Tropical cyclones (TCs) significantly influence coastal sedimentation, geomorphologic features, and morphodynamic processes through strong winds, heavy rains, and storm surges. These effects are particularly pronounced in the east China coastal ocean. However, the impacts of poleward and landward shifts in TC tracks on sedimentology, specifically sediment transport and erosion-deposition processes, remain insufficiently understood. This study utilizes the Delft3D-FM numerical model integrated with TC best track data and field measurements to investigate sediment transport patterns under historical TC tracks and to quantify erosion responses to poleward and landward track shifts. From the historical sediment transport pattern derived from the typical historical TC track, results reveal that sediment in waters shallower than 30 m is highly sensitive to TC activity, with four distinct zones where net sediment transport is sensitive to the change of typical historical TC tracks. Coastal erosion depth changes due to poleward and landward shifts of typical TC tracks during the peak TC intensity period are quantified as 0.24–1.63 cm°N⁻¹ and 0.05–1.06 cm°E⁻¹, respectively. Under global warming scenarios, these values are projected to increase by 2.45%–8.00% and 4.71%–13.33%, respectively. Identifying the coastal areas more susceptible to TC-induced sediment transport and quantitatively assessing the effects of poleward and landward track shifts are important for understanding local TC variability and supporting research on sedimentology during TCs and the future protection of coastal areas.

Healthy urban greenery is a fundamental asset to mitigate climate change phenomena such as extreme heat and air pollution. However, urban trees are often affected by abiotic and biotic stressors that hamper their functionality, and whenever not timely managed, even their survival. While the current greenery inspection techniques can help in taking effective measures, they often require a high amount of human labor, making frequent assessments infeasible at city-wide scales. In this article, we present GreenScan, a ground-based sensing system designed to provide health assessments of urban trees at high spatio-temporal resolutions, with low costs. The system uses thermal and multispectral imaging sensors fused using a custom computer vision model to estimate two tree health indexes. The evaluation of the system was performed through data collection experiments in Cambridge, USA. Overall, this work illustrates a novel approach for autonomous mobile ground-based tree health monitoring on city-wide scales at high temporal resolutions with low costs.

Accurate fishing activity detection from the trajectories of fishing vessels can not only achieve high-precision fishery management but also ensure the reasonable and sustainable development of marine fishery resources. This paper proposes a new method to detect fishing vessels’ fishing activities based on the defined local dynamic parameters and global statistical characteristics of vessel trajectories. On a local scale, the stop points and points of interest (POIs) in the vessel trajectory are extracted. Voyage extraction can then be conducted on this basis. After that, multiple characteristics based on motion and morphology on a global scale are defined to construct a logistic regression model for fishing behavior detection. To verify the effectiveness and feasibility of the method, vessel trajectory data, and fishing log data collected from Chinese ocean squid fishing vessels in Argentine waters in 2020 are integrated for fishing operation detection. Multiple evaluation metrics show that the proposed method can provide robust and accurate recognition results. Moreover, further analysis of the temporal and spatial distribution and seasonal changes in squid fishing activities in Argentine waters has been performed. A more refined assessment of the fishing activities of individual fishing vessels can also be provided quantitatively. All the results above can benefit the regulation of fishing activities.

This article aims to realize event-triggered constrained consensus tracking for high-order nonlinear multiagent networks subject to full-state constraints. The main challenge of achieving such goals lies in the fact that the standard designs [e.g., backstepping, event-triggered control, and barrier Lyapunov functions (BLFs)] successfully developed for low-order dynamics fail to work for high-order dynamics. To tackle these issues, a novel high-order event-triggered mechanism is devised to update the actual control input, lowering the communication and computation burden. More precisely, compared with the conventional event-triggered mechanism, not only the amplitudes of control signals and a fixed threshold are considered but a monotonically decreasing function is introduced to allow a relatively big threshold, while guaranteeing consensus tracking error to be small. Then, a high-order tan-type BLF working for both constrained and unconstrained scenarios is incorporated into the distributed adding-one-power-integrator design for the purpose of confining full states within some compact sets all the time. A finite-time convergent differentiator (FTCD) is introduced to circumvent the ``explosion of complexity.'' The consensus tracking error is shown to eventually converge to a residual set whose size can be adjusted as small as desired through choosing appropriate design parameters. Comparative simulations have been conducted to highlight the superiorities of the developed scheme.

This paper studies distributed singularity-free finite-time consensus tracking control for quite a large class of high-order (powers are positive odd integers) nonlinear multi-agent networks in the presence of unknown asymmetric dead zone. Achieving finite-time consensus tracking for such dynamics is extremely challenging because feedback linearization and backstepping methods successfully developed for low-order systems fail to work, and some appropriate exponential terms typically arising from finite-time stability are difficult to design due to the existence of high powers and strong couplings among distinct agents. To this purpose, an adding-one-power-integrator methodology is skillfully incorporated into the finite-time stability theory so as to stabilize the closed-loop system. Over the course of design, a variable-separable lemma is utilized to extract the unknown asymmetric dead-zone input in a “linear-like” manner and fuzzy logic systems are utilized to estimate the unknown system continuous nonlinearities over some compact sets. The singularity issue typical of finite-time control is overcome by delicately introducing a switching function. It is rigorously proved that the consensus tracking error eventually converges to a residual set, whose size can be made as small as desired, in finite time, while guaranteeing the boundedness of all closed-loop signals. Comparative simulations are finally provided to verify the effectiveness of the presented scheme on the existing control methods.

