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.

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.

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.

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.

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.

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.

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”.

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.

Explainable Artificial Intelligence-Based Model Drift Detection Applicable to Unsupervised Environments

Cybersecurity increasingly relies on machine learning(ML)models to respond to and detect attacks.However,the rapidly changing data environment makes model life-cycle management after deployment essential.Real-time detection of drift signals from various threats is fundamental for effectively managing deployed models.However,detecting drift in unsupervised environments can be challenging.This study introduces a novel approach leveraging Shapley additive explanations(SHAP),a widely recognized explainability technique in ML,to address drift detection in unsupervised settings.The proposed method incorporates a range of plots and statistical techniques to enhance drift detection reliability and introduces a drift suspicion metric that considers the explanatory aspects absent in the current approaches.To validate the effectiveness of the proposed approach in a real-world scenario,we applied it to an environment designed to detect domain generation algorithms(DGAs).The dataset was obtained from various types of DGAs provided by NetLab.Based on this dataset composition,we sought to validate the proposed SHAP-based approach through drift scenarios that occur when a previously deployed model detects new data types in an environment that detects real-world DGAs.The results revealed that more than 90%of the drift data exceeded the threshold,demonstrating the high reliability of the approach to detect drift in an unsupervised environment.The proposed method distinguishes itself fromexisting approaches by employing explainable artificial intelligence(XAI)-based detection,which is not limited by model or system environment constraints.In conclusion,this paper proposes a novel approach to detect drift in unsupervised ML settings for cybersecurity.The proposed method employs SHAP-based XAI and a drift suspicion metric to improve drift detection reliability.It is versatile and suitable for various realtime data analysis contexts beyond DGA detection environments.This study significantly contributes to theMLcommunity by addressing the critical issue of managing ML models in real-world cybersecurity settings.Our approach is distinguishable from existing techniques by employing XAI-based detection,which is not limited by model or system environment constraints.As a result,our method can be applied in critical domains that require adaptation to continuous changes,such as cybersecurity.Through extensive validation across diverse settings beyond DGA detection environments,the proposed method will emerge as a versatile drift detection technique suitable for a wide range of real-time data analysis contexts.It is also anticipated to emerge as a new approach to protect essential systems and infrastructures from attacks.

SUNDER:Self-organized grouping and entrapping method for swarms in multitarget environments

For swarm robots moving in a harsh or uncharted outdoor environment without GPS guidance and global communication,algorithms that rely on global-based information are infeasible.Typically,traditional gene regulatory networks(GRNs)that achieve superior performance in forming trapping pattern towards targets require accurate global positional information to guide swarm robots.This article presents a gene regulatory network with Self-organized grouping and entrapping method for swarms(SUNDER-GRN)to achieve adequate trapping performance with a large-scale swarm in a confined multitarget environment with access to only local information.A hierarchical self-organized grouping method(HSG)is proposed to structure subswarms in a distributed way.In addition,a modified distributed controller,with a relative coordinate system that is established to relieve the need for global information,is leveraged to facilitate subswarms entrapment toward different targets,thus improving the global multi-target entrapping performance.The results demonstrate the superiority of SUNDERGRN in the performance of structuring subswarms and entrapping 10 targets with 200 robots in an environment confined by obstacles and with only local information accessible.

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.

Hydrogenic donor impurity states and intersubband optical absorption spectra of monolayer transition metal dichalcogenides in dielectric environments

The hydrogenic donor impurity states and intersubband optical absorption spectra in monolayer transition metal dichalcogenides(ML TMDs) under dielectric environments are theoretically investigated based on a two-dimensional(2D)nonorthogonal associated Laguerre basis set. The 2D quantum confinement effect together with the strongly reduced dielectric screening results in the strong attractive Coulomb potential between electron and donor ion, with exceptionally large impurity binding energy and huge intersubband oscillator strength. These lead to the strong interaction of the electron with light in a 2D regime. The intersubband optical absorption spectra exhibit strong absorption lines of the non-hydrogenic Rydberg series in the mid-infrared range of light. The strength of the Coulomb potential can be controlled by changing the dielectric environment. The electron affinity difference leads to charge transfer between ML TMD and the dielectric environment, generating the polarization-electric field in ML TMD accompanied by weakening the Coulomb interaction strength. The larger the dielectric constant of the dielectric environment, the more the charge transfer is, accompanied by the larger polarization-electric field and the stronger dielectric screening. The dielectric environment is shown to provide an efficient tool to tune the wavelength and output of the mid-infrared intersubband devices based on ML TMDs.

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.

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.

