Hyperspectral remote sensing identification of marine oil spills and emulsions using feature bands and double-branch dual-attention mechanism network

The accurate identification of marine oil spills and their emulsions is of great significance for emergency response to oil spill pollution.The selection of characteristic bands with strong separability helps to realize the rapid calculation of data on aircraft or in orbit,which will improve the timeliness of oil spill emergency monitoring.At the same time,the combination of spectral and spatial features can improve the accuracy of oil spill monitoring.Two ground-based experiments were designed to collect measured airborne hyperspectral data of crude oil and its emulsions,for which the multiscale superpixel level group clustering framework(MSGCF)was used to select spectral feature bands with strong separability.In addition,the double-branch dual-attention(DBDA)model was applied to identify crude oil and its emulsions.Compared with the recognition results based on original hyperspectral images,using the feature bands determined by MSGCF improved the recognition accuracy,and greatly shortened the running time.Moreover,the characteristic bands for quantifying the volume concentration of water-in-oil emulsions were determined,and a quantitative inversion model was constructed and applied to the AVIRIS image of the deepwater horizon oil spill event in 2010.This study verified the effectiveness of feature bands in identifying oil spill pollution types and quantifying concentration,laying foundation for rapid identification and quantification of marine oil spills and their emulsions on aircraft or in orbit.

Hyperspectral remote sensing identification of marine oil emulsions based on the fusion of spatial and spectral features

Marine oil spill emulsions are difficult to recover,and the damage to the environment is not easy to eliminate.The use of remote sensing to accurately identify oil spill emulsions is highly important for the protection of marine environments.However,the spectrum of oil emulsions changes due to different water content.Hyperspectral remote sensing and deep learning can use spectral and spatial information to identify different types of oil emulsions.Nonetheless,hyperspectral data can also cause information redundancy,reducing classification accuracy and efficiency,and even overfitting in machine learning models.To address these problems,an oil emulsion deep-learning identification model with spatial-spectral feature fusion is established,and feature bands that can distinguish between crude oil,seawater,water-in-oil emulsion(WO),and oil-in-water emulsion(OW)are filtered based on a standard deviation threshold–mutual information method.Using oil spill airborne hyperspectral data,we conducted identification experiments on oil emulsions in different background waters and under different spatial and temporal conditions,analyzed the transferability of the model,and explored the effects of feature band selection and spectral resolution on the identification of oil emulsions.The results show the following.(1)The standard deviation–mutual information feature selection method is able to effectively extract feature bands that can distinguish between WO,OW,oil slick,and seawater.The number of bands was reduced from 224 to 134 after feature selection on the Airborne Visible Infrared Imaging Spectrometer(AVIRIS)data and from 126 to 100 on the S185 data.(2)With feature selection,the overall accuracy and Kappa of the identification results for the training area are 91.80%and 0.86,respectively,improved by 2.62%and 0.04,and the overall accuracy and Kappa of the identification results for the migration area are 86.53%and 0.80,respectively,improved by 3.45%and 0.05.(3)The oil emulsion identification model has a certain degree of transferability and can effectively identify oil spill emulsions for AVIRIS data at different times and locations,with an overall accuracy of more than 80%,Kappa coefficient of more than 0.7,and F1 score of 0.75 or more for each category.(4)As the spectral resolution decreasing,the model yields different degrees of misclassification for areas with a mixed distribution of oil slick and seawater or mixed distribution of WO and OW.Based on the above experimental results,we demonstrate that the oil emulsion identification model with spatial–spectral feature fusion achieves a high accuracy rate in identifying oil emulsion using airborne hyperspectral data,and can be applied to images under different spatial and temporal conditions.Furthermore,we also elucidate the impact of factors such as spectral resolution and background water bodies on the identification process.These findings provide new reference for future endeavors in automated marine oil spill detection.

Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research focus.This paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this problem.The proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among nodes.Three new metrics are defined:the intensity of node social relationships,node activity,and community connectivity.Within the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node activity.When a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between communities.The proposed algorithm was compared to three existing routing algorithms in simulation experiments.Results indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

基于Kano模型的心房颤动射频消融术患者健康教育需求调查分析

目的调查心房颤动射频消融术患者的健康教育需求属性,为制定针对性健康教育方案提供参考。方法基于Kano模型自行设计心房颤动射频消融术患者的健康教育需求调查问卷,并于2023年2月至7月对云南省某三级甲等医院收治的190例房颤射频消融术患者进行调查,根据Kano模型确定其健康教育需求属性。结果共发放问卷190份,回收有效问卷180份,问卷有效回收率为94.74%。心房颤动射频消融术患者的健康教育32项需求中,包括6项必备型需求、13项期望型需求、11项魅力型需求、2项无差异型需求,未发现反向需求。重要度—满意度矩阵图显示,12项位于优势区,11项位于待改进区,7项位于维持区,2项位于次要改进区。结论Kano模型的能够多维度分析心房颤动射频消融术患者的健康教育需求,为医护人员进一步制定以患者需求为导向的健康教育内容和形式提供参考。

Optimal duration of percutaneous microballoon compression for treatment of trigeminal nerve injury

Percutaneous microballoon compression of the trigeminal ganglion is a brand new operative technique for the treatment of trigeminal neuralgia. However, it is unclear how the procedure mediates pain relief, and there are no standardized criteria, such as compression pressure, com- pression time or balloon shape, for the procedure. In this study, percutaneous microballoon compression was performed on the rabbit trigeminal ganglion at a mean inflation pressure of 1,005 ＋ 150 mmHg for 2 or 5 minutes. At 1, 7 and 14 days after percutaneous microballoon compression, the large-diameter myelinated nerves displayed axonal swelling, rupture and demy- elination under the electron microscope. Fragmentation of myelin and formation of digestion chambers were more evident after 5 minutes of compression. Image analyzer results showed that the diameter of trigeminal ganglion cells remained unaltered after compression. These experi- mental findings indicate that a 2-minute period of compression can suppress pain transduction. Immunohistochemical staining revealed that vascular endothelial growth factor expression in the ganglion cells and axons was significantly increased 7 days after trigeminal ganglion compression, however, the changes were similar after 2-minute compression and 5-minute compression. The upregulated expression of vascular endothelial growth factor in the ganglion cells after percu- taneous microballoon compression can promote the repair of the injured nerve. These findings suggest that long-term compression is ideal for patients with recurrent trigeminal neuralgia.

Isolation and Experimental Study on Bacteriostasis of Bacillus haynesii

This experiment aims to isolate and inhibit three bacteria strains to provide candidate strains for the development and application of probiotics.Using bacterial morphological identification,16S rDNA sequence alignment,and genetic evolution analysis,three strains were identified as Bacillus haynesii,named HP01,HD02,and HK03.Through biosurfactant activity tests,C-TAB tests,hemolysis tests,and antibacterial activity analyses,the results showed that all three strains of B.haynesii exhibited significant biosurfactant activity.Additionally,the solutions of the three strains demonstrated a pronounced antibacterial effect on Staphylococcus aureus.The resistance and safety of commonly used drugs were evaluated using the tablet diffusion method and a mouse feeding test.The results indicated that the three strains were not resistant to commonly used antibacterial drugs,and the oral bacterial solution was not pathogenic and had high safety in mice.The study concluded that all three B.haynesii strains met the basic conditions for use,with B.haynesii HP01 being the most promising candidate.

