Complex Environmental Conditions Research Articles

Spectral uncertainty analysis of grassland and its camouflage materials based on land-based hyperspectral images

Abstract Spectral uncertainty is one of the most prominent spectral characteristics of hyperspectral images. Compared to the process of remote sensing hyperspectral imaging, hyperspectral imaging under land-based imaging conditions has the characteristics of variable detection directions, random imaging times, and complex environmental conditions, resulting in increased spectral uncertainty of targets in land-based hyperspectral images. The spectral uncertainty of the target mainly refers to the phenomenon of “Same spectral reflectance but different objects” and “Same object but different spectral reflectance” which poses significant difficulties for subsequent hyperspectral image target detection and recognition. In order to analyze the spectral uncertainty of hyperspectral images in land-based applications and address the issue of spectral uncertainty in similar targets, a spectral uncertainty evaluation index based on standard deviation vector was proposed. For the overall spectral separability between different types of targets, a quantitative index based on Jaccard Distance (JD-SSI) is proposed to measure the spectral separability of different kinds of targets. The experiment focused on grassland and its four typical camouflage materials, analyzing the spectral intra class differences and inter class separability of each target with grassland. It is a fundamental work for studying the spectral characteristics of land-based hyperspectral images, providing a new approach for subsequent spectral band extraction, hyperspectral image classification, and target detection tasks.

Biotoxicity attenuation and the underlying physicochemical mechanism of biochar aged under simulated natural environmental conditions

Biochar (BC), with the benefits of enhancing soil fertility, absorbing heavy metals, carbon sequestration, and mitigating the greenhouse effect, has been extensively used for soil remediation. However, the long-term changes in the biotoxicity of BC under complex environmental conditions, which are the key factors influencing the sustainable application of BC in soil, are still unclear. Herein, the biotoxicity of BC aged with various processes, including dry‒wet cycle (DW) aging, freeze‒thaw cycle (FT) aging, ultraviolet irradiation (UV) aging, and low molecular weight organic acid (OA) aging, was systematically investigated by Escherichia coli (E. coli) culture experiments. The toxicity attenuation rate (%·week−1) was proposed to more concisely and clearly compare the influence of different aging methods on BC toxicity. The results indicated that after 5 weeks of aging, the toxicity attenuation rate during the four aging modes followed the order OA aging > FT aging > UV aging > DW aging. BC was nontoxic after 1 week of OA aging, 4 weeks of FT aging, 7 weeks of UV aging, and 14 weeks of DW aging. Spectroscopic characterizations revealed that humic acids in the dissolved organic matter of BC were the main reason for the biotoxicity. In addition, the attenuation of environmentally persistent free radicals on BC during aging was also an important factor for reducing environmental toxicity. This work provides insight into the detoxification mechanism of the BC aging process under ordinary environmental conditions and guidance for the safe application of BC in soil.

A Fully Amorphous, Dynamic Cross-Linked Polymer Electrolyte for Lithium-Sulfur Batteries Operating at Subzero-Temperatures.

Solid-state lithium-sulfur batteries have shown prospects as safe, high-energy electrochemical storage technology for powering regional electrified transportation. Owing to limited ion mobility in crystalline polymer electrolytes, the battery is incapable of operating at subzero temperature. Addition of liquid plasticizer into the polymer electrolyte improves the Li-ion conductivity yet sacrifices the mechanical strength and interfacial stability with both electrodes. In this work, we showed that by introducing a spherical hyperbranched solid polymer plasticizer into a Li+ -conductive linear polymer matrix, an integrated dynamic cross-linked polymer network was built to maintain fully amorphous in a wide temperature range down to subzero. A quasi-solid polymer electrolyte with a solid mass content >90 % was prepared from the cross-linked polymer network, and demonstrated fast Li+ conduction at a low temperature, high mechanical strength, and stable interfacial chemistry. As a result, solid-state lithium-sulfur batteries employing the new electrolyte delivered high reversible capacity and long cycle life at 25 °C, 0 °C and -10 °C to serve energy storage at complex environmental conditions.

