Search Results (23)

Raw Chinese lacquer, tapped from the sap of Toxicodendron vernicifluum, is a natural water-in-oil microemulsion containing urushiol, polysaccharides, proteins, and laccase. Because this reactive system continues to oxidize and polymerize after harvesting, handling conditions directly determine water content, viscosity, and later film-forming performance. This review analyzes potential in situ stabilization routes that couple purification, low-temperature vacuum dehydration, and quality conditioning at, or near, the collection site. Emphasis is placed on how laccase retention, oxygen exposure, and urushiol polymerization are controlled together to limit transport losses and premature crusting. Portable filtration devices, reported centrifugal filtration systems, and proposed vacuum dehydration strategies are compared in terms of throughput, field compatibility, and process control. Physical and bio-based conditioning strategies, including shear adjustment, oxygen management, and natural film-forming aids, are further considered for on-site regulation. Surface-enhanced Raman spectroscopy (SERS) and portable spectroscopic devices are examined as feedback tools for parameter adjustment under field temperatures, humidity, and storage variation; however, these signals are treated as decision-support indicators that still require lacquer-specific calibration after tapping. The central task is to define a field-compatible process window for water removal, laccase retention, viscosity control, drying behavior, and storage stability before downstream coating preparation. The remaining challenges involve miniaturized equipment, standardized evaluation, evidence-level classification, and dynamic control of coupled variables. Full article

High-permeability reservoirs at the extra-high water-cut stage commonly exhibit preferential flow, limited sweep expansion, and complex remaining-oil occurrence. To clarify the pore-scale mechanisms controlling waterflood sweep and remaining-oil retention, this study integrates CT-assisted core flooding and microfluidic chip visualization using a high-permeability sandstone core from the Guantao Formation in the Bohai Bay Basin. The CT-assisted core flooding experiment was used to quantify the stage-wise evolution of pores swept by the water phase, while the microfluidic experiment was used to visualize displacement pathways, local bypassing, and remaining-oil morphology under controlled pore-network conditions. The results show that waterflood sweep exhibits clear stage-wise evolution. During the low water-cut stage, injected water preferentially advances through large pore channels, resulting in limited sweep efficiency. With increasing water cut, pores newly swept by the water phase gradually shift from large pores to medium and small pores, accompanied by increasing displacement pressure. Under the present experimental conditions, the lower radius limit of pores newly swept by the water phase is approximately 7.54 μm, corresponding to a capillary force of about 0.9 MPa. When the injected volume exceeds approximately 2.5 PV, the sweep efficiency approaches a plateau and increases only from 0.72 to 0.75 at 5.0 PV, indicating that approximately 25% of the pore space remains difficult to be effectively swept. Image-based classification indicates that remaining oil can be divided into six occurrence types: clustered, porous, columnar, dead-end, film-like, and granular. Clustered and porous are the dominant occurrence types, accounting for a combined 59.7% of the total remaining oil. Pore-structure heterogeneity controls the microscopic sweep boundary through the combined effects of intra-unit structural dispersion and cross-unit structural contrast, which together regulate capillary resistance, seepage resistance, preferential flow, local bypassing, and remaining-oil retention. Microfluidic observations further show that permeability contrast and displacement velocity affect pore-scale displacement pathways and remaining-oil morphology. These findings provide experimental evidence for understanding the lower sweep-radius limit and remaining-oil occurrence mechanisms in high-permeability heterogeneous reservoirs at the extra-high water-cut stage, while the chip-scale velocity effects should be interpreted as pore-scale mechanistic evidence and require further validation before field-scale application. Full article

The continuous expansion of global maritime trade and shipping networks has increased the risk of marine oil spills. The increasing frequency of oil spill incidents has seriously threatened nearshore ecosystems, marine biological resources, and the sustainable development of coastal regions. An improved Monarch Butterfly Optimization (MBO) algorithm was proposed to achieve accurate identification and segmentation of oil spill regions in radar images. The original radar images underwent preprocessing, including grayscale conversion and background pixel removal, to preserve the informative pixels of oil films and improve the contrast between oil films and the seawater background. Subsequently, a genetic algorithm was employed to optimize the radial basis function (RBF) neural network, and a three-dimensional pixel feature space was constructed for oil spill Region of Interest (ROI) extraction. Finally, the improved MBO algorithm was applied to design a multi-objective fitness function integrating within-class variance, between-class differences, and class proportion constraints. Global optimization of the segmentation threshold was achieved via dynamic parameter adjustment, reverse learning, and elite reproduction, enabling accurate oil spill extraction. The precision, recall, F1 score, and IoU of the algorithm were 92.4%, 93.3%, 92.8%, and 86.6%, respectively. The proposed method achieves a well-balanced performance in both detection accuracy and region overlap, exhibits clear advantages over the compared methods in overall segmentation quality. The results demonstrated that the improved MBO algorithm mitigated segmentation challenges induced by low contrast and strong clutter, achieving superior classification accuracy and region completeness for offshore oil spill monitoring. Full article

