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.

Compositional and Hollow Engineering of Silicon Carbide/Carbon Microspheres as High-Performance Microwave Absorbing Materials with Good Environmental Tolerance

Microwave absorbing materials(MAMs)characterized by high absorption efficiency and good environmental tolerance are highly desirable in practical applications.Both silicon carbide and carbon are considered as stable MAMs under some rigorous conditions,while their composites still fail to produce satisfactory microwave absorption performance regardless of the improvements as compared with the individuals.Herein,we have successfully implemented compositional and structural engineering to fabricate hollow Si C/C microspheres with controllable composition.The simultaneous modulation on dielectric properties and impedance matching can be easily achieved as the change in the composition of these composites.The formation of hollow structure not only favors lightweight feature,but also generates considerable contribution to microwave attenuation capacity.With the synergistic effect of composition and structure,the optimized SiC/C composite exhibits excellent performance,whose the strongest reflection loss intensity and broadest effective absorption reach-60.8 dB and 5.1 GHz,respectively,and its microwave absorption properties are actually superior to those of most SiC/C composites in previous studies.In addition,the stability tests of microwave absorption capacity after exposure to harsh conditions and Radar Cross Section simulation data demonstrate that hollow SiC/C microspheres from compositional and structural optimization have a bright prospect in practical applications.

The coupling control of biological precursors and environmental factors onβ-carotane enrichment in alkaline lacustrine source rocks:A case study from the Fengcheng formation in the western Junggar Basin,NW China

The organic-rich mudstones and dolostones of the Permian Fengcheng Formation(Fm.)are typically alkaline lacustrine source rocks,which are typified by impressively abundantβ-carotane.Abundant β-carotane has been well acknowledged as an effective indicator of biological sources or depositional environments.However,the specific biological sources of β-carotane and the coupling control of biological sources and environmental factors on the enrichment of β-carotane in the Fengcheng Fm.remains obscure.Based on a comprehensive investigation of the bulk,molecular geochemistry,and organic petrology of sedimentary rocks and the biochemistry of phytoplankton in modern alkaline lakes,we proposed a new understanding of the biological precursors of β-carotane and elucidated the enrichment mechanism of β-carotane in the Fengcheng Fm.The results show that the biological precursors crucially control the enrichment of β-carotane in the Fengcheng Fm.The haloalkaliphilic cyanobacteria are the primary biological sources of β-carotane,which is suggested by a good positive correlation between the 2-methylhopane index,7-+8-methyl heptadecanes/C_(max),C_(29%),and β-carotane/C_(max)in sedimentary rocks and the predominance of cyanobacteria with abundantβ-carotene in modern alkaline lakes.The enrichment of β-carotane requires the reducing condition,and the paleoredox state that affects the enrichment of β-carotane appears to have a threshold.The paleoclimate conditions do not considerably impact the enrichment of β-carotane,but they have some influence on the water's paleosalinity by affecting evaporation and precipitation.While it does not directly affect the enrichment of β-carotane in the Fengcheng Fm.,paleosalinity does have an impact on the cyanobacterial precursor supply and the preservation conditions.

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.

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.

Tree mycorrhizal associations determine how biodiversity,large trees,and environmental factors drive aboveground carbon stock in temperate forests

Biodiversity,large trees,and environmental conditions such as climate and soil have important effects on forest carbon stocks.However,recent studies in temperate forests suggest that the relative importance of these factors depends on tree mycorrhizal associations,whereby large-tree effects may be driven by ectomycorrhizal(EM)trees,diversity effects may be driven by arbuscular mycorrhizal(AM)trees,and environment effects may depend on differential climate and soil preferences of AM and EM trees.To test this hypothesis,we used forest-inventory data consisting of over 80,000 trees from 631 temperate-forest plots(30 m×30 m)across Northeast China to examine how biodiversity(species diversity and ecological uniqueness),large trees(top 1%of tree diameters),and environmental factors(climate and soil nutrients)differently regulate aboveground carbon stocks of AM trees,EM trees,and AM and EM trees combined(i.e.total aboveground carbon stock).We found that large trees had a positive effect on both AM and EM tree carbon stocks.However,biodiversity and environmental factors had opposite effects on AM vs.EM tree carbon stocks.Specifically,the two components of biodiversity had positive effects on AM tree carbon stocks,but negative effects on EM tree carbon stocks.Environmental heterogeneity(mean annual temperature and soil nutrients)also exhibited contrasting effects on AM and EM tree carbon stocks.Consequently,for the total carbon stock,the positive large-tree effect far surpasses the diversity and environment effect.This is mainly because when integrating AM and EM tree carbon stock into total carbon stock,the opposite diversity-effect(also environment-effect)on AM vs.EM tree carbon stock counteracts each other while the consistent positive large-tree effect on AM and EM tree carbon stock is amplified.In summary,this study emphasized a mycorrhizal viewpoint to better understand the determinants of overarching aboveground carbon profile across regional forests.