A Fully Amorphous, Dynamic Cross‐Linked Polymer Electrolyte for Lithium‐Sulfur Batteries Operating at Subzero‐Temperatures

AbstractSolid‐state lithium‐sulfur batteries have shown prospects as safe, high‐energy electrochemical storage technology for powering regional electrified transportation. Owing to limited ion mobility in crystalline polymer electrolytes, the battery is incapable of operating at subzero temperature. Addition of liquid plasticizer into the polymer electrolyte improves the Li‐ion conductivity yet sacrifices the mechanical strength and interfacial stability with both electrodes. In this work, we showed that by introducing a spherical hyperbranched solid polymer plasticizer into a Li+‐conductive linear polymer matrix, an integrated dynamic cross‐linked polymer network was built to maintain fully amorphous in a wide temperature range down to subzero. A quasi‐solid polymer electrolyte with a solid mass content >90 % was prepared from the cross‐linked polymer network, and demonstrated fast Li+ conduction at a low temperature, high mechanical strength, and stable interfacial chemistry. As a result, solid‐state lithium‐sulfur batteries employing the new electrolyte delivered high reversible capacity and long cycle life at 25 °C, 0 °C and −10 °C to serve energy storage at complex environmental conditions.

Influence of Fly Ash Content on the Durability of Mortar Specimens under Dry/Wet Sulfate Attack.

To investigate the durability of cementitious materials under complex environmental conditions in Xinjiang, this study conducted durability tests on mortar specimens with different fly ash contents under dry/wet sulfate attack conditions, with standard curing and steam curing at 70 °C. The appearance loss and flexural and compressive strength variations in the specimens were analyzed, and an evolution model of the mortar strength under a dry/wet sulfate attack was established. Moreover, XRD and SEM techniques were used to characterize the erosion products and microstructure, and to explore the erosion resistance mechanism of fly ash cementitious materials. The results showed that, after 160 cycles of erosion, the flexural strength of the specimens decreased with the increase in the fly ash content. In the context of steam-cured mortar specimens, throughout the entire erosion period, specimens with a fly ash content of 45% exhibited the highest relative compressive strength. The established strength evolution model had a minimum determination coefficient of 0.879, indicating a good agreement between the model and experimental results. Microscopic research showed that fly ash would undergo a pozzolanic reaction under the action of sulfate and calcium hydroxide, which was beneficial to the improvement of the erosion resistance. As the fly ash content increased, the erosion products of the specimens gradually became dominated by gypsum.

Direction-of-Arrival Estimation for a Random Sparse Linear Array Based on a Graph Neural Network.

This article proposes a direction-of-arrival (DOA) estimation algorithm for a random sparse linear array based on a novel graph neural network (GNN). Unlike convolutional layers and fully connected layers, which do not interact well with information between different antennas, the GNN model can adapt to the goniometry problem of non-uniform random sparse linear arrays without any prior information by applying neighbor nodes' aggregation and update operations. This helps the model in learning signal features under complex environmental conditions. We train the model in an end-to-end way to reduce the complexity of the network. Experiments are conducted on the uniform and sparse linear arrays for various signal-to-noise ratio (SNR) and numbers of snapshots for comparison. We prove that the GNN model has superior angle estimation performance on arrays with large sparsity that cannot be used by traditional algorithms and surpasses existing deep learning models based on convolutional or fully connected structures. The proposed algorithm shows excellent DOA estimation performance under the complex conditions of limited snapshots, low signal-to-noise ratio, and large array sparsity as well. In addition, the algorithm has a low time calculation cost and is suitable for scenarios that require low latency.

Speckle suppression in holographic phase fringe patterns with different level noises based on FFDNet.

In this paper, an ANLVENet speckle suppression method in holographic phase fringe patterns with different level noises is proposed based on FFDNet, combined with asymmetric pyramid non-local block with a verge extraction module. The experimental results are compared to three network models and several representative algorithms. It is shown that the ANLVENet method not only has better superiority in the speckle suppression with different noise levels, but also preserves more details of the image edge. In addition, another speckle noise model is applied in the phase fringe patterns to prove the stronger generalization of the ANLVENet algorithm. The proposed method is suitable for suppressing the speckle with different levels in a large noise range under complex environmental conditions.

