ALBERT

Description: Growing evidence suggests that ocean acidification (OA) may affect animal behaviors such as feeding. Although gustation plays a crucial role in evaluating the quality and palatability of food and ultimately influences whether or not teleosts consume the food, the potential impact of OA on gustation-mediated feeding behavior remains unknown. In this study, gustation mediated-feeding behavior, as indicated by the consumption rate (CR) and swallowing rate (SR) of agar pellets with or without feed upon OA exposure was investigated in black sea bream (Acanthopagrus schlegelii). Results showed that the exposure to acidified seawater led to significant reductions in the CR and SR of feed-containing agar pellets. In addition, the in vivo contents of three neurotransmitters and expression of genes from the gustatory signal transduction pathway were all significantly suppressed by the OA treatment. In general, the data obtained indicated that OA may hinder the gustation-mediated feeding behavior of A. schlegelii by disrupting gustatory signal transduction, which may aggravate the issue of food shortage for wild populations of black sea bream.

Description: Ensuring that oocytes are fertilized by a single sperm during broadcast spawning is crucial for the fertilization success of many marine invertebrates. Although the adverse impacts of ocean acidification (OA) on various marine species have been revealed in recent years, its impact on polyspermy and the underlying mechanisms involved remain largely unknown. Therefore, in the present study, the effect of OA on polyspermy risk was assessed in a broadcast spawning bivalve, Tegillarca granosa. In addition, the impacts of OA on the two polyspermy blocking processes, the fast block (membrane depolarization) and the permanent block (cortical reaction), were investigated. The results show that the exposure of oocytes to two future OA scenarios (pH 7.8 and pH 7.4) leads to significant increases in polyspermy risk, about 1.70 and 2.38 times higher than the control, respectively. The maximum change in the membrane potential during oocyte membrane depolarization markedly decreased to 15.79% (pH 7.8) and 34.06% (pH 7.4) of the control value. Moreover, the duration of oocyte membrane depolarization was significantly reduced to approximately 63.38% (pH 7.8) and 21.91% (pH 7.4) of the control. In addition, cortical granule exocytosis, as well as microfilament migration, were significantly arrested by OA treatment. Exposure to future OA scenarios also led to significant reductions in the ATP and Ca2+ content of the oocytes, which may explain the hampered polyspermy blocking. Overall, the present study suggests that OA may significantly increase polyspermy risk in T. granosa by inhibiting membrane depolarization and arresting cortical granule exocytosis.

Description: In this article, event-triggered attitude consensus is considered for multiagent systems with guaranteed fixed-time convergence. Due to the non-Euclidean property of the attitude configuration space, the attitude consensus is more challenging to achieve under the sampled-data setting. An event-triggered attitude consensus protocol and event-triggered condition are proposed based on the axis–angle attitude representation. The fixed-time attitude consensus is reached if the initial attitudes lie in local regions on the attitude configuration space. The theoretical results reveal that the settling time is related to the interevent interval and the algebraic connectivity of the topology graph. We further consider the consensus protocol under a jointly connected graph, and establish the settling time estimation that depends on the switching instants. Numerical simulations are conducted to verify the validity of the theoretical results finally.

Description: The papers in this special section focus on computational intelligence for perception and decision making of autonomous systems. Due to powerful capabilities in environmental perception, real-time computing, and intelligent decision-making, autonomous systems have demonstrated their great potential to efficiently accomplish a variety of complex tasks that humans cannot. Hence, autonomous systems are able to facilitate the development of almost every walk of life and have attracted increasing attention from both academia and industry. However, given high dimensional, heterogeneous, unstructured, and unpredictable data sampled from different modalities of sensors, autonomous systems with conventional algorithms may fail to acquire the accurate information related to the environment, and make the appropriate decision to complete assigned tasks. Notice that recent advanced computational intelligence algorithms including deep neural networks and evolutionary algorithms have the unique ability to efficiently extract useful information from the multi-source heterogeneous data, and thus have been successfully applied in the fields of computer vision, natural language processing, and so on. Therefore, it is promising to have a thorough and tight integration between computational intelligence and autonomous systems by upgrading advanced and innovative computational intelligence algorithms to ensure high-level environmental perception and decision-making of autonomous systems.

Description: Signal propagation in complex networks drives epidemics, is responsible for information going viral, promotes trust and facilitates moral behavior in social groups, enables the development of misinformation detection algorithms, and it is the main pillar supporting the fascinating cognitive abilities of the brain, to name just some examples. The geometry of signal propagation is determined as much by the network topology as it is by the diverse forms of nonlinear interactions that may take place between the nodes. Advances are therefore often system dependent and have limited translational potential across domains. Given over two decades worth of research on the subject, the time is thus certainly ripe, indeed the need is urgent, for a comprehensive review of signal propagation in complex networks. We here first survey different models that determine the nature of interactions between the nodes, including epidemic models, Kuramoto models, diffusion models, cascading failure models, and models describing neuronal dynamics. Secondly, we cover different types of complex networks and their topologies, including temporal networks, multilayer networks, and neural networks. Next, we cover network time series analysis techniques that make use of signal propagation, including network correlation analysis, information transfer and nonlinear correlation tools, network reconstruction, source localization and link prediction, as well as approaches based on artificial intelligence. Lastly, we review applications in epidemiology, social dynamics, neuroscience, engineering, and robotics. Taken together, we thus provide the reader with an up-to-date review of the complexities associated with the network’s role in propagating signals in the hope of better harnessing this to devise innovative applications across engineering, the social and natural sciences as well as to inspire future research.

