Complex Natural Environments Research Articles

Optimal screening method for dosage of calcium-based nanosilica modified expansive soils improver based on cost-effectiveness

Under complex natural environments, the properties of lime-modified expansive soil may deteriorate. This facilitates the need for newer materials to develop composite modifiers to improve the anti-deterioration performance. Nanosilica (NS), which is a highly reactive material, is a good promoter of reaction systems with volcanic ash. In this paper, lime is mixed with NS according to a specified ratio to form calcium-based nanosilica (NS-C). NS-C significantly increased the unconfined compressive strength, cohesion, and internal friction angle and decreased the plasticity index, free swelling ratio, and permeability coefficient. The optimum ratio of nano-silica to lime in compound modifier was determined to be 1:8. NS can facilitate the production of a large amount of hydration products in the early stage of the reaction of stabilized soil, thus strengthening the connection between soil particles. In addition, NS exhibits good filling effect on the tiny pores between soil particles. The properties of NS-C stabilized soils with different ratios and curing times were determined to evaluate the modification effect and mechanism of composite modifier and obtain the optimal dosage for Hefei expansive soils in road-base edging technology. The optimum content of the compound modifier for expansive soils in the Hefei area should be 6 %, and the curing time should be at least 14 days.

Integrating Artificial Intelligence and UAV-Acquired Multispectral Imagery for the Mapping of Invasive Plant Species in Complex Natural Environments

The proliferation of invasive plant species poses a significant ecological threat, necessitating effective mapping strategies for control and conservation efforts. Existing studies employing unmanned aerial vehicles (UAVs) and multispectral (MS) sensors in complex natural environments have predominantly relied on classical machine learning (ML) models for mapping plant species in natural environments. However, a critical gap exists in the literature regarding the use of deep learning (DL) techniques that integrate MS data and vegetation indices (VIs) with different feature extraction techniques to map invasive species in complex natural environments. This research addresses this gap by focusing on mapping the distribution of the Broad-leaved pepper (BLP) along the coastal strip in the Sunshine Coast region of Southern Queensland in Australia. The methodology employs a dual approach, utilising classical ML models including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) in conjunction with the U-Net DL model. This comparative analysis allows for an in-depth evaluation of the performance and effectiveness of both classical ML and advanced DL techniques in mapping the distribution of BLP along the coastal strip. Results indicate that the DL U-Net model outperforms classical ML models, achieving a precision of 83%, recall of 81%, and F1–score of 82% for BLP classification during training and validation. The DL U-Net model attains a precision of 86%, recall of 76%, and F1–score of 81% for BLP classification, along with an Intersection over Union (IoU) of 68% on the separate test dataset not used for training. These findings contribute valuable insights to environmental conservation efforts, emphasising the significance of integrating MS data with DL techniques for the accurate mapping of invasive plant species.

Bioinspiration from bats and new paradigms for autonomy in natural environments

Achieving autonomous operation in complex natural environment remains an unsolved challenge. Conventional engineering approaches to this problem have focused on collecting large amounts of sensory data that are used to create detailed digital models of the environment. However, this only postpones solving the challenge of identifying the relevant sensory information and linking it to action control to the domain of the digital world model. Furthermore, it imposes high demands in terms of computing power and introduces large processing latencies that hamper autonomous real-time performance. Certain species of bats that are able to navigate and hunt their prey in dense vegetation could be a biological model system for an alternative approach to addressing the fundamental issues associated with autonomy in complex natural environments. Bats navigating in dense vegetation rely on clutter echoes, i.e. signals that consist of unresolved contributions from many scatters. Yet, the animals are able to extract the relevant information from these input signals with brains that are often less than 1 g in mass. Pilot results indicate that information relevant to location identification and passageway finding can be directly obtained from clutter echoes, opening up the possibility that the bats’ skill can be replicated in man-made autonomous systems.