Evaluation of sustainable development and utilization-oriented renewable energy systems based on CRITIC-SWARA-CODAS method using interval valued picture fuzzy sets

Multi-criteria decision making (MCDM) methods used to overcome complex real-life problems is the process of finding the most suitable alternative among alternative options under multiple tangible or intangible criteria. In particular, the use of MCDM methods is an important tool for policy makers, managers and decision makers (DMs) to easily and effectively address today's MCDM problems such as renewable energy. However, since the classical MCDM methods are based on crisp numerical values, they are insufficient when considering insufficient information about the systems, complex environment and real-life conditions. Especially since many real-life problems within the scope of sustainability contain uncertain information, using them together with fuzzy sets is extremely important for making the right decisions. This study presents a new model based on the interval-valued picture fuzzy (IVPF)-MCDM integrated approach in an environment of uncertainty for the evaluation of sustainable development and utilization oriented renewable energy systems. In the study, firstly, renewable energy alternatives focused on sustainable development and utilization for Turkey and the criteria affecting them have been determined. Secondly, for the objective and subjective weights of the criteria, the CRiteria Importance Through Intercriteria Correlation (CIRITIC) and Stepwise Weight Assessment Ratio Analysis (SWARA) methods have been extended to interval-valued picture fuzzy sets (IVPFSs), respectively, and the weights have been obtained. Then, the weights have been combined by the coefficient parameter determined by the expert. Later, the COmbinative Distance-based ASsessment (CODAS) method has been extended to IVPFSs to rank renewable energy alternatives focused on sustainable development and use under uncertainty. A comprehensive sensitivity analysis has been conducted to confirm the stability and effectiveness of the method. Moreover, a comparative analysis has been presented with other MCDM methods in the literature showing the superiority and advantages of the developed method.

Graphene oxide dispersed mesoporous ZIF-8-encapsulated laccase for removal of toluidine blue with multiple enhanced stability.

The extensive use and discharge of toluidine blue have caused serious problems to the water environment. As a green biocatalyst, laccase has the ability to decolorize the dyes, but it is limited by poor reusability and low stability. Metal-organic frameworks (MOFs) are a good platform for enzyme immobilization. However, due to the weak dispersion of MOFs, the enzyme activity is inevitably inhibited. Herein, we proposed to use graphene oxide (GO) as the dispersion medium of mesoporous ZIF-8 to construct MZIF-8/GO bi-carrier for laccase (FL) immobilization. On account of the narrower bandgap energy of FL@MZIF-8/GO (4.07eV) than that of FL@MZIF-8 (4.69eV), electron transport was enhanced which later increased the catalytic activity of the immobilized enzyme. Meanwhile, the improved hydrophilicity characterized by contact angle and full infiltration time further promoted the efficiency of the enzymatic reaction. Benefiting from such regulatory effects of GO, the composite showed excellent storage stability and reusability, as well as multifaceted enhancements including pH, thermal, and solvent adaptation. On the basis of the characterized synergistic effect of adsorption and degradation, FL@MZIF-8/GO was successfully applied to the degradation of toluidine blue (TB) with a removal rate of 94.8%. Even in actual treated wastewater, the highest removal rate still reached more than 80%. Based on the inner mechanism analysis and the universality study, this material is expected to be widely used in the degradation of pollutants in real water under complex environmental conditions.

Node fault diagnosis algorithm for wireless sensor networks based on BN and WSN

Wireless sensor networks, as an emerging information exchange technology, have been widely applied in many fields. However, nodes tend to become damaged in harsh and complex environmental conditions. In order to effectively diagnose node faults, a Bayesian model-based node fault diagnosis model was proposed. Firstly, a comprehensive analysis was conducted into the operative principles of wireless sensor systems, whereby fault-related features were then extrapolated. A Bayesian diagnostic model was constructed using the maximum likelihood method with sufficient sample features, and a joint tree model was introduced for node diagnosis. Due to the insufficient accuracy of Bayesian models in processing small sample data, a constrained maximum entropy method was proposed as the prediction module of the model. The use of small sample data to obtain the initial model parameters leads to improved performance and accuracy of the model. During parameter learning tests, the limited maximum entropy model outperformed the other two learning models on a smaller dataset of 35 with a distance value of 2.65. In node fault diagnosis, the diagnostic time of the three models was compared, and the average diagnostic time of the proposed diagnostic model was 41.2 seconds. In the node diagnosis accuracy test, the proposed model has the highest node fault diagnosis accuracy, with an average diagnosis accuracy of 0.946, which is superior to the other two models. In summary, the node fault diagnosis model based on Bayesian model proposed in this study has important research significance and practical application value in wireless sensor networks. By improving the reliability and maintenance efficiency of the network, this model provides strong support for the development and application of wireless sensor networks.