Deep learning classification of coastal wetland hyperspectral image combined spectra and texture features: A case study of Huanghe(Yellow) River Estuary wetland

This paper develops a deep learning classification method with fully-connected 8-layers characteristics to classification of coastal wetland based on CHRIS hyperspectral image. The method combined spectral feature and multi-spatial texture feature information has been applied in the Huanghe(Yellow) River Estuary coastal wetland.The results show that:(1) Based on testing samples, the DCNN model combined spectral feature and texture feature after K-L transformation appear high classification accuracy, which is up to 99%.(2) The accuracy by using spectral feature with all the texture feature is lower than that using spectral only and combing spectral and texture feature after K-L transformation. The DCNN classification accuracy using spectral feature and texture feature after K-L transformation was up to 99.38%, and the outperformed that of all the texture feature by 4.15%.(3) The classification accuracy of the DCNN method achieves better performance than other methods based on the whole validation image, with an overall accuracy of 84.64% and the Kappa coefficient of 0.80.(4) The developed DCNN model classification algorithm ensured the accuracy of all types is more balanced, and it also greatly improved the accuracy of tidal flat and farmland, while kept the classification accuracy of main types almost invariant compared to the shallow algorithms. The classification accuracy of tidal flat and farmland is up to 79.26% and 56.72%respectively based on the DCNN model. And it improves by about 2.51% and 10.6% compared with that of the other shallow classification methods.

Detection of oil spill based on CBF-CNN using HY-1C CZI multispectral images

Accurate detection of an oil spill is of great significance for rapid response to oil spill accidents.Multispectral images have the advantages of high spatial resolution,short revisit period,and wide imaging width,which is suitable for large-scale oil spill monitoring.However,in wide remote sensing images,the number of oil spill samples is generally far less than that of seawater samples.Moreover,the sea surface state tends to be heterogeneous over a large area,which makes the identification of oil spills more difficult because of various sea conditions and sunglint.To address this problem,we used the F-Score as a measure of the distance between forecast value and true value,proposed the Class-Balanced F loss function(CBF loss function)that comprehensively considers the precision and recall,and rebalances the loss according to the actual sample numbers of various classes.Using the CBF loss function,we constructed convolution neural networks(CBF-CNN)for oil spill detection.Based on the image acquired by the Coastal Zone Imager(CZI)of the Haiyang-1C(HY-1C)satellite in the Andaman Sea(study area 1),we carried out parameter adjustment experiments.In contrast to experiments of different loss functions,the F1-Score of the detection result of oil emulsions is 0.87,which is 0.03–0.07 higher than cross-entropy,hinge,and focal loss functions,and the F1-Score of the detection result of oil slicks is 0.94,which is 0.01–0.09 higher than those three loss functions.In comparison with the experiment of different methods,the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.05–0.12 higher than that of the deep neural networks,supports vector machine and random forests models,and the F1-Score of the detection result of oil slicks is 0.15–0.22 higher than that of the three methods.To verify the applicability of the CBF-CNN model in different observation scenes,we used the image obtained by HY-1C CZI in the Karimata Strait to carry out experiments,which include two studies areas(study area 2 and study area 3).The experimental results show that the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.88,which is 0.16–0.24 higher than that of other methods,and the F1-Score of the detection result of oil slicks is 0.96–0.97,which is 0.06–0.23 higher than that of other methods.Based on all the above experiments,we come to the conclusions that the CBF loss function can restrain the influence of oil spill and seawater sample imbalance on oil spill detection of CNN model thus improving the detection accuracy of oil spills,and our CBF-CNN model is suitable for the detection of oil spills in an area with weak sunglint and can be applied to different scenarios of CZI images.