Marine oil spill incidents are one of the major global marine pollution issues, which pose significant threats to ocean ecosystems. However, traditional monitoring methods often suffer from time delays, high costs, and limited real-time capability, making them inadequate for timely and large-scale oil spill detection. With the development of remote sensing (RS) technology and artificial intelligence (AI) methods, as well as the increasing frequency of marine oil spill accidents, plenty of AI-based methods using RS imagery have been proposed for more efficient and accurate oil spill monitoring. This review presents a comprehensive and systematic overview of recent progress in marine oil spill analysis using RS imagery, emphasizing the integration of AI methods across three key tasks: detection, classification, and thickness estimation. Specifically, we first introduce the main types of RS data and discuss the significance of publicly available datasets, which can facilitate method validation and model comparison. Second, we briefly review the application of RS imagery from different sensors in oil spill detection, highlighting the strengths of various spectral and polarimetric methods. Third, we summarize advances in oil spill classification, including AI-based methods that enable differentiation between mineral oil, biogenic films, and various emulsified oils. Fourth, we discuss emerging techniques for oil spill thickness estimation. Finally, we analyze the challenges of existing methods and future directions, including the need for real-time monitoring, the integration of multi-source RS data, and the development of robust models that can generalize across different environmental conditions. This review adopts a comprehensive perspective from both AI methods and RS technology, provides a systematic overview of recent advancements, identifies critical gaps in current methodologies, and serves as a valuable reference for researchers and practitioners working on oil spill monitoring. Full article

Postmenopausal osteoporosis (PMOP) has become an important public health issue. The diagnosis of PMOP relies on clinical symptoms and radiology. However, most patients with PMOP do not exhibit obvious symptoms in the early stages of this disease. This study aimed to explore the feasibility of surface-enhanced Raman scattering (SERS) technology in the auxiliary screening of PMOP. PMOP rats were induced by ovariectomy (OVX) surgery, with a Sham group and an icariin (ICA) treatment group serving as controls. A monolayer film of Au nanoparticles (NPs) was prepared using the Marangoni effect in an oil/water/oil three-phase system, and was used to detect serum SERS signals in the Sham, OVX, and ICA treatment groups. Then, the spectral diagnostic model for PMOP screening was established utilizing partial least squares (PLS) and support vector machine (SVM) algorithms. Histopathology confirmed the establishment of the PMOP rat model. The assignment of Raman peaks and the analysis of spectral differences revealed the biochemical changes associated with PMOP, including the upregulation of tyrosine levels and the downregulation of arginine, tryptophan, lipids, and collagen. When employing the PLS-SVM algorithm to simultaneously classify and discriminate three groups of samples, the diagnostic sensitivity for PMOP is 93.33%, the specificity is 96.67%, and the accuracy of three-class classification is 91.11%. This study demonstrated the potential of SERS for the auxiliary screening of PMOP. Full article

Deep learning models have obvious advantages in detecting oil spills, but the training of deep learning models heavily depends on a large number of samples of high quality. However, due to the accidental nature, unpredictability, and urgency of oil spill incidents, it is difficult to obtain a large number of labeled samples in real oil spill monitoring scenarios. Surprisingly, few-shot learning can achieve excellent classification performance with only a small number of labeled samples. In this context, a new cross-domain few-shot SAR oil spill detection network is proposed in this paper. Significantly, the network is embedded with a hybrid attention feature extraction block, which consists of a coordinate attention module to perceive the channel information and spatial location information, as well as a global self-attention transformer module capturing the global dependencies and a multi-scale self-attention module depicting the local detailed features, thereby achieving deep mining and accurate characterization of image features. In addition, to address the problem that it is difficult to distinguish between the suspected oil film in seawater and real oil film using few-shot due to the small difference in features, this paper proposes a double loss function category determination block, which consists of two parts: a well-designed category-perception loss function and a traditional cross-entropy loss function. The category-perception loss function optimizes the spatial distribution of sample features by shortening the distance between similar samples while expanding the distance between different samples. By combining the category-perception loss function with the cross-entropy loss function, the network’s performance in discriminating between real and suspected oil films is thus maximized. The experimental results effectively demonstrate that this study provides an effective solution for high-precision oil spill detection under few-shot conditions, which is conducive to the rapid identification of oil spill accidents. Full article