Multiband task related components enhance rapid cognition decoding for both small and similar objects

The cortically-coupled target recognition system based on rapid serial visual presentation (RSVP) has a wide range of applications in brain computer interface (BCI) fields such as medical and military. However, in the complex natural environment backgrounds, the identification of event-related potentials (ERP) of both small and similar objects that are quickly presented is a research challenge. Therefore, we designed corresponding experimental paradigms and proposed a multi-band task related components matching (MTRCM) method to improve the rapid cognitive decoding of both small and similar objects. We compared the areas under the receiver operating characteristic curve (AUC) between MTRCM and other 9 methods under different numbers of training sample using RSVP-ERP data from 50 subjects. The results showed that MTRCM maintained an overall superiority and achieved the highest average AUC (0.6562 ± 0.0091). We also optimized the frequency band and the time parameters of the method. The verification on public data sets further showed the necessity of designing MTRCM method. The MTRCM method provides a new approach for neural decoding of both small and similar RSVP objects, which is conducive to promote the further development of RSVP-BCI.

Design and experimental study of electron beam treatment for composite insulator sheds

Composite insulators, widely used in high-voltage transmission lines, feature silicone rubber sheds that age in complex natural environments. Applying electron beam irradiation to the silicone rubber can enhance their ageing resistance and hydrophobic properties. To advance the study of sheet silicone rubber to the entire insulator, this study proposes a method utilising a 5 MeV Dynamitron accelerator for treating a irregularly structured insulator. The insulator is irradiated in a state of tilted placement and rotation about its axis to achieve a uniform and consistent dose across the sheds. The irradiation process of the insulator with electron beams is simulated using Geant4, considering both instantaneous and cumulative states separately. The deflected particles from scattering and reflection of high-energy electrons interacting with insulator components have a significant impact on the dose distribution in each shed. Moreover, an optimal tilt of 20° ensures similar irradiation doses and high uniformity across all sheds. An experimental platform is developed for rotational irradiation of the insulator to ensure stable operation. Preliminary experimental results confirm that the doses across all sheds are uniform and consistent.

A method for multi-target segmentation of bud-stage apple trees based on improved YOLOv8

The bud stage is a crucial period in the growth and development of apple trees. Accurate detections of the physiological changes in the organs (branches, buds, leaves, and the connecting parts between buds and branches) are essential for the scientific management of orchards. In the field of intelligent orchard management, image segmentation is a fundamental method for obtaining the phenotypes of fruit tree organs, making it especially critical. To address this, we have created a dataset for apple tree organ segmentation during the bud stage and have incorporated several advanced convolutional network modules (ConvNeXt V2, Multi-scale Extended Attention Module (MSDA), Dynamic Snake Convolution (DSConv)) to enhance YOLOv8 and improve the accuracy of organ segmentation in complex natural environments. In the backbone network, we have integrated ConvNeXt V2 and MSDA modules to increase the extraction of contextual information and improve the network’s ability to recognize multi-scale and multi-shaped targets. Additionally, we have embedded DSConv in the network’s head and utilized deformable convolution to enable adaptive sampling of the feature map, capturing a wider range of context information and improving the local feature processing capability for objects of varying sizes, ultimately leading to improved segmentation accuracy. Our models significantly outperform existing models, achieving 82.58 % mean Precision (mP), 74.58 % mean Recall (mR), 77.94 % mean Dice(mDice), 64.91 % mean IoU(mIoU), and 79.75 % mean Average Precision (mAP). Ablation studies confirm the contributions of each module to intelligent orchard management and suggest potential benefits for precise agricultural decision-making and operations.

Climate change unevenly affects the dependence of multiple climate-related hazards in China

Due to the complex natural environment and large regional differentiation in China, the dependence of multiple climate-related hazards on compound events (CEs) and their response to climate change are still unclear. Using daily meteorological observations (1961–2020) and climate simulations from the Coupled Model Intercomparison Project Phase 6, statistically strong dependences among hot, dry, and high fire risk are revealed in China. The average temperature from 1991 to 2020 was 1 °C higher than that from 1961 to 1990, and the probabilities of CEs exhibiting extreme hot-dry, dry-high fire risk, and extreme hot-dry-high fire risk increased significantly by 74.8%, 60.5%, and 26.8%, respectively. Although most CEs occur more frequently in China at the end of the 21st century, the increase rates in low emissions are lower. These findings have implications for developing climate adaptation and mitigation strategies to cope with increased CEs in critical geographical regions.

