Ecological environmental quality evaluation and driving factor analysis of the Lijiang River Basin,based on Google Earth Engine

For regional ecological management,it is important to evaluate the quality of ecosystems and analyze the underlying causes of ecological changes.Using the Google Earth Engine(GEE)platform,the remote sensing ecological index(RSEI)was calculated for the Lijiang River Basin in Guangxi Zhuang Autonomous Region for 1991,2001,2011,and 2021.Spatial autocorrelation analysis was employed to investigate spatiotemporal variations in the ecological environmental quality of the Lijiang River Basin.Furthermore,geographic detectors were used to quantitatively analyze influencing factors and their interaction effects on ecological environmental quality.The results verified that:1)From 1991 to 2021,the ecological environmental quality of the Lijiang River Basin demonstrated significant improvement.The area with good and excellent ecological environmental quality in proportion increased by 19.69%(3406.57 km^(2)),while the area with fair and poor ecological environmental quality in proportion decreased by 10.76%(1860.36 km^(2)).2)Spatially,the ecological environmental quality of the Lijiang River Basin exhibited a pattern of low quality in the central region and high quality in the periphery.Specifically,poor ecological environmental quality characterized the Guilin urban area,Pingle County,and Lingchuan County.3)From 1991 to 2021,a significant positive spatial correlation was observed in ecological environmental quality of the Lijiang River Basin.Areas with high-high agglomeration were predominantly forests and grasslands,indicating good ecological environmental quality,whereas areas with low-low agglomeration were dominated by cultivated land and construction land,indicating poor ecological environmental quality.4)Annual average precipitation and temperature exerted the most significant influence on the ecological environmental quality of the basin,and their interactions with other factors had the great influence.This study aimed to enhance understanding of the evolution of the ecological environment in the Lijiang River Basin of Guangxi Zhuang Autonomous Region and provide scientific guidance for decision-making and management related to ecology in the region.

基于Google earth engine渭-库绿洲果园遥感提取

针对干旱区果园大面积遥感提取困难、识别精度低等问题,本研究基于GEE(Google earth engine)平台,综合应用Sentinel-1/Sentinel-2影像构建特征集。通过对比原始特征组合、Jeffries-Matusita(J-M)距离、属性重要度3种优化方式,结合随机森林(Random forest,RF)分类方法,对比得到最佳优化方式,探索果园最优分类特征集。结果表明:识别效果最好的方案为G17 JM,总体精度为91.25%,kappa系数为0.89,面积精度为82.55%。最优特征集为B8_asm、B8_ent、B8_idm、NDVI re3、B6、B7、a、e、b、EVI、B11、B8A、B8、VV。使用J-M距离进行特征集优化,有效降低数据量、提高计算效率,更有利于精确遥感识别果园种植面积。表明GEE快速、准确获取果园种植面积的可行性,为获取果园动态变化提供强有力的基础。

基于GEOVIS Earth Studio的高中地理教学案例开发——以“河流地貌”为例

信息技术与教学的融合发展是教育改革发展的方向,地理信息技术在教育信息化2.0时代给地理教育提供了丰富的教学资源,可有效提高教学效果。本文通过挖掘GEOVIS Earth Studio平台在地理教学中的功能优势,构建其教学应用模式,以人教版必修《地理1》“河流地貌”为例阐明平台操作过程并进行案例教学设计,创新地理教学技术手段,促进中学地理教学的信息化进程。

Development of a building information model-guided post-earthquake building inspection framework using 3D synthetic environments

Computer vision-based inspection methods show promise for automating post-earthquake building inspections.These methods survey a building with unmanned aerial vehicles and automatically detect damage in the collected images.Nevertheless,assessing the damage′s impact on structural safety requires localizing damage to specific building components with known design and function.This paper proposes a BIM-based automated inspection framework to provide context for visual surveys.A deep learning-based semantic segmentation algorithm is trained to automatically identify damage in images.The BIM automatically associates any identified damage with specific building components.Then,components are classified into damage states consistent with component fragility models for integration with a structural analysis.To demonstrate the framework,methods are developed to photorealistically simulate severe structural damage in a synthetic computer graphics environment.A graphics model of a real building in Urbana,Illinois,is generated to test the framework;the model is integrated with a structural analysis to apply earthquake damage in a physically realistic manner.A simulated UAV survey is flown of the graphics model and the framework is applied.The method achieves high accuracy in assigning damage states to visible structural components.This assignment enables integration with a performance-based earthquake assessment to classify building safety.

