Application of remote sensing,an artificial neural network leaf area model,and a process-based simulation model to estimate carbon storage in Florida slash pine plantations.

Carbon sequestration in forests is of great interest due to concerns about global climate change.Carbon storage rates depend on ecosystem fluxes（photosynthesis and ecosystem respiration）,typically quantified as net ecosystem exchange（NEE）.Methods to estimate forest NEE without intensive site sampling are needed to accurately assess rates of carbon sequestration at stand-level and larger scales.We produced spatially-explicit estimates of NEE for 9 770 ha of slash pine（Pinus elliottii） plantations in North-Central Florida for a single year by coupling remote sensing-based estimates of leaf area index（LAI） with a process-based growth simulation model.LAI estimates produced from a neural-network modeling of ground plot and Landsat TM satellite data had a mean of 1.06（range 0-3.93,including forest edges）.Using the neural network LAI values as inputs,the slash pine simulation model（SPM2） estimates of NEE ranged from-5.52 to 11.06 Mg·ha^-1·a^-1with a mean of 3.47 Mg·ha^-1·a^-1Total carbon storage for the year was 33920 t,or about 3.5 tons per hectare.Both estimated LAI and NEE were highly sensitive to fertilization.

Evolution and Governance of the Structure of Marine Economic Networks in China’s Coastal Areas: Based on Sea-related A-share Listed Companies

Data on discrete,isolated attributes of the marine economy are often used in traditional marine economic research.However,as the focus of urban research shifts from internal static attributes to external dynamic linkages,the importance of marine economic net-work research is beginning to emerge.The construction of the marine economic network in China’s coastal areas is necessary to change the flow of land and sea resources and optimize regional marine economic development.Employing data from headquarters and branches of sea-related A-share listed enterprises to construct the marine economic network in China,we use social network analysis(SNA)to discuss the characteristics of its evolution as of 2010,2015,and 2020 and its governance.The following results were obtained.1)In terms of topological characteristics,the scale of the marine economic network in China’s coastal areas has accelerated and expan-ded,and the connections have become increasingly close;thus,this development has complex network characteristics.2)In terms of spatial structure,the intensity of the connection fluctuates and does not form stable development support;the group structure gradually becomes clear,but the overall pattern is fragmented;there are spatial differences in marine economic agglomeration radiation;the radi-ation effect of the eastern marine economic circle is obvious;and the polarization effect of northern and southern marine economic circles is significant.On this basis,we construct a framework for the governance of a marine economic network with the market,the government,and industry as the three governing bodies.By clarifying the driving factors and building objectives of marine economic network construction,this study aims to foster the high-quality development of China’s marine economy.

A hybrid physics-informed data-driven neural network for CO_(2) storage in depleted shale reservoirs

To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most current CO_(2) sequestration models do not adequately consider multiple transport mechanisms.Moreover,the evaluation of CO_(2) storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and decision-making.In this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO_(2) storage in depleted shale reservoirs,supporting the establishment of a training database.On this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and inversion.By incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion processes.Furthermore,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO_(2) storage capacity under complicated scenarios.The validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise sources.This study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO_(2) storage projects in depleted shale reservoirs.

New Rural Community Construction or Retention Development:A Comparative Analysis of Rural Settlement Transition Mechanism in Plain Agriculture Area of China Based on Actor Network Theory

It is an important way to realize rural revitalization and sustainable development to guide rural settlement transition(RST)in an appropriate way.This paper uses actor network theory(ANT)to construct a theoretical framework for the study of RST.Taking two typical villages with different transition paths in rural areas of North China Plain as examples,this paper reveals the mechanism of RST and makes a comparative analysis.The results show that:1)after identifying problems and obligatory passage point,key actors recruit heterogeneous actors into the actor network by entrusting them with common interests,and realize RST under the system operation.2)Rural settlements under different transition paths have similarities in the problems to be solved,collective actions and policy factors,but there are differences in the transition process,mechanism and effect.The actor network and mechanism of RST through the path of new rural community construction are more complex and the transition effect is more thorough.In contrast,the degree of RST of retention development path is limited if there is no resource and location advantage.3)Based on the applicable conditions of different paths,this paper designs a logical framework of‘Situation-Structure-Behavior-Result’to scientifically guide the identification of RST paths under the background of rural revitalization.

