Integration of Multiple Spectral Data via a Logistic Regression Algorithm for Detection of Crop Residue Burned Areas:A Case Study of Songnen Plain,Northeast China

The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate estimation of cropland burned area is both crucial and challenging,especially for the small and fragmented burned scars in China.Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument(MSI)data and its effectiveness was tested taking Songnen Plain,Northeast China as a case using satellite image of 2020.We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator,and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer(MODIS)MCD64A1 burned area product.The overall accuracy of the single variable logistic regression was 77.38%to 86.90%and 73.47%to 97.14%for the 52TCQ and 51TYM cases,respectively.In comparison,the accuracy of the burned area map was improved to 87.14%and 98.33%for the 52TCQ and 51TYM cases,respectively by multiple variable logistic regression of Sentind-2 images.The balance of omission error and commission error was also improved.The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection,offering a highly automated process with an automatic threshold determination mechanism.This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit.It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data.

Identified the hydrochemical and the sulfur cycle process in subsidence area of Pingyu mining area using multi-isotopes combined with hydrochemistry methods

Groundwater serves as an important water source for residents in and around mining areas.To achieve scientific planning and efficient utilization of water resources in mining areas,it is essential to figure out the chemical formation process and the ground water sulfur cycle that transpire after the coal mining activities.Based on studies of hydrochemistry and D,^(18)O-H_(2)O,^(34)S-SO_(4)isotopes,this study applied principal component analysis,ion ratio and other methods in its attempts to reveal the hydrogeochemical action and sulfur cycle in the subsidence area of Pingyu mining area.The study discovered that,in the studied area,precipitation provides the major supply of groundwater and the main water chemistry effects are dominated by oxidation dissolution of sulfide minerals as well as the dissolution of carbonate and silicate rocks.The sulfate in groundwater primarily originates from oxidation and dissolution of sulfide minerals in coal-bearing strata and human activities.The mixed sulfate formed by the oxidation of sulfide minerals and by human activities continuously recharges the groundwater,promoting the dissolution of carbonate rock and silicate rock in the process.

Grouping tree species to estimate basal area increment in temperate multispecies forests in Durango,Mexico

Multispecies forests have received increased scientific attention,driven by the hypothesis that biodiversity improves ecological resilience.However,a greater species diversity presents challenges for forest management and research.Our study aims to develop basal area growth models for tree species cohorts.The analysis is based on a dataset of 423 permanent plots(2,500 m^(2))located in temperate forests in Durango,Mexico.First,we define tree species cohorts based on individual and neighborhood-based variables using a combination of principal component and cluster analyses.Then,we estimate the basal area increment of each cohort through the generalized additive model to describe the effect of tree size,competition,stand density and site quality.The principal component and cluster analyses assign a total of 37 tree species to eight cohorts that differed primarily with regard to the distribution of tree size and vertical position within the community.The generalized additive models provide satisfactory estimates of tree growth for the species cohorts,explaining between 19 and 53 percent of the total variation of basal area increment,and highlight the following results:i)most cohorts show a"rise-and-fall"effect of tree size on tree growth;ii)surprisingly,the competition index"basal area of larger trees"had showed a positive effect in four of the eight cohorts;iii)stand density had a negative effect on basal area increment,though the effect was minor in medium-and high-density stands,and iv)basal area growth was positively correlated with site quality except for an oak cohort.The developed species cohorts and growth models provide insight into their particular ecological features and growth patterns that may support the development of sustainable management strategies for temperate multispecies forests.

