Complex Environmental Conditions Research Articles

Estimation of Time of Death through Observation of Microbiota Changes in the Oral Cavity

Abstract: Various methods for estimating post-mortem (PMI) have been investigated such as rigor mortis, livor mortis, molecular, chemical, and forensic entomology (insect) methods. However, these methods have weaknesses, therefore researchers are looking for more accurate methods in determining the time of death of a person. Molecular methods are unreliable due to the degradation of DNA, RNA, and proteins in corpses over time. In different parts of the body, the amount of diversity of microbes will be different. The oral cavity is the most abundant microbial area among other parts of the body due to its continuous exposure to the respiratory and digestive systems. This area has great potential in estimating time intervals of death because of its ease of access and the types of microbiomes that predominate at any given time. Therefore, this narrative review was conducted to describe studies that used oral microbiota communities to estimate post-mortem intervals. Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes are the dominant microbial types found in corpses. Firmicutes became one of the dominant bacterial phyla in the early stages of decomposition. Actinobacteria were found to decrease as PMI increased. Studies have shown that the oral microbiome has excellent potential as a parameter to determine the post-mortem interval. However, further research is needed with more complex environmental conditions such as different humidity and temperature. In addition, further research requires more samples of human remains to obtain more valid results. Keywords: oral microbiome; post-mortem interval; human identification; forensic odontology

Enhancing Wind Farm Reliability: A Field of View Enhanced Convolutional Neural Network-Based Model for Fault Diagnosis and Prevention

Wind farms play a crucial role in renewable energy generation, but their reliability is often compromised by complex environmental and equipment conditions. This study proposes a field of view enhanced convolutional neural network (CNN) model for fault diagnosis and prevention in wind farms. The model is developed by collecting and processing wind farm fault data and compared with support vector machine (SVM) and k-nearest neighbor (KNN) models. The results showed that the proposed CNN model outperformed the other models in terms of convergence speed (17 iterations to reach the minimum loss), fault diagnosis accuracy (99.3% and 99.2% for inner and outer circle faults, respectively), and stable power output improvement. The model’s application to maintenance scheduling and economic benefit analysis in a real wind farm case demonstrated its high consistency and accuracy in fault prediction and maintenance optimization. The proposed approach has the potential to enhance wind farm reliability, efficiency, and economy by enabling accurate fault diagnosis, early warning, and preventive maintenance.

Exposure to nanopollutants (nZnO) enhances the negative effects of hypoxia and delays recovery of the mussels’ immune system

Aquatic environments face escalating challenges from multiple stressors like hypoxia and nanoparticle exposure, with impact of these combined stressors on mussel immunity being poorly understood. We investigated the individual and combined effects of short-term and long-term hypoxia and exposure to zinc oxide nanoparticles (nZnO) on immune system of the mussels (Mytilus edulis). Hemocyte functional traits (mortality, adhesion capacity, phagocytosis, lysosomal abundance, and oxidative burst), and transcript levels of immune-related genes involved in pathogen recognition (the Toll-like receptors, the complement system components, and the adaptor proteins MyD88) were assessed. Short-term hypoxia minimally affected hemocyte parameters, while prolonged exposure led to immunosuppression, impacting hemocyte abundance, viability, phagocytosis, and defensin gene expression. Under normoxia, nZnO stimulated immune responses of mussel hemocytes. However, combined nZnO and hypoxia induced more pronounced and rapid immunosuppression than hypoxia alone, indicating a synergistic interaction. nZnO exposure hindered immune parameter recovery during post-hypoxic reoxygenation, suggesting persistent impact. Opposing trends were observed in pathogen-sensing and pathogen-elimination mechanisms, with a positive correlation between pathogen-recognition system activation and hemocyte mortality. These findings underscore a complex relationship and potential conflict between pathogen-recognition ability, immune function, and cell survival in mussel hemocytes under hypoxia and nanopollutant stress, and emphasize the importance of considering multiple stressors in assessing the vulnerability and adaptability of mussel immune system under complex environmental conditions of anthropogenically modified coastal ecosystems.