DNBP-CCA:A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

Priority Based Energy Efficient MAC Protocol by Varying Data Ratefor Wireless Body Area Network

Wireless Body Area Network(WBAN)is a cutting-edge technology that is being used in healthcare applications to monitor critical events in the human body.WBAN is a collection of in-body and on-body sensors that monitor human physical parameters such as temperature,blood pressure,pulse rate,oxygen level,body motion,and so on.They sense the data and communicate it to the Body Area Network(BAN)Coordinator.The main challenge for the WBAN is energy consumption.These issues can be addressed by implementing an effective Medium Access Control(MAC)protocol that reduces energy consumption and increases network lifetime.The purpose of the study is to minimize the energy consumption and minimize the delay using IEEE 802.15.4 standard.In our proposed work,if any critical events have occurred the proposed work is to classify and prioritize the data.We gave priority to the highly critical data to get the Guarantee Tine Slots(GTS)in IEEE 802.15.4 standard superframe to achieve greater energy efficiency.The proposed MAC provides higher data rates for critical data based on the history and current condition and also provides the best reliable service to high critical data and critical data by predicting node similarity.As an outcome,we proposed a MAC protocol for Variable Data Rates(MVDR).When compared to existing MAC protocols,the MVDR performed very well with low energy intake,less interruption,and an enhanced packet-sharing ratio.

Research on Scheduling Strategy of Flexible Interconnection Distribution Network Considering Distributed Photovoltaic and Hydrogen Energy Storage

Distributed photovoltaic(PV)is one of the important power sources for building a new power system with new energy as the main body.The rapid development of distributed PV has brought new challenges to the operation of distribution networks.In order to improve the absorption ability of large-scale distributed PV access to the distribution network,the AC/DC hybrid distribution network is constructed based on flexible interconnection technology,and a coordinated scheduling strategy model of hydrogen energy storage(HS)and distributed PV is established.Firstly,the mathematical model of distributed PV and HS system is established,and a comprehensive energy storage system combining seasonal hydrogen energy storage(SHS)and battery(BT)is proposed.Then,a flexible interconnected distribution network scheduling optimization model is established to minimize the total active power loss,voltage deviation and system operating cost.Finally,simulation analysis is carried out on the improved IEEE33 node,the NSGA-II algorithm is used to solve specific examples,and the optimal scheduling results of the comprehensive economy and power quality of the distribution network are obtained.Compared with the method that does not consider HS and flexible interconnection technology,the network loss and voltage deviation of this method are lower,and the total system cost can be reduced by 3.55%,which verifies the effectiveness of the proposed method.

Research on the Impact and Strategies of Rail Transit Network Integration in the Context of Metropolitan Area Development:A Case Study of the Wuhan Metropolitan Area

The metropolitan area is one of the key focal points in the construction and development of China’s new urbanization.Urban integration is an emerging trend in metropolitan areas.This paper explores the traffic demand characteristics and economic aspects of rail transit within metropolitan regions and argues that the construction of an integrated urban rail transit network is an effective approach to support their development.Rail transit in metropolitan areas offers both technical and economic advantages,improving the efficiency of time and space resource utilization,fostering economic cooperation,and ultimately contributing to an integrated development model.However,the integration of rail transit networks faces several challenges,including road network planning,technical standards,and operational organization.Using the Wuhan metropolitan area as a case study,this paper analyzes the challenges of rail transit network integration and proposes strategic solutions for development.

Climate Characteristics in Three Gorges Reservoir Area after Water Storage and Its Impact on the Production Potential

Based on the meteorological data from 33 stations of Three Gorges Reservoir from 1960 to 2008,climate yield of rice,corn and winter wheat and the changes of climatic potential productivity after water storage in Three Gorges Reservoir were calculated by the dynamic statistic model of crop growth.The results showed that the temperature in Three Gorges Reservoir was fluctuant decreased before late 1980s,and warmed rapidly after the late 1980s.The precipitation had little change before the late 1990s and had a slight decrease after the late 1990s.Sunshine hours were more in 1960s and 1970s,and then it changed little after 1980s.After water storage,the temperature increased in Three Gorges Reservoir as a whole.The precipitation decreased in the south of Three Gorges Reservoir,while it increased in the northwest of Three Gorges Reservoir.The sunshine hours were reduced except that in the vicinity of Dianjiang.After water storage,climatic potential productivity of rice decreased in the northwest and the northeast,while it increased in the south of Three Gorges Reservoir.The climatic potential productivity of corn decreased in the northeast and the southwest,but increased in the rest of Three Gorges Reservoir.The climatic potential productivity of winter wheat increased almost in total.

Classification and Integration of Storage and Transportation Engineering Technologies in Potato Producing Areas of China

Considering the development of potato （Solanum tuberosum） industry in China, the existing technologies of potato storage and transportation in the produc- ing area were analyzed through investigation on four main potato production areas. Unear classification was used to conduct the technology classification. According to the technical attributes and characteristics, the potato technologies of storage and transportation in producing area were classified with large classes, middle classes, small classes and subclasses, into the agricultural production area processing and storage engineering technology system, to reveal the structure and functions. Mean- while, the widely used technologies were integrated and summarized into 5 principal technology integration programs, which could be used for the technology integration of the new management subjects such as planting professional cooperatives, family farms, enterprises and so on.