Navigating Eutrophication in Aquatic Environments: Understanding Impacts and Unveiling Solutions for Effective Wastewater Management

Eutrophication is the term used to describe the presence of natural and artificial nutrients like phosphorus and nitrogen in aquatic ecosystems.The water quality in various bodies of water such as ponds,lakes,rivers,etc.is deteriorating as a result of an abundance of plant nutrients in these water sources.Over-enrichment of aquatic ecosystems with nutrients is a major hazard to the well-being of aquatic ecosystems worldwide.In addition,the circulations have lowered the requirements for home and agricultural consumption of water.The main origins of these plant nutrients within aquatic ecosystems stem from the discharges of industries engaged in activities like livestock farming,agriculture,fertilizer production,manufacturing of textiles,and clothing production.Therefore,a variety of methods and approaches have already been developed as safety measures to avoid the negative consequences of water tainted with those undesired minerals.Eutrophication presents many obstacles,but with the right public awareness campaign and global scientific efforts,its negative impacts may be lessened.This research seeks to pinpoint the primary origins of plant nutrients within the aquatic ecosystem and explore potential triggers for eutrophication.Additionally,it proposes innovative regulatory methods and offers suggestions for sustainable wastewater management practices.

Humanizing the Culture of Technology Teams: Strategies for Creating Healthier and More Productive Work Environments

With the advancement of technology, companies are increasingly dependent on technology teams to stay competitive. However, members of these teams often work in stressful and unhealthy environments, which can undermine their productivity and well-being. The humanization of the culture of technology teams is an approach that aims to create healthier and more productive work environments for team members, promoting balance between personal and professional life. Despite the importance of a healthy and productive work environment, many companies do not invest in strategies to humanize the culture of their technology teams. This can lead to high levels of stress, staff turnover and low productivity. The objective of this project is to identify effective strategies to humanize the culture of technology teams and create healthier and more productive work environments in digital companies. For this, factors such as management styles, psychological safety, human-centered development, individual beliefs and time and energy management will be analyzed. The project’s methodology will include a literature review on the subject and qualitative data analysis will be performed using a content analysis approach. This project will contribute to the advancement of knowledge about the humanization of the culture of technology teams in digital companies. The results can be applied to companies that want to create healthier and more productive work environments for their team members.

基于环境型鉴定技术划分生态区综合评价黄淮海青贮玉米品种

气候因子对农作物区域试验丰产性和适应性的影响较大。为准确评价青贮玉米品种在黄淮海夏播区的适应性、丰产性和稳定性,采用2002-2021年20 a的气象数据资料,依据环境型鉴定技术(ET)对2022年青贮玉米区域试验中12个试点进行生态区(ME)划分,依据品种-性状(GT)双标图和品种-产量×性状(GYT)双标图对15个参试品种的生物干重、干物质含量、倒伏率、倒折率、空秆率、小斑病、弯孢叶斑病、南方锈病、茎腐病、瘤黑粉病、生育期、株高和穗位高13个农艺性状以及全株淀粉含量、中性洗涤纤维含量、酸性洗涤纤维含量和粗蛋白质含量4个品质指标进行综合评价。结果表明,加性主效应和积性互作效应(AMMI)方差分析被测的13个农艺性状中基因型效应和环境效应均达到了极显著水平(P<0.01),除穗位高外其余性状基因型与环境互作效应也达到了极显著水平。6个省份的12个试点被划分为4个生态区,不同生态区间气象因子呈较大的变化趋势。生物干重与株高、穗位高呈极显著正相关,而与倒伏率、倒折率呈极显著负相关。GYT双标图与生态区结合,可以鉴别出不同生态区的优势品种。参试品种中渝单805在划定的4个生态区中均表现出丰产性突出、稳定性较好的特征,属于丰产稳产型品种。皖农科青贮8号、成单3601、正大511和衡玉1996等品种在ME2、ME3和ME4中丰产性和稳定性较好。安科青2号和KNX2202等品种在ME1和ME4中丰产性较差,金诚6在ME2和ME3中丰产性和稳定性均较差。基于环境型鉴定技术划分生态区和GYT双标图相结合评价青贮玉米品种的丰产性、稳定性和适应性,可以实现品种推广的精细定位。

从知识困境到认知陷阱:生成式技术驱动型信息生态系统安全问题研究

生成式技术驱动型信息生态系统以生成式人工智能技术为核心,对整个信息环境中的知识传递与共享、认知流动与扩散过程发挥支撑和推动作用。然而,这种创新型的信息生态系统也伴随着知识安全和认知安全问题的出现。本文从知识环境和认知环境两个层面入手,对生成式技术驱动型信息生态系统的特点、优势与风险进行深入剖析,探索如何在符合人类价值观和社会伦理的前提下,充分发挥生成式人工智能的潜力,推动构建高效、安全、可持续发展的生成式技术驱动型信息生态系统。

人工环境中“舒适”的医学意义——关于舒适与健康的思考

当今社会,人工环境的营造日渐受到重视。室内环境不仅应满足舒适性的要求,更应考虑其动态变化对人体健康与免疫力的潜在影响。本文以人居环境营造为出发点,结合已有的文献研究与中西医观点,首先列举了外部环境对人体健康及体内环境的影响,其次介绍了“舒适”营造对提高机体健康水平的作用,最后从宏观上阐述了物理环境中“舒适”对人体健康的医学意义,为本专业同医学的交叉研究提供新思路。