Resource allocation modeling for autonomous prediction of plant cell phenotypes

Predicting the plant cell response in complex environmental conditions is a challenge in plant biology. Here we developed a resource allocation model of cellular and molecular scale for the leaf photosynthetic cell of Arabidopsis thaliana, based on the Resource Balance Analysis (RBA) constraint-based modeling framework. The RBA model contains the metabolic network and the major macromolecular processes involved in the plant cell growth and survival and localized in cellular compartments. We simulated the model for varying environmental conditions of temperature, irradiance, partial pressure of CO2 and O2, and compared RBA predictions to known resource distributions and quantitative phenotypic traits such as the relative growth rate, the C:N ratio, and finally to the empirical characteristics of CO2 fixation given by the well-established Farquhar model. In comparison to other standard constraint-based modeling methods like Flux Balance Analysis, the RBA model makes accurate quantitative predictions without the need for empirical constraints. Altogether, we show that RBA significantly improves the autonomous prediction of plant cell phenotypes in complex environmental conditions, and provides mechanistic links between the genotype and the phenotype of the plant cell.

Metabolic perturbations in zebrafish (Danio rerio) larvae exposed to sulfentrazone and imidacloprid

The intensive and widespread application of pesticides in agroecosystems can lead to the simultaneous exposure of non-target aquatic organisms to insecticides and herbicides. However, the underlying mechanisms through which aquatic organisms undergo metabolic reprogramming to withstand the combined effects of the insecticide imidacloprid (IMI) and herbicide sulfentrazone (SUL) remain poorly elucidated. This study employs metabolomics to investigate the effects of individual and combined exposures to IMI and SUL on zebrafish (Danio rerio), aiming to simulate complex environmental conditions. Metabolomics analysis revealed extensive metabolic reprogramming in larvae induced by the selected agrochemicals. Both individual and combined exposures disrupted nucleotide metabolism, inhibited glycolysis, and led to the accumulation of acetylcholine through the shared modulation of differential metabolites. Notably, individual exposure exhibited a unique mode of action. Larvae exposed to IMI alone showed mitochondrial dysfunction, potentially stemming from interference with the electron transport chain, while SUL-induced disruptions were associated with glycerophospholipid accumulation, marking it as a critical target. Additionally, calculations of the metabolic effect level index indicated antagonistic interactions between SUL and IMI mixtures at an overall metabolic level. The results obtained through investigating the lethal and sub-lethal effects also revealed that the simultaneous application of SUL and IMI may have the potential to diminish acute and developmental toxicity in zebrafish. This study underscores the significance of metabolomics as a valuable and effective strategy for deciphering the toxicity and interactions of agrochemical mixtures.

Hydrogen Peroxide Signaling in the Maintenance of Plant Root Apical Meristem Activity

Hydrogen peroxide (H2O2) is a prevalent reactive oxygen species (ROS) found in cells and takes a central role in plant development and stress adaptation. The root apical meristem (RAM) has evolved strong plasticity to adapt to complex and changing environmental conditions. Recent advances have made great progress in explaining the mechanism of key factors, such as auxin, WUSCHEL-RELATED HOMEOBOX 5 (WOX5), PLETHORA (PLT), SHORTROOT (SHR), and SCARECROW (SCR), in the regulation of RAM activity maintenance. H2O2 functions as an emerging signaling molecule to control the quiescent center (QC) specification and stem cell niche (SCN) activity. Auxin is a key signal for the regulation of RAM maintenance, which largely depends on the formation of auxin regional gradients. H2O2 regulates the auxin gradients by the modulation of intercellular transport. H2O2 also modulates the expression of WOX5, PLTs, SHR, and SCR to maintain RAM activity. The present review is dedicated to summarizing the key factors in the regulation of RAM activity and discussing the signaling transduction of H2O2 in the maintenance of RAM activity. H2O2 is a significant signal for plant development and environmental adaptation.