基于GoogleEarthEngine的黄土高原植被覆盖度时空变化特征分析

[目的]探究黄土高原植被覆盖度变化的时空特征,揭示植被对气候因子变化的时滞效应,进而为地区生态保护与高质量发展提供数据支撑。[方法]基于2001年至2020年的黄土高原地区NDVI数据、气温和降水数据,利用像元二分法、一元线性回归和时滞偏相关分析等方法,开展地区植被与气候因子变化关系的研究。[结果]过去20年间,黄土高原植被覆盖度以0.076/10 a的速率增加,在空间上主要呈现极显著增加,但占总面积38.29%的区域植被覆盖变化波动较大。黄土高原月植被覆盖度与气温和降水呈现显著正相关关系,其中降水是影响植被变化的主要因素。植被对降水的响应滞后时间主要集中在3个月,而气温的滞后时间在空间上存在较大差异,东南部植被主要滞后0至1个月,而西北部植被主要滞后2至3个月。[结论]黄土高原植被变化主要受降水影响,20年间植被恢复情况良好,但变化波动较大,未来应继续生态保护工作进行巩固。

基于Google Earth Engine的八门湾红树林年际变化监测

该文基于Google Earth Engine(GEE)云平台,选择Landsat系列卫星数据,采用支持向量机(support vector machines,SVM)分类方法对八门湾地区进行地物类型分类,并基于分类结果监测该地区红树林年际变化。结果表明:红树林与陆地树木之间除红外波段外反射光谱曲线极其相似,利用红外波段特征指数并结合地形数据可进行有效区分,分类结果总体精度达到0.91;研究区红树林存在先减少后增加的变化趋势,在2009—2013年减少,2014—2016年基本保持不变,2017—2021年缓慢增加,红树林增加和坑塘减少时期是在“南红北柳”政策颁布之后,说明退塘还林政策成效显著;红树林主要是和坑塘相互变化转移,说明毁林造塘和退塘还林是影响该地区红树林变化的重要因素。红树林年际变化监测结果可以精细化分析红树林演变过程,并能精确量化红树林与其他土地类型的转化过程,从而在经济、政策上分析红树林演变因素,更有效地保护红树林。

Effects of thinning on the understory light environment of different stands and the photosynthetic performance and growth of the reforestation species Phoebe bournei

Light levels determine regeneration in stands and a key concern is how to regulate the light environment of different stand types to the requirements of the understory.In this study,we selected three stands typical in south China(a Cryptomeria japonica plantation,a Quercus acutissima plantation,and a mixed stand of both)and three thinning intensities to determine the best understory light environ-ment for 3-year-old Phoebe bournei seedlings.The canopy structure,understory light environment,and photosynthe-sis and growth indicators were assessed following thin-ning.Thinning improved canopy structure and understory light availability of each stand;species composition was the reason for differences in the understory light environ-ment.Under the same thinning intensity,the mixed stand had the greatest light radiation and most balanced spectral composition.P.bournei photosynthesis and growth were closely related to the light environment;all three stands required heavy thinning to create an effective and sustained understory light environment.In a suitable understory light environment,the efficiency of light interception,absorption,and use by seedlings was enhanced,resulting in a higher carbon assimilation the main limiting factor was stomatal conductance.As a shade-avoidance signal,red/far-red radia-tion is a critical factor driving changes in photosynthesis and growth of P.bournei seedlings,and a reduction increased light absorption and use capacity and height:diameter ratios.The growth advantage transformed from diameter to height,enabling seedlings to access more light.Our findings suggest that the regeneration of shade-tolerant species such as P.bournei could be enhanced if a targeted approach to thinning based on stand type was adopted.