Distribution and invasion of Spartina alterniflora within the Jiaozhou Bay monitored by remote sensing image

Spartina alterniflora as an alien invasive plant,poses a serious threat to the ecological functions of the coastal wetland of the Jiaozhou Bay.As of 2019,the distribution area of S.alterniflora in the Jiaozhou Bay has reached more than 500 hm^(2).For this reason,combined with field surveys,remote sensing monitoring of the invasion S.alterniflora in the Jiaozhou Bay has been carried out.To accurately identify S.alterniflora within the Jiaozhou Bay coastal wetland,we used a new method which is an implement of deep convolutional neural network,and by which we got a higher accuracy than the traditional method.Based on distribution of S.alterniflora extracted by the proposed method,the temporal and spatial distribution characteristics of S.alterniflora were analyzed.And then combined with environmental factors,the invasion mechanism of S.alterniflora in the Jiaozhou Bay was analyzed in detail.From the monitoring results,it can be seen that S.alterniflora in Jiaozhou Bay is mainly distributed in the beaches near the Yanghe River Estuary and its southern side,the Dagu River Estuary and the Nügukou.Spartina alterniflora first broke out near the Yanghe River Estuary and gradually spread to the tidal flats near the Nügukou.The Dagu River Estuary is dominated by S.anglica,whose area has not changed much over the years,and a small amount of S.alterniflora has invaded later.

Wideband Direction-of-Arrival Estimation Based on Deep Learning

The performance of traditional high-resolution direction-of-arrival(DOA)estimation methods is sensitive to the inaccurate knowledge on prior information,including the position of ar-ray elements,array gain and phase,and the mutual coupling between the array elements.Learning-based methods are data-driven and are expected to perform better than their model-based counter-parts,since they are insensitive to the array imperfections.This paper presents a learning-based method for DOA estimation of multiple wideband far-field sources.The processing procedure mainly includes two steps.First,a beamspace preprocessing structure which has the property of fre-quency invariant is applied to the array outputs to perform focusing over a wide bandwidth.In the second step,a hierarchical deep neural network is employed to achieve classification.Different from neural networks which are trained through a huge data set containing different angle combinations,our deep neural network can achieve DOA estimation of multiple sources with a small data set,since the classifiers can be trained in different small subregions.Simulation results demonstrate that the proposed method performs well both in generalization and imperfections adaptation.

Infiltrating ductal breast carcinoma with monoclonal gammopathy of undetermined significance:A case report

BACKGROUND Infiltrating ductal breast carcinoma with monoclonal gammopathy of undetermined significance(MGUS)is rare and easily misdiagnosed.Most patients are first diagnosed with MGUS.We report a rare case of MGUS secondary to infiltrating ductal breast carcinoma.We also review the literature to analyze the clinical characteristics and diagnostic methods.CASE SUMMARY A 51-year-old woman underwent modified radical mastectomy for infiltrating ductal carcinoma of the right breast and was then treated with radiation and chemotherapy.A decreased platelet count was found on routine blood examination,and MGUS was subsequently diagnosed.This is the first report of the occurrence of MGUS after breast cancer surgery.CONCLUSION Vigilance is required to distinguish this rare comorbidity from breast plasmacytoma.

Pharmacokinetics and pharmacodynamics of cisatracurium in children undergoing cleft lip or cleft palate repair surgery

OBJECTIVE The aim of the study was to characterize the pharmacokinetics(PK) and pharmacodynamics(PD) profile of cisatracurium in 0-2 years and 2-5 years old children patients with cheilopalatognathus,to find if there are some connections between the different muscle relaxation action and different PK procedure.METHODS 14 children patients were divided into two groups,≤2 years and 2-5 years group,venous samples were taken before injection of a 0.15 mg·kg-1 dose of cisatracurium and then at 2,5,10,30,60,90,and 120 min.Cisatracurium plasma concentrations were determined by ultra-performance liquid chromatography/electrospray ionization/triple quadrupole tandem mass spectrometer system(UPLC/MS/MS).The degree of neuromuscular block was measured by train of four(TOF) testing.An indirect PK-PD link model with a sigmoid E max model was established using Win Nonlin software.The model were applied to PK and PD data analysis,respectively.RESULTS The TOF monitor parameters showed that cisatracurium works very quickly,the onset time were(2.64±0.93) min and(2.59 ± 0.90) min for ≤2 years and 2-5 years group respectively.Young children ≤2 years have longer muscle blocking duration time(62.5 ± 6.01 min vs 53.86 ± 12.18 min) and slower recovery index(32.14±7.10 min and 27.43±10.63 min) than those children in group of 2-5 years.More children ≤2 years have postoperative complication than that in 2-5 children.PK parameters showed that there were no statistical differences in blood concentration and pharmacokinetic parameters.While the concentration of cisatracurium in muscle site calculated by using PK/PD model were higher and longer for ≤2 year children than that of 2-5 year children.This means that cisatracurium could stay at high concentration for a longer time in younger children' muscle tissue.CONCLUSION As a result young children tend to have postoperative complications related to slower muscle recovery action and increased concentration in skeletal muscle.So more careful observation and monitor are needed for younger children,our study could be of use in clinical practice for the administration of cisatracurium to children patients.