Research on Deformation Monitoring of Roadway Surrounding Rock Based on Mobile 3D Laser Scanning Technology

Aiming at the problems of low data acquisition efficiency, unintuitive data form, low relative accuracy, and inability to continuously monitor the whole cross-section in traditional roadway rock deformation monitoring methods, a roadway rock deformation monitoring method based on mobile 3D laser scanning technology is proposed. Firstly, the mobile 3D laser scanning technology is used to obtain the real 3D point cloud data of the roadway surrounding rock; secondly, the multi-scale dimensional features are used to classify the 3D point cloud data, filter the invalid point cloud data, and form the accurate point cloud data of the roadway surrounding rock surface; then the 3D laser scanning is carried out on a regular basis for the deformation-prone areas of the roadway, and the Iterative Closest Point Algorithm (ICP algorithm) is used to align the overlapping part of the point cloud to achieve the point cloud splicing and point cloud stitching of the two scans. The point cloud splicing and point cloud overlapping of the two scans are achieved; finally, the multi-scale model-to-model point cloud comparison algorithm (M3C2 algorithm) is used to compare the point cloud data of the two periods of the roadway, and the appropriate parameters are selected to indicate the displacement change of the surface of the roadway rock with the size of the color value, so as to realize the monitoring of the deformation of the roadway rock in the three-dimensional full cross-section of the roadway. The method is applied to the deformation monitoring of the surrounding rock in the return roadway of 21407 working face in a coal mine, and the results show that: the mobile laser scanning technology can be used to obtain the complete point cloud data of the roadway, and the deformation area of the surrounding rock is successfully identified after the classification of the scanned point clouds in different time periods, the alignment of the point clouds, and the comparison of the point clouds, and the deformation condition of the surrounding rock of the roadway is visually reflected in the three-dimensional full-section deformation condition of the surrounding rock of the roadway. This method is suitable for monitoring the overall de-formation of the surrounding rock of the roadway under complex environmental conditions, and the de-formation of the surrounding rock is identified by different color areas.

Semirational Design Strategy To Enhance the Thermostability and Catalytic Activity of Cytochrome P450 105D7 for the Degradation of the Pharmaceutically Active Compounds: Diclofenac.

Pharmaceutically active compounds are an important category of emerging pollutants, and their biological transformation processes in the environment are crucial for understanding and evaluating the migration, transformation, and environmental fate of emerging pollutants. The cytochrome P450 105 enzyme family has been proven to play an important role in the degradation of exogenous environmental pollutants. However, its thermostability and catalytic activity still need to be improved to better adapt to complex environmental conditions. This work elucidates the key mechanisms and important residues of the degradation reaction through multiple computational strategies, establishes a mutation library, and obtains 21 single-point mutation designs. Experimental verification showed that 16 single mutants had enhanced thermostability, with the R89F and L197Y mutants showing the highest increases in thermostability at 135 and 119% relative to the wild-type enzyme, respectively. Additionally, as a result of the higher specific activity of D390Q, it was selected for combination mutagenesis, ultimately resulting in three combination mutants (R89F/L197Y, R89F/D390Q, and R89F/L197Y/D390Q) with enhanced thermostability and catalytic activity. This study provides a modification approach for constructing efficient enzyme variants through semirational design and can contribute to the development of control technologies for emerging pollutants.

Lightweight object detection network for multi‐damage recognition of concrete bridges in complex environments

AbstractTo solve the challenges of low recognition accuracy, slow speed, and weak generalization ability inherent in traditional methods for multi‐damage recognition of concrete bridges, this paper proposed an efficient lightweight damage recognition model, constructed by improving the you only look once v4 (YOLOv4) with MobileNetv3 and fused inverted residual blocks, named YOLOMF. First, a novel lightweight network named MobileNetv3 with fused inverted residual (MobileNetv3‐FusedIR) is constructed as the backbone network for YOLOMF. This is achieved by integrating the fused mobile inverted bottleneck convolution (Fused‐MBConv) into the shallow layers of MobileNetv3. Second, the standard convolution in YOLOv4 is replaced with the depthwise separable convolution, resulting in a reduction in the number of parameters and complexity of the model. Third, the effects of different activation functions on the damage recognition performance of YOLOMF are thoroughly investigated. Finally, to verify the effectiveness of the proposed method in complex environments, a data enhancement library named Imgaug is used to simulate concrete bridge damage images under challenging conditions such as motion blur, fog, rain, snow, noise, and color variations. The results indicate that the YOLOMF shows excellent multi‐damage recognition proficiency for concrete bridges across varying field‐of‐view sizes as well as complex environmental conditions. The detection speed of YOLOMF reaches 85f/s, facilitating effective real‐time multi‐damage detection for concrete bridges under complex environments.