基于Google Earth Engine遥感大数据云平台的盐城盐沼植被精细分类研究

江苏省自1982年开始引种互花米草以来,滨海盐沼植被景观格局发生了巨大变化,芦苇和互花米草面积不断增长,挤占了碱蓬生态位,破坏了生态系统平衡。滨海盐沼湿地是三大蓝碳生态系统之一,具有高固碳和缓解温室效应的生态功能。利用遥感云平台手段监测滨海盐沼植被时空动态变化规律,可为滨海盐沼湿地生态系统保护以及修复提供科学支撑。本文选取植被类型丰富的江苏沿海地区为研究区,基于Google Earth Engine(GEE)平台,选取2000-2020年的Landsat 5、Landsat 7、Landsat 8以及Sentinel-2影像,提出了一种湿地植被提取方法。首先,构建盐城盐沼湿地地表覆盖分类体系,选取样本点,计算特征指数,进行地物分类;然后,利用时间谐波分析法对盐沼植被归一化植被指数(NDVI)变化特征进行分析,确定三类盐沼植被的物候差异;最后,基于物候特征,利用随机森林(Random Forest,RF)分类法提取盐城滨海湿地盐沼植被地表覆盖信息。结果表明:(1)相较传统监测方法,本文所构建的滨海盐沼植被精细化提取方法能够对滨海盐沼植被进行有效细分和监测;(2)2000-2020年,芦苇和互花米草面积呈扩张趋势,碱蓬面积不断缩减;(3)盐城三种典型盐沼植被NDVI值呈现不同变化特征;(4)降水对盐沼植被生长的影响较为显著,气温对盐沼植被生长的直接影响较小。研究结果可为盐城盐沼湿地自然遗产保护以及湿地生态可持续发展提供科学支撑。

基于Google Earth Engine的洱海流域建成区范围及生态质量变化分析

以洱海流域为研究区,借助Google Earth Engine云平台,利用Landsat 8 OLI系列影像,合成研究区域的增强的指数型建筑指数(EIBI)并对洱海流域建成区进行提取,再对研究区域遥感生态指数进行合成,通过对建成区范围与流域范围内遥感生态指数进行空间相关分析,从而对2014—2021年洱海流域及其建成区进行生态评估。结果表明,EIBI可有效增强建筑信息,使用EIBI可有效、准确地提取研究区域建成区,经过EIBI提取,洱海流域2014—2021年建成区扩张面积共14.712 km2;2014—2021年洱海流域生态明显改善,遥感生态指数增加0.008,其上升速率为0.001/年,流域内超过65%的区域生态得到改善;洱海流域建成区与生态未得到改善区域大致匹配,建成区经过生态修复后生态质量未见显著提升,且建成区生态质量呈逐年下降趋势,说明建成区对环境改善具有阻力。

基于Google Earth Engine的前郭县春季农田覆膜提取

本文基于Google Earth Engine(GEE)云平台,综合考虑光学影像的波段反射率、光谱指数特征和雷达影像的极化、纹理特征,分别构建仅使用光学特征、仅使用雷达特征以及光学和雷达特征组合3种特征输入组合;根据精度确定最佳输入特征后,分别结合机器学习中的分类与回归树、支持向量机、最小距离分类法、梯度提升树和随机森林5种方法建立覆膜提取模型,依据结果精度评估不同方法的性能,并基于最优化模型提取出最终的覆膜农田面积。结果表明:①最佳输入特征为波段反射率特征+光谱指数特征+极化特征+纹理特征;②采用随机森林方法建立的模型精度最高,研究区I的总体精度达到了95.84%,Kappa系数为0.95,地物错分率为1.2%,明显优于其他4种方法(地物错分率较分类与回归树、支持向量机、最小距离和梯度提升树法降低0.8%、7.3%、38.0%和0.3%),研究区II的验证精度达到了87.84%,证明该模型在覆膜提取中可以取得更加准确的结果;③使用本文方法得到2022年研究区I覆膜农田面积为1302.48 km2,估算地膜使用量约为7585.62 t。本文综合考虑光学和雷达影像在地物识别中的特点建立模型,可以准确、高效的识别农田地膜,掌握地膜面积,对环境治理与防治具有重要意义。

Earth Summit Mission 2022:Scientific Expedition and Research on Mt.Qomolangma Helps Reveal the Synergy between Westerly Winds and Monsoon and the Resulting Climatic and Environmental Effects

“Earth summit mission 2022”is one of the landmark scientific research activities of the Second Tibetan Plateau Scientific Expedition and Research(STEP).This scientific expedition firstly used advanced technology and methods to detect vertical meteorological elements and produce forecasts for mountain climbing.The“Earth summit mission 2022”Qomolangma scientific expedition exceeded an altitude of over 8000 meters for the first time and carried out a comprehensive scientific investigation mission on the summit of Mt.Qomolangma.Among the participants,the westerly–monsoon synergy and influence team stationed in the Mt.Qomolangma region had two tasks:1)detecting the vertical structure of the atmosphere for parameters such as wind,temperature,humidity,and pressure with advanced instruments for high-altitude detection at the Mt.Qomolangma base camp;and 2)observing extreme weather processes to ensure that members of the mountaineering team could successfully reach the top.Through this scientific expedition,a better understanding of the vertical structure and weather characteristics of the complex area of Mt.Qomolangma is gained.

