GRU Enabled Intrusion Detection System for IoT Environment with Swarm Optimization and Gaussian Random Forest Classification

In recent years,machine learning(ML)and deep learning(DL)have significantly advanced intrusion detection systems,effectively addressing potential malicious attacks across networks.This paper introduces a robust method for detecting and categorizing attacks within the Internet of Things(IoT)environment,leveraging the NSL-KDD dataset.To achieve high accuracy,the authors used the feature extraction technique in combination with an autoencoder,integrated with a gated recurrent unit(GRU).Therefore,the accurate features are selected by using the cuckoo search algorithm integrated particle swarm optimization(PSO),and PSO has been employed for training the features.The final classification of features has been carried out by using the proposed RF-GNB random forest with the Gaussian Naïve Bayes classifier.The proposed model has been evaluated and its performance is verified with some of the standard metrics such as precision,accuracy rate,recall F1-score,etc.,and has been compared with different existing models.The generated results that detected approximately 99.87%of intrusions within the IoT environments,demonstrated the high performance of the proposed method.These results affirmed the efficacy of the proposed method in increasing the accuracy of intrusion detection within IoT network systems.

Introduction to Urban and Community Forestry in the United States of America: History, Accomplishments, Issues and Trends

The urban and community forestry movement in the United States has matured over the last 20 years from managing street trees, to understanding the benefits of trees in urban ecosystems, and now to managing urban green infrastructure. This paper introduced the history, development, and major accomplishments of the urban and community forestry movement, highlighted the economic, ecological, environmental, and social values of forests and trees to communities, and discussed issues and trends of the urban and community forestry program in the United States.

Uncertainties of landslide susceptibility prediction:influences of different study area scales and mapping unit scales

This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou City in China,its eastern region(Ganzhou East),and Ruijin County in Ganzhou East were chosen.Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m,as well as slope units that were extracted by multi-scale segmentation method.The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs.Then,landslide susceptibility maps(LSMs)of Ganzhou City,Ganzhou East and Ruijin County are pro-duced using a support vector machine(SVM)and random forest(RF),respectively.The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City,along with the LSMs of Ruijin County from Ganzhou East.Additionally,LSMs of Ruijin at various mapping unit scales are generated in accordance.Accuracy and landslide suscepti-bility indexes(LSIs)distribution are used to express LSP uncertainties.The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City,Ganzhou East to Ruijin County,whereas those under slope units are less affected by study area scales.Of course,attentions should also be paid to the broader representativeness of large study areas.The LSP accuracy of slope units increases by about 6%–10%compared with those under grid units with 30 m and 60 m resolution in the same study area's scale.The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large.The importance of environmental factors varies greatly with the 60 m grid unit,but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.

Deep-Ensemble Learning Method for Solar Resource Assessment of Complex Terrain Landscapes

As the global demand for renewable energy grows,solar energy is gaining attention as a clean,sustainable energy source.Accurate assessment of solar energy resources is crucial for the siting and design of photovoltaic power plants.This study proposes an integrated deep learning-based photovoltaic resource assessment method.Ensemble learning and deep learning methods are fused for photovoltaic resource assessment for the first time.The proposed method combines the random forest,gated recurrent unit,and long short-term memory to effectively improve the accuracy and reliability of photovoltaic resource assessment.The proposed method has strong adaptability and high accuracy even in the photovoltaic resource assessment of complex terrain and landscape.The experimental results show that the proposed method outperforms the comparison algorithm in all evaluation indexes,indicating that the proposed method has higher accuracy and reliability in photovoltaic resource assessment with improved generalization performance traditional single algorithm.