Automatic Road Tunnel Crack Inspection Based on Crack Area Sensing and Multiscale Semantic Segmentation

The detection of crack defects on the walls of road tunnels is a crucial step in the process of ensuring travel safetyand performing routine tunnel maintenance. The automatic and accurate detection of cracks on the surface of roadtunnels is the key to improving the maintenance efficiency of road tunnels. Machine vision technology combinedwith a deep neural network model is an effective means to realize the localization and identification of crackdefects on the surface of road tunnels.We propose a complete set of automatic inspection methods for identifyingcracks on the walls of road tunnels as a solution to the problem of difficulty in identifying cracks during manualmaintenance. First, a set of equipment applied to the real-time acquisition of high-definition images of walls inroad tunnels is designed. Images of walls in road tunnels are acquired based on the designed equipment, whereimages containing crack defects are manually identified and selected. Subsequently, the training and validationsets used to construct the crack inspection model are obtained based on the acquired images, whereas the regionscontaining cracks and the pixels of the cracks are finely labeled. After that, a crack area sensing module is designedbased on the proposed you only look once version 7 model combined with coordinate attention mechanism (CAYOLOV7) network to locate the crack regions in the road tunnel surface images. Only subimages containingcracks are acquired and sent to the multiscale semantic segmentation module for extraction of the pixels to whichthe cracks belong based on the DeepLab V3+ network. The precision and recall of the crack region localizationon the surface of a road tunnel based on our proposed method are 82.4% and 93.8%, respectively. Moreover, themean intersection over union (MIoU) and pixel accuracy (PA) values for achieving pixel-level detection accuracyare 76.84% and 78.29%, respectively. The experimental results on the dataset show that our proposed two-stagedetection method outperforms other state-of-the-art models in crack region localization and detection. Based onour proposedmethod, the images captured on the surface of a road tunnel can complete crack detection at a speed often frames/second, and the detection accuracy can reach 0.25 mm, which meets the requirements for maintenanceof an actual project. The designed CA-YOLO V7 network enables precise localization of the area to which a crackbelongs in images acquired under different environmental and lighting conditions in road tunnels. The improvedDeepLab V3+ network based on lightweighting is able to extract crack morphology in a given region more quicklywhile maintaining segmentation accuracy. The established model combines defect localization and segmentationmodels for the first time, realizing pixel-level defect localization and extraction on the surface of road tunnelsin complex environments, and is capable of determining the actual size of cracks based on the physical coordinatesystemafter camera calibration. The trainedmodelhas highaccuracy andcanbe extendedandapplied to embeddedcomputing devices for the assessment and repair of damaged areas in different types of road tunnels.

Multi-scenario Simulation of the Impact of Land Use Change on the Ecosystem Service Value in the Suzhou-Wuxi-Changzhou Metropolitan Area,China

As the most economically developed metropolitan area in China’s Yangtze River Delta,the rapid changing land use patterns of Suzhou-Wuxi-Changzhou(Su-Xi-Chang) metropolitan area have profound impacts on the ecosystem service value(ESV).Based on the patterns of land use change and the ESV change in Su-Xi-Chang metropolitan area from 2000 to 2020,we set up four scenarios:natural development scenario,urban development scenario,arable land protection scenario and ecological protection scenario,and simulated the impact of land use changes on the ESV in these scenarios.The results showed that:1) the area of built-up land in the Su-XiChang metropolitan area increased significantly from 2000 to 2020,and the area of other types of land decreased.Arable land underwent the highest transfer-out area,and was primarily converted into built-up land.The total ESV of Su-Xi-Chang metropolitan area increased initially then declined from 2000–2020,and the value of almost all individual ecosystem services decreased.2) Population density,GDP per area,night lighting intensity,and road network density can negatively impact the ESV.3) The total ESV loss under the natural development and urban development scenarios was high,and the expansion of the built-up land and the drastic shrinkage of the arable land contributed to the ESV decline under both scenarios.The total ESV under arable land protection and ecological protection scenarios increases,and therefore these scenarios are suitable for future land use optimization in Su-Xi-Chang.This study could provide a certain reference for land use planning and allocation,and offer guidance for the rational allocation of land resources.