Description: Autonomous systems possess the features of inferring their own state, understanding their surroundings, and performing autonomous navigation. With the applications of learning systems, like deep learning and reinforcement learning, the visual-based self-state estimation, environment perception, and navigation capabilities of autonomous systems have been efficiently addressed, and many new learning-based algorithms have surfaced with respect to autonomous visual perception and navigation. In this review, we focus on the applications of learning-based monocular approaches in ego-motion perception, environment perception, and navigation in autonomous systems, which is different from previous reviews that discussed traditional methods. First, we delineate the shortcomings of existing classical visual simultaneous localization and mapping (vSLAM) solutions, which demonstrate the necessity to integrate deep learning techniques. Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks. Then, we focus on the visual navigation based on learning systems, mainly including reinforcement learning and deep reinforcement learning. Finally, we examine several challenges and promising directions discussed and concluded in related research of learning systems in the era of computer science and robotics.

Description: Extreme climate events constitute a major risk to global food production. Among these, extreme rainfall is often dismissed from historical analyses and future projections, the impacts and mechanisms of which remain poorly understood. Here we used long-term nationwide observations and multi-level rainfall manipulative experiments to explore the magnitude and mechanisms of extreme rainfall impacts on rice yield in China. We find that rice yield reductions due to extreme rainfall were comparable to those induced by extreme heat over the last two decades, reaching 7.6 ± 0.9% (one standard error) according to nationwide observations and 8.1 ± 1.1% according to the crop model incorporating the mechanisms revealed from manipulative experiments. Extreme rainfall reduces rice yield mainly by limiting nitrogen availability for tillering that lowers per-area effective panicles and by exerting physical disturbance on pollination that declines per-panicle filled grains. Considering these mechanisms, we projected ~8% additional yield reduction due to extreme rainfall under warmer climate by the end of the century. These findings demonstrate that it is critical to account for extreme rainfall in food security assessments.

Description: The multiple scale atmospheric or oceanic prediction has been a challenging issue due to different dynamical and thermodynamic processes involved and the uncertainty inherent in highly nonlinear and stochastic forcing, such as the prediction of Asian summer monsoon (ASM) precipitation. The seamless prediction was proposed to address this issue. The idea behind this approach is that temporal averaging reduces the spread of the prediction ensemble but retains the approximate first moment, which effectively increases the signal-to-noise ratio of the prediction. In this study, the superiority of the seamless prediction was assessed for the ASM precipitation at timescales from days to weeks. The hindcasts from the European Center for Medium-Range Weather Forecasts in the Subseasonal to Seasonal ensemble dataset were used for the detailed assessment. Results show significant advantages by the seamless approach in evaluating the ASM precipitation prediction on timescales ranging from two days to three weeks. Furthermore, the source of the average window predictability was analyzed for two lead times with significantly superior prediction accuracy: lead times of 4d4d and 3w3w. For 4d4d, the Madden–Julian Oscillation is dominant, while the role of the El Niño–Southern Oscillation phase becomes substantial for 3w3w. These results offer a detailed analysis for the prediction of ASM precipitation within a framework of seamless prediction, benefiting further interpretation of seamless prediction of ASM precipitation.

Description: In this study, a new index based on the potential vorticity (PV) framework is proposed for the quantification of the Tibetan Plateau (TP) surface thermodynamic and dynamic forcing. The results show that the derived TP surface PV (SPV) includes the topographical effect, near-surface absolute vorticity, and land–air potential temperature differences. The climatological annual cycle of the SPV suggests that the TP transitions from a cooling to a heating source in April. The SPV reaches a maximum from June to August, which is consistent with the evolution of the Asian summer monsoon precipitation. Further analysis suggests that the intensified SPV in the boreal summer results in a low-level cyclonic circulation anomaly associated with increased precipitation over the southeastern slope of the TP and South China and decreased precipitation over the Indian Ocean. In winter, the intensified SPV is associated with local cold air and divergence at the TP surface.

Description: Due to the construction and operation of cascade reservoirs, the natural hydrological regimes have inevitably been altered and most fish spawning grounds have been destroyed, thus causing ecological impacts on riverine ecosystems and fish reproduction. An effective way to restore fish habitats and fulfill their spawning requirements is to generate an appropriate ecological flow regime guided by Natural-based solutions (NbS) that can help restore valuable functions provided by nature. With a focus on one major existing spawning ground for four major Chinese carps (FMCC) in the Hongshui River (HSR), China, this study first evaluated temporal variations of flow regimes and their ecological responses in multiple time scales by employing several eco-hydrological indicators. A fuzzy logic-based one- and two-dimensional-coupled fish habitat model was then built to estimate a suitable ecological flow to ensure the existence of continuous and connected habitats. Furthermore, an improved fluctuation identification algorithm was proposed to quantitatively identify naturally effective high-flow processes to trigger fish spawning behaviors following NbS guidance. Thus, suitable ecological flow regimes were established based on the notion of thresholds of flow discharge while meeting the requirements for water temperature during the spawning period of FMCC. The results recommended high-flow processes characterized with thresholds of flow indicators while mimicking natural flow regimes to be generated to stimulate FMCC spawning in different periods in HSR. The findings of this study provide actionable insights into the conservation of fish in dammed rivers and such NbS-oriented practices can create achievable strategies for reservoir operations.