A HybridManufacturing ProcessMonitoringMethod Using Stacked Gated Recurrent Unit and Random Forest

This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations.Since real-time production process monitoring is critical in today’s smart manufacturing.The more robust the monitoring model,the more reliable a process is to be under control.In the past,many researchers have developed real-time monitoring methods to detect process shifts early.However,thesemethods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties.In this paper,a robust monitoring model combining Gated Recurrent Unit(GRU)and Random Forest(RF)with Real-Time Contrast(RTC)called GRU-RF-RTC was proposed to detect process shifts rapidly.The effectiveness of the proposed GRU-RF-RTC model is first evaluated using multivariate normal and nonnormal distribution datasets.Then,to prove the applicability of the proposed model in a realmanufacturing setting,the model was evaluated using real-world normal and non-normal problems.The results demonstrate that the proposed GRU-RF-RTC outperforms other methods in detecting process shifts quickly with the lowest average out-of-control run length(ARL1)in all synthesis and real-world problems under normal and non-normal cases.The experiment results on real-world problems highlight the significance of the proposed GRU-RF-RTC model in modern manufacturing process monitoring applications.The result reveals that the proposed method improves the shift detection capability by 42.14%in normal and 43.64%in gamma distribution problems.

Regeneration Potential of Woody Species at the Side of Secondary Roads Post-Logging of Loundoungou-Toukoulaka Forest Management Unit, Republic of the Congo

Natural regeneration is the basis of a dynamic and demographic balance of plant populations. The objective of this study was to assess the natural regeneration potential of woody species along secondary roads post-logging abandoned since 2008 and 2018. In the two Annual Allowable Cuts (AAC 2008 and AAC 2018), 24 regenerating sub-plots (i.e. 12 sub-plots for AAC 2008 and 12 sub-plots for AAC 2018) with a unit area of 5 m × 5 m were delimited with a total area of 0.06 ha (i.e. 0.03 ha for each AAC). The abundance and diversity of woody species were respectively inventoried and estimated. Two estimators of the specific richness were used to estimate the floristic diversity of each Annual Allowable Cuts (AAC). The results reveal globally 88 woody species in the AAC 2008 and 241 woody species in the AAC 2018, with respective average densities of 2933 stem/ha and 8033 stem/ha. There was a very highly significant difference between the mean densities of the two AAC (Kruskal-Wallis test;H = 2.36, p-value < 0.000). The results also highlight a great diversity and a relatively high abundance of woody species in the 2018 AAC compared to the 2008 AAC. Also, the spatial structuring of the sub-plots on the basis of Principal Component Analysis (PCA) demonstrates that the floristic composition of the two AAC is globally different. The study suggests silvicultural interventions and the long-term assessment of regenerating woody species along abandoned secondary roads in order to guarantee the sustainable management of their population.

Comparison and Analysis of Agricultural and Forest Land Changes in Typical Agricultural Regions of Northern Mid-latitudes

The northeastern China, the United States, and the western Europe are important agricultural regions both on the global and regional scales. The westem Europe has a longer history of agricultural land development than the eastem United States. These two regions have changed from the deforestation and reclamation phase in the past to the current land abandonment and reforestation phase. Compared with the two regions, large-scale land exploitation has only been practiced in the northeastern China during the last century. After a short high-intensity deforestation and reclamation period, agricultural and forest lands are basically in a dynamic steady state. By comparing domestic and international agro-forestry development and considering the ecological environment and socio-economic bene- fits that can be derived from agro-forestry, this paper suggests that large area of reforestation would be inevitable in future though per- sistent and large agricultural demand in coming decades even more. And local reforestation at slope farmland with ecological vulner- ability should be imperative at present to avoid severer damage. At the same time, from the perspective of Land Change Science, the results demonstrate that the research on land use change in the agro-forestry ecotone is typical and critical, particularly those dealing with the analysis of spatial and temporal characteristics and the simulation of climate, hydrology, and other environmental effects.

Timberland Investing and Private Property Rights in the United States of America

Investments in rural land for agriculture, timber, and other natural resource purposes occur frequently and globally. Fundamental principles of liberty and property found in the United States of America’s (“US”) legal system, from its origins to recent US Supreme Court decisions, continue to positively benefit holders of real estate in the Southern US, through a deep-rooted public policy of supporting private property rights and rural economic development. This stable rule of law enhances the long-term adaptability and sustainability of timberland as an asset class. This article is a commentary. It combines legal research methodology with the observations and conclusions of the authors. Its purpose is to demonstrate that the existence of alienable, documentable ownership, and related property rights create inherent stability and security. These principles form the basis of a culture that is defined by the rule of law and is “open for business.” This business mindset is particularly prevalent in the Southern US.