Study on the effect of forced convection and thermal radiation on foam stability

High expansion foam is widely used to mitigate cryogenic LNG spills, which is a major type of process safety accident. The stability of the foam affects the performance of hazard mitigation after cryogenic LNG spills. Forced convection and thermal radiation are key external factors affecting the foam stability, which depends on the foam drainage and breakage rates. The goal of this paper is to research the stability of high expansion foam under convection intensity of 0∼6 m/s and thermal radiation intensity of 0∼420 W/m2 following the actual environmental conditions through experiments and establish correlation models for predicting foam drainage rate and breakage rate based on the experimental results. The results show that increasing forced convection and thermal radiation will accelerate foam breaking, with forced convection having a greater effect, shortening the time of effective prevention in high expansion foam. The results of this work will guide high expansion foam mitigating LNG leakage accidents in complex environmental conditions.

Mode I fracture toughness with fiber bridging of unidirectional composite laminates under cryogenic temperature

Carbon fiber reinforced polymer (CFRP) composites have widely been used in the aerospace field owing to their excellent mechanical properties. However, composite structures often fail under complex space environmental conditions. In particular, the fiber bridging and cryogenic temperature may greatly change the fracture toughness of CFRP composites. Herein, the effects of cryogenic temperature and fiber bridging on the fracture toughness of unidirectional composite laminates were experimentally examined by double cantilever beam (DCB) testing at different temperatures (room temperature, −50 °C, −70 °C, −100 °C, −150 °C, and −180 °C). Different calculation methods of fracture toughness were used and the results were compared. A method without measuring the delamination length at low temperatures was used and the results were consistent with other methods. The average difference in fracture toughness values was less than 5 %. The fiber-bridging microstructures of composites under cryogenic temperatures were examined by confocal microscopy along with the geometric morphology and parameters of surface roughness. The results suggested changes in the matrix failure mechanism at −50 °C, resulting in maximum fracture toughness, which is 26.45 % higher than that at room temperature. A semi-analytic approach was then proposed to express the bridge stress, and a bilinear cohesive zone model was established to simulate the delamination of composite laminates at low temperatures. Overall, the numerical data agreed well with the experimental results (peak force maximum error of 17 %), suggesting the usefulness of the proposed method for the prediction of interlamination properties and structural damage design of composite structures at cryogenic temperature.

Integration of digital shearography and FEM for non-destructive evaluation of internal cracks in GRE pipes

Pipes made of glass fiber-reinforced epoxy (GRE) are often exposed to complex environmental conditions, which can make them prone to internal cracking. Quantitative, non-destructive evaluation of cracks can reduce unintended failures and improve system reliability. The use of digital shearography, a modern non-destructive method based on laser interferometry, in the diagnosis of composite materials has drawn attention in recent years. In this paper, a model for the quantitative evaluation of subsurface cracks in various locations on GRE pipes was developed by integrating the finite element method and shearography with thermal stimulation. The response surface method was used to design the simulations, and the effect of crack dimensions and location on the phase map and out-of-plane surface strain component was investigated. The results demonstrated that the crack placement angle relative to the shearography device is effective on crack detectability due to the change in the actual shearing size. A model was then presented to estimate the length and depth of the crack based on the displacement derivative graph by using analysis of variance and regression techniques. By determining the out-of-plane displacement derivative, the proposed model enables precise estimation of the dimensions and location of subsurface cracks.

