Distribution and geochemical significance of alkylbenzenes for crude oil with different depositional environments and thermal maturities

A total of 45 alkylbenzenes were detected and identified in crude oils with different depositional environments and thermal maturities from the Tarim Basin,Beibuwan Basin,and Songliao Basin using comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry(GC×GCTOFMS).By analyzing the distribution characteristics of C0-C5alkylbenzenes,it is found that the content of some alkylbenzenes varies greatly in crude oils.Based on the distribution characteristics of 1,2,4,5-tetramethylbenzene(Te MB)and 1,2,3,4-Te MB,the ratio of 1,2,4,5-Te MB to 1,2,3,4-Te MB is proposed to indicate the organic matter origin and depositional environment of ancient sediments.Oil samples originated mainly from lower hydrobiont,algae,bacteria and source rocks deposited under reducing/anoxic conditions have low 1,2,4,5-/1,2,3,4-Te MB values(less than 0.6),while oil samples originated mainly from terrestrial higher plants and source rocks deposited under oxic/sub-oxic conditions have higher 1,2,4,5-/1,2,3,4-Te MB values(greater than 1.0).The significant difference of 1,2,4,5-/1,2,3,4-Te MB values is controlled by 1,2,4,5-Te MB content.1,2,4,5-Te MB content in oils derived from source rocks deposited in oxidized sedimentary environment(greater than 1.0 mg/g whole oil)is higher than that in oils from source rocks deposited in reduced sedimentary environment(less than 1.0 mg/g whole oil).1,2,4,5-/1,2,3,4-Te MB ratio might not or slightly be affected by evaporative fractionation,biodegradation and thermal maturity.1,2,4,5-/1,2,3,4-Te MB ratio and 1,2,4,5-Te MB content can be used as supplementary parameter for the identification of sedimentary environment and organic matter input.It should be noted that compared to the identification of organic matter sources,the 1,2,4,5-/1,2,3,4-Te MB parameter is more effective in identifying sedimentary environments.

Tomato detection method using domain adaptive learning for dense planting environments

This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.

Changes in human settlement environments and their drivers in valley cities located in arid and semi-arid regions:A case study of Lanzhou in Western China

Development of urban human settlement environments(HSEs)is an integral part of promoting high-quality and sustainable regional development and constructing a beautiful China.The city of Lanzhou,located at the geometric center of China,is the only provincial capital traversed by the Yellow River.Given the constraints posed by the valley topography and the need for economic development,the development of this HSE,which is located within an arid region,poses considerable challenges.Evidently,an understanding of the evolution of HSEs and drivers of changes in them contributes to high-quality,sustainable urban development in arid and semi-arid regions.An analytical model was developed using the parameters of relief degree of land surface,human comfort days,the land cover index,nighttime light index,and precipitation.This model was used in combination with population density and the gross domestic product to analyze the spatial distribution of Lanzhou's HSE and its drivers.The results showed that landscapes in Lanzhou underwent significant changes between 2000 and 2022,with an increase in building-up land(+0.946%),cultivated land(+0.134%),and forest land(+0.018%)and a decrease in grassland(-1.10%).There was significant outward expansion of the main urban zone of Lanzhou and of various county towns,with the increase in building-up land being most prominent.During this period,there were significant changes in the periphery of the core urban area and county towns in Lanzhou,with decreases moving from the urban center(the highest value)to the surrounding areas(Yongdeng County had the lowest value).The correlation between the HSE and population density grew stronger in Anning and Chengguan Districts but became weaker in Xigu and Qilihe Districts.Spatiotemporal variations in the HSE were primarily caused by climate change,followed by human activities,and were also influenced by the valley topography.Overall,the spatial distribution of population density and the HSE in Lanzhou demonstrated good consistency under the in-fluence of economic development and urbanization.

Effects of virtual agents on interaction efficiency and environmental immersion in MR environments

Background Physical entity interactions in mixed reality(MR)environments aim to harness human capabilities in manipulating physical objects,thereby enhancing virtual environment(VEs)functionality.In MR,a common strategy is to use virtual agents as substitutes for physical entities,balancing interaction efficiency with environmental immersion.However,the impact of virtual agent size and form on interaction performance remains unclear.Methods Two experiments were conducted to explore how virtual agent size and form affect interaction performance,immersion,and preference in MR environments.The first experiment assessed five virtual agent sizes(25%,50%,75%,100%,and 125%of physical size).The second experiment tested four types of frames(no frame,consistent frame,half frame,and surrounding frame)across all agent sizes.Participants,utilizing a head mounted display,performed tasks involving moving cups,typing words,and using a mouse.They completed questionnaires assessing aspects such as the virtual environment effects,interaction effects,collision concerns,and preferences.Results Results from the first experiment revealed that agents matching physical object size produced the best overall performance.The second experiment demonstrated that consistent framing notably enhances interaction accuracy and speed but reduces immersion.To balance efficiency and immersion,frameless agents matching physical object sizes were deemed optimal.Conclusions Virtual agents matching physical entity sizes enhance user experience and interaction performance.Conversely,familiar frames from 2D interfaces detrimentally affect interaction and immersion in virtual spaces.This study provides valuable insights for the future development of MR systems.