A Multi-Center Randomized Controlled Study UsingΔP_(CO2)/Ca-v_(O2)as the Target to Guide Early Tissue Hypoperfusion in Sepsis in Plateau Areas

Objective:To explore the value of using the venous-arterial carbon dioxide partial pressure difference and the arterial-venous oxygen content difference ratio(ΔP_(CO2)/Ca-v_(O2))as targets to guide early tissue hypoperfusion in sepsis in plateau areas.Methods:90 sepsis patients admitted to the Third People’s Hospital of Xining and Golmud People’s Hospital from June 2017 to December 2022 were selected as the research subjects,and they were divided into the Scv_(O2)(central venous oxygen saturation)group and theΔP_(CO2)/Ca-v_(O2)group,with 45 cases in each group.The two groups were treated with early shock resuscitation according to different protocols.The hemodynamic characteristics of the two groups of patients before and after resuscitation were observed,and the volume responsiveness was evaluated.The ROC(receiver operating characteristic)curve was used to analyze the significance ofΔP_(CO2)/Ca-v_(O2),Scv_(O2),lactate,lactate clearance,and urine output in evaluating patient prognosis and the correlation betweenΔP_(CO2)/Ca-v_(O2)and the above indicators was explored.Results:Compared with before resuscitation,after fluid resuscitation,the heart rate(HR),mean arterial pressure(MAP),central venous pressure(CVP),cardiac index(CI),lactate,lactate clearance rate,and urine output of the two groups of patients were significantly improved(P<0.05);in terms of therapeutic effect,the 28-day mortality rate,6-hour fluid balance,and lactic acid clearance of theΔP_(CO2)/Ca-v_(O2)group were better than the Scv_(O2)group.The ROC characteristic curve showed that theΔP_(CO2)/Ca-v_(O2)value can effectively predict the prognosis of patients(AUC=0.907,sensitivity was 97%,specificity was 72.4%,and critical value was 1.84).ΔP_(CO2)/Ca-v_(O2)significantly correlated with Scv_(O2),lactic acid,and lactic acid clearance rate.Conclusion:TheΔP_(CO2)/Ca-v_(O2)value can be used to guide fluid resuscitation in early hypoperfusion in sepsis in plateau areas,improve patients’hemodynamics,reduce lactate indicators,and increase urine output.ΔP_(CO2)/Ca-v_(O2)level>1.84 can effectively improve patient prognosis.

Multi-source Data-driven Identification of Urban Functional Areas:A Case of Shenyang,China

Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.

Energy‐based analysis of seismic damage mechanism of multi‐anchor piles in tunnel crossing landslide area

To study the damage mechanism of multi‐anchor piles in tunnel crossing landslide area under earthquake,the damping performance of multi‐anchor piles was discussed.The energy dissipation springs were used as the optimization device of the anchor head to carry out the shaking table comparison test on the reinforced slope.The Hilbert spectrum and Hilbert marginal spectrum were proposed to analyze the seismic damage mechanism of the multi‐anchor piles,and the peak Fourier spectrum amplitude(PFSA)was used to verify the effectiveness of the method.The results show that the seismic energy is concentrated in the high‐frequency component(30-40Hz)of the Hilbert spectrum and the low‐frequency component(12-30 Hz)of the marginal spectrum.This indicates that they can be combined with the distribution law of the PFSA to identify the overall and local dynamic responses of the multi‐anchored piles,respectively.The stretchable deformation of the energy‐dissipation springs improves the coordination of the multi‐anchor piles,resulting in better pile integrity.The damage mechanism of the multi‐anchor piles is elucidated based on the energy method:local damage at the top and middle areas of the multi‐anchor piles is mainly caused by the low‐frequency component(12-30 Hz)of the marginal spectrum under the action of 0.15g and 0.20g seismic intensities.As the seismic intensity increases to 0.30g,the dynamic response of the slope is further amplified by the high‐frequency component(30-40 Hz)of the Hilbert energy spectrum,which leads to the overall damage of the multi‐anchor piles.