Study on Distribution of Four Pseudomonas Species in Living Environment Using Multiplex PCR

Purpose: The genus Pseudomonas is a ubiquitous microorganism frequently detected from immunocompromised patients. The inherent resistance to numerous antimicrobial agents contributes to the opportunistic character of this pathogen exhaustive monitoring of this pathogen is considered of critical importance to public health organizations. The reliable identification method able to distinguish genetic close Pseudomonas species is needed, because these organisms are difficult to differentiate by phenotypic or biochemical methods. The purpose of the present study was to design species-specific primers in order to identify and detect four Pseudomonas species which are frequently detected from the human oral cavities, and to investigate the distribution of these organisms in the living environment using a multiplex PCR. Methods: Polymerase chain reaction (PCR) primers were designed based on partial sequences of the rpoD gene of four Pseudomonas species. Swab samples were collected from fifty washstands, and the distribution of Pseudomonas species was investigated using a conventional PCR at genus level and a multiplex PCR at species level. Results: Multiplex PCR method developed in this study was able to distinguish four Pseudomonas species clearly. The genus Pseudomonas was detected from all samples (100%), whereas P. putida, P, aeruginosa, P. stutzeri and P. fluorescens were detected at 44%, 8%, 4% and 2% in fifty swab samples, respectively. Conclusion: Our developed one-step multiplex PCR method is accurate, specific, cost-effective, time-saving, and works without requiring DNA extraction. It was indicated that washstands were the uninhabitable environment for P. putida, P, aeruginosa, P. stutzeri and P. fluorescens.

Biomedical rare-earth magnesium alloy:Current status and future prospects

Biomedical magnesium(Mg)alloys have garnered significant attention because of their unique biodegradability,favorable biocompatibility,and suitable mechanical properties.The incorporation of rare earth(RE)elements,with their distinct physical and chemical properties,has greatly contributed to enhancing the mechanical performance,degradation behavior,and biological performance of biomedical Mg alloys.Currently,a series of RE-Mg alloys are being designed and investigated for orthopedic implants and cardiovascular stents,achieving substantial and encouraging research progress.In this work,a comprehensive summary of the state-of-the-art in biomedical RE-Mg alloys is provided.The physiological effects and design standards of RE elements in biomedical Mg alloys are discussed.Particularly,the degradation behavior and mechanical properties,including their underlying action are studied in-depth.Furthermore,the preparation techniques and current application status of RE-Mg alloys are reviewed.Finally,we address the ongoing challenges and propose future prospects to guide the development of high-performance biomedical Mg-RE alloys.

Rare earth element signatures of Paleoproterozoic Sallopat phosphorites of Aravalli Basin,India:implications for diagenetic effects and depositional environment

As a repository of REE,the Paleoproterozoic phosphorite of the Aravalli Basin in India is explored time to time.Paleoproterozoic phosphorite deposits of the Sallopat sub-basin Banswara district of the Aravalli Supergroup show moderate REE concentrations much lower than that of average marine phosphorites.Different ratios and PAAS-normalized REE distribution patterns reflect MREE enrichment over LREE and HREE in these phosphorites.MREE enrichment confirmed by(Sm/Yb)Nversus(Sm/Pr)Nrelationship and attributed to the selective uptake by cyanobacteria and mixing of riverine inputs.Y/Ho,La/Ce and Er/Nd ratios confirm that the REE in Sallopat phosphorites might have been affected by the argillaceous terrigenous and plagioclase-rich sediments probably derived from Banded Gneissic Complex.Diagenetic modeling indicates that REE were concentrated during early diagenetic processes with limited weathering influence.Moderate negative to low Ce anomaly depicts oxic to sub-oxic conditions of seawater due to oxidation of Ce^(3+)to Ce^(4+)whereas Eu^(3+)reduced to Eu^(2+)indicating positive Euanomaly and reducing conditions as well during the deposition.These anomalies may be due to the stratified restricted marine environment of upwelling and mingling of organic-rich anoxic deep water with oxic upper seawater prior to the formation of phosphorite deposits in the embayment.Enrichment of REE in Sallopat phosphorite relies on various physico-chemical conditions.These phosphorites may have been formed in restricted marine water which was influenced by extra clastic sediment input carried out by fluvial systems.