Performance analysis of an iSCSI-based unified storage network

In this paper, we introduced a novel storage architecture 'Unified Storage Network', which merges NAC( Network Attached Channel) and SAN( Storage Area Network) , and provides the file I/O services as NAS devices and provides the block I/O services as SAN. To overcome the drawbacks from FC, we employ iSCSI to implement the USN( Unified Storage Network) . To evaluate whether iSCSI is more suitable for implementing the USN, we analyze iSCSI protocol and compare it with FC protocol from several components of a network protocol which impact the performance of the network. From the analysis and comparison, we can conclude that the iSCSI is more suitable for implementing the storage network than the FC under condition of the wide-area network. At last, we designed two groups of experiments carefully.

Re-assessing Vegetation Carbon Storage and Emissions from Land Use Change in China Using Surface Area

Land surface area estimation can provide basic information for accurately estimating vegetation carbon storage under complex terrain. This study selected China, a country dominated by mountains, as an example, and calculated terrestrial vegetation carbon storage(VCS) for 2000 and 2015 using land surface area and traditional ellipsoid area. The land surface area is estimated by a triangular network on the high precision digital elevation model.The results showed that: 1) The VCS estimated by the surface area measurement in 2000 and 2015 were 0.676 and0.692 Pg C(1 Pg = 1015 g) higher than the VCS calculated using the ellipsoid area, respectively. 2) As the elevation increases, the differences between VCS estimated by surface area measurement and ellipsoid area measurement are expanding. Specially, a clear gap was present starting from an elevation of 500 m, with the relative error exceeds8.99%. 3) The total amount of carbon emitted due to land use change reached 0.114 Pg C. The conversions of forestland and grassland to other land use type are the main reasons of the loss of vegetation carbon storage, resulting in a total amount of biomass carbon storage decreased by 0.942 and 0.111 Pg C, respectively. This study was a preliminary exploration of incorporating land surface area as a factor in resource estimation, which can help more accurately understand the status of resources and the environment in the region.

A Human Body Posture Recognition Algorithm Based on BP Neural Network for Wireless Body Area Networks

Human body posture recognition has attracted considerable attention in recent years in wireless body area networks(WBAN). In order to precisely recognize human body posture,many recognition algorithms have been proposed.However, the recognition rate is relatively low. In this paper, we apply back propagation(BP) neural network as a classifier to recognizing human body posture, where signals are collected from VG350 acceleration sensor and a posture signal collection system based on WBAN is designed. Human body signal vector magnitude(SVM) and tri-axial acceleration sensor data are used to describe the human body postures. We are able to recognize 4postures: Walk, Run, Squat and Sit. Our posture recognition rate is up to 91.67%. Furthermore, we find an implied relationship between hidden layer neurons and the posture recognition rate. The proposed human body posture recognition algorithm lays the foundation for the subsequent applications.

Stand basal area modelling for Chinese fir plantations using an artificial neural network model

Artificial neural network models are a popular estimation tool for fitting nonlinear relationships because they require no assumptions about the form of the fitting function,non-Gaussian distributions,multicollinearity,outliers and noise in the data.The problems of backpropagation models using artificial neural networks include determination of the structure of the network and overlearning courses.According to data from 1981 to 2008 from 15 permanent sample plots on Dagangshan Mountain in Jiangxi Province,a back-propagation artificial neural network model(BPANN)and a support vector machine model(SVM)for basal area of Chinese fir(Cunninghamia lanceolata)plantations were constructed using four kinds of prediction factors,including stand age,site index,surviving stem numbers and quadratic mean diameters.Artificial intelligence methods,especially SVM,could be effective in describing stand basal area growth of Chinese fir under different growth conditions with higher simulation precision than traditional regression models.SVM and the Chapman–Richards nonlinear mixed-effects model had less systematic bias than the BPANN.

Optimization of Air Route Network Nodes to Avoid ″Three Areas″ Based on An Adaptive Ant Colony Algorithm

Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.

Aquaculture area extraction and vulnerability assessment in Sanduao based on richer convolutional features network model

Sanduao is an important sea-breeding bay in Fujian,South China and holds a high economic status in aquaculture.Quickly and accurately obtaining information including the distribution area,quantity,and aquaculture area is important for breeding area planning,production value estimation,ecological survey,and storm surge prevention.However,as the aquaculture area expands,the seawater background becomes increasingly complex and spectral characteristics differ dramatically,making it difficult to determine the aquaculture area.In this study,we used a high-resolution remote-sensing satellite GF-2 image to introduce a deep-learning Richer Convolutional Features(RCF)network model to extract the aquaculture area.Then we used the density of aquaculture as an assessment index to assess the vulnerability of aquaculture areas in Sanduao.The results demonstrate that this method does not require land and water separation of the area in advance,and good extraction can be achieved in the areas with more sediment and waves,with an extraction accuracy>93%,which is suitable for large-scale aquaculture area extraction.Vulnerability assessment results indicate that the density of aquaculture in the eastern part of Sanduao is considerably high,reaching a higher vulnerability level than other parts.