基于Multi-Agent的无人机集群体系自主作战系统设计

针对无人集群自主作战体系设计中的关键问题,提出基于Multi-Agent的无人集群自主作战系统设计方法。建立无人集群各节点的Agent模型及其推演规则;对于仿真系统模块化和通用化的需求,设计系统互操作式接口和无人集群自主作战的交互关系;开展无人集群系统仿真推演验证。仿真结果表明,所提设计方案不仅能够有效开展并完成自主作战网络生成-集群演化-效能评估的全过程动态演示验证,而且能够通过重复随机试验进一步评估无人集群的协同作战效能,最后总结了集群协同作战的策略和经验。

联合物理层与MAC层的multi-TRP上行重叠传输处理机制

针对非理想回程下现有协议难以有效处理多传输接收节点(multi-TRP)场景中多定时提前(multi-TA)导致的严重上行链路(UL)重叠传输问题,联合改进物理层复用技术和介质访问控制(MAC)令牌桶技术,提出了一种新型的UL重叠传输处理机制。该新型机制通过改进物理层重叠信道识别流程、复用要求及复用规则,将物理层复用信息与重叠信息反馈至MAC层,并对MAC层令牌桶技术进行优化。通过仿真实验对所提机制与现有协议机制进行对比,结果表明,在逻辑时隙不可重叠和可重叠2种情形下,物理上行控制信道(PUCCH)实际复用数量性能平均提升了57.58%和49.40%,物理上行共享信道(PUSCH)实际可用资源数量性能平均提升了12.09%和26.03%;优先级最高逻辑信道实际占用资源数量性能平均提升了33.33%和45.48%。

基于Multi-Agent的水电站变压器故障诊断系统

为了精准、快速完成水电站变压器的故障诊断,设计基于Multi-Agent的水电站变压器故障诊断系统。变压器状态监控agent将检测到的变压器故障信息发送给系统管理agent,系统管理agent通过通信agent将变压器故障信息发送给变压器故障诊断agent,变压器故障诊断agent利用小波变换方法提取变压器故障特征,并将其作为IFOA-SVM模型输入,完成变压器故障分类后,获取变压器故障诊断结果,该结果通过通信agent显示给用户。实验表明,该系统可有效诊断变压器故障诊断,诊断成功率受系统故障信息丢失率的影响较小,诊断耗时、耗能小,并具有较高故障诊断成功率。

Correlation of periodontal inflamed surface area with glycated hemoglobin,interleukin-6 and lipoprotein(a)in type 2 diabetes with retinopathy

BACKGROUND The two-way relationship between periodontitis and type 2 diabetes mellitus(T2DM)is well established.Prolonged hyperglycemia contributes to increased periodontal destruction and severe periodontitis,accentuating diabetic complications.An inflammatory link exists between diabetic retinopathy(DR)and periodontitis,but the studies regarding this association and the role of lipoprotein(a)[Lp(a)]and interleukin-6(IL-6)in these conditions are scarce in the literature.AIM To determine the correlation of periodontal inflamed surface area(PISA)with glycated Hb(HbA1c),serum IL-6 and Lp(a)in T2DM subjects with retinopathy.METHODS This cross-sectional study comprised 40 T2DM subjects with DR and 40 T2DM subjects without DR.All subjects were assessed for periodontal parameters[bleeding on probing(BOP),probing pocket depth,clinical attachment loss(CAL),oral hygiene index-simplified,plaque index(PI)and PISA],and systemic parameters[HbA1c,fasting plasma glucose and postprandial plasma glucose,fasting lipid profile,serum IL-6 and serum Lp(a)].RESULTS The proportion of periodontitis in T2DM with and without DR was 47.5%and 27.5%respectively.Severity of periodontitis,CAL,PISA,IL-6 and Lp(a)were higher in T2DM with DR group compared to T2DM without DR group.Significant difference was observed in the mean percentage of sites with BOP between T2DM with DR(69%)and T2DM without DR(41%),but there was no significant difference in PI(P>0.05).HbA1c was positively correlated with CAL(r=0.351,P=0.001),and PISA(r=0.393,P≤0.001)in study subjects.A positive correlation was found between PISA and IL-6(r=0.651,P<0.0001);PISA and Lp(a)(r=0.59,P<0.001);CAL and IL-6(r=0.527,P<0.0001)and CAL and Lp(a)(r=0.631,P<0.001)among study subjects.CONCLUSION Despite both groups having poor glycemic control and comparable plaque scores,the periodontal parameters were higher in DR as compared to T2DM without DR.Since a bidirectional link exists between periodontitis and DM,the presence of DR may have contributed to the severity of periodontal destruction and periodontitis may have influenced the progression of DR.