A robotic platform for integrating biomimetic sonar and AI

To gain a first-hand view of the biosonar inputs that a bat receives when operating in a complex natural environment, a bioinspired sonar head to mimic the auditory periphery of the bats' biosonar system is currently under development. The sonar head seeks to replicate the static geometric complexity of the baffles that emit the biosonar pulses and receive the returning echoes in horseshoe bats. Furthermore, some of the dynamic deformations of these structures are mimicked using tension-driven soft-robotics actuated by a set of 12 motors (two for the noseleaf and five for the each pinna). The periphery has been interfaced with acoustic emission and reception systems as well as a modular system control system for the acoustic input and output as well as the motors. Besides low-level control, the sonar head has onboard computing resources that can support deep-learning inference in the loop with acoustic data acquisition and pulse generation. All functional components have been integrated into a custom-designed shell that allows operating the system in natural outdoor environments. A wireless user interface allows remote control over the system so that the tasks of carrying and orienting the sonar head and controlling its function can be shared between two individuals.

A smart fruit size measuring method and system in natural environment

The measurement of fruit size (length, width, and area) is one of the key components in the assessment of fruit ripeness for consumption and fruit quality. Due to issues with fruit shape and ambient occlusion, noncontact measurement of fruits in their natural growth state in natural environment is more difficult. This study proposes a fast and low-cost noncontact measurement solution combining deep learning, image analysis and robotic platform. First, based on the YOLOv5 object detection algorithm and converged dataset, a 95.6% detection accuracy and 0.05 s detection speed was achieved. Second, a Cycle GAN model for fruit occlusion recovery was established, and the occlusion of the fruit under different natural conditions was adaptively repaired, with a 5.48% average relative error under different occlusion conditions. Third, through image morphological operations, the size measurements were achieved, with an average relative error of 8.43% for individual fruits and the 10.12% overall error under the complex natural environment. This method and platform provide a systematic, fast (0.2s) and low-cost solution for the noncontact measurement of fruit size.

Research Progress on Cuttings of Malus Rootstock Resources in China

Apple (Malus Mill.) is one of the most important fruits in China, and it boasts the world’s largest cultivation area and yield. It needs to be grafted onto rootstocks to maintain a variety of characteristics. China has many apple rootstock resources that exhibit high resistance and strong adaptability; for these reasons, they are highly suited to China’s complex and diverse natural environment. In China, apple rootstock breeding began in the 1970s, and now, several rootstocks, such as the ‘GM256’ and ‘SH’ series, are widely used. However, domestic rootstock resources and varieties are difficult to root. This affects the selection, utilization, and promotion of apple rootstocks. Cutting is an important method of rooting for apple rootstocks. This study discusses the main factors that affect rooting in rootstock cutting propagation; it also summarizes the rooting ability of different apple rootstocks and presents analyses of the demand for rootstocks in the major areas of apple production in China. We present the apple rootstock resources that are suitable for the soil and climate conditions of this production. We also call for research on the cutting roots of these specific apple rootstock resources to be expanded and strengthened. It is hoped that cutting rootstocks suitable for major areas of apple production may be more easily screened and bred successfully in China in the near future.

Neural correlates of audiovisual narrative speech perception in children and adults on the autism spectrum: A functional magnetic resonance imaging study.