The frequent occurrence of marine oil spills underscores the need for efficient methods to identify spilled substances and analyze their thickness. Traditional models based on Laser-Induced Fluorescence (LIF) technology often focus on a single functionality, limiting their ability to simultaneously perform qualitative and quantitative analyses. This study introduces a novel LIF-based spectral analysis method that integrates a self-designed detection system and a multi-task framework, the Wavelet CNN-sLSTM-KAN-Enhanced Transformer (WaveConv-sLSTM-KET). By combining a Wavelet Transform CNN block, a scalar LSTM block, and a Kolmogorov–Arnold Network-Enhanced Transformer block, the framework enables simultaneous oil-type identification and thickness prediction without preprocessing or fully connected layers. It achieves high classification accuracy and precise regression for oil film thicknesses (50 µm–0.5 mm). Its reliability, real-time operation, and lightweight structure address limitations of conventional methods, offering a promising solution for non-destructive, efficient oil spill detection. Full article

In reality, the remaining oil in the ultra-high water cut period is highly dispersed, so a thorough investigation is required to understand the microscopic remaining oil. This will directly influence the technological direction and allow for countermeasures such as enhanced oil recovery (EOR). Therefore, this study aims to investigate the state, classification method and utilization mechanism of the microscopic remaining oil in the late period of the ultra-high water cut. To achieve this, the classification of microscopic remaining oil based on mechanical mechanism was developed using displacement CT scan and micro-scale flow simulation methods. Three carefully selected mechanical characterization parameters were used: oil–water connectivity, oil–mass specific surface and oil–water area ratio. These give five types of microscopic remaining oil, which are as follows: A (capillary and viscous oil cluster type), B (capillary and viscous oil drop type), C (viscous oil film type), D (capillary force control throat type), and E (viscous control blind end type). The state of the microscopic remaining oil in classified oil reservoirs was defined after high-expansion water erosion. Based on micro-flow simulation and analysis of different forces during the displacement process, the main microscopic remaining oil recognized is in class-I, class-II and class-III reservoirs. Within the Eastern sandstone oilfields in China, the ultra-high water-cut stage is a good indicator that the class-I oil layer is dominated by capillary and viscous oil drop types distributed in large connected holes. The class-II oil layer has capillary and viscous force-controlled clusters distributed in small and medium pores with high connectivity. In the case of the class-III oil layer, it enjoys the support of capillary force control throats that are mainly distributed in small holes with high connectivity. Integrating mechanisms of different types of micro-remaining oil indicates that, enhancing utilization conditions requires increasing pressure gradient and shear force while reducing capillary resistance. An effective way to improve the remaining oil utilization is to increase the pressure gradient and change the flow direction during the water-drive development process. Hence, this forms a theoretical basis and a guide for the potential exploitation of remaining oil. Likewise, it provides a strategy for optimizing enhanced oil recovery in the ultra-high water-cut stage of mid-high permeability oil reservoirs worldwide. Full article

The extraction and collection of crude oil will result in the formation of numerous complex emulsions, which will not only decrease crude oil production, raise the cost of extraction and storage, and worsen pipeline equipment loss, but also seriously pollute the environment because the oil in the emulsion can fill soil pores, lower the soil’s permeability to air and water, and create an oil film on the water’s surface to prevent air–water contact. At present, a variety of demulsification technologies have been developed, such as physical, chemical, biological and other new emulsion breaking techniques, but due to the large content of colloid and asphaltene in many crude oils, resulting in the increased stability of their emulsions and oil–water interfacial tension, interfacial film, interfacial charge, crude oil viscosity, dispersion, and natural surfactants have an impact on the stability of crude oil emulsions. Therefore, the development of efficient, widely applicable, and environmentally friendly demulsification technologies for crude oil emulsions remains an important research direction in the field of crude oil development and application. This paper will start from the formation, classification and hazards of crude oil emulsion, and comprehensively summarize the development and application of demulsification technologies of crude oil emulsion. The demulsification mechanism of crude oil emulsion is further analyzed, and the problems of crude oil demulsification are pointed out, so as to provide a theoretical basis and technical support for the development and application of crude oil demulsification technology in the future. Full article