Ecological-Based Mining:A Coal-Water-Thermal Collaborative Paradigm in Ecologically Fragile Areas in Western China

A substantial reduction in groundwater level,exacerbated by coal mining activities,is intensifying water scarcity in western China’s ecologically fragile coal mining areas.China’s national strategic goal of achieving a carbon peak and carbon neutrality has made eco-friendly mining that prioritizes the protection and efficient use of water resources essential.Based on the resource characteristics of mine water and heat hazards,an intensive coal-water-thermal collaborative co-mining paradigm for the duration of the mining process is proposed.An integrated system for the production,supply,and storage of mining companion resources is achieved through technologies such as roof water inrush prevention and control,hydrothermal quality improvement,and deep-injection geological storage.An active preventive and control system achieved by adjusting the mining technology and a passive system centered on multiobjective drainage and grouting treatment are suggested,in accordance with the original geological characteristics and dynamic process of water inrush.By implementing advanced multi-objective drainage,specifically designed to address the“skylight-type”water inrush mode in the Yulin mining area of Shaanxi Province,a substantial reduction of 50%in water drillings and inflow was achieved,leading to stabilized water conditions that effectively ensure subsequent safe coal mining.An integrated-energy complementary model that incorporates the clean production concept of heat utilization is also proposed.The findings indicate a potential saving of 8419 t of standard coal by using water and air heat as an alternative heating source for the Xiaojihan coalmine,resulting in an impressive energy conservation of 50.2%and a notable 24.2%reduction in carbon emissions.The ultra-deep sustained water injection of 100 m^(3)·h^(-1)in a single well would not rupture the formation or cause water leakage,and 7.87×10^(5)t of mine water could be effectively stored in the Liujiagou Formation,presenting a viable method for mine-water management in the Ordos Basin and providing insights for green and low-carbon mining.

Research on the Relationship Between Environmental and Economic Coupling Systems in Bohai Bay Area Based on a Vector Autoregression(VAR)Model

This study analyzed the impact of land-based contaminants and tertiary industrial structure on economic development in the selected Bohai Bay area,China.Based on panel data spanning 2011-2020,a vector autoregressive(VAR)model is used to analyze and forecast the short-run and long-run relationships between three industrial structures,pollutant discharge,and economic development.The results showed that the environmental index had a long-term cointegration relationship with the industrial structure economic index.Per capital chemical oxygen demand(PCOD)and per capita ammonia nitrogen(PNH_(3)N)had a positive impact on delta per capita GDP(dPGDP),while per capita solid waste(PSW),the secondary industry rate(SIR)and delta tertiary industry(dTIR)had a negative impact on dPGDP.The VAR model under this coupling system had stability and credibility.The impulse response results showed that the short-term effect of the coupling system on dPGDP was basically consistent with the Granger causality test results.In addition,variance decomposition was used in this study to predict the long-term impact of the coupling system in the next ten periods(i.e.,ten years).It was found that dTIR had a great impact on dPGDP,with a contribution rate as high as 74.35%in the tenth period,followed by the contribution rate of PCOD up to 3.94%,while the long-term contribution rates of PSW,SIR and PNH3N were all less than 1%.The results show that the government should support the development of the tertiary industry to maintain the vitality of economic development and prevent environmental deterioration.