Autistic individuals show substantially reduced benefit from observing visual articulations during audiovisual speech perception, a multisensory integration deficit that is particularly relevant to social communication. This has mostly been studied using simple syllabic or word-level stimuli and it remains unclear how altered lower-level multisensory integration translates to the processing of more complex natural multisensory stimulus environments in autism. Here, functional neuroimaging was used to examine neural correlates of audiovisual gain (AV-gain) in 41 autistic individuals to those of 41 age-matched non-autistic controls when presented with a complex audiovisual narrative. Participants were presented with continuous narration of a story in auditory-alone, visual-alone, and both synchronous and asynchronous audiovisual speech conditions. We hypothesized that previously identified differences in audiovisual speech processing in autism would be characterized by activation differences in brain regions well known to be associated with audiovisual enhancement in neurotypicals. However, our results did not provide evidence for altered processing of auditory alone, visual alone, audiovisual conditions or AV- gain in regions associated with the respective task when comparing activation patterns between groups. Instead, we found that autistic individuals responded with higher activations in mostly frontal regions where the activation to the experimental conditions was below baseline (de-activations) in the control group. These frontal effects were observed in both unisensory and audiovisual conditions, suggesting that these altered activations were not specific to multisensory processing but reflective of more general mechanisms such as an altered disengagement of Default Mode Network processes during the observation of the language stimulus across conditions.

Re-framing bio-plausible collision detection: identifying shared meta-properties through strategic prototyping

Insects exhibit remarkable abilities in navigating complex natural environments, whether it be evading predators, capturing prey, or seeking out con-specifics, all of which rely on their compact yet reliable neural systems. We explore the field of bio-inspired robotic vision systems, focusing on the locust inspired Lobula Giant Movement Detector (LGMD) models. The existing LGMD models are thoroughly evaluated, identifying their common meta-properties that are essential for their functionality. This article reveals a common framework, characterized by layered structures and computational strategies, which is crucial for enhancing the capability of bio-inspired models for diverse applications. The result of this analysis is the Strategic Prototype, which embodies the identified meta-properties. It represents a modular and more flexible method for developing more responsive and adaptable robotic visual systems. The perspective highlights the potential of the Strategic Prototype: LGMD-Universally Prototype (LGMD-UP), the key to re-framing LGMD models and advancing our understanding and implementation of bio-inspired visual systems in robotics. It might open up more flexible and adaptable avenues for research and practical applications.

Occlusion-aware fruit segmentation in complex natural environments under shape prior

Widespread occlusion in complex natural environments severely restricts the accurate segmentation and localization of approximately spherical fruits by existing automatic picking devices, resulting in the confinement of efficient automatic picking to a controlled laboratory environment. While occlusion can be mitigated to some extent by optimizing annotations and refining the algorithm, the absence of partial fruit information due to occlusion may still result in misplacement or incorrect localization of the picking point. In this study, by introducing approximately spherical fruit shape priors, the widespread partial occlusion, slice occlusion, and self-occlusion found in complex natural environments can be effectively addressed. First, with the help of a boundary interest point–based fruit region contour segmentation method, the proposed method can achieve more accurate fruit segmentation in a complex environment. Self-occlusion can be mitigated with better separation and accurate localization for under-segmented overlapping fruit regions, which is achieved in this study through a local minima point–based fruit region contour segmentation method. This research demonstrates that occlusion-aware fruit segmentation can be achieved simply by introducing the approximately spherical fruit shape priors that are easily available in real production. Additional experiments are also performed to illustrate that the shape priors can be flexibly adjusted to ensure the generalizability of the proposed method to different species of fruits. The proposed method, which utilizes only plant phenotypic information and refrains from reliance on additional training data or equipment, can effectively solve the ubiquitous occlusion problem in complex natural environments and is important for future large-scale automatic fruit picking in smart agriculture.

Experimental evidences of bio-chemo-mechanical processes in contaminated sediments