Protected simultaneous quantum remote state preparation scheme by weak and reversal measurements in noisy environments

We discuss a quantum remote state preparation protocol by which two parties, Alice and Candy, prepare a single-qubit and a two-qubit state, respectively, at the site of the receiver Bob. The single-qubit state is known to Alice while the two-qubit state which is a non-maximally entangled Bell state is known to Candy. The three parties are connected through a single entangled state which acts as a quantum channel. We first describe the protocol in the ideal case when the entangled channel under use is in a pure state. After that, we consider the effect of amplitude damping(AD) noise on the quantum channel and describe the protocol executed through the noisy channel. The decrement of the fidelity is shown to occur with the increment in the noise parameter. This is shown by numerical computation in specific examples of the states to be created. Finally, we show that it is possible to maintain the label of fidelity to some extent and hence to decrease the effect of noise by the application of weak and reversal measurements. We also present a scheme for the generation of the five-qubit entangled resource which we require as a quantum channel. The generation scheme is run on the IBMQ platform.

Passive IoT Localization Technology Based on SD-PDOA in NLOS and Multi-Path Environments

Addressing the challenges of passive Radio Frequency Identification(RFID)indoor localization technology in Non-Line-of-Sight(NLoS)and multipath environments,this paper presents an innovative approach by introducing a combined technology integrating an improved Kalman Filter with Space Domain Phase Difference of Arrival(SD-PDOA)and Received Signal Strength Indicator(RSSI).This methodology utilizes the distinct channel characteristics in multipath and NLoS contexts to effectively filter out interference and accurately extract localization information,thereby facilitating high precision and stability in passive RFID localization.The efficacy of this approach is demonstrated through detailed simulations and empirical tests conducted on a custom-built experimental platform consisting of passive RFID tags and an R420 reader.The findings are significant:in NLoS conditions,the four-antenna localization system achieved a notable localization accuracy of 0.25 m at a distance of 5 m.In complex multipath environments,this system achieved a localization accuracy of approximately 0.5 m at a distance of 5 m.When compared to conventional passive localization methods,our proposed solution exhibits a substantial improvement in indoor localization accuracy under NLoS and multipath conditions.This research provides a robust and effective technical solution for high-precision passive indoor localization in the Internet of Things(IoT)system,marking a significant advancement in the field.

Influences of different alkaline and acidic diagenetic environments on diagenetic evolution and reservoir quality of alkaline lake shales

Thin section and argon-ion polishing scanning electron microscope observations were used to analyze the sedimentary and diagenetic environments and main diagenesis of the Permian Fengcheng Formation shales in different depositional zones of Mahu Sag in the Junggar Basin,and to reconstruct their differential diagenetic evolutional processes.The diagenetic environment of shales in the lake-central zone kept alkaline,which mainly underwent the early stage(Ro<0.5%)dominated by the authigenesis of Na-carbonates and K-feldspar and the late stage(Ro>0.5%)dominated by the replacement of Na-carbonates by reedmergnerite.The shales from the marginal zone underwent a transition from weak alkaline to acidic diagenetic environments,with the early stage dominated by the authigenesis of Mg-bearing clay and silica and the late stage dominated by the dissolution of feldspar and carbonate minerals.The shales from the transitional zone also underwent a transition from an early alkaline diagenetic environment,evidenced by the formation of dolomite and zeolite,to a late acidic diagenetic environment,represented by the reedmergnerite replacement and silicification of feldspar and carbonate minerals.The differences in formation of authigenic minerals during early diagenetic stage determine the fracability of shales.The differences in dissolution of minerals during late diagenetic stage control the content of free shale oil.Dolomitic shale in the transitional zone and siltstone in the marginal zone have relatively high content of free shale oil and strong fracability,and are favorable“sweet spots”for shale oil exploitation and development.