CHARACTERISTICS ANALYSIS FOR HIGH-RISE BUILDINGS DURING TOP-DOWN CONSTRUCTION

New advances in top-down construction are developing with the construction of supertall buildings in China. This study presents a finite element analysis for high-rise structural construction with a top-down method (TDM) considering complex environmental conditions. Based on this analysis model, the forces and the deformation of the diaphragm wall, beams, and soldier piles at various stages of construction are computed. Taking a super high-rise building with a 5-story basement in Nanjing as an example, the reliability and accuracy of the model is verified by comparing the measured and simulated results of displacement and stress values at various locations. The research results reveal the relationship between excavation depth, soil settlement and pile displacement, which is convenient for finding the optimal construction critical surface. It lays a foundation for the study of the critical height of the subsequent construction and facilitates the prediction of the weak link in the process. At present, this project is under construction, so this study has reference value for subsequent construction projects.

Risk assessment of antimony-arsenic contaminated soil remediated using zero-valent iron at different pH values combined with freeze-thaw cycles.

Soil in mining wastelands is seriously polluted with heavy metals. Zero-valent iron (ZVI) is widely used for remediation of heavy metal-polluted soil because of its excellent adsorption properties; however, the remediation process is affected by complex environmental conditions, such as acid rain and freeze-thaw cycles. In this study, the effects of different pH values and freeze-thaw cycles on remediation of antimony (Sb)- and arsenic (As)-contaminated soil by ZVI were investigated in laboratory simulation experiments. The stability and potential human health risks associated with the remediated soil were evaluated. The results showed that ZVI has a significant stabilizing effect on Sb and As in both acidic and alkaline soils contaminated with dual levels of Sb and As, and the freeze-thaw process in different pH value solution systems further enhances the ability of ZVI to stabilize Sb and As, especially in acidic soils. However, it should be noted that apart from the pH=1.0 solution environment, ZVI's ability to stabilize As is attenuated under other circumstances, potentially leading to leaching of its unstable form and thereby increasing contamination risks. This indicates that the F1 (2% ZVI+pH=1 solution+freeze-thaw cycle) processing exhibits superior effectiveness. After F1 treatment, the bioavailability of Sb and As in both soils also significantly decreased during the gastric and intestinal stages (about 60.00%), the non-carcinogenic and carcinogenic risks of Sb and As in alkaline soils are eliminated for children and adults, with a decrease ranging from 60.00% to 70.00%, while in acidic soil, the non-carcinogenic and carcinogenic risks of As to adults and children is acceptable, but Sb still poses non-carcinogenic risks to children, despite reductions of about 65.00%. These findings demonstrate that soil pH is a crucial factor influencing the efficacy of ZVI in stabilizing Sb and As contaminants during freeze-thaw cycles. This provides a solid theoretical foundation for utilizing ZVI in the remediation of Sb- and As-contaminated soils, emphasizing the significance of considering both pH levels and freeze-thaw conditions to ensure effective and safe treatment.