The research shows the results of a micro to macro testing programme carried out on contaminated marine sediments from a natural deposit to assess the effects of bio-chemo-mechanical coupled processes which may act in complex natural environments and affect the geotechnical properties of the clays. The research has been triggered by the emblematic case of the contaminated Mar Piccolo (MP) basin in Taranto (Southern Italy), where the high degree of pollution recorded in the clayey sediments at the sea bottom, has been found to worsen water quality and promote bioaccumulation of pollutants in several species. Several samples of sediments collected in the basin from the top layer exhibited peculiar geotechnical properties, in terms of plasticity and activity indexes, compressibility and hydraulic permeability. The results of the geotechnical testing were interpreted taking into account the XRD mineralogical results and the chemical composition of the soil matrix. Furthermore, thermogravimetry tests results were examined to explore the nature of the sediment skeleton and of its organic matter content, based on the main thermal reactions occurring within different temperature ranges. Lastly, scanning electron microscopy and mercury intrusion porosimetry were performed to assess the sediment pore size distribution as well as their content in microfossils and diatoms. The original multiscale analysis carried out on some selected sediment samples showed that the biogeochemical degradation of organic matter and the presence of microfossils and diatoms significantly affect the micro to macro behaviour of marine sediments.

Bio-Inspired Small Target Motion Detection with Spatio-Temporal Feedback in Natural Scenes.

Small moving objects at far distance always occupy only one or a few pixels in image and exhibit extremely limited visual features, which bring great challenges to motion detection. Highly evolved visual systems endow flying insects with remarkable ability to pursue tiny mates and prey, providing a good template to develop image processing method for small target motion detection. The insects' excellent sensitivity to small moving objects is believed to come from a class of specific neurons called small target motion detectors (STMDs). However, existing STMD-based methods often experience performance degradation when coping with complex natural scenes. In this paper, we propose a bio-inspired visual system with spatio-temporal feedback mechanism (called Spatio-Temporal Feedback STMD) to suppress false positive background movement while enhancing system responses to small targets. Specifically, the proposed visual system is composed of two complementary subnetworks and a feedback loop. The first subnetwork is designed to extract spatial and temporal movement patterns of cluttered background by neuronal ensemble coding. The second subnetwork is developed to capture small target motion information where its output and signal from the first subnetwork are integrated together via the feedback loop to filter out background false positives in a recurrent manner. Experimental results demonstrate that the proposed spatio-temporal feedback visual system is more competitive than existing methods in discriminating small moving targets from complex natural environments.

A photothermal MoS2 decorated biomass carbon-based aerogel with a directionally aligned porous structure for mitigating heavy metal stress under seawater acidification.

Marine animals and human are threatened by seawater acidification and metal contamination. Especially, the toxicity of copper (Cu) is expected to be boosted with seawater acidification. However, studies on the removal of Cu under seawater acidification are limited for practical applications, owing to obstacles such as instability, secondary contamination, and low adsorption efficiency. In this work, coconut shells were utilized for the synthesis of biomass carbon, which was then decorated with MoS2. A novel porous MoS2/carbon-based aerogel (MCA) with the synergistic effect of photothermal conversion and adsorption was constructed via directional freeze-drying technology. The adsorption properties of MCA were a precise match with Freundlich isotherm and pseudo-second-order kinetic models with a high correlation coefficient (R2) of more than 0.995. Under solar illumination, the surface temperature of MCA reached up to 36.3 °C and the adsorption capacity of MCA increased to 833.8 mg g-1, indicating that the remarkable thermal properties of MCA contributed to achieving high adsorption capacity. The adsorption mechanisms of MCA involved in the removal of Cu(ii) ions were dominated by chemisorption rather than surface physical adsorption. Owing to its outstanding photothermal conversion performance and directionally aligned porous structure, MCA was able to remove Cu(ii) species from seawater, and the adsorption ability of MCA reached 247.1 mg g-1 after ten adsorption cycles. MCA exhibited excellent stability to resist the complex natural environment and was easy to reuse. Overall, MCA with a series of merits, including high adsorption efficiency, excellent photothermal conversion property, and outstanding cycling stability, was confirmed to contribute to addressing heavy metal stress under seawater acidification.