A CombustionModel for Explosive Charge Affected by a Bottom Gap in the Launch Environment

Launch safety of explosive charges has become an urgent problem to be solved by all countries in the world aslaunch situation of ammunition becomes consistentlyworse.However, the existing numericalmodels have differentdefects. This paper formulates an efficient computational model of the combustion of an explosive charge affectedby a bottom gap in the launch environment in the context of the material point method. The current temperatureis computed accurately from the heat balance equation, and different physical states of the explosive charges areconsidered through various equations of state. Microcracks in the explosive charges are described with respectto the viscoelastic statistical crackmechanics (Visco–SCRAM) model. Themethod for calculating the temperatureat the bottomof the explosive charge with respect to the bottomgap is described. Based on this combustionmodel,the temperature history of a Composition B (COMB) explosive charge in the presence of a bottom gap is obtainedduring the launch process of a 155-mm artillery. The simulation results show that the bottom gap thickness shouldbe no greater than 0.039 cm to ensure the safety of the COM B explosive charge in the launch environment. Thisconclusion is consistent with previous results and verifies the correctness of the proposed model. Ultimately, thispaper derives amathematical expression for themaximumtemperature of the COMB explosive chargewith respectto the bottomgap thickness (over the range of 0.00–0.039 cm), and establishes a quantitative evaluationmethod forthe launch safety of explosive charges.The research results provide some guidance for the assessment and detectionof explosive charge safety in complex launch environments.

The chemical environment and structural ordering in liquid Mg-Y-Zn system:An ab-initio molecular dynamics investigation of melt for the formation mechanism of LPSO structure

In an effort to clarify the formation mechanism of LPSO structure in Mg-Y-Zn alloy,the chemical environment and structural ordering in liquid Mg-rich Mg-Y-Zn system are investigated with the aid of ab-initio molecular dynamics simulation.In liquid Mg-rich Mg-Y alloys,the strong Mg-Y interaction is determined,which promotes the formation of fivefold symmetric local structure.For Mg-Zn alloys,the weak Mg-Zn interaction results in the fivefold symmetry weakening in the liquid structure.Due to the coexistence of Y and Zn,the strong attractive interaction is introduced in liquid Mg-Y-Zn ternary alloy,and contributes to the clustering of Mg,Y,Zn launched from Zn.What is more,the distribution of local structures becomes closer to that in pure Mg compared with that in binary Mg-Y and Mg-Zn alloys.These results should relate to the origins of the Y/Zn segregation zone and close-packed stacking mode in LPSO structure,which provides a new insight into the formation mechanism of LPSO structure at atomic level.

Formation environment and hydrocarbon potential of the Paleogene Enping Formation coal measures in the ZhuⅠDepression of northern South China Sea