Assessing forest understory biomass in northwest Florida (USA) and its potential to meet the state energy needs

A stratified random sampling approach was employed to quantify total biomass across prevalent non-commercial forest understory species found in six counties of northwest Florida, USA. The moisture content （wet basis） and calorific values of these species were also measured. Total green biomass from forest understory species was estimated to be around 12 million metric tons, mostly comprised of Cliftonia monophylla （titi, buckwheat tree） and Cyrilla racemiflora （white titi, swamp titi）. This understory forest biomass would be sufficient to generate about 28.8 million GJ of electricity or 1589.25 million liters of ethanol. A need was identified to determine the inventory of forest understory biomass at the state level and assess the overall sustainability of utilizing forest understory biomass for bioenergy.

ESTABLISHMENT OF TAI PIN DI COMPREHENSIVE PROTECTING FOREST SYSTEM

A multi-function protecting forest system was planed and arranged elaborately for im-provement of the local ecological conditions and high economical benefit. The system in-cludes level farmland shelter belt network, hillside farmland shelter belt network, stereoscop-ic sparse-wood pasture, erosion control fuel forest, fast growing commercial forest, eco-nomical forest, salt-soda controlling project and salt-soda protecting forest on salt-sodaland, ect..

Mapping regional forest management units:a road-based framework in Southeastern Coastal Plain and Piedmont

Management practices are one of the most important factors affecting forest structure and function.Landowners in southern United States manage forests using appropriately sized areas,to meet management objectives that include economic return,sustainability,and esthetic enjoyment.Road networks spatially designate the socioenvironmental elements for the forests,which represented and aggregated as forest management units.Road networks are widely used for managing forests by setting logging roads and firebreaks.We propose that common types of forest management are practiced in road-delineated units that can be determined by remote sensing satellite imagery coupled with crowd-sourced road network datasets.Satellite sensors do not always capture roadcaused canopy openings,so it is difficult to delineate ecologically relevant units based only on satellite data.By integrating citizen-based road networks with the National Land Cover Database,we mapped road-delineated management units across the regional landscape and analyzed the size frequency distribution of management units.We found the road-delineated units smaller than 0.5 ha comprised 64%of the number of units,but only0.98%of the total forest area.We also applied a statistical similarity test(Warren's Index)to access the equivalency of road-delineated units with forest disturbances by simulating a serious of neutral landscapes.The outputs showed that the whole southeastern U.S.has the probability of road-delineated unit of 0.44 and production forests overlapped significantly with disturbance areas with an average probability of 0.50.

Evaluating the impact of sampling schemes on leaf area index measurements from digital hemispherical photography in Larix principis-rupprechtii forest plots

Background:Digital hemispherical photography(DHP)is widely used to estimate the leaf area index(LAI)of forest plots due to its advantages of high efficiency and low cost.A crucial step in the LAI estimation of forest plots via DHP is choosing a sampling scheme.However,various sampling schemes involving DHP have been used for the LAI estimation of forest plots.To date,the impact of sampling schemes on LAI estimation from DHP has not been comprehensively investigated.Methods:In this study,13 commonly used sampling schemes which belong to five sampling types(i.e.dispersed,square,cross,transect and circle)were adopted in the LAI estimation of five Larix principis-rupprechtii plots(25m×25 m).An additional sampling scheme(with a sample size of 89)was generated on the basis of all the sample points of the 13 sampling schemes.Three typical inversion models and four canopy element clumping index(Ωe)algorithms were involved in the LAI estimation.The impacts of the sampling schemes on four variables,including gap fraction,Ωe,effective plant area index(PAIe)and LAI estimation from DHP were analysed.The LAI estimates obtained with different sampling schemes were then compared with those obtained from litter collection measurements.Results:Large differences were observed for all four variable estimates(i.e.gap fraction,Ωe,PAIe and LAI)under different sampling schemes.The differences in impact of sampling schemes on LAI estimation were not obvious for the three inversion models,if the fourΩe algorithms,except for the traditional gap-size analysis algorithm were adopted in the estimation.The accuracy of LAI estimation was not always improved with an increase in sample size.Moreover,results indicated that with the appropriate inversion model,Ωe algorithm and sampling scheme,the maximum estimation error of DHP-estimated LAI at elementary sampling unit can be less than 20%,which is required by the global climate observing system,except in forest plots with extremely large LAI values(~>6.0).However,obtaining an LAI from DHP with an estimation error lower than 5%is impossible regardless of which combination of inversion model,Ωe algorithm and sampling scheme is used.Conclusion:The LAI estimation of L.principis-rupprechtii forests from DHP was largely affected by the sampling schemes adopted in the estimation.Thus,the sampling scheme should be seriously considered in the LAI estimation.One square and two transect sampling schemes(with sample sizes ranging from 3 to 9)were recommended to be used to estimate the LAI of L.principis-rupprechtii forests with the smallest mean relative error(MRE).By contrast,three cross and one dispersed sampling schemes were identified to provide LAI estimates with relatively large MREs.