Seismic response of a mid-story isolated structure considering SSI in mountainous areas under long-period earthquakes

At present,there is not much research on mid-story isolated structures in mountainous areas.In this study,a model of a mid-story isolated structure considering soil-structure interaction(SSI)in mountainous areas is established along with a model that does not consider SSI.Eight long-period earthquake waves and two ordinary earthquake waves are selected as inputs for the dynamic time history analysis of the structure.The results show that the seismic response of a mid-story isolated structure considering SSI in mountainous areas can be amplified when compared with a structure that does not consider SSI.The structure response under long-period earthquakes is larger than that of ordinary earthquakes.The structure response under far-field harmonic-like earthquakes is larger than that of near-fault pulse-type earthquakes.The structure response under near-fault pulse-type earthquakes is larger than that of far-field non-harmonic earthquakes.When subjected to long-period earthquakes,the displacement of the isolated bearings exceeded the limit value,which led to instability and overturning of the structure.The structure with dampers in the isolated story could adequately control the nonlinear response of the structure,effectively reduce the displacement of the isolated bearings,and provide a convenient,efficient and economic method not only for new construction but also to retrofit existing structures.

Can Multiple Livelihood Interventions Improve Livelihood Resilience of Out-of-poverty Farmers in Mountain Areas?A Case Study of Longnan Mountain Area,China

Clarifying the impact of livelihood interventions on the livelihood resilience of farmers in undeveloped mountain areas can not only optimize interventions,but also provide experiential support for global poverty alleviation.To analyze the impact of multiple livelihood interventions on livelihood resilience,we constructed an analytical framework and analyzed the enjoyment of livelihood interventions and the heterogeneity of livelihood resilience among out-of-poverty farmers in the Longnan mountain areas,China.Then,we studied the impact of intervention intensity on livelihood resilience through the multiple linear regression model.The results revealed that:1)the livelihood interventions enjoyed by out-of-poverty farmer in mountain areas were multiple.The proportion of farmers enjoyed diversified livelihood interventions was in descending order of high mountain areas,semi-mountain area and Chuanba valley areas.2)The overall livelihood resilience of farmers in Longnan mountain areas was generally low,with an average of 0.299.There were significant differences in the livelihood resilience of farmers across different geographic areas in the study area and types of interventions.3)The effects of industry interventions,employment interventions and education interventions were significant.The endogenous power of farmers not only had a significant positive effect on livelihood resilience,but also positively moderated the impact of the intensity of interventions on livelihood resilience.In addition,the household dependency ratio and the average altitude of the village area had a significant negative impact on their livelihood resilience.

Multi-Agent Dynamic Area Coverage Based on Reinforcement Learning with Connected Agents

Dynamic area coverage with small unmanned aerial vehicle(UAV)systems is one of the major research topics due to limited payloads and the difficulty of decentralized decision-making process.Collaborative behavior of a group of UAVs in an unknown environment is another hard problem to be solved.In this paper,we propose a method for decentralized execution of multi-UAVs for dynamic area coverage problems.The proposed decentralized decision-making dynamic area coverage(DDMDAC)method utilizes reinforcement learning(RL)where each UAV is represented by an intelligent agent that learns policies to create collaborative behaviors in partially observable environment.Intelligent agents increase their global observations by gathering information about the environment by connecting with other agents.The connectivity provides a consensus for the decision-making process,while each agent takes decisions.At each step,agents acquire all reachable agents’states,determine the optimum location for maximal area coverage and receive reward using the covered rate on the target area,respectively.The method was tested in a multi-agent actor-critic simulation platform.In the study,it has been considered that each UAV has a certain communication distance as in real applications.The results show that UAVs with limited communication distance can act jointly in the target area and can successfully cover the area without guidance from the central command unit.

Occurrence state and oil content evaluation of Permian Fengcheng Formation in the Hashan area as constrained by NMR and multistage Rock-Eval