A comprehensive review of lunar lava tube base construction and field research on a potential Earth test site

The Moon,as the closest celestial body to the Earth,plays a pivotal role in the progression of deep space exploration,and the establishment of research outposts on its surface represents a crucial step in this mission.Lunar lava tubes are special underground caves formed by volcanic eruptions and are considered as ideal natural shelters and scientific laboratories for lunar base construction.This paper begins with an in-depth overview of the geological origins,exploration history,and distribution locations of lunar lava tubes.Subsequently,it delves into the presentation of four distinctive advantages and typical concepts for constructing bases within lava tubes,summarizing the ground-based attempts made thus far in lunar lava tube base construction.Field studies conducted on a lava tube in Hainan revealed rock compositions similar to those found during the Apollo missions and clear lava tube structures,making it a promising analog site.Lastly,the challenges and opportunities encountered in the field of geotechnical engineering regarding the establishment of lunar lava tube bases are discussed,encompassing cave exploration technologies,in-situ testing methods,geomechanical properties under lunar extreme environments,base design and structural stability assessment,excavation and reinforcement techniques,and simulated Earth-based lava tube base.

基于Google Earth Engine和Sentinel-2数据的耕地分类研究

【目的】采用遥感影像对不同种类农作物耕地进行高效精准的分类,以期获取最优的农作物种植信息提取方案,为农业生产提质增效提供决策支持。【方法】以陕西省宝鸡市扶风县为研究区,利用Google Earth Engine(GEE)平台,基于2020年10月至2021年12月的Sentinel-2影像和目视解译与野外定位相结合选取的样本点,使用随机森林(random forest,RF)算法,结合归一化植被指数(normalized difference vegetation index,NDVI)、增强植被指数(enhanced vegetation index,EVI)、物候参数特征、遥感影像红边波段,且将根据NDVI计算出的物候参数的波段记为N,根据EVI计算出的物候参数的波段记为E,引入红边波段记为1,未引入红边波段记为2,在此基础上构建了8种不同的分类模型(NDVI-N1、EVI-E1、N1、E1、NDVI-N2、EVI-E2、N2、E2),并与4种样本分割比例(训练样本数与检验样本数的比例分别为5∶5,6∶4,7∶3和8∶2)组合,共计32种分类方案,利用这些方案对扶风县的不同种类农作物耕地进行分类,并计算了不同分类方案的总体精度和Kappa系数。【结果】(1)经S-G滤波法处理后,6种地物类型的NDVI和EVI时序曲线噪声减小且更为平滑,且NDVI和EVI曲线差异明显。(2)在32种分类方案中,只有样本分割比例为7∶3时的EVI-E1模型、EVI-E2模型和样本分割比例为6∶4时E2模型的总体精度高于90%,说明在扶风地区用EVI分类效果更好一些。(3)在样本分割比例为7∶3的情况下,EVI-E1模型的总体精度和Kappa系数均最高,分别为91.66%和89.41%,为最优分类方案。但是在该方案中引入红边波段后,其总体精度下降了1.18%,Kappa系数下降了1.53%,可知运用该分类方案时应剔除红边波段。(4)运用最优分类方案对小麦地、玉米地、土豆地的提取结果与实际调查情况大致相同。【结论】基于GEE平台和Sentinel-2数据实现了对县域级农作物种植面积的精确分类,可为扶风县农业生产提供决策支持。

Influence of static cartoons combined with dynamic virtual environments on preoperative anxiety of preschool-aged children undergoing surgery