The snubber circuit is a cost-effective solution to reduce the severe turn-off overvoltage and oscillation caused by the fast switching characteristics of SiC MOSFET. However, the turn-on switching losses will significantly increase as the snubber decouples the power loop parasitic inductance during the switching process. In this article, the effect of the power loop parasitic inductance on the switch performance of SiC MOSFETs and the quasi-zero voltage switching (QZVS) turn-on condition are studied. On this basis, an SiC MOSFET overvoltage and oscillation suppression circuit (OVSC) with not only the switching losses optimization feature but also the clamping energy feedback characteristic is proposed. The overvoltage and oscillation can be effectively suppressed by clamping capacitors, and those capacitors do not participate in the switching process until the overvoltage occurs, which is of benefit to the switching losses reduction compared with the other normal snubber circuits. Besides, the turn-off overvoltage and oscillation energy stored in the clamping capacitors can be feedback to dc and load side through a passive branch composed of an energy feedback inductor and a diode. The experimental studies and comprehensive comparison studies are carried out to verify the validity of the proposed OVSC. The experimental results show that OVSC has excellent overvoltage and oscillation suppression performance and can significantly reduce the switching losses.

The tethered formation system has been widely studied due to its extensive use in aerospace engineering, such as Earth observation, orbital location, and deep space exploration. The deployment of such a multitethered system is a problem because of the oscillations and complex formation maintenance caused by the space tether's elasticity and flexibility. In this article, a triangle tethered formation system is modeled, and an exact stable condition for the system's maintaining is carefully analyzed, which is given as the desired trajectories; then, a new control scheme is designed for its spinning deployment and stable maintenance. In the proposed scheme, a novel second-order sliding mode controller is given with a designed nonsingular sliding-variable. Based on the theoretical proof, the addressed sliding variable from the arbitrary initial condition can converge to the manifold in finite time, and then sliding to the equilibrium in finite time as well. The simulation results show that compared with classic second sliding-mode control, the proposed scheme can speed up the convergence of the states and sliding variables.

In smart buildings, the models used for energy management and those used for maintenance scheduling differ in scope and structure: while the models for energy management describe continuous states (energy, temperature), the models used for maintenance scheduling describe only a few discrete states (healthy/faulty equipment, and fault typology). In addition, models for energy management typically assume the Heating, Ventilation, and Air Conditioning (HVAC) equipment to be healthy, whereas the models for maintenance scheduling are rarely human-centric, i.e. they do not take possible human factors (e.g. discomfort) into account. As a result, it is very difficult to integrate energy management and maintenance scheduling strategies in an efficient way. In this work, a holistic framework for energy-aware and comfort-driven maintenance is proposed: energy management and maintenance scheduling are integrated in the same optimization framework. Continuous and discrete states are embedded as hybrid dynamics of the system, while considering both continuous controls (for energy management) and discrete controls (for maintenance scheduling). To account for the need to estimate the equipment efficiency online, the solution to the problem is addressed via an adaptive dual control formulation. We show, via a zone-boiler-radiator simulator, that the best economic cost of the system is achieved by active learning strategies, in which control interacts with estimation (dual control design).

This work proposes for the first time a model reference adaptive asymptotic tracking method for a class of uncertain switched linear systems with nonnegative orthant state constraints and dwell time switching. In contrast with the state-of-the-art switched adaptive methods, the main characteristic of this method consists in guaranteeing that the systems states remain in the nonnegative orthant of the state space. To achieve asymptotic tracking, an interpolated Lyapunov function is adopted. To ensure the positivity of the system states, a parameter projection law is needed, because some knowledge of the bounds of the parameter estimates is required to guarantee that the closed-loop system is a switched positive system. A benchmark traffic flow example (a switched positive linear system commonly used in literature) is adopted to verify the effectiveness of the proposed approach.

At present, the supervision mode of inland ferrying was conducted passively by mainly human analysis and judgement based on VTS and AIS, which is not favorable to reply increasingly serious situation of marine security supervision. The historical AIS data of the ferryboat was analyzed to explore its motion pattern and obtain the probability density spatial distribution of motion characteristics of location, course and speed by using the kernel density estimation. Then on this basis, the detection algorithm of the ferryboat abnormal behaviors was established for location and speed abnormity, and actual AIS data was used to verify the algorithm. Experimental results show that the proposed algorithm can accurately recognize the abnormal behaviors of the ferryboat, which is helpful to the marine supervision.

Traffic congestion leads to delays and increased carbon dioxide (CO2) emissions. Traffic management measures such as providing information on environmental route costs have been proposed to mitigate congestion. Multi-criteria routing dynamic traffic assignment (MCR-DTA) models are needed to evaluate the effectiveness of such measures. This paper presents a simulation-based bi-level optimization method to solve the MCR-DTA problem, which works as follows. Route costs include travel times and emissions, but those are updated inside two different loops. In the inner loop, emission costs are considered fixed; the assignment is performed by updating route travel times, using a traditional DTA tool. Then, in the outer loop, emissions are calculated based on link loads and fed back to the DTA tool, which performs a new assignment. The MCR user equilibrium is found when emissions or predefined generalized costs converge to an equilibrium. The bi-level method is first tested on a small network, showing that the proposed method is able to effectively solve the MCR-DTA problem. Next, the method is applied to a medium-size urban network. The results show that if drivers choose routes based on emissions besides travel time, the average travel time and emissions per vehicle decrease. This occurs because congested links have a higher impact on route costs; hence the equilibrium is pushed away from the single-criteria routing (SCR) user optimum towards the SCR system optimum. Results support the conclusion that informing drivers about CO2 emissions per route can potentially lead to decreased delay and emissions in real networks.