Analysis on the Construction Safety of Hongkong-Zhuhai-Macao Cross-sea Bridge

With the continuous development of highway and bridge construction in China, the number of cross-sea projects is increasing. The large scale and bad construction environment of cross-sea bridge project put forward a severe test to the construction safety. At present, although China has completed a large number of cross-sea projects, the analysis of construction safety is still insufficient. As a large cross-sea bridge, the Hongkong-Zhuhai-Macao Bridge is representative with harsh construction environment, complex geological structure and high environmental safety requirements. Therefore, this paper takes the Hongkong-Zhuhai-Macao Bridge as an example to analyze the construction safety. Firstly, the safety of construction platforms in different sea areas are discussed, and then, the construction of bridge substructure under complex geological conditions and natural environment are also displayed. In the face of these harsh construction conditions, the Hongkong-Zhuhai-Macao Bridge has adopted some targeted schemes, which provides a good reference model for the construction safety of other cross-sea projects.

Thermal sensitivity of field metabolic rate predicts differential futures for bluefin tuna juveniles across the Atlantic Ocean

Changing environmental temperatures impact the physiological performance of fishes, and consequently their distributions. A mechanistic understanding of the linkages between experienced temperature and the physiological response expressed within complex natural environments is often lacking, hampering efforts to project impacts especially when future conditions exceed previous experience. In this study, we use natural chemical tracers to determine the individual experienced temperatures and expressed field metabolic rates of Atlantic bluefin tuna (Thunnus thynnus) during their first year of life. Our findings reveal that the tuna exhibit a preference for temperatures 2–4 °C lower than those that maximise field metabolic rates, thereby avoiding temperatures warm enough to limit metabolic performance. Based on current IPCC projections, our results indicate that historically-important spawning and nursery grounds for bluefin tuna will become thermally limiting due to warming within the next 50 years. However, limiting global warming to below 2 °C would preserve habitat conditions in the Mediterranean Sea for this species. Our approach, which is based on field observations, provides predictions of animal performance and behaviour that are not constrained by laboratory conditions, and can be extended to any marine teleost species for which otoliths are available.

Trail using ants follow idiosyncratic routes in complex landscapes

A large volume of research on individually navigating ants has shown how path integration and visually guided navigation form a major part of the ant navigation toolkit for many species and are sufficient mechanisms for successful navigation. One of the behavioural markers of the interaction of these mechanisms is that experienced foragers develop idiosyncratic routes that require that individual ants have personal and unique visual memories that they use to guide habitual routes between the nest and feeding sites. The majority of ants, however, inhabit complex cluttered environments and social pheromone trails are often part of the collective recruitment, organisation and navigation of these foragers. We do not know how individual navigation interacts with collective behaviour along shared trails in complex natural environments. We thus asked here if wood ants that forage through densely cluttered woodlands where they travel along shared trails repeatedly follow the same routes or if they choose a spread of paths within the shared trail. We recorded three long homing trajectories of 20 individual wood ants in their natural woodland habitat. We found that wood ants follow idiosyncratic routes when navigating along shared trails through highly complex visual landscapes. This shows that ants rely on individual memories for habitual route guidance even in cluttered environments when chemical trail information is available. We argue that visual cues are likely to be the dominant sensory modality for the idiosyncratic routes. These experiments shed new light on how ants, or insects in general, navigate through complex multimodal environments.

A wave knowledge description of a surf break in Southern Brazil

Surf breaks are complex natural environments that provide ecosystem services to coastal communities and suffer from both natural and anthropogenic impacts. Surfers have specific knowledge about these environments, due to their direct relationship with the ecosystem. Through a social-ecological approach such knowledge (wave knowledge) becomes a valuable source of information and is increasingly being incorporated into coastal management processes. From an ethno-oceanographic perspective, this article investigates the wave knowledge of surfers in Pontal do Sul surf break, state of Paraná, southern Brazil. This surf break is located at an estuary mouth, being subject to natural and anthropic changes, especially due to nearby port activities. A detailed set of knowledge about the oceanographic dynamics that act on this surf break was identified, from descriptions of waves, wind, and tidal conditions, to complex local morphodynamics. For surfers, the presence of port activities is one of the factors that modified the sediment dynamics in the region and in the surf break. Such information can contribute to the understanding of local dynamics and the predictability of future changes, offering effectiveness and legitimacy to conservation of these environments.