signals. The main technique challenges lie in that the homomorphic encryption is integrated into the control scheme without destructing the performance of systems. Therefore, the iterative learning control algorithm based on the quantized feedback and saturated input is presented to support the design of encrypted controller, and the conditions on the parameters of cryptosystem are established to ensure that the inputs obtained by actuator are reliable. As the results, the calculation of secure control law only relies on the encrypted data, thus immunizing the disclosure attacks of adversaries. To the best of our knowledge, there is currently little work to investigate the secure iterative learning control scheme, which can enhance the practicability and security of systems due to the data-driven strategy and encryption mechanism.

This paper presents a volumetric efficiency model for high-pressure motor-driven 2D piston pumps, incorporating key factors such as axial internal and external leakage, circumferential leakage, and reverse flow. The model integrates hydraulic fluid compressibility and variations in flow coefficients to improve simulation accuracy. Co-simulation using AMESim and Simulink, along with experimental validation, demonstrates the model’s reliability across various operating conditions. The results show that rotational speed enhances volumetric efficiency due to its minimal effect on leakage and reverse flow, while pressure significantly reduces efficiency, particularly through reverse flow and circumferential leakage. Reducing trapped volume proves to be an effective strategy for minimizing reverse flow and improving overall pump performance. Additionally, refining flow coefficients to improve the accuracy of the simulation model remains an important area for further development.

The two-dimensional (2D) piston pump utilizes a cam guide-roller two-degree-of-freedom (2DOF) motion mechanism, making it adaptable to a wide range of variable operating conditions and frequent loaded startups in the context of motor-driven systems. Volumetric efficiency, which measures the ratio of actual to theoretical output flow, is vital for optimizing hydraulic pump performance and reducing energy loss. This study established a mathematical model for the 2D piston pump, considering factors like axial internal and external leakage, circumferential leakage, backflow, fluid compressibility, turbulence, and flow coefficients. The model was built using a co-simulation environment integrating AMESim and Simulink. Simulation results showed that volumetric efficiency increases with higher rotational speeds and decreases with higher pressures. High-pressure backflow is a key factor in adversely affecting volumetric efficiency and requires special attention. Experimental validation was conducted across a speed range of 500--3000 r/min and pressure range of 1--28 MPa. The lowest efficiency recorded was 64.81\% at 500 r/min and 28 MPa, with a maximum deviation of 3.28\% from the simulation. At 3000 r/min and 28 MPa, the efficiency was 89.53\%, deviating by 1.69\% from the simulation. The close correspondence between experimental and simulation results validates the model's reliability in predicting volumetric efficiency.