Exploration on Talents Through-Cultivating Model of Bachelor, Master and Doctor Degrees

Talents through-cultivating of bachelor master and doctor degrees(shortened for throughcultivating)refers to organic integration of the cultivating process of bachelor,master and doctoral students integrating high-quality educational resources,optimizing the talents cultivating model to achieve the goal of cultivating outstanding innovative talents.Analyzing the status of the talents through-cultivating model in universities pointing out the current problems in the talents throughcultivating model,this paper discusses from the perspectives of cultivating system construction,admission of bachelor-master-doctoral students,diversion and elimination mechanism,international exchanges and cooperation,and support policies.

Effect of the Real Irregular Terrain on the Lightning-radiated Electromagnetic Field Propagation

The propagation of the lightning-radiated electromagnetic field along the real irregular terrain around Yangzhou Direction Finder(DF)site(119°42′E,32°39′N)and Nanjing DF site(118°46′E,32°03′N)of Jiangsu Province in China is estimated.The results show that the rough irregular terrain results in the rapid magnitude attenuation and the increase of the rise-time of the field waveform.For example,for the Yangzhou DF site as the circumference of a radius of 45 km,the root-mean-squared height(RMSH)of the real irregular terrain varies from 7 to 33 m;the extra field attenuation relative to the ideal ground surface ranges from 1%to 11%,and the extra rise-time increment varies from 0.1 to 0.6μs.Therefore,the extracted current peak of lightning return stroke may be underestimated from the remotely measured electromagnetic field,and the error varies along with different azimuths.

生物天敌暴发导致珊瑚礁退化的高分遥感监测与分析——以南海太平岛为例

珊瑚生物天敌黑皮海绵、长棘海星在世界范围内暴发严重威胁珊瑚礁生态健康,然而目前珊瑚礁领域尚缺乏生物天敌暴发后对珊瑚礁地貌类型影响的定量研究。文章利用太平岛珊瑚生物天敌黑皮海绵、长棘海星2次暴发事件前后,覆盖太平岛2016—2022年的26期Sentinel-2遥感影像,结合高分辨率GF-2(PMS)遥感影像和卫星遥感影像地理信息系统Google Earth平台中的数据,开展中国南海太平岛珊瑚礁地貌类型遥感影像分类实验,对密集珊瑚沉积区、稀疏珊瑚沉积区、珊瑚丛生区、沙坪、浅礁前斜坡等珊瑚礁地貌类型演变进行特征分析。结果表明:1)结合专家解译知识和支持向量机(Support Vector Machine,SVM)分类算法开展的太平岛珊瑚礁地貌类型分类,最高总体精度和Kappa系数分别为96.46%和0.94。2)在2种珊瑚生物天敌暴发期间,太平岛的珊瑚礁丛生区、密集珊瑚沉积区、稀疏珊瑚沉积区等珊瑚礁地貌类型面积有显著下降;黑皮海绵暴发后对密集珊瑚沉积区影响最大,其面积减少72.92%;长棘海星暴发后对珊瑚丛生区影响最大,其面积减少59.17%。3)2016—2022年,太平岛珊瑚礁退化率高于恢复率,其中2017年3—6月珊瑚礁退化率最高,为23.88%;在2017年6—9月珊瑚礁恢复率最高,为18.03%。