The coal-measure source rock in the Chinese sea area plays a significant role as a hydrocarbon source rock,with its genetic environment,development and distribution,and hydrocarbon generation potential serving as essential factors for the exploration of coal-type oil and gas fields.This study focuses on the coal-measure source rock of the Paleogene Enping Formation in the ZhuⅠDepression,located in the northern South China Sea.The main geological insights obtained are as follows.The coal measures of the Enping Formation are developed in a warm and wet tropical-subtropical climate.The development environment of the coal-measure source rock in the Enping Formation includes the braided river delta upper plain peat swamp,characterized by dry forest swamp coal facies with relatively thick coal seams and a small number of layers.The braided river delta lower plain swamp-interdistributary bay of braided river delta front represents a forest edge-wetland herbaceous swamp coal facies with numerous layers of thin coal seams and poor stability.The shore swamp corresponds to an open water swamp coal facies with multiple layers of thin coal seams and poor stability.The organic matter abundance in the braided river delta upper plain is the highest,followed by the braided river delta lower plain-braided river delta front,and the shore-shallow lake.The organic matter type is predominantly typeⅡ1.Thermal evolution analysis suggests that the organic matter has progressed into a substantial oil generation stage.The hydrocarbon generation potential of the coal-measure source rock in the Enping Formation is the highest in the braided river delta upper plain,followed by the braided river delta lower plain-braided river delta front and the shore-shallow lake.Overall,this study proposes three organic facies in the coal-measure source rock of the Enping Formation:upper-plain swamp-dry forest swamp facies,lower plain-interdistributary bay-forest-herbaceous swamp facies,and lake swamp-herbaceous swamp facies.

Theoretical Framework for a Polymorphic Network Environment

The question of whether an ideal network exists with global scalability in its full life cycle has always been a first-principles problem in the research of network systems and architectures.Thus far,it has not been possible to scientifically practice the design criteria of an ideal network in a unimorphic network system,making it difficult to adapt to known services with clear application scenarios while supporting the ever-growing future services with unexpected characteristics.Here,we theoretically prove that no unimorphic network system can simultaneously meet the scalability requirement in a full cycle in three dimensions—the service-level agreement(S),multiplexity(M),and variousness(V)—which we name as the“impossible SMV triangle”dilemma.It is only by transforming the current network development paradigm that the contradiction between global scalability and a unified network infrastructure can be resolved from the perspectives of thinking,methodology,and practice norms.In this paper,we propose a theoretical framework called the polymorphic network environment(PNE),the first principle of which is to separate or decouple application network systems from the infrastructure environment and,under the given resource conditions,use core technologies such as the elementization of network baselines,the dynamic aggregation of resources,and collaborative software and hardware arrangements to generate the capability of the“network of networks.”This makes it possible to construct an ideal network system that is designed for change and capable of symbiosis and coexistence with the generative network morpha in the spatiotemporal dimensions.An environment test for principle verification shows that the generated representative application network modalities can not only coexist without mutual influence but also independently match well-defined multimedia services or custom services under the constraints of technical and economic indicators.

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.

Application of near-infrared spectroscopy for fast germplasm analysis and classification in multi-environment using intact-seed peanut(Arachis hypogaea L.)

Peanut is a worldwide oilseed crop and the need to assess germplasm in a non-destructive manner is important for seed nutritional breeding.In this study,Near Infrared Spectroscopy(NIRS)was applied to rapidly assess germplasm variability from whole seed of 699 samples,field-collected and assembled in four genetic and environmentbased sets:one set of 300 varieties of a core-collection and three sets of 133 genotypes of an interspecific population,evaluated in three environments in a large spatial scale of two countries,Mbalmayo and Bafia in Cameroon and Nioro in Senegal,under rainfed conditions.NIR elemental spectra were gathered on six subsets of seeds of each sample,after three rotation scans,with a spectral resolution of 16 cm-1over the spectral range of867 nm to 2530 nm.Spectra were then processed by principal component analysis(PCA)coupled with Partial least squares-discriminant analysis(PLS-DA).As results,a huge variability was found between varieties and genotypes for all NIR wavelength within and between environments.The magnitude of genetic variation was particularly observed at 11 relevant wavelengths such as 1723 nm,usually related to oil content and fatty acid composition.PCA yielded the most chemical attributes in three significant PCs(i.e.,eigenvalues>10),which together captured 93%of the total variation,revealing genetic and environment structure of varieties and genotypes into four clusters,corresponding to the four samples sets.The pattern of genetic variability of the interspecific population covers,remarkably half of spectrum of the core-collection,turning out to be the largest.Interestingly,a PLS-DA model was developed and a strong accuracy of 99.6%was achieved for the four sets,aiming to classify each seed sample according to environment origin.The confusion matrix achieved for the two sets of Bafia and Nioro showed 100%of instances classified correctly with 100%at both sensitivity and specificity,confirming that their seed quality was different from each other and all other samples.Overall,NIRS chemometrics is useful to assess and distinguish seeds from different environments and highlights the value of the interspecific population and core-collection,as a source of nutritional diversity,to support the breeding efforts.