The Hashan area,neighboring the Mahu Sag that is rich in the shale oil resources,showed commercial oil flow in the corresponding lacustrine shales of the Lower Permian Fengcheng Formation(P)with reserve scale approximately 789 million tons,presenting great potential for oil exploration.Despite their geographical proximity,the hydrocarbon occurrence and oil-bearing capacity of shale in the Hashan area and Mahu Sag greatly differ owing to the complex tectonic evolution.Therefore,understanding the occurrence state and oil content of the Pif in the Hashan area is crucial for ongoing shale oil exploration activities and the development of the northwestern margin of the Junggar Basin.In this study,an in-tegrated investigation,including petrological observations,scanning electron microscopy(SEM)obser-vation,analysis of nuclear magnetic resonance(NMR)Ti-T2 spectra,and conventional and multistage Rock-Eval pyrolysis methods were conducted to evaluate the occurrence state and oil content of the Pif shale in the Hashan area.The results indicate that plagioclase(average 30.7%)and quartz(24.1%)dominate the mineral compositions of the Pf shale samples.A method involving quartz-plagioclase-carbonate minerals is proposed to conduct lithofacies classification.In the Hashan area,the organic matter abundance in the Pf shale is scaled in fair to good range,the thermal maturity ranges from immature to early mature stage,and the primary organic matter types are Types I and Ilj.Intergranular and dissolution pores are the two most common pore types.The free oil is mostly found in the pores and microfractures of the mineral matrix,whereas the adsorbed oil is mostly adsorbed on the surfaces of kerogen and clay minerals.The high organic matter abundance,quartz content,and porosity account for substantial increase in the oil content,the area rich in shale oil resources coincides with that rich in free oil.The most favorable lithofacies in the Hashan area is the calcareous mudstone/shale,which hosts the highest free oil content(average 2.49 mg),total oil content(15.02 mg/g),organic matter abundance CTOC:1.88% and S_(1)+S_(2)=20.54mg/g and orositv(5.97%)

Diversity of Orchids in Terms of Their Distribution, Uses and Conservation in Annapurna Conservation Area of Nepal

The Annapurna Conservation Area (ACA), the first conservation area and the largest protected area (PA) in Nepal, is incredibly rich in biodiversity. Notwithstanding this, orchids in the ACA have not been explored enough yet thus making the need for ambitious research to be carried out. Previous study only included 81 species of orchids within ACA. This study aims to update the record of species and genera richness in the ACA. In total 198 species of orchids, belonging to 67 genera (40% and 62% of the total recorded orchid species and genera in Nepal) has been recorded in ACA. This represents an increase of 144% in species and 56% in genera over the previous data. Out of the 198 species, 99 were epiphytes, 6 were holomycotrophic and 93 were terrestrial. Among the 67 genera, Bulbophyllum (17) species were dominant, followed by Dendrobium (16), Herminium (10), Coelogyne, Plantanthera (9 each), Eria, Habenaria, Oberonia (8 each), Calanthe (7), and Liparis (6). Fifty-six species were found to be ornamentally significant and 85 species medicinally significant.

A Robust Approach for Multi Classification-Based Intrusion Detection through Stacking Deep Learning Models

Intrusion detection is a predominant task that monitors and protects the network infrastructure.Therefore,many datasets have been published and investigated by researchers to analyze and understand the problem of intrusion prediction and detection.In particular,the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)is an extensively used benchmark dataset for evaluating intrusion detection systems(IDSs)as it incorporates various network traffic attacks.It is worth mentioning that a large number of studies have tackled the problem of intrusion detection using machine learning models,but the performance of these models often decreases when evaluated on new attacks.This has led to the utilization of deep learning techniques,which have showcased significant potential for processing large datasets and therefore improving detection accuracy.For that reason,this paper focuses on the role of stacking deep learning models,including convolution neural network(CNN)and deep neural network(DNN)for improving the intrusion detection rate of the NSL-KDD dataset.Each base model is trained on the NSL-KDD dataset to extract significant features.Once the base models have been trained,the stacking process proceeds to the second stage,where a simple meta-model has been trained on the predictions generated from the proposed base models.The combination of the predictions allows the meta-model to distinguish different classes of attacks and increase the detection rate.Our experimental evaluations using the NSL-KDD dataset have shown the efficacy of stacking deep learning models for intrusion detection.The performance of the ensemble of base models,combined with the meta-model,exceeds the performance of individual models.Our stacking model has attained an accuracy of 99%and an average F1-score of 93%for the multi-classification scenario.Besides,the training time of the proposed ensemble model is lower than the training time of benchmark techniques,demonstrating its efficiency and robustness.