أثر تطبيق نظام التكاليف المعيارية على تحسين الرقابة على التكاليف في قطاع مطاحن الغلال (تم الإصلاح)

the study examined the effect of applying the standard cost system on improving cost control in the grain mills sector. The problem of the study was the inability of grain mills to tighten costs control in light of the complex economic, environmental and industrial conditions in Sudan. The study aimed to identify the extent of using the standard cost system in Grain mills in Sudan, highlighting the obstacles and obstacles to using this system, studying the ability of grain companies to set precise standards according to the scientific foundations of standard costs and clarifying and clarifying the impact of using the standard cost system on improving cost control, as the importance of the study was that this technical development witnessed by grain mills The commodity strategy that it produces requires a system that reflects the ability of these mills to compete locally and globally, and the standard cost system is the appropriate tool to achieve this. The study also reached several results, including: The application of the standard cost system in grain mills is an indication of the quality of cost control, the study recommended some recommendations, including: Create a separate cost division from the financial section and support it with specialized cost cadres, While continuing the training approach to increase the scientific capabilities in the field of costs and management accounting

Identifying microbiota community patterns important for plant protection using synthetic communities and machine learning

Plant-associated microbiomes contribute to important ecosystem functions such as host resistance to biotic and abiotic stresses. The factors that determine such community outcomes are inherently difficult to identify under complex environmental conditions. In this study, we present an experimental and analytical approach to explore microbiota properties relevant for a microbiota-conferred host phenotype, here plant protection, in a reductionist system. We screened 136 randomly assembled synthetic communities (SynComs) of five bacterial strains each, followed by classification and regression analyses as well as empirical validation to test potential explanatory factors of community structure and composition, including evenness, total commensal colonization, phylogenetic diversity, and strain identity. We find strain identity to be the most important predictor of pathogen reduction, with machine learning algorithms improving performances compared to random classifications (94-100% versus 32% recall) and non-modelled predictions (0.79-1.06 versus 1.5 RMSE). Further experimental validation confirms three strains as the main drivers of pathogen reduction and two additional strains that confer protection in combination. Beyond the specific application presented in our study, we provide a framework that can be adapted to help determine features relevant for microbiota function in other biological systems.

Robust anti-perturbation safety control of multi-heterogeneous unmanned vessels

The identical nature of unmanned vessels in a homogeneous formation imposes limitations on their adaptability to diverse task demands in complex environmental conditions or constantly evolving task requirements, hindering their ability to achieve optimal performance. This paper presents the heterogeneous formation comprising fully actuated and underactuated vessels to overcome these limitations. Firstly, an affine transformation is employed to implement arbitrary configuration transformations within the formation system. Secondly, a composite learning formation controller is proposed for the underactuated vessels acting as the leaders to track the arbitrary formation transformation. Thirdly, considering that certain followers have access to the state information of the leaders, a new virtual leader–follower structure is constructed to achieve formation maneuver control by designing distributed estimators to estimate the state information of the virtual leader. Finally, simulation results validate the proposed control law’s effectiveness.

Blood Transcriptome Analysis Provides Responsive Changes in Gene Expression between Ex Situ and Captive Yangtze Finless Porpoises (Neophocaena asiaeorientalis asiaeorientalis)

The Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis, YFP) is an endangered species endemic to the Yangtze River in China, and it is the only freshwater whale in the genus Neophocaena. In terms of protection, three effective conservation strategies exist: in situ conservation, ex situ conservation, and artificial breeding, all of which have been implemented by the Chinese government. Of these, ex situ conservation involves the relocation of Yangtze finless porpoises to semi-natural waters with less human interference, and artificial breeding involves the relocation of Yangtze finless porpoises to a controlled environment that is more strictly managed. To compare and analyze the responsive changes in gene expression of the YFPs between the ex situ and controlled environments, we performed the RNA sequencing of blood tissues from these YFPs. A total of 1201 differentially expressed genes (DEGs) were identified, of which 423 were up-regulated in the ex situ population and 778 were up-regulated in the controlled-environment population. Gene enrichment analysis showed that 1201 DEGs between the ex situ and controlled-environment populations were generally enriched for vision-, digestion- and immune-system-related pathways. Further analysis revealed that several key immune system pathways, such as the chemokine signaling pathway and B cell receptor signal pathway, were activated in the ex situ population. In addition, the key pathways related to vision, including phototransduction and the inflammatory mediator regulation of TRP channels, as well as the pathways related to the digestive system, such as protein digestion and absorption and salivary secretion, were activated in the controlled-environment population. These results suggest that the ex situ populations may respond to complex environmental conditions in semi-natural waters by enhancing their immune function through the increased expression of immune-related genes and that the visual function and protein digestion of the YFPs were improved compared to those of the ex situ population based on the conditions of artificial feeding, such as the higher transparency of the water and regular feeding. This study provides clues for evaluating the adaptability of YFPs to different environments and is a useful reference for future ex situ conservation and artificial breeding.