Marine oil spills can cause serious damage to marine ecosystems and biological species, and the pollution is difficult to repair in the short term. Accurate oil type identification and oil thickness quantification are of great significance for marine oil spill emergency response and damage assessment. In recent years, hyperspectral remote sensing technology has become an effective means to monitor marine oil spills. The spectral and spatial features of oil spill images at different levels are different. To accurately identify oil spill types and quantify oil film thickness, and perform better extraction of spectral and spatial features, a multilevel spatial and spectral feature extraction network is proposed in this study. First, the graph convolutional neural network and graph attentional neural network models were used to extract spectral and spatial features in non-Euclidean space, respectively, and then the designed modules based on 2D expansion convolution, depth convolution, and point convolution were applied to extract feature information in Euclidean space; after that, a multilevel feature fusion method was developed to fuse the obtained spatial and spectral features in Euclidean space in a complementary way to obtain multilevel features. Finally, the multilevel features were fused at the feature level to obtain the oil spill information. The experimental results show that compared with CGCNN, SSRN, and A2S2KResNet algorithms, the accuracy of oil type identification and oil film thickness classification of the proposed method in this paper is improved by 12.82%, 0.06%, and 0.08% and 2.23%, 0.69%, and 0.47%, respectively, which proves that the method in this paper can effectively extract oil spill information and identify different oil spill types and different oil film thicknesses. Full article

An elastomer is a material that undergoes large deformation under force and quickly recovers its approximate initial shape and size after withdrawing the external force. Furthermore, an elastomer can heal itself and increase volume when in contact with certain liquids. They have been widely used as sealing elements and packers in different oil drilling and development operations. With the development of drilling fluids, elastomer materials have also been gradually used as drilling fluid additives in drilling engineering practices. According to the material type classification, elastomer materials can be divided into polyurethane elastomer, epoxy elastomer, nanocomposite elastomer, rubber elastomer, etc. According to the function classification, elastomers can be divided into self-healing elastomers, expansion elastomers, etc. This paper systematically introduces the research progress of elastomer materials based on material type classification and functional classification. Combined with the requirements for drilling fluid additives in drilling fluid application practice, the application prospects of elastomer materials in drilling fluid plugging, fluid loss reduction, and lubrication are discussed. Oil-absorbing expansion and water-absorbing expansion elastomer materials, such as polyurethane, can be used as lost circulation materials, and enter the downhole to absorb water or absorb oil to expand, forming an overall high-strength elastomer to plug the leakage channel. When graphene/nano-composite material is used as a fluid loss additive, flexibility and elasticity facilitate the elastomer particles to enter the pores of the filter cake under the action of differential pressure, block a part of the larger pores, and thus, reduce the water loss, while it would not greatly change the rheology of drilling fluid. As a lubricating material, elastic graphite can form a protective film on the borehole wall, smooth the borehole wall, behaving like a scaly film, so that the sliding friction between the metal surface of the drill pipe and the casing becomes the sliding friction between the graphite flakes, thereby reducing the friction of the drilling fluid. Self-healing elastomers can be healed after being damaged by external forces, making drilling fluid technology more intelligent. The research and application of elastomer materials in the field of drilling fluid will promote the ability of drilling fluid to cope with complex formation changes, which is of great significance in the engineering development of oil and gas wells. Full article

Despite the fact that skin has a stronger potential to regenerate than other tissues, wounds have become a serious healthcare issue. Much effort has been focused on developing efficient therapeutical approaches, especially biological ones. This paper presents a comprehensive review on the wound healing process, the classification of wounds, and the particular characteristics of each phase of the repair process. We also highlight characteristics of the normal process and those involved in impaired wound healing, specifically in the case of infected wounds. The treatments discussed here include proteins, lipids, and carbohydrates. Proteins are important actors mediating interactions between cells and between them and the extracellular matrix, which are essential interactions for the healing process. Different strategies involving biopolymers, blends, nanotools, and immobilizing systems have been studied against infected wounds. Lipids of animal, mineral, and mainly vegetable origin have been used in the development of topical biocompatible formulations, since their healing, antimicrobial, and anti-inflammatory properties are interesting for wound healing. Vegetable oils, polymeric films, lipid nanoparticles, and lipid-based drug delivery systems have been reported as promising approaches in managing skin wounds. Carbohydrate-based formulations as blends, hydrogels, and nanocomposites, have also been reported as promising healing, antimicrobial, and modulatory agents for wound management. Full article