BACKGROUND Preschoolers become anxious when they are about to undergo anesthesia and surgery,warranting the development of more appropriate and effective interventions.AIM To explore the effect of static cartoons combined with dynamic virtual environments on preoperative anxiety and anesthesia induction compliance in preschool-aged children undergoing surgery.METHODS One hundred and sixteen preschool-aged children were selected and assigned to the drug(n=37),intervention(n=40),and control(n=39)groups.All the children received routine preoperative checkups and nursing before being transferred to the preoperative preparation room on the day of the operation.The drug group received 0.5 mg/kg midazolam and the intervention group treatment consisting of static cartoons combined with dynamic virtual environments.The control group received no intervention.The modified Yale Preoperative Anxiety Scale was used to evaluate the children’s anxiety level on the day before surgery(T0),before leaving the preoperative preparation room(T1),when entering the operating room(T2),and at anesthesia induction(T3).Compliance during anesthesia induction(T3)was evaluated using the Induction Compliance Checklist(ICC).Changes in mean arterial pressure(MAP),heart rate(HR),and respiratory rate(RR)were also recorded at each time point.RESULTS The anxiety scores of the three groups increased variously at T1 and T2.At T3,both the drug and intervention groups had similar anxiety scores,both of which were lower than those in the control group.At T1 and T2,MAP,HR,and RR of the three groups increased.The drug and control groups had significantly higher MAP and RR than the intervention group at T2.At T3,the MAP,HR,and RR of the drug group decreased and were significantly lower than those in the control group but were comparable to those in the intervention group.Both the drug and intervention groups had similar ICC scores and duration of anesthesia induction(T3),both of which were higher than those of the control group.CONCLUSION Combining static cartoons with dynamic virtual environments as effective as medication,specifically midazolam,in reducing preoperative anxiety and fear in preschool-aged children.This approach also improve their compliance during anesthesia induction and helped maintain their stable vital signs.

Corrosion behavior of HVOF Inconel 625 coating in the simulated marine environment

High velocity oxygen fuel(HVOF)spraying process is commonly used to produce superalloy coatings.Inconel 625 coating was prepared on Q235B low carbon steel by HVOF.A series of experiments were conducted to examine the surface and corrosion resistance properties of Inconel 625 HVOF coating.In this paper,potentiodynamic polarization tests and electrochemical impedance spectroscopy(EIS)tests were carried out to evaluate the corrosion resistance of Inconel 625 coating under simulated marine environment.The experiment-al results showed that Inconel 625 coating revealed low porosity and desired coating thickness.Shift in the corrosion potential(E_(corr))to-wards the noble direction combined with much low corrosion current density(i_(corr))indicating a significant improvement of HVOF Inconel 625 coating compared with the substrate.

Lateral earth pressure of granular backfills on retaining walls with expanded polystyrene geofoam inclusions under limited surcharge loading

Existing studies have focused on the behavior of the retaining wall equipped with expanded polystyrene(EPS)geofoam inclusions under semi-infinite surcharge loading rather than limited surcharge loading.In this paper,the failure mode and the earth pressure acting on the rigid retaining wall with EPS geofoam inclusions and granular backfills(henceforth referred to as EPS-wall),under limited surcharge loading are investigated through two-and three-dimensional model tests.The testing results show that different from the sliding of almost all the backfill in the EPS-wall under semi-infinite surcharge loading,only an approximately triangular backfill slides in the wall under limited surcharge loading.The distribution of the lateral earth pressure on the EPS-wall under limited surcharge loading is non-linear,and the distribution changes from the increase of the wall depth to the decrease with the increase of the limited surcharge loading.An approach based on the force equilibrium of a differential element is developed to predict the lateral earth pressure behind the EPS-wall subjected to limited surcharge loading,and its performance was fully validated by the three-dimensional model tests.

Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet.Traditional signature-based IDS are effective in detecting known attacks,but they are unable to detect unknown emerging attacks.Therefore,there is the need for an IDS which can learn from data and detect new threats.Ensemble Machine Learning(ML)and individual Deep Learning(DL)based IDS have been developed,and these individual models achieved low accuracy;however,their performance can be improved with the ensemble stacking technique.In this paper,we have proposed a Deep Stacked Neural Network(DSNN)based IDS,which consists of two stacked Convolutional Neural Network(CNN)models as base learners and Extreme Gradient Boosting(XGB)as the meta learner.The proposed DSNN model was trained and evaluated with the next-generation dataset,TON_IoT.Several pre-processing techniques were applied to prepare a dataset for the model,including ensemble feature selection and the SMOTE technique.Accuracy,precision,recall,F1-score,and false positive rates were used to evaluate the performance of the proposed ensemble model.Our experimental results showed that the accuracy for binary classification is 99.61%,which is better than in the baseline individual DL and ML models.In addition,the model proposed for IDS has been compared with similar models.The proposed DSNN achieved better performance metrics than the other models.The proposed DSNN model will be used to develop enhanced IDS for threat mitigation in smart industrial environments.