Analysis on the Application of Image Processing in UAV and Autopilot

The purpose of this paper is to analyze the application of image processing in UAV and autopilot. UAV and autonomous driving technology are important development directions in the field of intelligent transportation and agricultural production, and image processing, as one of the core technologies supporting its perception and decision-making, plays a vital role. For UAV, in agricultural plant protection and natural disaster monitoring, the rapid evaluation and identification of farmland growth status and disaster situation can be realized through image processing technology, which provides important support for crop production and rescue work. For the automatic driving system, image processing technology can detect and track the road environment, traffic signs and pedestrians, and improve the driving safety and comfort of vehicles. However, image processing technology also faces some challenges in application, such as complex environmental conditions and real-time requirements. In the future, through the improvement of sensor technology, algorithm optimization and data sharing, the application of image processing technology in UAV and autonomous driving will be continuously improved, bringing more innovation and development opportunities for intelligent transportation and agricultural production.

Spatial response of water level and quality shows more significant heterogeneity during dry seasons in large river-connected lakes.

The spatial response mechanism of hydrology and water quality of large river-connected lakes is very complicated. In this study, we developed a spatial response analysis method that couples wavelet correlation analysis (WTC) with self-organizing maps (SOM), revealing the spatial response and variation of water level and water quality in Poyang Lake, China's largest river-connected lake, over the past decade. The results show that: (1) there was significant spatial heterogeneity in water level and quality during the dry seasons (2010-2018) compared to other hydrological stages. (2) We identified a more pronounced difference in response of water level and quality between northern and southern parts of Poyang Lake. As the distance increases from the northern lake outlet, the impact of rising water levels on water quality deterioration intensified during the dry seasons. (3) The complex spatial heterogeneity of water level and quality response in the dry seasons is primarily influenced by water level fluctuations from the northern region and the cumulative pollutant entering the lake from the south, which particularly leads to the reversal of the response in the central area of Poyang Lake. The results of this study can contribute to scientific decision-making regarding water environment zoning management in large river-connected lakes amidst complex environment conditions.

Rapidly count crop seedling emergence based on waveform Method(WM) using drone imagery at the early stage

Effectively monitoring seedling emergence is critical to identify missing cotton seedling at early stages, allowing the prompt replenishment of the seedlings to maintain crop yield. However, traditional manual inspections have the limitations with low efficiency, poor timeliness, and large counting errors. Although UAV imagery has been applied in seedling monitoring, there are still chance in cotton seedling monitoring under complex environment conditions. Therefore, there is a urgent need for an efficient, rapid, and low-cost method for monitoring early seedling emergence in cotton crops. This study utilizes RGB images of early seedling emergence captured from an unmanned aerial vehicle (UAV) at a flight height of 10 m and a resolution of 0.33 cm in Shihezi, Xinjiang, on 7 and 12 days after seeding (DAS). The modified Hough transform method is constructed for line detection, and crop rows are subsequently extracted from the lines and masked images. After masking, the waveform is extracted from the excess green index (ExG) greyscale map and smoothed to determine the peaks. Seedlings are counted and located by numbering and positioning the peaks, meanwhile their coordinates and distance determine the number and location of missing seedlings. Images with varying resolutions (ranging from 0.33 to 2.5 cm) and intensity changes (0.8 to 1.2) verify the accuracy and efficiency of the proposed waveform method(WM). The results shown that at 7 DAS, the root mean square error (RMSE) is less than 2.0 plants/row, the relative root mean squared error (RRMSE) is less than 2 %, and the coefficient of determination (R2) is stable at 0.95–0.96 when the resolution is below 1.0 cm. The RMSE, RRMSE and R2 fluctuate more when the resolution is less than 1.0 cm. At 12 DAS, the counting accuracy is stable under different conditions, and the RMSE and RRMSE are distributed smoothly over the range of 1.76–4.39 plants/row and 1.29–5.47 % with different resolutions. The R2 is in the range of 0.88–0.97, RRMSE is 1.90–3.39 % and the RMSE is 1.65–2.90 plants/row under different intensities. The results of cotton seedling counting using the waveform method are less affected by image resolution and intensity changes. Therefore, the proposed WM provides efficient, accurate, and automatic counting for cotton seedlings in response to complex weather conditions. Furthermore, it would give a novel approach to accurately countequally spaced row-sown crops based on UAV images.