Declining Vegetation Growth Rates in the Eastern United States from 2000 to 2010

Negative trends in the monthly MODerate resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time-series were found to be widespread in natural (non-cropland) ecosystems of the eastern United Statesfrom 2000 to 2010. Four sub-regions were detected with significant declines in summed growing season (May-September) EVI, namely theUpper Great Lakes, the Southern Appalachian, the Mid-Atlantic, and the southeastern Coastal Plain forests ecosystems. More than 20% of the undeveloped ecosystem areas in the four sub-regions with significant negative EVI growing season trends were classified as forested land cover over the entire study period. We detected relationships between annual temperature and precipitation patterns and negative forest EVI trends across these regions. Change patterns in both the climate moisture index (CMI) and growing degree days (GDD) were associated with declining forest EVI growing season trends. We conclude that temperature warming-induced change and variability of precipitation at local and regional scales may have altered the growth trends of large forested areas of the easternUnited Statesover the past decade.

The American College of ＂five in one＂ mode of governance

the governance of American University by the president and the board of directors, the state government, internal management, corporate governance, risk transfer and other dimensions, the governance mode of advanced and first-class personnel training quality, followed by the majority of countries in the world. Interpretation of the governance of the University of the United States, can help to make clear the United States as the development history of higher education is the oldest country, how to quickly improve university governance mode in a short period of time, to enhance the quality of higher education, has the important reference value to the reform of the governance mode of higher schools in china.

Detection of Alzheimer’s Disease Progression Using Integrated Deep Learning Approaches

Alzheimer’s disease(AD)is an intensifying disorder that causes brain cells to degenerate early and destruct.Mild cognitive impairment(MCI)is one of the early signs of AD that interferes with people’s regular functioning and daily activities.The proposed work includes a deep learning approach with a multimodal recurrent neural network(RNN)to predict whether MCI leads to Alzheimer’s or not.The gated recurrent unit(GRU)RNN classifier is trained using individual and correlated features.Feature vectors are concate-nated based on their correlation strength to improve prediction results.The feature vectors generated are given as the input to multiple different classifiers,whose decision function is used to predict the final output,which determines whether MCI progresses onto AD or not.Our findings demonstrated that,compared to individual modalities,which provided an average accuracy of 75%,our prediction model for MCI conversion to AD yielded an improve-ment in accuracy up to 96%when used with multiple concatenated modalities.Comparing the accuracy of different decision functions,such as Support Vec-tor Machine(SVM),Decision tree,Random Forest,and Ensemble techniques,it was found that that the Ensemble approach provided the highest accuracy(96%)and Decision tree provided the lowest accuracy(86%).

基于双向门控式宽度学习系统的监测数据结构变形预测

监测数据深度学习预测模型运算量大、实时性差,为此结合宽度学习系统(BLS)和双向长短时记忆(Bi-LSTM)模型的优势,提出基于双向门控式宽度学习系统(Bi-G-BLS)的结构变形预测模型.对BLS的特征节点增加循环反馈和遗忘门结构,提高当前节点对前一节点的依赖关系,分别从正向和反向提取时间序列的内部特征,充分挖掘数据的双向特征,在提高模型预测精确度的同时减少模型预测时间.基于实测的地铁基坑沉降监测数据的测试结果显示,所提预测模型与门控循环单元(GRU)、BLS、Bi-LSTM、G-BLS模型相比,均方根误差(RMSE)、平均绝对误差(MAE)、平均绝对百分比误差(MAPE)平均分别降低了21.04%、12.81%、24.41%;在预测精度相近的情况下,所提模型的预测时间比Bi-LSTM模型降低了99.59%.结果表明,所提模型在预测速度和精确度上较对比模型有明显提升.