Ultrafine nano-copper derived from dopamine polymerization & synchronous adsorption achieve electrochemical purification of nitrate to ammonia in complex water environments

Electrochemical-nitrate-reduction-reaction (eNitRR) synthesis of ammonia is an effective way to treat nitrate wastewater and alleviate the pressure of the Haber-Bosch ammonia production industry. How to develop effective catalysts to electrochemically reduce nitrate to ammonia and purify sewage under complex environmental conditions is the focus of current research. Herein, the dopamine polymerization process and the [(C₁₂H₈N₂)2Cu]2+ complex embedding process were run simultaneously in time and space, and ultrafine Cu nanoparticles (Cu/CN) were effectively loaded on nitrogen-doped carbon after heat treatment. Using Cu/CN as the catalyst, the ammonia yield rate and Faradaic efficiency of the electrochemical conversion of NO3− to NH3 are highly 8984.0 µg h−1 mgcat.−1 and 95.6%, respectively. Even in the face of complex water environments, such as neutral media, acidic media, coexisting ions, and actual nitrate wastewater, nitrate wastewater can be effectively purified to form high value-added ammonia. The strategy of simultaneous embedding increases the exposure rate of Cu sites, and the support of CN is also beneficial to reduce the energy barrier of *NO3 activation. This study rationally designed catalysts that are beneficial to eNitRR, and considered the situation faced by practical applications during the research stage, reducing the performance gap between laboratory exploration and industrial applications.

Fourier ptychographic layer-based imaging of hazy environments

Macroscopic Fourier ptychographic imaging method can significantly improve the resolution of imaging systems. Nevertheless, under far-field complex environment conditions, such as strong scattering medium (e.g.,hazy), the inhomogeneous distribution of the particles inside the hazy cause random interference to the propagation of the photons carrying the target information, resulting in distortion and blurring of the wavefront information. This severely impacts the quality of the final reconstructed image. In this paper, we propose an improved Resnet network to extract and enhance the features from distorted intensity images in hazy environment, and effectively merge information through multi-scale feature fusion. We successfully recover the target intensity information sequences hidden by different concentrations of hazy and even dynamic non-uniform hazy, and present the final Fourier ptychographic reconstruction results. To the best of our knowledge, we are the first to demonstrate the feasibility of far-field Fourier ptychographic imaging through hazy environments using deep learning methods. Experimental results indicate that it can effectively overcome the images blurring and distortion problems caused by the scattering of hazy environment, significantly improving the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) of final macroscopic Fourier ptychographic reconstructed images under different hazy levels. This study not only reveals the great value of deep learning in solving the imaging problems of complex scenes such as dynamic non-uniform strong scattering media, but also effectively suppresses the adverse effect of hazy scattering environment on macroscopic Fourier ptychographic imaging, which greatly promotes the progress and development of the application of the Fourier ptychographic imaging technology in the field of imaging complex environments in the long distance.

Binocular Vision-Based Pole-Shaped Obstacle Detection and Ranging Study

(1) Background: In real road scenarios, various complex environmental conditions may occur, including bright lights, nighttime, rain, and snow. In such a complex environment for detecting pole-shaped obstacles, it is easy to lose the feature information. A high rate of leakage detection, false positives, and measurement errors are generated as a result. (2) Methods: The first part of this paper utilizes the improved YOLOv5 algorithm to detect and classify pole-shaped obstacles. Then, the identified target frame information is combined with binocular stereo matching to obtain more accurate distance information. (3) Results: The experimental results demonstrate that this method achieves a mean average precision (mAP) of 97.4% for detecting pole-shaped obstacles, which is 3.1% higher than the original model. The image inference time is only 1.6 ms, which is 1.8 ms faster than the original algorithm. Additionally, the model size is only 19.0 MB. Furthermore, the range error of this system is less than 7% within the range of 3–15 m. (4) Conclusions: Therefore, the algorithm not only achieves real-time and accurate identification and classification but also ensures precise measurement within a specific range. Meanwhile, the model is lightweight and better suited for deploying sensing systems.