Extraction of Water Bodies from High-Resolution Aerial and Satellite Images Using Visual Foundation Models

Water, indispensable for life and central to ecosystems, human activities, and climate dynamics, requires rapid and accurate monitoring. This is vital for sustaining ecosystems, enhancing human welfare, and effectively managing land, water, and biodiversity on both the local and global level. In the rapidly evolving domain of remote sensing and deep learning, this study focuses on water body extraction and classification through the use of recent deep learning models of visual foundation models (VFMs). Specifically, the Segment Anything Model (SAM) and Contrastive Language-Image Pre-training (CLIP) models have shown promise in semantic segmentation, dataset creation, change detection, and instance segmentation tasks. A novel two-step approach involving segmenting images via the Automatic Mask Generator method of the SAM and the zero-shot classification of segments using CLIP is proposed, and its effectiveness is tested on water body extraction problems. The proposed methodology was applied to both remote sensing imagery acquired from LANDSAT 8 OLI and very high-resolution aerial imagery. Results revealed that the proposed methodology accurately delineated water bodies across complex environmental conditions, achieving a mean intersection over union (IoU) of 94.41% and an F1 score of 96.97% for satellite imagery. Similarly, for the aerial imagery dataset, the proposed methodology achieved a mean IoU of 90.83% and an F1 score exceeding 94.56%. The high accuracy achieved in selecting segments predominantly classified as water highlights the effectiveness of the proposed model in intricate environmental image analysis.

Size-dependent vector effects of microplastics on bioaccumulation of hydrophobic organic contaminants in earthworm: A dual-dosing study

The potential of microplastics to act as a vector for anthropogenic contaminants is of rising concern. However, directly quantitatively determining the vector effects of microplastics has been rarely studied. Here, we present a dual-dosing method that simulates the chemical bioaccumulation from soil and microplastics simultaneously, wherein unlabeled hydrophobic organic contaminants (HOCs) were spiked in the soil and their respective isotope-labeled reference compounds were spiked on the polyethylene microplastics. The comparison of the bioavailability, i.e., the freely dissolved concentration in soil porewater and bioaccumulation by earthworm, between the unlabeled and isotope-labeled HOCs was carried out. Relatively higher level of bioavailability of the isotope-labeled HOCs was observed compared to the unlabeled HOCs, which may be attributed to the irreversible desorption of HOCs from soil particles. The average relative fractions of bioaccumulated isotope-labeled HOCs in the soil treated with 1 % microplastics ranged from 6.9 % to 46.4 %, which were higher than those in the soil treated with 0.1 % microplastics. Treatments with the smallest microplastic particles were observed to have the highest relative fractions of bioaccumulated isotope-labeled HOCs, with the exception of phenanthrene, suggesting greater vector effects of smaller microplastic particles. Biodynamic model analysis indicated that the contribution of dermal uptake to the bioaccumulation of isotope-labeled HOCs was higher than that for unlabeled HOCs. This proposed method can be used as a tool to assess the prospective vector effects of microplastics in complex environmental conditions and would enhance the comprehensive understanding of the microplastic vector effects for HOC bioaccumulation.

A Compound Approach for Monitoring the Variation in Wind Turbine Power Performance with SCADA Data

The performance of wind turbines directly determines the profitability of wind farms. However, the complex environmental conditions and influences of various uncertain factors make it difficult to accurately assess and monitor the actual power generation performance of wind turbines. A data-driven approach is proposed to intelligently monitor the power generation performance evolution of wind turbines based on operational data. Considering the inherent nonlinearity and structural complexity of wind turbine systems, a data-derived characteristic construction and dimensionality reduction method based on KPCA is adopted as a prerequisite. Additionally, an AdaBoost-enhanced regressor is applied to wind power prediction with adequate inputs, and day-oriented deviation indicators are further constructed for quantifying performance fluctuations. The final validation phase includes two application cases: In the first case, the results show that the proposed method is sensitive enough to capture the early characteristics of blade damage faults. In the second case, an uncertainty error within ±0.5% demonstrates that the proposed method has high-level accuracy in the quantitative assessment of the power performance and good practical effectiveness in real engineering applications.