Impact of Outdoor Activities in Different Landscape Environments on the Physical and Mental Health of College Students

To investigate whether outdoor activities have an improvement effect on the emotions of young people and the differences in the impact of outdoor activities on the emotions of young people under different landscape environmental conditions,the participating students are asked to engage in outdoor activities in different landscape environments and divided into four groups.Each group is assigned to engage in outdoor activities in four types of landscape areas:open lawn area,low shrub area,dense forest area,and waterfront area.The activities include outdoor yoga and making leaf mandalas.The results show that outdoor yoga has the best effect on reducing pulse and blood pressure and regulating emotions in open lawn area and waterfront area,followed by leaf mantras in waterfront area and outdoor yoga in dense forest area.However,both activities have no significant effect in low shrub area.

Cold environments and human metabolism: A traditional chinese medicine perspective

The concept of Traditional Chinese Medicine(TCM)emphasizes the intrinsic connection between human beings and nature,positing that the human body undergoes distinct physiological changes in response to various natural environments.Cold,as a primary external factor in cold areas,necessitates the body's autonomous adaptation to uphold optimal living conditions.The repercussions of cold on the body are both far-reaching and profound,with metabolic equilibrium adjustments playing a pivotal role.This article,rooted in the TCM principle of Yin-Yang balance,delves into the metabolic intricacies and adaptive responses to the human body in cold environments.The effects manifest in heat-producing tissues,systemic substance consumption,the blood substance concentrations,liver function,and metabolic rhythms.The article subsequently presents TCM recommendations for maintaining health in cold climates.It concludes by advocating the exploration of metabolic homeostasis changes as a key avenue for investigating the metabolic traits s of populations in cold regions.We posit that such insights will enhance comprehension of the metabolic shifts in cold region populations and advance the evolution of regional medicine.

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.

Impact of coupling coordination development of port and city environments on urban economic competitiveness:Evidence from the coastal port cities in eastern China

Promoting the coupling coordination development of port and its hinterland city environments is an important way to improve urban economic competitiveness.Based on relevant data of 13 coastal port cities in eastern China from 2000 to 2018,this study explores the coupling coordination development of port and city environments and its impact on urban economic competitiveness by constructing the coupling coordination degree model and the panel threshold model.The research results show that:(1)In terms of the coupling coordination development of port and city environments,most coastal ports and their hinterland cities are in a state of moderate or serious disorder.Overall,the degree of coupling coordination of port and city environments needs to be further improved;(2)The coupling coordination degree of port and city environments has a significant impact on urban economic competitiveness,and this effect gradually increases with the development of the ports and the urban economy.Among the variables that impact the urban economic competitiveness,fixed assets investment and foreign trade are significant factors that can enhance urban economic competitiveness.(3)At present,there is a“U-shaped”relationship between the coupling coordination degree of port-city environments and the urban economic competitiveness.This relationship lies on the right side of the inflection point of the“U-shaped”curve.Therefore,following the concept of assigning priority to ecological development,expanding fixed assets investment and actively developing foreign trade can further enhance the urban economic competitiveness.

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.

A Study on the Effects of Different Interaction Combinations and Language Levels on Continuous Writing in Online Environments

This study was conducted with non-English sophomore students,aiming to explore the effects of different interaction combinations and language levels on continuous writing in an online environment,and compare the differences in lexical alignments and composition quality of learners with different interaction combinations and language levels in the same continuous writing task through experiments.The results show that the mean values of the word-phrase alignment of the paired group were higher than those of the individual group in different interaction combinations,and the two groups showed significant differences;in terms of composition quality,the individual group was better than the paired group,but there was no significant difference between the two groups in terms of task continuation.Secondly,the word-phrase alignment and composition scores of the different language-level groups were higher than those of the same language-level groups,and there was a significant difference between the two groups in terms of word-phrase alignments,but not in terms of composition scores.The results of this study can be useful and informative for second language teachers in future continuous teaching in online environments.

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.

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.

Correc tion:TiO_(2) nanoparticles in aquatic environments:impact on heavy metals distribution in sediments and overlying water

Correction to:Acta Geochimica(2022)41:968–981 https://doi.org/10.1007/s11631-022-00555-w In the original publication of the article,the affi liation of“Behzad Aghabarari”was published incorrectly.The correct affi liation should read as follows“Department of Nanotechnology and Advanced Materials,Material and Energy Research Center,Karaj,Iran”.