An Experimental Study of Network-based Language Learning in Less-developed Areas

The advent of the Age of Information brings about bright prospects to Network-based Language Learning(NBLL).The thesis adopts the Engagement Theory as guided principles.The purpose is to use the novel NBLL model effectively with the help of modern technology especially in less-developed areas.This thesis focuses on network-based experimental study.The research shows that the students under NBLL environment have cultivated the capabilities in information collection,computer operation,and information evaluation,as well as the abilities in problem solving,reasoning with criticism,and cooperating with others.

HUSTserver:Implementation for Reliable and High-Performance Network Attached Storage System

Network attached storage (NAS) with the properties of improved scalability, simplified management, low cost and balanced price performance, is desirable for high performance storage systems applied to extensive areas. Unfortunately, it also has some disadvantages such as increased network workload, and inconvenience in disaster recovery. To overcome these disadvantages, we propose a channel bonding technique and provide hot backup functions in the designed NAS system, named HUSTserver. Channel bonding means merging multiple Ethernet channels into integrated one, and that the data packets can be transferred through any available network channels in a parallel mode. The hot backup function provides automatic data mirroring among servers. In this paper, we first describe the whole system prototype from a software and hardware architecture view. Then, multiple Ethernet and hot backup technologies that distinguish HUSTserver from others are discussed in detail. The findings presented demonstrate that network bandwidth can be scaled by the use of multiple commodity networks. Dual parallel channels of commodity 100 Mbps Ethernet are both necessary and sufficient to support the data rates of multiple concurrent file transfers. And the hot backup function introduced in our system provides high data accessibility.

Artificial neural network models predicting the leaf area index:a case study in pure even-aged Crimean pine forests from Turkey

Background: Leaf Area Index(LAI) is an important parameter used in monitoring and modeling of forest ecosystems. The aim of this study was to evaluate performance of the artificial neural network(ANN) models to predict the LAI by comparing the regression analysis models as the classical method in these pure and even-aged Crimean pine forest stands.Methods: One hundred eight temporary sample plots were collected from Crimean pine forest stands to estimate stand parameters. Each sample plot was imaged with hemispherical photographs to detect the LAI. The partial correlation analysis was used to assess the relationships between the stand LAI values and stand parameters, and the multivariate linear regression analysis was used to predict the LAI from stand parameters. Different artificial neural network models comprising different number of neuron and transfer functions were trained and used to predict the LAI of forest stands.Results: The correlation coefficients between LAI and stand parameters(stand number of trees, basal area, the quadratic mean diameter, stand density and stand age) were significant at the level of 0.01. The stand age, number of trees, site index, and basal area were independent parameters in the most successful regression model predicted LAI values using stand parameters(R_(adj)~2=0.5431). As corresponding method to predict the interactions between the stand LAI values and stand parameters, the neural network architecture based on the RBF 4-19-1 with Gaussian activation function in hidden layer and the identity activation function in output layer performed better in predicting LAI(SSE(12.1040), MSE(0.1223), RMSE(0.3497), AIC(0.1040), BIC(-77.7310) and R^2(0.6392)) compared to the other studied techniques.Conclusion: The ANN outperformed the multivariate regression techniques in predicting LAI from stand parameters. The ANN models, developed in this study, may aid in making forest management planning in study forest stands.

Spatial Accessibility of Road Network in Wuhan Metropolitan Area Based on Spatial Syntax

Based on space syntax theory, the spatial accessibility of the road network in Wuhan Metropolitan Area has been quantitatively analyzed by building a series of accessibility variables. Topologic connectivity in the accessible rings appears to be broken;traffic axis network is in spatial structure of hub-and-spoke and fishbone-like. Meanwhile, the differences in classified road network have led to inefficiency of its network servo and its ever-worsening capability to respond to traffic jams. Besides, two band-like integrated cores of which one is east to west along the Yangtze River and the other is north to south along Beijing to Guangzhou Railway, have become the first level traffic axis in the whole network, which is responsible for the connectivity of the entire metropolitan area network. This consequently has strengthened the dominant position of Wuhan which is located on the bands’ crossing. In short, the spatial accessibility of that classified space morphology, the urban system, the transport infrastructure as well as the social and economic development of Wuhan Metropolitan Area are highly interrelated to each other, especially to the high level highway network featured by freeways, the development level of which is well in line with that of road network accessibility.