Asymmetrically superwetting Janus membrane constructed by laser-induced graphene (LIG) for on-demand oil–water separation and electrothermal anti-/de-icing

Superwetting functional surfaces based on laser-induced graphene (LIG) have a wide range of applications in many fields. However, single wettability LIG films are difficult to respond to complex and changing environmental conditions. Despite the large number of experiments were carried out to demonstrate the formation of LIG, there is a lack of studies on the process and formation mechanisms of LIG. In this study, the conversion of PEI to graphene was systematically investigated by both experimental and molecular dynamics simulations. A new method for obtaining Janus membranes with both superhydrophobicity and superhydrophilicity by ethanol and plasma treatment of LIG is proposed. The asymmetrically superwetting Janus membrane are shown to realize the on-demand controllable separation of light oil–water and heavy oil–water. In addition, the superhydrophobic LIG is used as a coating material with excellent electrothermal properties, which provide the performance of anti-icing and electrothermal de-icing. The asymmetrically superwetting Janus membranes prepared by the proposed simple and environmentally friendly method are shown to have a variety of prospective applications.

Research on Visual Perception of Speed Bumps for Intelligent Connected Vehicles Based on Lightweight FPNet.

In the field of intelligent connected vehicles, the precise and real-time identification of speed bumps is critically important for the safety of autonomous driving. To address the issue that existing visual perception algorithms struggle to simultaneously maintain identification accuracy and real-time performance amidst image distortion and complex environmental conditions, this study proposes an enhanced lightweight neural network framework, YOLOv5-FPNet. This framework strengthens perception capabilities in two key phases: feature extraction and loss constraint. Firstly, FPNet, based on FasterNet and Dynamic Snake Convolution, is developed to adaptively extract structural features of distorted speed bumps with accuracy. Subsequently, the C3-SFC module is proposed to augment the adaptability of the neck and head components to distorted features. Furthermore, the SimAM attention mechanism is embedded within the backbone to enhance the ability of key feature extraction. Finally, an adaptive loss function, Inner-WiseIoU, based on a dynamic non-monotonic focusing mechanism, is designed to improve the generalization and fitting ability of bounding boxes. Experimental evaluations on a custom speed bumps dataset demonstrate the superior performance of FPNet, with significant improvements in key metrics such as the mAP, mAP50_95, and FPS by 38.76%, 143.15%, and 51.23%, respectively, compared to conventional lightweight neural networks. Ablation studies confirm the effectiveness of the proposed improvements. This research provides a fast and accurate speed bump detection solution for autonomous vehicles, offering theoretical insights for obstacle recognition in intelligent vehicle systems.

Oil-in-water nanoemulsion adhesive system: Preparation by ultrasonic homogenization and its application in 3D direct writing composite energetic materials

Stable and low-particle size nanoemulsions were prepared using ultrasound homogenization. The conditions for preparing low-viscosity nanoemulsions were optimized using Response Surface Methodology (RSM) experimental design. The results showed that the addition of composite surfactants at 1%, an ultrasound amplitude of 35%, an ultrasound time of 4 min, and an ultrasound temperature of 20 °C yielded nanoemulsions with the best particle size and stability. The particle distribution was uniform, and the cold and heat stability were good. Compared with traditional emulsions, the low-viscosity nanoemulsion had better environmental stability, storage stability, and exhibited a phenomenon of self-reducing particle size at 40–55 °C, indicating that the emulsion had good performance under complex environmental conditions. The performance test of composite energetic materials using two types of emulsions as bonding systems showed that the low-viscosity nanoemulsion composite energetic material effectively reduced friction sensitivity, increased detonation velocity, and had better thermal stability and smaller propagation size. The results confirmed that the low-viscosity nanoemulsion perfectly met the requirements of bonding agents for composite energetic materials. This study provides a new approach for bonding agent systems of composite energetic materials and can serve as a new method to meet the special requirements of composite energetic materials.