Comparison of Chick Quality, Health, and Inflammation from Two Hatchery Environments

Hatchery contamination can result in ingested or inhaled microbes that may modify colonization of the intestinal and respiratory tract, with potential to influence early growth, inflammation, and overall health. Six experiments were completed to compare chick quality, inflammation, and health between two hatcheries (H1 and H2). On embryonic d0, 45 eggs from the same breeder flock were set at each hatchery. On d0, length, abdominal height, navel and leg abnormalities, and self-righting were measured for 36 chicks/hatchery, yolk sacs were weighed, and crop/cloaca swabs were cultured from 12 chicks/hatchery. On d7, mid-ileum and ceca were cultured from 12 chicks/hatchery. On d0 and d7, body weight (BW) and intestinal weight were measured, lung/air sac swabs and liver were cultured, and liver and air sacs were scored for health. Blood was collected on d0 and d7 for serum alpha-1-acid glycoprotein concentrations in Exp 1 and 2. Data was analyzed using Student’s t-test or χ2, significance p < 0.05. On d0, BW, length, yolk free BW, and intestinal weight were greater for H2 chicks (p < 0.05). Liver bacterial recovery was decreased in H2 on d0 (p < 0.05) and there were fewer average leg and righting abnormalities in H2 (p < 0.05). Decreased lactase positive Enterobacteriaceae were noted in H2 in crop/cloaca and lung/air sac swabs (p < 0.05), and of alpha and beta hemolysis in crop/cloaca swabs, and alpha and gamma hemolysis in lung/air sac swabs (p < 0.05) on d0. By d7, only alpha hemolytic bacteria were increased in lung/air sac swabs of H2. Based on factors measured, chicks from H2 showed favorable microbial colonization, starting quality, and improved health on d0. While not sustained through d7, differences in d0 microbial recovery may have shifted microbial development and potentially influenced immune response development. These experiments elucidated the importance of hatchery environment on early chick quality, microbial colonization, overall inflammation, and chick health.

Vision‐audio fusion SLAM in dynamic environments

Moving humans,agents,and subjects bring many challenges to robot self‐localisation and environment perception.To adapt to dynamic environments,SLAM researchers typically apply several deep learning image segmentation models to eliminate these moving obstacles.However,these moving obstacle segmentation methods cost too much computation resource for the onboard processing of mobile robots.In the current industrial environment,mobile robot collaboration scenario,the noise of mobile robots could be easily found by on‐board audio‐sensing processors and the direction of sound sources can be effectively acquired by sound source estimation algorithms,but the distance estimation of sound sources is difficult.However,in the field of visual perception,the 3D structure information of the scene is relatively easy to obtain,but the recognition and segmentation of moving objects is more difficult.To address these problems,a novel vision‐audio fusion method that combines sound source localisation methods with a visual SLAM scheme is proposed,thereby eliminating the effect of dynamic obstacles on multi‐agent systems.Several heterogeneous robots experiments in different dynamic scenes indicate very stable self‐localisation and environment reconstruction performance of our method.

Will high-entropy carbides and borides be enabling materials for extreme environments?

The concept of multi-principal component has created promising opportunities for the development of novel high-entropy ceramics for extreme environments encountered in advanced turbine engines, nuclear reactors, and hypersonic vehicles, as it expands the compositional space of ceramic materials with tailored properties within a single-phase solid solution. The unique physical properties of some high-entropy carbides and borides, such as higher hardness, high-temperature strength, lower thermal conductivity, and improved irradiation resistance than the constitute ceramics, have been observed. These promising properties may be attributed to the compositional complexity, atomic-level disorder, lattice distortion, and other fundamental processes related to defect formation and phonon scattering.This manuscript serves as a critical review of the recent progress in high-entropy carbides and borides, focusing on synthesis and evaluations of their performance in extreme high-temperature, irradiation, and gaseous environments.

Many-Objective Optimization-Based Task Scheduling in Hybrid Cloud Environments

Due to the security and scalability features of hybrid cloud architecture,it can bettermeet the diverse requirements of users for cloud services.And a reasonable resource allocation solution is the key to adequately utilize the hybrid cloud.However,most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling,even ignoring the conflicts between its security privacy features and other requirements.Based on the above problems,a many-objective hybrid cloud task scheduling optimization model(HCTSO)is constructed combining risk rate,resource utilization,total cost,and task completion time.Meanwhile,an opposition-based learning knee point-driven many-objective evolutionary algorithm(OBL-KnEA)is proposed to improve the performance of model solving.The algorithm uses opposition-based learning to generate initial populations for faster convergence.Furthermore,a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search range.By comparing the experiments with other excellent algorithms on HCTSO,OBL-KnEA achieves excellent results in terms of evaluation metrics,initial populations,and model optimization effects.