Semi-supervised learning based hybrid beamforming under time-varying propagation environments

Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi-supervised Incremental Learning(IL),we propose an online hybrid beamforming scheme.Firstly,given the constraint of constant modulus on analog beamformer and combiner,we propose a new broadnetwork-based structure for the design model of hybrid beamforming.Compared with the existing network structure,the proposed network structure can achieve better transmission performance and lower complexity.Moreover,to enhance the efficiency of IL further,by combining the semi-supervised graph with IL,we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning,where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk.Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update,all transmissions,even the ones during the model update,would make contributions to model update in the proposed method.During the model update,the amount of unlabelled transmissions is very large and they also carry some information,the prediction performance can be enhanced to some extent by these unlabelled channel data.Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach.Besides,we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach.

Recent Developments in Metallic Degradable Micromotors for Biomedical and Environmental Remediation Applications

Synthetic micromotor has gained substantial attention in biomedicine and environmental remediation.Metal-based degradable micromotor composed of magnesium(Mg),zinc(Zn),and iron(Fe)have promise due to their nontoxic fuel-free propulsion,favorable biocompatibility,and safe excretion of degradation products Recent advances in degradable metallic micromotor have shown their fast movement in complex biological media,efficient cargo delivery and favorable biocompatibility.A noteworthy number of degradable metal-based micromotors employ bubble propulsion,utilizing water as fuel to generate hydrogen bubbles.This novel feature has projected degradable metallic micromotors for active in vivo drug delivery applications.In addition,understanding the degradation mechanism of these micromotors is also a key parameter for their design and performance.Its propulsion efficiency and life span govern the overall performance of a degradable metallic micromotor.Here we review the design and recent advancements of metallic degradable micromotors.Furthermore,we describe the controlled degradation,efficient in vivo drug delivery,and built-in acid neutralization capabilities of degradable micromotors with versatile biomedical applications.Moreover,we discuss micromotors’efficacy in detecting and destroying environmental pollutants.Finally,we address the limitations and future research directions of degradable metallic micromotors.

A Novel Hybrid Architecture for Superior IoT Threat Detection through Real IoT Environments

As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense mechanisms.This paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world settings.Our proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive framework.This integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT systems.We evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated attacks.Our research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive quality.Our model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security paradigms.Through our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of IoT.Our model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat landscape.This achievement marks a significant stride towards protecting the integrity of IoT infrastructure,ensuring operational resilience,and building privacy in this groundbreaking technology.

Recent advances in core-shell organic framework-based photocatalysts for energy conversion and environmental remediation

Direct conversion of solar energy into chemical energy in an environmentally friendly manner is one of the most promising strategies to deal with the environmental pollution and energy crisis.Among a variety of materials developed as photocatalysts,the core-shell metal/covalent-organic framework(MOF or COF)photocatalysts have garnered significant attention due to their highly porous structure and the adjustability in both structure and functionality.The existing reviews on core-shell organic framework photocatalytic materials have mainly focused on core-shell MOF materials.However,there is still a lack of indepth reviews specifically addressing the photocatalytic performance of core-shell COFs and MOFs@COFs.Simultaneously,there is an urgent need for a comprehensive review encompassing these three types of core-shell structures.Based on this,this review aims to provide a comprehensive understanding and useful guidelines for the exploration of suitable core-shell organic framework photocatalysts towards appropriate photocatalytic energy conversion and environmental governance.Firstly,the classification,synthesis,formation mechanisms,and reasonable regulation of core-shell organic framework were summarized.Then,the photocatalytic applications of these three kinds of core-shell structures in different areas,such as H_(2)evolution,CO_(2)reduction,and pollutants degradation are emphasized.Finally,the main challenges and development prospects of core-shell organic framework photocatalysts were introduced.This review aims to provide insights into the development of a novel generation of efficient and stable core-shell organic framework materials for energy conversion and environmental remediation.