Marine oil spills are one of the major threats to marine ecological safety, and the rapid identification of oil films is of great significance to the emergency response. Marine radar can provide data for marine oil spill detection; however, to date, it has not been commonly reported. Traditional marine radar oil spill research is mostly based on grayscale segmentation, and its accuracy depends entirely on the selection of the threshold. With the development of algorithm technology, marine radar oil spill extraction has gradually come to focus on artificial intelligence, and the study of oil spills based on machine learning has begun to develop. Based on X-band marine radar images collected from the Dalian 716 incident, this study used image texture features, the BP neural network classifier, and threshold segmentation for oil spill extraction. Firstly, the original image was pre-processed, to eliminate co-channel interference noise. Secondly, texture features were extracted and analyzed by the gray-level co-occurrence matrix (GLCM) and principal component analysis (PCA); then, the BP neural work was used to obtain the effective wave region. Finally, threshold segmentation was performed, to extract the marine oil slicks. The constructed BP neural network could achieve 93.75% classification accuracy, with the oil film remaining intact and the segmentation range being small; the extraction results were almost free of false positive targets, and the actual area of the oil film was calculated to be 42,629.12 m². The method proposed in this paper can provide a reference for real-time monitoring of oil spill incidents. Full article

Synthetic aperture radar (SAR) has been widely applied in oil spill detection on the sea surface due to the advantages of wide area coverage, all-weather operation, and multi-polarization characteristics. Sentinel-1 satellites can provide dual-polarized SAR data, and they have high potential for successful application to oil spill detection. However, the characteristics of the sea surface and oil film on different images are not the same when imaging at different locations and in different conditions, which leads to the inconsistent accuracy of these images with the application of the current oil spill detection methods. In order to avoid the above limitation, we propose an oil spill detection method using image stretching based on superpixels and a convolutional neural network. Experiments were carried out on eight Sentinel-1 dual-pol data, and the optimal superpixel number and image stretching parameters are discussed. Mean intersection over union (MIoU) was used to evaluate classification accuracy. The proposed method could effectively improve the classification accuracy; when the expansion and inhibition coefficients of image stretching were set to 1.6 and 1.2 respectively, the experiments achieved a maximum MIoU of 85.4%, 7.3% higher than that without image stretching. Full article

It is known that a ship’s shafting system can be adversely affected by hull deformation, variations in the engine power, the propeller load, and eccentric propeller thrusts, thereby increasingly affecting the behavior of the shaft’s movement. A deformed shafting system may also lead to a potential risk of bearing damage by causing a change in the local load of the rear part of the after-stern tube bearing of the propeller shaft. With this concern, a series of previous studies were focused on optimizing the effects of hull deformation by securing a proper level of propulsion shaft stability and optimizing the relative inclination angle and oil film retention based on a quasi-static state, that is, Rules for the Classification of Steel Ships and experiences of shipyards. However, despite our efforts to resolve this issue, marine accidents involving stern tube bearing damage have continued to occur under relatively unattended ship motions in a transient state, that is, a quasi-static state that can possibly cause sudden stern flow field changes. Therefore, to improve the stability of the propulsion shaft, it is necessary to understand ship motions and the conditions of shafting systems in a dynamic state and a transient state when the system is designed. From this point of view, this study investigated the effect of changes in eccentric propeller forces on the motion of the propeller shaft in a representative transient state of a 50,000 deadweight tonnage tanker by means of the strain gauge method and a displacement sensor. The research findings demonstrate that propeller thrust fluctuations have a direct effect on the shaft stability by significant changes in the shaft motion that can lead to unbalanced supporting loads on the stern tube bearing. These results clearly reproduce the cause of damage to the ship in which the accident occurred at a reliable level and will be a reference for establishing pragmatic guidelines for preventing damage to similar ships in the future. Full article