Immp2l Mutation Induces Mitochondrial Membrane Depolarization and Complex Ⅲ Activity Suppression after Middle Cerebral Artery Occlusion in Mice

Objective We previously reported that mutations in inner mitochondrial membrane peptidase 2-like(Immp2l)increase infarct volume,enhance superoxide production,and suppress mitochondrial respiration after transient cerebral focal ischemia and reperfusion injury.The present study investigated the impact of heterozygous Immp2l mutation on mitochondria function after ischemia and reperfusion injury in mice.Methods Mice were subjected to middle cerebral artery occlusion for 1 h followed by 0,1,5,and 24 h of reperfusion.The effects of Immp2l^(+/−)on mitochondrial membrane potential,mitochondrial respiratory complex III activity,caspase-3,and apoptosis-inducing factor(AIF)translocation were examined.Results Immp2l^(+/−)increased ischemic brain damage and the number of TUNEL-positive cells compared with wild-type mice.Immp2l^(+/−)led to mitochondrial damage,mitochondrial membrane potential depolarization,mitochondrial respiratory complex III activity suppression,caspase-3 activation,and AIF nuclear translocation.Conclusion The adverse impact of Immp2l^(+/−)on the brain after ischemia and reperfusion might be related to mitochondrial damage that involves depolarization of the mitochondrial membrane potential,inhibition of the mitochondrial respiratory complex III,and activation of mitochondria-mediated cell death pathways.These results suggest that patients with stroke carrying Immp2l^(+/−)might have worse and more severe infarcts,followed by a worse prognosis than those without Immp2l mutations.

基础无机化学实验课程思政的探索与实践——以氧化型石墨烯制备实验为例

在中国高等教育剧烈变革的时代,作为化学专业及相关专业大学一年级学生入校后开设的第一门实验课,在无机化学实验课的教学过程中对学生进行全方位的培养,注重教学思政,对于提升实验操作技能、夯实学生基础理论知识、激发学生爱国情怀、认识科学发展客观规律以及开拓思维创新等方面有着至关重要的作用。本文以氧化型石墨烯制备实验为例,探讨了如何在实验教学中引入思政元素,使学生在提高科学研究能力的同时,充分认识科学研究在强国中的重要性,培养学生科技报国的理念。

多维度构建和实施线上实验课堂的探索与实践

基于虚拟仿真实验教学项目,结合Blackborad(Bb)教学平台和腾讯课堂/腾讯会议等线上交流媒介,多维度实施线上实验教学,为学生上了一堂立体的“线上实验课”。不仅解决了新冠病毒疫情防控期间实验教学的难题,同时也为实验教学的形式拓展、渠道丰富和质量提升提供了有意义的借鉴和帮助。

面向一流课程多维度推进无机化学实验课程改革与实践

详细总结陕西师范大学化学化工学院在无机化学实验课程改革的实施情况,从教学内容、考核模式、教学平台、课程思政以及教学总结等方面进行阐述,并通过典型案例“二氧化碳分子量的测定”介绍了各项改革内容如何具体实施,希望为相关课程的改革提供借鉴。

多模态建构下的无机化学课程学习体验与感受

陕西师范大学化学化工学院无机化学课程组根据课程内容和学生学习特点,有针对性地设计并采取了不同的授课方式,构建了多模态教学方式,尝试了创新型课堂,以调动学生的学习积极性,培养学生的自主学习能力。本文从学生的角度出发,依据无机化学课程学习情况,对不同的教学方式下的学习感受进行了总结,剖析了学生在多模态建构下的无机化学课程中的学习体验与感受,以期在教学改革的探索中实现师生共学相长,共同提升教学质量,打造适合提升学生综合能力的“入门课程”。