贵州碧江石林地质公园地质遗迹特征及其地学意义

贵州碧江石林地质公园是以石林喀斯特为主,溶洞、侵蚀峡谷、泉水溪流为辅,集地质剖面、地质构造、古油茶树等景观资源为一体的省级小型地质公园。公园处在贵州高原向湘西丘陵过渡的斜坡地带,地质遗迹资源较典型,且具观赏和美学价值,主要分为地质剖面、地质构造、地貌景观、水体景观4个大类,6个类,共30余处地质遗迹景点。在山顶斜坡相对封闭的独立喀斯特单元中,有限可溶地层发育了规模较大的石林喀斯特,代表了贵州高原晚新生代喀斯特地貌发育演化的典型范例,具有区域对比意义,在地质学、地貌学及地学旅游等领域具有重要的研究价值。研究结果对于系统认识公园地质遗迹状况、地质遗迹保护和开发、建立地学科普研学基地等具有一定参考和借鉴意义。

基于人员出入的激光检索系统设计

激光由于具有方向性好,亮度高,单色性好等特点而被广泛应用,基于激光的特点,设计了一个基于人员出入的激光检索系统。本系统采用Arduino uno开发板来控制激光发射与接收,通过屏幕操作启动中央处理器进行数据处理,在集成开发板的控制下,对出入的人员进行计数,在LED屏幕上显示当前到达的总人数,通过实验测试,本系统实现无接触检测某些场所内的人流量。

基于不同评价单元的三峡库区滑坡易发性对比——以重庆市云阳县为例

为探究不同评价单元对区域滑坡易发性评估的影响,基于网格单元与斜坡单元对三峡库区典型县域重庆市云阳县开展了滑坡易发性研究。首先选取高程、坡度、曲率等22个评价因子,根据研究区988个历史滑坡数据,通过30 m×30 m的栅格数据提取斜坡单元,并基于网格单元及斜坡单元分别建立22个滑坡影响因子地理空间数据库;然后利用随机森林与贝叶斯优化算法来构建滑坡易发性模型,对研究区滑坡进行易发性评估;最后结合ROC(受试者工作特征)曲线与混淆矩阵结果检验评价单元的易发性模型预测精度。结果表明:易发性评估的结果可划分为低、较低、中、较高、高5个等级;基于网格单元的滑坡易发性模型中,高程、与道路距离、坡度这3个因子对滑坡发生的贡献率大,基于斜坡单元的模型中,I_(NDV)(归一化植被指数)、剖面曲率、平面曲率这3个因子对滑坡发生的贡献率大,并且2个模型的滑坡密度均随着滑坡易发性等级的升高而变大;与网格单元相比,斜坡单元能更好地解释地形间的联系,以斜坡单元(AUC=0.744)为最小评价单元的滑坡易发性模型比网格单元(AUC=0.714)精度更高。

镇域尺度下秦巴山区堆积层滑坡易发性不同单元评价性能对比研究

秦巴山区堆积层滑坡数量多、分布广、密度大、频次高,所造成的危害十分严重,且具有孕灾条件复杂多样和部分灾害评价数据获取难度大等特征。笔者选取秦巴山区小岭镇作为研究区,在地质灾害野外调查基础上,结合堆积层滑坡区域特点,采取栅格、斜坡两种单元类型,因地制宜的提取了滑坡孕灾因子,分析其相关性,提选出坡度、坡高、坡面形态、斜坡结构类型、堆积层厚度、距道路、矿区、断裂的距离等8个因子作为堆积层滑坡特征因子,运用随机森林模型方法对该镇域进行了滑坡易发性评价;并通过评价结果频率比、ROC曲线、易发性概率均值与标准差,对栅格单元、斜坡单元两种单元类型的精度与准确性进行了验证,结果表明:两种评价单元的预测结果都有良好的表现,但斜坡单元作为评价单元总体预测性能高于栅格单元,栅格单元在灾害防治具体空间部署上有着更精细的参考。研究成果对秦巴山区镇域地质灾害风险评价工作有一定的理论和实践意义。