SFC placement and dynamic resource allocation based on VNF performance-resource function and service requirement in cloud-edge environment

With the continuous development of network func-tions virtualization(NFV)and software-defined networking(SDN)technologies and the explosive growth of network traffic,the requirement for computing resources in the network has risen sharply.Due to the high cost of edge computing resources,coordinating the cloud and edge computing resources to improve the utilization efficiency of edge computing resources is still a considerable challenge.In this paper,we focus on optimiz-ing the placement of network services in cloud-edge environ-ments to maximize the efficiency.It is first proved that,in cloud-edge environments,placing one service function chain(SFC)integrally in the cloud or at the edge can improve the utilization efficiency of edge resources.Then a virtual network function(VNF)performance-resource(P-R)function is proposed to repre-sent the relationship between the VNF instance computing per-formance and the allocated computing resource.To select the SFCs that are most suitable to deploy at the edge,a VNF place-ment and resource allocation model is built to configure each VNF with its particular P-R function.Moreover,a heuristic recur-sive algorithm is designed called the recursive algorithm for max edge throughput(RMET)to solve the model.Through simula-tions on two scenarios,it is verified that RMET can improve the utilization efficiency of edge computing resources.

About Some Aspects of Use of Optical Sensors for Monitoring the Aquatic Environment

Multi-channel polarization optical technology is increasingly used for prompt monitoring of water systems.Optical devices during the assessment of water quality determine the intensity of light through the studied aquatic environment.Spectrophotometric devices measure the spectrum of weakening of light through the aquatic environment.Spectroellipsometric devices receive spectra in vertical and horizontal polarizations.The presented article develops an adaptive optical hardware and image system for monitoring water bodies.The system is combined.It consists of 2 parts:1)automated spectrophotometer-refractometer,and 2)adaptive spectroellipsometer.The system is equipped with a corresponding algorithmic and software,including algorithms for identifying spectral curves,databases and knowledge of spectral curves algorithms for solving reverse problems.The presented system is original since it differs from modern foreign systems by a new method of spectrophotometric and spectroellipsometric measurements,an original elemental base of polarization optics and a comprehensive mathematical approach to assessing the quality of a water body.There are no rotating polarization elements in the system.Therefore,this makes it possible to increase the signal-to-noise ratio and,as a result,improve measurement stability and simplify multichannel spectrophotometers and spectroellipsometers.The proposed system can be used in various water systems where it is necessary to assess water quality or identify the presence of a certain set of chemical elements.

Micro-nano-fabrication of green functional materials by multiphase microfluidics for environmental and energy applications

Multiphase microfluidic has emerged as a powerful platform to produce novel materials with tailor-designed functionalities,as microfluidic fabrication provides precise controls over the size,component,and structure of resultant materials.Recently,functional materials with well-defined micro-/nanostructures fabricated by microfluidics find important applications as environmental and energy materials.This review first illustrated in detail how different structures or shapes of droplet and jet templates are formed by typical configurations of microfluidic channel networks and multiphase flow systems.Subsequently,recent progresses on several representative energy and environmental applications,such as water purification,water collecting and energy storage,were overviewed.Finally,it is envisioned that integrating microfluidics and other novel materials will play increasing important role in contributing environmental remediation and energy storage in near future.

Contract Mechanism of Water Environment Regulation for Small and Medium Sized Enterprises Based on Optimal Control Theory

The small and scattered enterprise pattern in the county economy has formed numerous sporadic pollution sources, hindering the centralized treatment of the water environment, increasing the cost and difficulty of treatment. How enterprises can make reasonable decisions on their water environment behavior based on the external environment and their own factors is of great significance for scientifically and effectively designing water environment regulation mechanisms. Based on optimal control theory, this study investigates the design of contractual mechanisms for water environmental regulation for small and medium-sized enterprises. The enterprise is regarded as an independent economic entity that can adopt optimal control strategies to maximize its own interests. Based on the participation of multiple subjects including the government, enterprises, and the public, an optimal control strategy model for enterprises under contractual water environmental regulation is constructed using optimal control theory, and a method for calculating the amount of unit pollutant penalties is derived. The water pollutant treatment cost data of a paper company is selected to conduct empirical numerical analysis on the model. The results show that the increase in the probability of government regulation and public participation, as well as the decrease in local government protection for enterprises, can achieve the same regulatory effect while reducing the number of administrative penalties per unit. Finally, the implementation process of contractual water environmental regulation for small and medium-sized enterprises is designed.